https://adapt2.sis.pitt.edu/w/api.php?action=feedcontributions&feedformat=atom&user=K.thakerPAWS Lab - User contributions [en]2026-05-18T17:34:24ZUser contributionsMediaWiki 1.31.1https://adapt2.sis.pitt.edu/w/index.php?title=People&diff=4908People2025-06-25T15:55:51Z<p>K.thaker: </p>
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<div>== Faculty ==<br />
<gallery><br />
Image:Peter.jpg|'''[[User:Peterb | Peter Brusilovsky]]'''<br/>Director<br />
</gallery><br />
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== Doctoral Students ==<br />
<gallery perrow="4"><br />
Image:Jordan.jpeg|[http://pitt.edu/~jab464 Jordan Barria-Pineda]<br />
Image:Kamil.jpg|[http://pitt.edu/~kaa108 Kamil Akhuseyinoglu]<br />
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Image:rully.jpeg|[https://ruhendrawan.com/ Rully Hendrawan]<br />
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Image:moh70.jpg|[https://themhassany.github.io/ Mohammad Hassany]<br />
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Image:arun.jpeg|[https://a2un.github.io/ Arun Lekshmi-Narayanan]<br />
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Image:Rafaella.jpg|[https://www.linkedin.com/in/rafaella-sampaio-de-alencar/ Rafaella Sampaio]<br />
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</gallery><br />
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== Past Doctoral Students ==<br />
<gallery perrow="4"><br />
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Image:behnam.jpg|[http://pitt.edu/~ber58 Behnam Rahdari]<br /> PostDoc, Stanford<br />
Image:k.thaker.png|[https://www.linkedin.com/in/khushsi/ Khushboo Thaker]<br />
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Image:Arezou.jpg|[https://www.linkedin.com/in/arezou-farzaneh/ Arezou Farzaneh]<br />
Image:Avatar.jpg|[http://pitt.edu/~hkc6 Hung Chau]<br />Applied Scientist, Zillow<br />
Image:zrisha.png|[https://zakrisha.com Zak Risha] <br /> [https://facetlab.pitt.edu/ FACETLab]<br />
Image:Tsai.jpg|[http://www.cht77.com/ Chun-Hua Tsai] <br/>Assistant Professor, College of Information Science and Technology, University of Nebraska at Omaha<br />
Image:Roya.jpg|[[User:R.hosseini | Roya Hosseini]] <br/>Senior Applied Scientist, Microsoft<br />
Image:yunhuang.png|[http://columbus.exp.sis.pitt.edu/yunhuang/index.htm Yun Huang] <br/> Visiting professor, Universidad Austral de Chile<br />
Image:Julio.jpg|[[User:Julio | Julio Daniel Guerra]] <br/> Professor of Computer Science, Universidad Austral de Chile<br />
Image:xidao.jpg|[[User:Xidao| Xidao Wen]]<br/> Postdoctoral Researcher, Tsinghua University<br />
Image:Shaghayeghsahebi.jpg|[[User:Sherry | Shaghayegh Sahebi (Sherry)]] <br/>Associate Professor, Computer Science Department, University at Albany - State University of New York<br />
Image:Clau.JPG|[[User:Clau | Claudia López]] <br/>Associate Professor, Departamento de Informática, Universidad Técnica Federico Santa María, Chile<br />
Image:Kong.png|[[User:Chirayu | Chirayu Wongchokprasitti]] <br/> Department of Biomedical Informatics, University of Pittsburgh<br />
Image:jennifer.jpg|[[User:Jennifer | Jennifer (Yiling) Lin]]<br/>Associate Professor, Department of Information Management, National Sun Yat-Sen University.<br />
Image:Denis_PAWS_blog.jpg|[[User:Dparra | Denis Parra]]<br/>Associate Professor, Computer Science Department, School of Engineering, Pontificia Universidad Católica de Chile<br />
Image:Sergey.jpg|[[User:Sergey | Sergey Sosnovsky]]<br>Associate Professor, Utrecht University, Netherlands<br />
Image:Hsiao.jpg|[[User:Shoha99 | Sharon (I-Han) Hsiao]]<br/>Projects: [[AnnotEx]], [[QuizJET]], [[Progressor]], [[ProgressorPlus]]<br/>Assistant Professor and David Packard endowed junior fellow, Department of Computer Science and Engineering, Santa Clara University<br />
Image:Danielle.gif|[[User:Suleehs | Danielle H. Lee]]<br/>Projects: [[Eventur]], [[Proactive]]<br/>Associate Professor, Management Information Systems, Chung-Ang University, Korea<br />
Image:Michael_V_Yudelson.gif|'''[[User:Myudelson | Michael V. Yudelson]]'''<br/>Projects: [[Knowledge Tree]], [[CUMULATE]], [[PERSEUS]], [[NavEx]], [[CoPE]], [[WebEx]]<br/>Staff Data Scientist, Chegg<br />
Image:jaewook-1.jpg|[[User:Jahn | Jae-wook Ahn]]<br/>Projects: [[ADVISE]], [[Adaptive VIBE]], [[YourNews]], [[TaskSieve]], [[NameSieve]]<br/>Research Staff Member, Distributed AI, IBM Research<br />
Image:RostaFarzan.jpg|[[User:Rostaf | Rosta Farzan]]<br/>Professor, School of Computing and Information, University of Pittsburgh.<br />
</gallery><br />
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== Past Master Students ==<br />
<gallery perrow="4"><br />
Image:Gangeshwari_Rajavelu.png|[https://www.linkedin.com/in/gangeshwari-rajavelu-94991137/ Gangeshwari Rajavelu]<br />
Image:Vikrant Khenat.jpg|[http://www.sis.pitt.edu/~vkhenat/ Vikrant Khenat]<br />
Image:Chavan_girish.jpeg|[https://www.linkedin.com/in/girishchavan Girish Chavan]<br/>CTO Astrata Inc<br />
Image:Tibor Dumitriu.gif|[http://www.sis.pitt.edu/~dumitriu/ Tibor Dumitriu]<br/>Projects: [http://ir.exp.sis.pitt.edu/advise/ AdVisE]<br />
</gallery><br />
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== Past Visiting Faculty ==<br />
<gallery perrow="4"><br />
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Image:dr_bereket_yilma.jpg |[https://bekyilma.github.io/ Dr. Bereket Yilma]<br />
Image:Susan_bull.png|[https://www.researchgate.net/profile/Susan_Bull2 Susan Bull]<br />
Image:Jaakko peltonen 215x296.jpg|[http://users.ics.aalto.fi/jtpelto// Jaakko Peltonen]<br />
Image:IMG_0611.JPG|[http://www.dcs.warwick.ac.uk/~acristea/ Alexandra I. Cristea]<br />
Image:Sibel.jpg|[http://sibelsomyurek.com/ Sibel Somyürek]<br />
Image:KatrienVerbert.jpg|[http://people.cs.kuleuven.be/~katrien.verbert/KatrienVerbert/Katrien_Verbert.html Katrien Verbert]<br />
Image:Roman bednarik.png|[http://cs.uef.fi/~rbednari/ Roman Bednarik]<br />
Image:TanjaMitrovic.jpg|[http://www.cosc.canterbury.ac.nz/tanja.mitrovic/ Tanja Mitrovic]<br />
Image:Eva millan.gif|[http://www.lcc.uma.es/~eva/ Eva Millán Valldeperas]<br />
Image:Julita vasilleva.gif|[http://www.cs.usask.ca/faculty/julita/ Julita Vassileva]<br />
Image:NicolaHenze.gif|[http://www.kbs.uni-hannover.de/~henze/ Nicola Henze]<br />
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</gallery><br />
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== Past Visiting Scholars == <br />
<gallery perrow="4"><br />
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Image:alice.jpg |[https://research-portal.uu.nl/en/persons/alice-micheli Alice Micheli]<br />
Image:1648208030273.jpg | [https://www.linkedin.com/in/maxwellszymanski/ Maxwell Szymanski]<br />
Image:boet_hubert.jpg |[https://labsic.univ-paris13.fr/membres-cat/boet-hubert/ Hubert Boet]<br />
Image:Laia_alb%C3%B3.png|[https://scholar.google.es/citations?user=G8XglngAAAAJ&hl=ca Laia Albó] <br/> Universitat Pompeu Fabra (Barcelona)<br />
Image:huhtamaki-jukka-300.jpg|[http://www.linkedin.com/in/jukkahuhtamaki Jukka Huhtamäki]<br/>University of Tampere<br />
Image:Andrew.jpg|[https://www.researchgate.net/profile/Shuchen-Li Shuchen Li] <br/>Beijing University of Posts and Telecommunications<br />
Image:Rafael.png|[https://rafaelrda.wordpress.com Rafael Dias Araújo]<br/>Assistant Professor<br/>Federal University of Uberlandia, Brazil<br />
Image:Ayca cebi.jpg|[https://ktu.academia.edu/aycacebi Ayça ÇEBİ] <br/>Trabzon University<br />
Image:File crop1 1183908 y 384.jpg|[https://people.aalto.fi/index.html?profilepage=isfor#!teemu_sirkia Teemu Sirkiä]<br />
Image:Chris_face.jpg|[https://www.christophtrattner.info/ Christof Trattner]<br/>Professor<br/>University of Bergen<br />
Image:Michelle_liang.JPG|[https://www.linkedin.com/in/drmichelleliang/ Michelle Liang]<br />
Image:Yetunde.JPG|[https://wit.edu/directory/yetunde-folajimi Yetunde Folajimi] <br/>Associate Professor<br/>Wentworth Institute of Technology<br />
Image:Pkraker.jpg|[http://science20.wordpress.com Peter Kraker] <br/>Open Knowledge Maps<br />
Image:Kim.jpg|Jaekyung Kim<br />
Image:Jbravo.gif|[http://www.eps.uam.es/esp/personal/ficha.php?empid=367 Javier Bravo Agapito]<br />
Image:MarkusKetterl1.jpg|[http://studip.serv.uni-osnabrueck.de/extern.php?username=mketterl&page_url=http://www.virtuos.uni-osnabrueck.de/VirtUOS/TemplStudipMitarbDetails&global_id=4c8fb9ddd4dde83366119b2031d39ab3 Markus Ketterl]<br />
Image:Jillfreyne.jpg|[https://www.linkedin.com/in/jfreyne/ Jill Freyne]<br/>Deputy Chief Scientist<br/>CSIRO<br />
Image:Robert.jpg|[https://bhh.hamburg.de/prof-dr-robert-mertens/ Robert Mertens] <br/>Professor<br/>Berufliche Hochschule Hamburg<br />
Image:Roman bednarik.png|[https://cs.joensuu.fi/~rbednari/ Roman Bednarik]<br/>Professor<br/>University of Eastern Finland<br />
Image:Ewald ramp.jpg|[https://www.linkedin.com/in/ewaldramp/ Ewald W. A. Ramp]<br/>Strategic Systems & Technology Corporation<br />
Image:Jacopo.jpg|[https://www.linkedin.com/in/jacopoarmani/ Jacopo Armani] <br/>Director/Producer <br/>Fireflies<br />
Image:Liping_wang.jpg|Liping Wang<br/>JiLin University<br />
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</gallery></div>K.thakerhttps://adapt2.sis.pitt.edu/w/index.php?title=HELPeR_-_Health_e-Librarian_with_Personalized_Recommender&diff=4203HELPeR - Health e-Librarian with Personalized Recommender2023-02-15T15:48:22Z<p>K.thaker: Created page with "As the Internet has become a prominent source of health information to guide patients’ decision-making and self-management activities, patients strongly indicate they need n..."</p>
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<div>As the Internet has become a prominent source of health information to guide patients’ decision-making and self-management activities, patients strongly indicate they need navigational support to locate appropriate information on the Internet. The overall goal of our project is to build and implement a “Health E-Librarian with Personalized Recommendations (HELPeR)”, a personalized digital librarian that provides individualized, reliable online information relevant to the patient’s needs, interest and knowledge across the disease trajectory.<br />
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As a first step, we are now looking for participants who can help us to better understand the information needs and preferences of women with ovarian cancer and their family members. Help Us Now!<br />
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This is a collaborative project between the School of Nursing and the School of Computing and Information, University of Pittsburgh. This study is funded by the National Library of Medicine (1R01LM013038-01A1).</div>K.thakerhttps://adapt2.sis.pitt.edu/w/index.php?title=Open_Corpus_Personalized_Learning&diff=4183Open Corpus Personalized Learning2023-01-11T16:34:45Z<p>K.thaker: </p>
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<div>Goal: This project challenges the assumption that adaptive hypermedia systems require expensive knowledge engineering for domain and content modeling. It replaces carefully-crafted domain model with automatically-created domain models, lowering the cost of developing adaptive educational hypermedia software while also providing a wider range of instructional paths through the content. Adaptive educational hypermedia is known for its ability to improve learning outcomes and engagement maximizing educational opportunity for learners with different levels of knowledge. The development of this more automatic, open-corpus approach to adaptive educational hypermedia will increase the volume and the variety of resources available for meaningful online learning, especially for individuals learning on their own. Automatic knowledge indexing of educational content makes the system easy to maintain and update over time. These new open corpus user modeling techniques automatically adapt user models and personalized guidance to new materials as they are acquired. The ability to automatically organize, index, and adaptively recommend distributed educational content without the need of manual processing by system developers, enables new material to be integrated dynamically and with minimal effort in response to student needs.<br />
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This project merges research on text analysis, human learning, and personalization to enable open corpus personalized learning. It develops its models of the domain and human learning from an initial set of well-organized, manually selected materials. Automatic text analysis creates an ensemble of domain models with different characteristics. Each individual model may be flawed or incomplete, however, collectively they provide comprehensive coverage of the topic from several perspectives, thus reducing the manual effort required to create adaptive educational hypermedia. Multiple perspectives also give the system more flexibility in how to guide each student. These domain models are used as a foundation for building and maintaining dynamic models of user knowledge. The ensemble of domain and user models is used to deliver reactive and proactive adaptive guidance in an open corpus context. The growth of a person's knowledge is inferred by observing learner behavior and obtaining occasional feedback. This exploratory research opens the way to open corpus personalized learning. The domain modeling, user modeling, and personalization techniques developed in this research will be evaluated using a multi-layer framework that includes assessment by subject experts, performance prediction, cross-validation, and user studies.<br />
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==The Project Team==<br />
<gallery><br />
Image:Peter.jpg|'''[[User:Peterb | Peter Brusilovsky]]'''<br/>Director<br />
Image:Daqing_He.png|'''[http://www.pitt.edu/~dah44/ Daqing He]'''<br/>Co-director<br />
Image:Yun_Acadamic.png|[http://columbus.exp.sis.pitt.edu/yunhuang/index.htm Yun Huang]<br />
Image:shuguang.png|[http://www.pitt.edu/~shh69/ Shuguang Han]<br />
Image:rui.png|[http://memray.me/ Rui Meng]<br />
Image:Sanqiang.png|[http://pitt.edu/~sanqiang Sanqiang Zhao]<br />
Image:Jordan.jpeg|[http://pitt.edu/~jab464 Jordan Barria-Pineda]<br />
Image:Avatar.jpg|[http://pitt.edu/~hkc6 Hung Chau]<br />
Image:k.thaker.png|[http://kthaker.com Khushboo Thaker]<br />
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</gallery><br />
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==Education Content Linking==<br />
{|<br />
|valign="top" | [[ Image:Content-linking.png|thumb|left|'''100'''|Concepts Backbone connecting Educational Systems]]<br />
|valign="center" |<br />
Digitization has brought a significant change to the educational sector in the past ten years, making digital textbooks a convenient and popular resource for learning. Starting as merely a digital copy of paper textbooks, digital textbooks gradually integrated a number of innovative and advanced functionality. Rapid digitization also remarkably increased the volume of open learning resources, which have grown very popular, providing learners with great opportunities to expand their learning. Moreover, these new digital learning resources could be combined with the traditional learning process. For example, a learner may come across the concept of ``Indexing'' while reading a textbook on ``Information Retrieval''. This triggers her to engage extra online materials on ``Indexing'' to learn the state-of-the-art practical methods or research trends. <br />
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The goal of the project is to address this challenge by providing links to related content at different textbook materials. In this project, we would like to use concepts as a backbone for connecting different education materials. This is a challenging task since textbooks and online materials are written by different authors, for different audiences, and frequently use different terms/words to express the same concepts. Furthermore, many domains lack formal domain models or ontologies, where all domain concepts are listed and organized. Therefore, the association of terms and concepts, and the representation of concepts should be automatically discovered and constructed.<br />
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To address these challenges, our work applies distributed neural representations, which demonstrated an ability to solve the term-mismatch problem by transferring the representation of terms to an N-dimension continuous vector (also called embedding). The expectation is that terms or words which share the same semantic meaning will be nearer in this N-dimensional continuous vector space. <br />
|}<br />
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==Knowledge Extraction==<br />
{|<br />
|valign="top" | [[ Image:Knowledge-linking-Illustration.png|thumb|left|'''100'''|Dynamic Linking Textbooks]]<br />
|valign="center" |<br />
The Internet has dramatically increased both the volume and variety of online educational resources, such as online textbooks, online courses, and tutorials. The development of modern search techniques has further promoted the quick access of these resources. However, most of these educational resources are not well-structured, which imposed an important challenge -- readers without sufficient background knowledge may be difficult to understand its content. To achieve the goal of recommending ''the right content'' that matches individuals' knowledge levels, the first critical step is to provide a better organization for educational resources. The project visions two important components when organizing educational resources: (1) knowledge concept extraction; and (2) concept hierarchy extraction. Traditional solutions for these two problems heavily rely on experts' manual efforts which are time-consuming and unscalable. <br />
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Our goal for knowledge extraction is to provide a scalable solution for the above two problems. We pilot our study with extracting knowledge structures from textbooks since they provide a comprehensive list of concepts and are often used as major educational resources in schools, colleges and universities. In addition, textbooks are also equipped with structural information such as table of contents and glossaries, which are very helpful in identifying concepts and their relationships. <br />
<br />
<br />
However, the automated extraction of knowledge concepts. Accurately extracting knowledge concepts from educational content is a challenge since the miss of a large-scale knowledge concept labels for building reliable machine learning algorithms. Considering the high time cost for expert-based labeling, we explore an alternative crowdsourcing-based, with restricted quality control, approach. That is, we distribute our knowledge concept labeling work to massive crowdsourcing workers, and further aggregate the obtained labels based on well-developed quality control methods in crowdsourcing. So far, we have built our annotation system and conducted several pilot studies. In the future, we would like to conduct a live experiment to examine the validity of this approach.<br />
|}<br />
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== Learner Modeling ==<br />
{|<br />
|valign="top" | [[Image:ReadingLearningProcess.png|thumb|left|'''100'''|Dynamic Knowledge Modeling in Textbook Reading]]<br />
|valign="center" | We have recently proposed a data-driven framework for [[Dynamic Knowledge Modeling in Textbook-Based Learning| dynamic knowledge modeling in textbook-based learning]] (UMAP 2016). We formulated the problem of modeling learning from reading as a reading-time prediction problem, reconstructed existing popular student models (such as Knowledge Tracing) and explored two automatic text analysis approaches (bag-of-words-based and latent semantic-based) to build the KC model. This framework can be applied to a broader context of open-corpus personalized learning, empowering learners with the ability to access the right reading content at the right moment, despite the huge volume of online educational content. We are also working on applying [[Feature-Aware Student knowledge Tracing (FAST)|Feature-Aware Student knowledge Tracing (FAST)]] which is our new learner model proposed in 2014 with state-of-the-art predictive performance into textbook-based learning environment. <br />
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Over past years, our lab has developed [[CUMULATE]], a centralized user modeling server built for the [[ADAPT2]] architecture, to provide user modeling support for adaptive educational hypermedia (AEH) systems. We have proposed and implemented different learner models , including [[CUMULATE asymptotic knowledge assessment|asymptotic assessment of user knowledge]]. We have explored different aspects to improve learner modeling, including reducing the content model, better evaluation for practitioners and applying network (graph) analysis.<br />
<br />
* [[Dynamic Knowledge Modeling in Textbook-Based Learning|More about dynamic knowledge modeling in textbook-based learning]]<br />
* [[Learner Modeling|More about learner modeling]]<br />
* [[Feature-Aware Student knowledge Tracing (FAST)|More about Feature-Aware Student knowledge Tracing (FAST)]]<br />
* [[CUMULATE|More about CUMULATE]]<br />
* [[CUMULATE asymptotic knowledge assessment|More about asymptotic assessment of user knowledge]]<br />
|}<br />
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==The Experimental Platform==<br />
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In order to support students' learning in the classroom environment, we have implemented a web platform for students to access class materials including textbooks, research publications, web tutorials, etc. More importantly, the system automatically records users' reading behaviors in order to be able to build their student models based on this data. By now, the reading system is able to render material in two formats:<br />
* Image-based<br />
* HTML-based (not tested yet in classroom studies)<br />
We plan to support pdf document rendering soon.<br />
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[[Image:ReadingSystem_122016.jpg|thumb|left|alt=Current reading platform.|The experimental Reading System.]]<br />
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The reading system is basically formed by 2 main parts:<br />
* The reader itself (see right side of the figure)<br />
* The student reading data section (see left side of the figure)<br />
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In the student reading data section, the users can have access to two information sources. The first one is a sunburst hierarchical visualization tool (see upper section) that allows them to know their progress in the reading of the contents that are associated with the course using a color scale encoding from red (non-read) to green (totally read). The former version of this visualization tool is called [[ReadingCircle]]. <br />
The second one (see lower section) is the hierarchical index of the group, where each section<br />
The system is created for including learning material following a hierarchical structure in a similar way as books are structured (chapter, subchapter, section, etc.). In addition to this, the system allows the inclusion of multiple choice questions at the end of each section with the aim of test the acquired knowledge of the students.<br />
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==Publications==<br />
* Huang, Yun and Yudelson, Michael and Han, Shuguang and He, Daqing and Brusilovsky, Peter. "A Framework for Dynamic Knowledge Modeling in Textbook-Based Learning." In Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization, pp. 141-150. ACM, 2016 ([http://d-scholarship.pitt.edu/28248/ paper]).<br />
* Meng, Rui and Han, Shuguang and Huang, Yun and He, Daqing and Brusilovsky, Peter. "Knowledge-based Content Linking for Online Textbooks." In Proceeding of the 2016 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 13-16. IEEE Computer Society, 2016. ([http://d-scholarship.pitt.edu/30486/1/wi16-knowledge-linking.pdf paper]).<br />
* Meng, R., Zhao, S., Han, S., He, D., Brusilovsky, P., and Chi, Y. (2017) Deep Keyphrase Generation. In: Proceedings of ACL2017, Annual Meeting of the Association for Computational Linguistics, Vancouver, Canada, July 30-August 4, 2017 pp. 582-592.<br />
* Labutov, I., Huang, Y., Brusilovsky, P., and He, D. (2017) Semi-Supervised Techniques for Mining Learning Outcomes and Prerequisites. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, ACM, pp. 907-915.<br />
* Thaker, K., Huang, Y., Brusilovsky, P., and He, D. (2018) Dynamic Knowledge Modeling with Heterogeneous Activities for Adaptive Textbooks. In: Proceedings of the 11th International Conference on Educational Data Mining, Buffalo, USA, July 15-18, 2018, pp. 592-595. [http://educationaldatamining.org/files/conferences/EDM2018/papers/EDM2018_paper_199.pdf paper]<br />
* Thaker, K. M., Brusilovsky, P., and He, D. (2018) Concept Enhanced Content Representation for Linking Educational Resources. In: Proceedings of 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), Santiago, Chile, December 3-6, 2018, IEEE, pp. 413-420.<br />
* Barria-Pineda, J., Brusilovsky, P., and He, D. (2019) Reading Mirror: Social Navigation and Social Comparison for Electronic Textbooks. In: Proceedings of First Workshop on Intelligent Textbooks at 20th International Conference on Artificial Intelligence in Education (AIED 2019), Chicago, USA, June 25, 2019, CEUR, pp. 30-37.<br />
* Thaker, K., Brusilovsky, P., and He, D. (2019) Student Modeling with Automatic Knowledge Component Extraction for Adaptive Textbooks. In: Proceedings of First Workshop on Intelligent Textbooks at 20th International Conference on Artificial Intelligence in Education (AIED 2019), Chicago, USA, June 25, 2019, CEUR, pp. 95-102.<br />
* Thaker, K., Zhang, L., He, D., and Brusilovsky, P. (2020) Recommending Remedial Readings Using Student’s Knowledge State. In: Proceedings of 13th International Conference on Educational Data Mining, July 10-13, 2020, pp. 233-244.<br />
* Yuan, X., Wang, T., Meng, R., Thaker, K., Brusilovsky, P., He, D., and Trischler, A. (2020) One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases. In: D. Jurafsky, J. Chai, N. Schluter and J. R. Tetreault (eds.) Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online, July 5-10, 2020, Association for Computational Linguistics, pp. 7961-7975.<br />
* Chau, H., Labutov, I., Thaker, K., He, D., and Brusilovsky, P. (2021) Automatic Concept Extraction for Domain and Student Modeling in Adaptive Textbooks. International Journal of Artificial Intelligence in Education 31 (4), 820–846.<br />
* Wang, M., Chau, H., Thaker, K., Brusilovsky, P., and He, D. (2022) Knowledge Annotation for Intelligent Textbooks. Technology, Knowledge and Learning, in press.</div>K.thakerhttps://adapt2.sis.pitt.edu/w/index.php?title=People&diff=4182People2023-01-11T15:59:41Z<p>K.thaker: </p>
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<div>== Faculty ==<br />
<gallery><br />
Image:Peter.jpg|'''[[User:Peterb | Peter Brusilovsky]]'''<br/>Director<br />
</gallery><br />
<br />
== Doctoral Students ==<br />
<gallery perrow="4"><br />
Image:Avatar.jpg|[http://pitt.edu/~hkc6 Hung Chau]<br />
Image:Jordan.jpeg|[http://pitt.edu/~jab464 Jordan Barria-Pineda]<br />
Image:Kamil.jpg|[http://pitt.edu/~kaa108 Kamil Akhuseyinoglu]<br />
Image:zrisha.png|[https://zakrisha.com Zak Risha]<br />
Image:behnam.jpg|[http://pitt.edu/~ber58 Behnam Rahdari]<br />
Image:k.thaker.png|[http://kthaker.com Khushboo Thaker]<br />
</gallery><br />
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== Visiting Scholars ==<br />
<gallery perrow="4"><br />
<br />
</gallery><br />
<br />
== Past Doctoral Students ==<br />
<gallery perrow="4"><br />
Image:Tsai.jpg|[http://www.cht77.com/ Chun-Hua Tsai] <br/>Currently Assistant Research Professor in the IST Department at Penn State University<br />
Image:Roya.jpg|[[User:R.hosseini | Roya Hosseini]]<br />
Image:yunhuang.png|[http://columbus.exp.sis.pitt.edu/yunhuang/index.htm Yun Huang]<br />
Image:Julio.jpg|[[User:Julio | Julio Guerra]]<br />
Image:xidao.jpg|[[User:Xidao| Xidao Wen]]<br />
Image:Shaghayeghsahebi.jpg|[[User:Sherry | Shaghayegh Sahebi (Sherry)]] <br/>Currently Assistant Professor in the Computer Science Department at State University of New York (SUNY) at Albany<br />
Image:Clau.JPG|[[User:Clau | Claudia López]] <br/>Currently Assistant Professor in the Departamento de Informática, Universidad Técnica Federico Santa María, Chile<br />
Image:Kong.png|[[User:Chirayu | Chirayu Wongchokprasitti]] <br/> Currently at the Department of Biomedical Informatics, University of Pittsburgh<br />
Image:jennifer.jpg|[[User:Jennifer | Jennifer (Yiling) Lin]]<br/>Currently Assistant Professor in the department of Information Management at the National Sun Yat-Sen University.<br />
Image:Denis_PAWS_blog.jpg|[[User:Dparra | Denis Parra]]<br/>Currently Assistant Professor at the Computer Science Department, School of Engineering at PUC Chile.<br />
Image:jaewook-1.jpg|[[User:Jahn | Jae-wook Ahn]]<br/>Projects: [[ADVISE]], [[Adaptive VIBE]], [[YourNews]], [[TaskSieve]], [[NameSieve]]<br/>Research Staff Member, Distributed AI,IBM Research<br />
Image:RostaFarzan.jpg|[[User:Rostaf | Rosta Farzan]]<br/>Currently Assistant Professor at School of Computing and Information, University of Pittsburgh.<br />
Image:Michael_V_Yudelson.gif|'''[[User:Myudelson | Michael V. Yudelson]]'''<br/>Projects: [[Knowledge Tree]], [[CUMULATE]], [[PERSEUS]], [[NavEx]], [[CoPE]], [[WebEx]]<br/>Currently Postdoctoral Fellow at Carnegie Mellon University<br />
Image:Sergey.jpg|[[User:Sergey | Sergey Sosnovsky]]<br> Currently Assistant Professor at Utrecht University (the Netherlands)<br />
Image:Hsiao.jpg|[[User:Shoha99 | Sharon (I-Han) Hsiao]]<br/>Projects: [[AnnotEx]], [[QuizJET]], [[Progressor]], [[ProgressorPlus]]<br/>Currently Assistant Professor @ CIDSE, Arizona State University<br />
Image:Danielle.gif|[[User:Suleehs | Danielle H. Lee]]<br/>Projects: [[Eventur]], [[Proactive]]<br/>Currently Assistant Professor at the Department of Software, Sangmyung University, Korea<br />
</gallery><br />
<br />
== Past Visiting Faculty ==<br />
<gallery perrow="4"><br />
Image:Susan_bull.png|[https://www.researchgate.net/profile/Susan_Bull2 Susan Bull]<br />
Image:Jaakko peltonen 215x296.jpg|[http://users.ics.aalto.fi/jtpelto// Jaakko Peltonen]<br />
Image:IMG_0611.JPG|[http://www.dcs.warwick.ac.uk/~acristea/ Alexandra I. Cristea]<br />
Image:Sibel.jpg|[http://sibelsomyurek.com/ Sibel Somyürek]<br />
Image:KatrienVerbert.jpg|[http://people.cs.kuleuven.be/~katrien.verbert/KatrienVerbert/Katrien_Verbert.html Katrien Verbert]<br />
Image:Roman bednarik.png|[http://cs.uef.fi/~rbednari/ Roman Bednarik]<br />
Image:TanjaMitrovic.jpg|[http://www.cosc.canterbury.ac.nz/tanja.mitrovic/ Tanja Mitrovic]<br />
Image:Eva millan.gif|[http://www.lcc.uma.es/~eva/ Eva Millán Valldeperas]<br />
Image:Julita vasilleva.gif|[http://www.cs.usask.ca/faculty/julita/ Julita Vassileva]<br />
Image:NicolaHenze.gif|[http://www.kbs.uni-hannover.de/~henze/ Nicola Henze]<br />
<br />
</gallery><br />
<br />
== Past Master Students ==<br />
<gallery perrow="4"><br />
Image:Gangeshwari_Rajavelu.png|[https://www.linkedin.com/in/gangeshwari-rajavelu-94991137/ Gangeshwari Rajavelu]<br />
Image:Vikrant Khenat.jpg|[http://www.sis.pitt.edu/~vkhenat/ Vikrant Khenat]<br />
Image:Chavan_girish.jpeg|[https://www.linkedin.com/in/girishchavan Girish Chavan]<br/>CTO Astrata Inc<br />
Image:Tibor Dumitriu.gif|[http://www.sis.pitt.edu/~dumitriu/ Tibor Dumitriu]<br/>Projects: [http://ir.exp.sis.pitt.edu/advise/ AdVisE]<br />
</gallery><br />
<br />
== Past Visiting Scholars == <br />
<gallery perrow="4"><br />
Image:Laia_alb%C3%B3.png|Laia Albó <br> Postdoc Researcher, [https://www.upf.edu/web/tide TIDE], Universitat Pompeu Fabra (Barcelona)<br />
Image:huhtamaki-jukka-300.jpg|[http://www.linkedin.com/in/jukkahuhtamaki Jukka Huhtamäki]<br/>Postdoc Researcher, DSc (Tech), University of Tampere<br />
Image:Andrew.jpg|Shuchen Li (Andrew) <br/>From Beijing University of Posts and Telecommunications<br />
Image:Rafael.png|[https://rafaelrda.wordpress.com Rafael Dias Araújo]<br />
Image:Liping_wang.jpg|Liping Wang<br/>From JiLin University<br />
Image:Ayca cebi.jpg|[https://ktu.academia.edu/aycacebi Ayça ÇEBİ] <br/>From Karadeniz Technical University<br />
Image:File crop1 1183908 y 384.jpg|[https://people.aalto.fi/index.html?profilepage=isfor#!teemu_sirkia Teemu Sirkiä]<br />
Image:Michelle_liang.JPG|[http://www.tcs.fudan.edu.cn/~michelle/index.html Michelle Liang]<br />
Image:Pkraker.jpg|[http://science20.wordpress.com Peter Kraker] <br/>Marshall Plan Scholar <br />
Image:Kim.jpg|Jaekyung Kim<br />
Image:Jbravo.gif|[http://www.eps.uam.es/esp/personal/ficha.php?empid=367 Javier Bravo Agapito]<br />
Image:MarkusKetterl1.jpg|[http://studip.serv.uni-osnabrueck.de/extern.php?username=mketterl&page_url=http://www.virtuos.uni-osnabrueck.de/VirtUOS/TemplStudipMitarbDetails&global_id=4c8fb9ddd4dde83366119b2031d39ab3 Markus Ketterl]<br />
Image:Jillfreyne.jpg|[http://www.csi.ucd.ie/users/jill-freyne Jill Freyne]<br />
Image:Robert.jpg|Robert Mertens<br />
Image:Roman bednarik.png|[http://www.cs.joensuu.fi/~rbednari/ Roman bednarik]<br />
Image:Ewald ramp.jpg|Ewald W. A. Ramp<br />
Image:Jacopo.jpg|Jacopo Armani<br />
Image:Yetunde.JPG|Yetunde Folajimi<br />
Image:Chris_face.jpg|[http://www.austria-lexikon.at/af/User/Trattner%20Christoph Christof Trattner]<br />
</gallery></div>K.thakerhttps://adapt2.sis.pitt.edu/w/index.php?title=Open_Corpus_Personalized_Learning&diff=4181Open Corpus Personalized Learning2023-01-11T15:59:18Z<p>K.thaker: </p>
<hr />
<div>Goal: This project challenges the assumption that adaptive hypermedia systems require expensive knowledge engineering for domain and content modeling. It replaces carefully-crafted domain model with automatically-created domain models, lowering the cost of developing adaptive educational hypermedia software while also providing a wider range of instructional paths through the content. Adaptive educational hypermedia is known for its ability to improve learning outcomes and engagement maximizing educational opportunity for learners with different levels of knowledge. The development of this more automatic, open-corpus approach to adaptive educational hypermedia will increase the volume and the variety of resources available for meaningful online learning, especially for individuals learning on their own. Automatic knowledge indexing of educational content makes the system easy to maintain and update over time. These new open corpus user modeling techniques automatically adapt user models and personalized guidance to new materials as they are acquired. The ability to automatically organize, index, and adaptively recommend distributed educational content without the need of manual processing by system developers, enables new material to be integrated dynamically and with minimal effort in response to student needs.<br />
<br />
This project merges research on text analysis, human learning, and personalization to enable open corpus personalized learning. It develops its models of the domain and human learning from an initial set of well-organized, manually selected materials. Automatic text analysis creates an ensemble of domain models with different characteristics. Each individual model may be flawed or incomplete, however collectively they provide comprehensive coverage of the topic from several perspectives, thus reducing the manual effort required to create adaptive educational hypermedia. Multiple perspectives also give the system more flexibility in how to guide each student. These domain models are used as a foundation for building and maintaining dynamic models of user knowledge. The ensemble of domain and user models is used to deliver reactive and proactive adaptive guidance in an open corpus context. The growth of a person's knowledge is inferred by observing learner behavior and obtaining occasional feedback. This exploratory research opens the way to open corpus personalized learning. The domain modeling, user modeling, and personalization techniques developed in this research will be evaluated using a multi-layer framework that includes assessment by subject experts, performance prediction, cross-validation, and user studies.<br />
<br />
==The Project Team==<br />
<gallery><br />
Image:Peter.jpg|'''[[User:Peterb | Peter Brusilovsky]]'''<br/>Director<br />
Image:Daqing_He.png|'''[http://www.pitt.edu/~dah44/ Daqing He]'''<br/>Co-director<br />
Image:Yun_Acadamic.png|[http://columbus.exp.sis.pitt.edu/yunhuang/index.htm Yun Huang]<br />
Image:shuguang.png|[http://www.pitt.edu/~shh69/ Shuguang Han]<br />
Image:rui.png|[http://memray.me/ Rui Meng]<br />
Image:Sanqiang.png|[http://pitt.edu/~sanqiang Sanqiang Zhao]<br />
Image:Jordan.jpeg|[http://pitt.edu/~jab464 Jordan Barria-Pineda]<br />
Image:Avatar.jpg|[http://pitt.edu/~hkc6 Hung Chau]<br />
Image:k.thaker.png|[http://kthaker.com Khushboo Thaker]<br />
<br />
</gallery><br />
<br />
==Education Content Linking==<br />
{|<br />
|valign="top" | [[ Image:Content-linking.png|thumb|left|'''100'''|Concepts Backbone connecting Educational Systems]]<br />
|valign="center" |<br />
Digitization has brought a significant change to the educational sector in the past ten years, making digital textbooks a convenient and popular resource for learning. Starting as merely a digital copy of paper textbooks, digital textbooks gradually integrated a number of innovative and advanced functionality. Rapid digitization also remarkably increased the volume of open learning resources, which have grown very popular, providing learners with great opportunities to expand their learning. Moreover, these new digital learning resources could be combined with the traditional learning process. For example, a learner may come across the concept of ``Indexing'' while reading a textbook on ``Information Retrieval''. This triggers her to engage extra online materials on ``Indexing'' to learn the state-of-the-art practical methods or research trends. <br />
<br />
The goal of the project is to address this challenge by providing links to related content at different textbooks material. In this project we would like to use concepts as a backbone for connecting different education materials. This is a challenging task since textbooks and online materials are written by different authors, for different audiences, and frequently use different terms/words to express the same concepts. Furthermore, many domains lack formal domain models or ontologies, where all domain concepts are listed and organized. Therefore, the association of terms and concepts, and the representation of concepts should be automatically discovered and constructed.<br />
<br />
To address these challenges, our work applies distributed neural representations, which demonstrated an ability to solve the term-mismatch problem by transferring the representation of terms to a N-dimension continuous vector (also called embedding). The expectation is that terms or words which share the same semantic meaning will be nearer in this N-dimensional continuous vector space. However, these approaches cannot be applied efficiently to educational resources, as they need a good deal of training data to learn efficient representations (for example vectors learned from entire Google News Corpus), while the volume of educational resources for less popular domains might be limited. In addition, these embeddings do not bring insights to topical distribution of terms, for example ``Precision'', ``Recall'', ``Cross-validation'' are concepts related to same topic ``Evaluation'' in ``Machine Learning'' domain. <br />
|}<br />
<br />
<br />
==Knowledge Extraction==<br />
{|<br />
|valign="top" | [[ Image:Knowledge-linking-Illustration.png|thumb|left|'''100'''|Dynamic Linking Textbooks]]<br />
|valign="center" |<br />
The Internet has dramatically increased both the volume and variety of online educational resources, such as online textbooks, online courses, and tutorials. The development of modern search techniques has further promoted the quick access of these resources. However, most of these educational resources are not well-structured, which imposed an important challenge -- readers without sufficient background knowledge may be difficult to understand its content. To achieve the goal of recommending ''the right content'' that matches individuals' knowledge levels, the first critical step is to provide a better organization for educational resources. The project visions two important components when organizing educational resources: (1) knowledge concept extraction; and (2) concept hierarchy extraction. Traditional solutions for these two problems heavily rely on experts' manual efforts which are time-consuming and unscalable. <br />
<br />
Our goal for knowledge extraction is to provide a scalable solution for the above two problems. We pilot our study with extracting knowledge structures from textbooks since they provide a comprehensive list of concepts and are often used as major educational resources in schools, colleges and universities. In addition, textbooks are also equipped with structural information such as table of contents and glossaries, which are very helpful in identifying concepts and their relationships. <br />
<br />
<br />
However, the automated extraction of knowledge concepts. Accurately extracting knowledge concepts from educational content is a challenge since the miss of a large-scale knowledge concept labels for building reliable machine learning algorithms. Considering the high time cost for expert-based labeling, we explore an alternative crowdsourcing-based, with restricted quality control, approach. That is, we distribute our knowledge concept labeling work to massive crowdsourcing workers, and further aggregate the obtained labels based on well-developed quality control methods in crowdsourcing. So far, we have built our annotation system and conducted several pilot studies. In the future, we would like to conduct a live experiment to examine the validity of this approach.<br />
|}<br />
<br />
== Learner Modeling ==<br />
{|<br />
|valign="top" | [[Image:ReadingLearningProcess.png|thumb|left|'''100'''|Dynamic Knowledge Modeling in Textbook Reading]]<br />
|valign="center" | We have recently proposed a data-driven framework for [[Dynamic Knowledge Modeling in Textbook-Based Learning| dynamic knowledge modeling in textbook-based learning]] (UMAP 2016). We formulated the problem of modeling learning from reading as a reading-time prediction problem, reconstructed existing popular student models (such as Knowledge Tracing) and explored two automatic text analysis approaches (bag-of-words-based and latent semantic-based) to build the KC model. This framework can be applied to a broader context of open-corpus personalized learning, empowering learners with the ability to access the right reading content at the right moment, despite the huge volume of online educational content. We are also working on applying [[Feature-Aware Student knowledge Tracing (FAST)|Feature-Aware Student knowledge Tracing (FAST)]] which is our new learner model proposed in 2014 with state-of-the-art predictive performance into textbook-based learning environment. <br />
<br />
Over past years, our lab has developed [[CUMULATE]], a centralized user modeling server built for the [[ADAPT2]] architecture, to provide user modeling support for adaptive educational hypermedia (AEH) systems. We have proposed and implemented different learner models , including [[CUMULATE asymptotic knowledge assessment|asymptotic assessment of user knowledge]]. We have explored different aspects to improve learner modeling, including reducing the content model, better evaluation for practitioners and applying network (graph) analysis.<br />
<br />
* [[Dynamic Knowledge Modeling in Textbook-Based Learning|More about dynamic knowledge modeling in textbook-based learning]]<br />
* [[Learner Modeling|More about learner modeling]]<br />
* [[Feature-Aware Student knowledge Tracing (FAST)|More about Feature-Aware Student knowledge Tracing (FAST)]]<br />
* [[CUMULATE|More about CUMULATE]]<br />
* [[CUMULATE asymptotic knowledge assessment|More about asymptotic assessment of user knowledge]]<br />
|}<br />
<br />
==The Experimental Platform==<br />
<br />
In order to support students' learning in the classroom environment, we have implemented a web platform for students to access class materials including textbooks, research publications, web tutorials, etc. More importantly, the system automatically records users' reading behaviors in order to be able to build their student models based on this data. By now, the reading system is able to render material in two formats:<br />
* Image-based<br />
* HTML-based (not tested yet in classroom studies)<br />
We plan to support pdf document rendering soon.<br />
<br />
[[Image:ReadingSystem_122016.jpg|thumb|left|alt=Current reading platform.|The experimental Reading System.]]<br />
<br />
The reading system is basically formed by 2 main parts:<br />
* The reader itself (see right side of the figure)<br />
* The student reading data section (see left side of the figure)<br />
<br />
In the student reading data section, the users can have access to two information sources. The first one is a sunburst hierarchical visualization tool (see upper section) that allows them to know their progress in the reading of the contents that are associated with the course using a color scale encoding from red (non-read) to green (totally read). The former version of this visualization tool is called [[ReadingCircle]]. <br />
The second one (see lower section) is the hierarchical index of the group, where each section<br />
The system is created for including learning material following a hierarchical structure in a similar way as books are structured (chapter, subchapter, section, etc.). In addition to this, the system allows the inclusion of multiple choice questions at the end of each section with the aim of test the acquired knowledge of the students.<br />
<br />
==Publications==<br />
* Huang, Yun and Yudelson, Michael and Han, Shuguang and He, Daqing and Brusilovsky, Peter. "A Framework for Dynamic Knowledge Modeling in Textbook-Based Learning." In Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization, pp. 141-150. ACM, 2016 ([http://d-scholarship.pitt.edu/28248/ paper]).<br />
* Meng, Rui and Han, Shuguang and Huang, Yun and He, Daqing and Brusilovsky, Peter. "Knowledge-based Content Linking for Online Textbooks." In Proceeding of the 2016 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 13-16. IEEE Computer Society, 2016. ([http://d-scholarship.pitt.edu/30486/1/wi16-knowledge-linking.pdf paper]).<br />
* Meng, R., Zhao, S., Han, S., He, D., Brusilovsky, P., and Chi, Y. (2017) Deep Keyphrase Generation. In: Proceedings of ACL2017, Annual Meeting of the Association for Computational Linguistics, Vancouver, Canada, July 30-August 4, 2017 pp. 582-592.<br />
* Labutov, I., Huang, Y., Brusilovsky, P., and He, D. (2017) Semi-Supervised Techniques for Mining Learning Outcomes and Prerequisites. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, ACM, pp. 907-915.<br />
* Thaker, K., Huang, Y., Brusilovsky, P., and He, D. (2018) Dynamic Knowledge Modeling with Heterogeneous Activities for Adaptive Textbooks. In: Proceedings of the 11th International Conference on Educational Data Mining, Buffalo, USA, July 15-18, 2018, pp. 592-595. [http://educationaldatamining.org/files/conferences/EDM2018/papers/EDM2018_paper_199.pdf paper]<br />
* Thaker, K. M., Brusilovsky, P., and He, D. (2018) Concept Enhanced Content Representation for Linking Educational Resources. In: Proceedings of 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), Santiago, Chile, December 3-6, 2018, IEEE, pp. 413-420.<br />
* Barria-Pineda, J., Brusilovsky, P., and He, D. (2019) Reading Mirror: Social Navigation and Social Comparison for Electronic Textbooks. In: Proceedings of First Workshop on Intelligent Textbooks at 20th International Conference on Artificial Intelligence in Education (AIED 2019), Chicago, USA, June 25, 2019, CEUR, pp. 30-37.<br />
* Thaker, K., Brusilovsky, P., and He, D. (2019) Student Modeling with Automatic Knowledge Component Extraction for Adaptive Textbooks. In: Proceedings of First Workshop on Intelligent Textbooks at 20th International Conference on Artificial Intelligence in Education (AIED 2019), Chicago, USA, June 25, 2019, CEUR, pp. 95-102.<br />
* Thaker, K., Zhang, L., He, D., and Brusilovsky, P. (2020) Recommending Remedial Readings Using Student’s Knowledge State. In: Proceedings of 13th International Conference on Educational Data Mining, July 10-13, 2020, pp. 233-244.<br />
* Yuan, X., Wang, T., Meng, R., Thaker, K., Brusilovsky, P., He, D., and Trischler, A. (2020) One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases. In: D. Jurafsky, J. Chai, N. Schluter and J. R. Tetreault (eds.) Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online, July 5-10, 2020, Association for Computational Linguistics, pp. 7961-7975.<br />
* Chau, H., Labutov, I., Thaker, K., He, D., and Brusilovsky, P. (2021) Automatic Concept Extraction for Domain and Student Modeling in Adaptive Textbooks. International Journal of Artificial Intelligence in Education 31 (4), 820–846.<br />
* Wang, M., Chau, H., Thaker, K., Brusilovsky, P., and He, D. (2022) Knowledge Annotation for Intelligent Textbooks. Technology, Knowledge and Learning, in press.</div>K.thakerhttps://adapt2.sis.pitt.edu/w/index.php?title=Open_Corpus_Personalized_Learning&diff=4180Open Corpus Personalized Learning2023-01-11T15:46:36Z<p>K.thaker: </p>
<hr />
<div>Goal: This project challenges the assumption that adaptive hypermedia systems require expensive knowledge engineering for domain and content modeling. It replaces carefully-crafted domain model with automatically-created domain models, lowering the cost of developing adaptive educational hypermedia software while also providing a wider range of instructional paths through the content. Adaptive educational hypermedia is known for its ability to improve learning outcomes and engagement maximizing educational opportunity for learners with different levels of knowledge. The development of this more automatic, open-corpus approach to adaptive educational hypermedia will increase the volume and the variety of resources available for meaningful online learning, especially for individuals learning on their own. Automatic knowledge indexing of educational content makes the system easy to maintain and update over time. These new open corpus user modeling techniques automatically adapt user models and personalized guidance to new materials as they are acquired. The ability to automatically organize, index, and adaptively recommend distributed educational content without the need of manual processing by system developers, enables new material to be integrated dynamically and with minimal effort in response to student needs.<br />
<br />
This project merges research on text analysis, human learning, and personalization to enable open corpus personalized learning. It develops its models of the domain and human learning from an initial set of well-organized, manually selected materials. Automatic text analysis creates an ensemble of domain models with different characteristics. Each individual model may be flawed or incomplete, however collectively they provide comprehensive coverage of the topic from several perspectives, thus reducing the manual effort required to create adaptive educational hypermedia. Multiple perspectives also give the system more flexibility in how to guide each student. These domain models are used as a foundation for building and maintaining dynamic models of user knowledge. The ensemble of domain and user models is used to deliver reactive and proactive adaptive guidance in an open corpus context. The growth of a person's knowledge is inferred by observing learner behavior and obtaining occasional feedback. This exploratory research opens the way to open corpus personalized learning. The domain modeling, user modeling, and personalization techniques developed in this research will be evaluated using a multi-layer framework that includes assessment by subject experts, performance prediction, cross-validation, and user studies.<br />
<br />
==The Project Team==<br />
<gallery><br />
Image:Peter.jpg|'''[[User:Peterb | Peter Brusilovsky]]'''<br/>Director<br />
Image:Daqing_He.png|'''[http://www.pitt.edu/~dah44/ Daqing He]'''<br/>Co-director<br />
Image:Yun_Acadamic.png|[http://columbus.exp.sis.pitt.edu/yunhuang/index.htm Yun Huang]<br />
Image:shuguang.png|[http://www.pitt.edu/~shh69/ Shuguang Han]<br />
Image:rui.png|[http://memray.me/ Rui Meng]<br />
Image:Sanqiang.png|[http://pitt.edu/~sanqiang Sanqiang Zhao]<br />
Image:Jordan.jpeg|[http://pitt.edu/~jab464 Jordan Barria-Pineda]<br />
Image:Avatar.jpg|[http://pitt.edu/~hkc6 Hung Chau]<br />
Image:k.thaker.png|[http://pitt.edu/~kmt81 Khushboo Thaker]<br />
<br />
</gallery><br />
<br />
==Education Content Linking==<br />
{|<br />
|valign="top" | [[ Image:Content-linking.png|thumb|left|'''100'''|Concepts Backbone connecting Educational Systems]]<br />
|valign="center" |<br />
Digitization has brought a significant change to the educational sector in the past ten years, making digital textbooks a convenient and popular resource for learning. Starting as merely a digital copy of paper textbooks, digital textbooks gradually integrated a number of innovative and advanced functionality. Rapid digitization also remarkably increased the volume of open learning resources, which have grown very popular, providing learners with great opportunities to expand their learning. Moreover, these new digital learning resources could be combined with the traditional learning process. For example, a learner may come across the concept of ``Indexing'' while reading a textbook on ``Information Retrieval''. This triggers her to engage extra online materials on ``Indexing'' to learn the state-of-the-art practical methods or research trends. <br />
<br />
The goal of the project is to address this challenge by providing links to related content at different textbooks material. In this project we would like to use concepts as a backbone for connecting different education materials. This is a challenging task since textbooks and online materials are written by different authors, for different audiences, and frequently use different terms/words to express the same concepts. Furthermore, many domains lack formal domain models or ontologies, where all domain concepts are listed and organized. Therefore, the association of terms and concepts, and the representation of concepts should be automatically discovered and constructed.<br />
<br />
To address these challenges, our work applies distributed neural representations, which demonstrated an ability to solve the term-mismatch problem by transferring the representation of terms to a N-dimension continuous vector (also called embedding). The expectation is that terms or words which share the same semantic meaning will be nearer in this N-dimensional continuous vector space. However, these approaches cannot be applied efficiently to educational resources, as they need a good deal of training data to learn efficient representations (for example vectors learned from entire Google News Corpus), while the volume of educational resources for less popular domains might be limited. In addition, these embeddings do not bring insights to topical distribution of terms, for example ``Precision'', ``Recall'', ``Cross-validation'' are concepts related to same topic ``Evaluation'' in ``Machine Learning'' domain. <br />
|}<br />
<br />
<br />
==Knowledge Extraction==<br />
{|<br />
|valign="top" | [[ Image:Knowledge-linking-Illustration.png|thumb|left|'''100'''|Dynamic Linking Textbooks]]<br />
|valign="center" |<br />
The Internet has dramatically increased both the volume and variety of online educational resources, such as online textbooks, online courses, and tutorials. The development of modern search techniques has further promoted the quick access of these resources. However, most of these educational resources are not well-structured, which imposed an important challenge -- readers without sufficient background knowledge may be difficult to understand its content. To achieve the goal of recommending ''the right content'' that matches individuals' knowledge levels, the first critical step is to provide a better organization for educational resources. The project visions two important components when organizing educational resources: (1) knowledge concept extraction; and (2) concept hierarchy extraction. Traditional solutions for these two problems heavily rely on experts' manual efforts which are time-consuming and unscalable. <br />
<br />
Our goal for knowledge extraction is to provide a scalable solution for the above two problems. We pilot our study with extracting knowledge structures from textbooks since they provide a comprehensive list of concepts and are often used as major educational resources in schools, colleges and universities. In addition, textbooks are also equipped with structural information such as table of contents and glossaries, which are very helpful in identifying concepts and their relationships. <br />
<br />
<br />
However, the automated extraction of knowledge concepts. Accurately extracting knowledge concepts from educational content is a challenge since the miss of a large-scale knowledge concept labels for building reliable machine learning algorithms. Considering the high time cost for expert-based labeling, we explore an alternative crowdsourcing-based, with restricted quality control, approach. That is, we distribute our knowledge concept labeling work to massive crowdsourcing workers, and further aggregate the obtained labels based on well-developed quality control methods in crowdsourcing. So far, we have built our annotation system and conducted several pilot studies. In the future, we would like to conduct a live experiment to examine the validity of this approach.<br />
|}<br />
<br />
== Learner Modeling ==<br />
{|<br />
|valign="top" | [[Image:ReadingLearningProcess.png|thumb|left|'''100'''|Dynamic Knowledge Modeling in Textbook Reading]]<br />
|valign="center" | We have recently proposed a data-driven framework for [[Dynamic Knowledge Modeling in Textbook-Based Learning| dynamic knowledge modeling in textbook-based learning]] (UMAP 2016). We formulated the problem of modeling learning from reading as a reading-time prediction problem, reconstructed existing popular student models (such as Knowledge Tracing) and explored two automatic text analysis approaches (bag-of-words-based and latent semantic-based) to build the KC model. This framework can be applied to a broader context of open-corpus personalized learning, empowering learners with the ability to access the right reading content at the right moment, despite the huge volume of online educational content. We are also working on applying [[Feature-Aware Student knowledge Tracing (FAST)|Feature-Aware Student knowledge Tracing (FAST)]] which is our new learner model proposed in 2014 with state-of-the-art predictive performance into textbook-based learning environment. <br />
<br />
Over past years, our lab has developed [[CUMULATE]], a centralized user modeling server built for the [[ADAPT2]] architecture, to provide user modeling support for adaptive educational hypermedia (AEH) systems. We have proposed and implemented different learner models , including [[CUMULATE asymptotic knowledge assessment|asymptotic assessment of user knowledge]]. We have explored different aspects to improve learner modeling, including reducing the content model, better evaluation for practitioners and applying network (graph) analysis.<br />
<br />
* [[Dynamic Knowledge Modeling in Textbook-Based Learning|More about dynamic knowledge modeling in textbook-based learning]]<br />
* [[Learner Modeling|More about learner modeling]]<br />
* [[Feature-Aware Student knowledge Tracing (FAST)|More about Feature-Aware Student knowledge Tracing (FAST)]]<br />
* [[CUMULATE|More about CUMULATE]]<br />
* [[CUMULATE asymptotic knowledge assessment|More about asymptotic assessment of user knowledge]]<br />
|}<br />
<br />
==The Experimental Platform==<br />
<br />
In order to support students' learning in the classroom environment, we have implemented a web platform for students to access class materials including textbooks, research publications, web tutorials, etc. More importantly, the system automatically records users' reading behaviors in order to be able to build their student models based on this data. By now, the reading system is able to render material in two formats:<br />
* Image-based<br />
* HTML-based (not tested yet in classroom studies)<br />
We plan to support pdf document rendering soon.<br />
<br />
[[Image:ReadingSystem_122016.jpg|thumb|left|alt=Current reading platform.|The experimental Reading System.]]<br />
<br />
The reading system is basically formed by 2 main parts:<br />
* The reader itself (see right side of the figure)<br />
* The student reading data section (see left side of the figure)<br />
<br />
In the student reading data section, the users can have access to two information sources. The first one is a sunburst hierarchical visualization tool (see upper section) that allows them to know their progress in the reading of the contents that are associated with the course using a color scale encoding from red (non-read) to green (totally read). The former version of this visualization tool is called [[ReadingCircle]]. <br />
The second one (see lower section) is the hierarchical index of the group, where each section<br />
The system is created for including learning material following a hierarchical structure in a similar way as books are structured (chapter, subchapter, section, etc.). In addition to this, the system allows the inclusion of multiple choice questions at the end of each section with the aim of test the acquired knowledge of the students.<br />
<br />
==Publications==<br />
* Huang, Yun and Yudelson, Michael and Han, Shuguang and He, Daqing and Brusilovsky, Peter. "A Framework for Dynamic Knowledge Modeling in Textbook-Based Learning." In Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization, pp. 141-150. ACM, 2016 ([http://d-scholarship.pitt.edu/28248/ paper]).<br />
* Meng, Rui and Han, Shuguang and Huang, Yun and He, Daqing and Brusilovsky, Peter. "Knowledge-based Content Linking for Online Textbooks." In Proceeding of the 2016 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 13-16. IEEE Computer Society, 2016. ([http://d-scholarship.pitt.edu/30486/1/wi16-knowledge-linking.pdf paper]).<br />
* Meng, R., Zhao, S., Han, S., He, D., Brusilovsky, P., and Chi, Y. (2017) Deep Keyphrase Generation. In: Proceedings of ACL2017, Annual Meeting of the Association for Computational Linguistics, Vancouver, Canada, July 30-August 4, 2017 pp. 582-592.<br />
* Labutov, I., Huang, Y., Brusilovsky, P., and He, D. (2017) Semi-Supervised Techniques for Mining Learning Outcomes and Prerequisites. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, ACM, pp. 907-915.<br />
* Thaker, K., Huang, Y., Brusilovsky, P., and He, D. (2018) Dynamic Knowledge Modeling with Heterogeneous Activities for Adaptive Textbooks. In: Proceedings of the 11th International Conference on Educational Data Mining, Buffalo, USA, July 15-18, 2018, pp. 592-595. [http://educationaldatamining.org/files/conferences/EDM2018/papers/EDM2018_paper_199.pdf paper]<br />
* Thaker, K. M., Brusilovsky, P., and He, D. (2018) Concept Enhanced Content Representation for Linking Educational Resources. In: Proceedings of 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), Santiago, Chile, December 3-6, 2018, IEEE, pp. 413-420.<br />
* Barria-Pineda, J., Brusilovsky, P., and He, D. (2019) Reading Mirror: Social Navigation and Social Comparison for Electronic Textbooks. In: Proceedings of First Workshop on Intelligent Textbooks at 20th International Conference on Artificial Intelligence in Education (AIED 2019), Chicago, USA, June 25, 2019, CEUR, pp. 30-37.<br />
* Thaker, K., Brusilovsky, P., and He, D. (2019) Student Modeling with Automatic Knowledge Component Extraction for Adaptive Textbooks. In: Proceedings of First Workshop on Intelligent Textbooks at 20th International Conference on Artificial Intelligence in Education (AIED 2019), Chicago, USA, June 25, 2019, CEUR, pp. 95-102.<br />
* Thaker, K., Zhang, L., He, D., and Brusilovsky, P. (2020) Recommending Remedial Readings Using Student’s Knowledge State. In: Proceedings of 13th International Conference on Educational Data Mining, July 10-13, 2020, pp. 233-244.<br />
* Yuan, X., Wang, T., Meng, R., Thaker, K., Brusilovsky, P., He, D., and Trischler, A. (2020) One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases. In: D. Jurafsky, J. Chai, N. Schluter and J. R. Tetreault (eds.) Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online, July 5-10, 2020, Association for Computational Linguistics, pp. 7961-7975.<br />
* Chau, H., Labutov, I., Thaker, K., He, D., and Brusilovsky, P. (2021) Automatic Concept Extraction for Domain and Student Modeling in Adaptive Textbooks. International Journal of Artificial Intelligence in Education 31 (4), 820–846.<br />
* Wang, M., Chau, H., Thaker, K., Brusilovsky, P., and He, D. (2022) Knowledge Annotation for Intelligent Textbooks. Technology, Knowledge and Learning, in press.</div>K.thakerhttps://adapt2.sis.pitt.edu/w/index.php?title=Open_Corpus_Personalized_Learning&diff=4179Open Corpus Personalized Learning2023-01-11T15:46:13Z<p>K.thaker: </p>
<hr />
<div>Goal: This project challenges the assumption that adaptive hypermedia systems require expensive knowledge engineering for domain and content modeling. It replaces carefully-crafted domain model with automatically-created domain models, lowering the cost of developing adaptive educational hypermedia software while also providing a wider range of instructional paths through the content. Adaptive educational hypermedia is known for its ability to improve learning outcomes and engagement maximizing educational opportunity for learners with different levels of knowledge. The development of this more automatic, open-corpus approach to adaptive educational hypermedia will increase the volume and the variety of resources available for meaningful online learning, especially for individuals learning on their own. Automatic knowledge indexing of educational content makes the system easy to maintain and update over time. These new open corpus user modeling techniques automatically adapt user models and personalized guidance to new materials as they are acquired. The ability to automatically organize, index, and adaptively recommend distributed educational content without the need of manual processing by system developers, enables new material to be integrated dynamically and with minimal effort in response to student needs.<br />
<br />
This project merges research on text analysis, human learning, and personalization to enable open corpus personalized learning. It develops its models of the domain and human learning from an initial set of well-organized, manually selected materials. Automatic text analysis creates an ensemble of domain models with different characteristics. Each individual model may be flawed or incomplete, however collectively they provide comprehensive coverage of the topic from several perspectives, thus reducing the manual effort required to create adaptive educational hypermedia. Multiple perspectives also give the system more flexibility in how to guide each student. These domain models are used as a foundation for building and maintaining dynamic models of user knowledge. The ensemble of domain and user models is used to deliver reactive and proactive adaptive guidance in an open corpus context. The growth of a person's knowledge is inferred by observing learner behavior and obtaining occasional feedback. This exploratory research opens the way to open corpus personalized learning. The domain modeling, user modeling, and personalization techniques developed in this research will be evaluated using a multi-layer framework that includes assessment by subject experts, performance prediction, cross-validation, and user studies.<br />
<br />
==The Project Team==<br />
<gallery><br />
Image:Peter.jpg|'''[[User:Peterb | Peter Brusilovsky]]'''<br/>Director<br />
Image:Daqing_He.png|'''[http://www.pitt.edu/~dah44/ Daqing He]'''<br/>Co-director<br />
Image:Yun_Acadamic.png|[http://columbus.exp.sis.pitt.edu/yunhuang/index.htm Yun Huang]<br />
Image:shuguang.png|[http://www.pitt.edu/~shh69/ Shuguang Han]<br />
Image:rui.png|[http://memray.me/ Rui Meng]<br />
Image:Sanqiang.png|[http://pitt.edu/~sanqiang Sanqiang Zhao]<br />
Image:Jordan.jpeg|[http://pitt.edu/~jab464 Jordan Barria-Pineda]<br />
Image:Avatar.jpg|[http://pitt.edu/~hkc6 Hung Chau]<br />
Image:k.thaker.png|[http://pitt.edu/~kmt81 Khushboo Thaker]<br />
<br />
</gallery><br />
<br />
==Education Content Linking==<br />
{|<br />
|valign="top" | [[ Image:Content-linking.png|thumb|left|'''100'''|Backbone connecting Educational Systems]]<br />
|valign="center" |<br />
Digitization has brought a significant change to the educational sector in the past ten years, making digital textbooks a convenient and popular resource for learning. Starting as merely a digital copy of paper textbooks, digital textbooks gradually integrated a number of innovative and advanced functionality. Rapid digitization also remarkably increased the volume of open learning resources, which have grown very popular, providing learners with great opportunities to expand their learning. Moreover, these new digital learning resources could be combined with the traditional learning process. For example, a learner may come across the concept of ``Indexing'' while reading a textbook on ``Information Retrieval''. This triggers her to engage extra online materials on ``Indexing'' to learn the state-of-the-art practical methods or research trends. <br />
<br />
The goal of the project is to address this challenge by providing links to related content at different textbooks material. In this project we would like to use concepts as a backbone for connecting different education materials. This is a challenging task since textbooks and online materials are written by different authors, for different audiences, and frequently use different terms/words to express the same concepts. Furthermore, many domains lack formal domain models or ontologies, where all domain concepts are listed and organized. Therefore, the association of terms and concepts, and the representation of concepts should be automatically discovered and constructed.<br />
<br />
To address these challenges, our work applies distributed neural representations, which demonstrated an ability to solve the term-mismatch problem by transferring the representation of terms to a N-dimension continuous vector (also called embedding). The expectation is that terms or words which share the same semantic meaning will be nearer in this N-dimensional continuous vector space. However, these approaches cannot be applied efficiently to educational resources, as they need a good deal of training data to learn efficient representations (for example vectors learned from entire Google News Corpus), while the volume of educational resources for less popular domains might be limited. In addition, these embeddings do not bring insights to topical distribution of terms, for example ``Precision'', ``Recall'', ``Cross-validation'' are concepts related to same topic ``Evaluation'' in ``Machine Learning'' domain. <br />
|}<br />
<br />
<br />
==Knowledge Extraction==<br />
{|<br />
|valign="top" | [[ Image:Knowledge-linking-Illustration.png|thumb|left|'''100'''|Dynamic Linking Textbooks]]<br />
|valign="center" |<br />
The Internet has dramatically increased both the volume and variety of online educational resources, such as online textbooks, online courses, and tutorials. The development of modern search techniques has further promoted the quick access of these resources. However, most of these educational resources are not well-structured, which imposed an important challenge -- readers without sufficient background knowledge may be difficult to understand its content. To achieve the goal of recommending ''the right content'' that matches individuals' knowledge levels, the first critical step is to provide a better organization for educational resources. The project visions two important components when organizing educational resources: (1) knowledge concept extraction; and (2) concept hierarchy extraction. Traditional solutions for these two problems heavily rely on experts' manual efforts which are time-consuming and unscalable. <br />
<br />
Our goal for knowledge extraction is to provide a scalable solution for the above two problems. We pilot our study with extracting knowledge structures from textbooks since they provide a comprehensive list of concepts and are often used as major educational resources in schools, colleges and universities. In addition, textbooks are also equipped with structural information such as table of contents and glossaries, which are very helpful in identifying concepts and their relationships. <br />
<br />
<br />
However, the automated extraction of knowledge concepts. Accurately extracting knowledge concepts from educational content is a challenge since the miss of a large-scale knowledge concept labels for building reliable machine learning algorithms. Considering the high time cost for expert-based labeling, we explore an alternative crowdsourcing-based, with restricted quality control, approach. That is, we distribute our knowledge concept labeling work to massive crowdsourcing workers, and further aggregate the obtained labels based on well-developed quality control methods in crowdsourcing. So far, we have built our annotation system and conducted several pilot studies. In the future, we would like to conduct a live experiment to examine the validity of this approach.<br />
|}<br />
<br />
== Learner Modeling ==<br />
{|<br />
|valign="top" | [[Image:ReadingLearningProcess.png|thumb|left|'''100'''|Dynamic Knowledge Modeling in Textbook Reading]]<br />
|valign="center" | We have recently proposed a data-driven framework for [[Dynamic Knowledge Modeling in Textbook-Based Learning| dynamic knowledge modeling in textbook-based learning]] (UMAP 2016). We formulated the problem of modeling learning from reading as a reading-time prediction problem, reconstructed existing popular student models (such as Knowledge Tracing) and explored two automatic text analysis approaches (bag-of-words-based and latent semantic-based) to build the KC model. This framework can be applied to a broader context of open-corpus personalized learning, empowering learners with the ability to access the right reading content at the right moment, despite the huge volume of online educational content. We are also working on applying [[Feature-Aware Student knowledge Tracing (FAST)|Feature-Aware Student knowledge Tracing (FAST)]] which is our new learner model proposed in 2014 with state-of-the-art predictive performance into textbook-based learning environment. <br />
<br />
Over past years, our lab has developed [[CUMULATE]], a centralized user modeling server built for the [[ADAPT2]] architecture, to provide user modeling support for adaptive educational hypermedia (AEH) systems. We have proposed and implemented different learner models , including [[CUMULATE asymptotic knowledge assessment|asymptotic assessment of user knowledge]]. We have explored different aspects to improve learner modeling, including reducing the content model, better evaluation for practitioners and applying network (graph) analysis.<br />
<br />
* [[Dynamic Knowledge Modeling in Textbook-Based Learning|More about dynamic knowledge modeling in textbook-based learning]]<br />
* [[Learner Modeling|More about learner modeling]]<br />
* [[Feature-Aware Student knowledge Tracing (FAST)|More about Feature-Aware Student knowledge Tracing (FAST)]]<br />
* [[CUMULATE|More about CUMULATE]]<br />
* [[CUMULATE asymptotic knowledge assessment|More about asymptotic assessment of user knowledge]]<br />
|}<br />
<br />
==The Experimental Platform==<br />
<br />
In order to support students' learning in the classroom environment, we have implemented a web platform for students to access class materials including textbooks, research publications, web tutorials, etc. More importantly, the system automatically records users' reading behaviors in order to be able to build their student models based on this data. By now, the reading system is able to render material in two formats:<br />
* Image-based<br />
* HTML-based (not tested yet in classroom studies)<br />
We plan to support pdf document rendering soon.<br />
<br />
[[Image:ReadingSystem_122016.jpg|thumb|left|alt=Current reading platform.|The experimental Reading System.]]<br />
<br />
The reading system is basically formed by 2 main parts:<br />
* The reader itself (see right side of the figure)<br />
* The student reading data section (see left side of the figure)<br />
<br />
In the student reading data section, the users can have access to two information sources. The first one is a sunburst hierarchical visualization tool (see upper section) that allows them to know their progress in the reading of the contents that are associated with the course using a color scale encoding from red (non-read) to green (totally read). The former version of this visualization tool is called [[ReadingCircle]]. <br />
The second one (see lower section) is the hierarchical index of the group, where each section<br />
The system is created for including learning material following a hierarchical structure in a similar way as books are structured (chapter, subchapter, section, etc.). In addition to this, the system allows the inclusion of multiple choice questions at the end of each section with the aim of test the acquired knowledge of the students.<br />
<br />
==Publications==<br />
* Huang, Yun and Yudelson, Michael and Han, Shuguang and He, Daqing and Brusilovsky, Peter. "A Framework for Dynamic Knowledge Modeling in Textbook-Based Learning." In Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization, pp. 141-150. ACM, 2016 ([http://d-scholarship.pitt.edu/28248/ paper]).<br />
* Meng, Rui and Han, Shuguang and Huang, Yun and He, Daqing and Brusilovsky, Peter. "Knowledge-based Content Linking for Online Textbooks." In Proceeding of the 2016 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 13-16. IEEE Computer Society, 2016. ([http://d-scholarship.pitt.edu/30486/1/wi16-knowledge-linking.pdf paper]).<br />
* Meng, R., Zhao, S., Han, S., He, D., Brusilovsky, P., and Chi, Y. (2017) Deep Keyphrase Generation. In: Proceedings of ACL2017, Annual Meeting of the Association for Computational Linguistics, Vancouver, Canada, July 30-August 4, 2017 pp. 582-592.<br />
* Labutov, I., Huang, Y., Brusilovsky, P., and He, D. (2017) Semi-Supervised Techniques for Mining Learning Outcomes and Prerequisites. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, ACM, pp. 907-915.<br />
* Thaker, K., Huang, Y., Brusilovsky, P., and He, D. (2018) Dynamic Knowledge Modeling with Heterogeneous Activities for Adaptive Textbooks. In: Proceedings of the 11th International Conference on Educational Data Mining, Buffalo, USA, July 15-18, 2018, pp. 592-595. [http://educationaldatamining.org/files/conferences/EDM2018/papers/EDM2018_paper_199.pdf paper]<br />
* Thaker, K. M., Brusilovsky, P., and He, D. (2018) Concept Enhanced Content Representation for Linking Educational Resources. In: Proceedings of 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), Santiago, Chile, December 3-6, 2018, IEEE, pp. 413-420.<br />
* Barria-Pineda, J., Brusilovsky, P., and He, D. (2019) Reading Mirror: Social Navigation and Social Comparison for Electronic Textbooks. In: Proceedings of First Workshop on Intelligent Textbooks at 20th International Conference on Artificial Intelligence in Education (AIED 2019), Chicago, USA, June 25, 2019, CEUR, pp. 30-37.<br />
* Thaker, K., Brusilovsky, P., and He, D. (2019) Student Modeling with Automatic Knowledge Component Extraction for Adaptive Textbooks. In: Proceedings of First Workshop on Intelligent Textbooks at 20th International Conference on Artificial Intelligence in Education (AIED 2019), Chicago, USA, June 25, 2019, CEUR, pp. 95-102.<br />
* Thaker, K., Zhang, L., He, D., and Brusilovsky, P. (2020) Recommending Remedial Readings Using Student’s Knowledge State. In: Proceedings of 13th International Conference on Educational Data Mining, July 10-13, 2020, pp. 233-244.<br />
* Yuan, X., Wang, T., Meng, R., Thaker, K., Brusilovsky, P., He, D., and Trischler, A. (2020) One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases. In: D. Jurafsky, J. Chai, N. Schluter and J. R. Tetreault (eds.) Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online, July 5-10, 2020, Association for Computational Linguistics, pp. 7961-7975.<br />
* Chau, H., Labutov, I., Thaker, K., He, D., and Brusilovsky, P. (2021) Automatic Concept Extraction for Domain and Student Modeling in Adaptive Textbooks. International Journal of Artificial Intelligence in Education 31 (4), 820–846.<br />
* Wang, M., Chau, H., Thaker, K., Brusilovsky, P., and He, D. (2022) Knowledge Annotation for Intelligent Textbooks. Technology, Knowledge and Learning, in press.</div>K.thakerhttps://adapt2.sis.pitt.edu/w/index.php?title=File:Content-linking.png&diff=4178File:Content-linking.png2023-01-11T15:45:05Z<p>K.thaker: </p>
<hr />
<div></div>K.thakerhttps://adapt2.sis.pitt.edu/w/index.php?title=Open_Corpus_Personalized_Learning&diff=4176Open Corpus Personalized Learning2023-01-11T15:31:17Z<p>K.thaker: </p>
<hr />
<div>Goal: This project challenges the assumption that adaptive hypermedia systems require expensive knowledge engineering for domain and content modeling. It replaces carefully-crafted domain model with automatically-created domain models, lowering the cost of developing adaptive educational hypermedia software while also providing a wider range of instructional paths through the content. Adaptive educational hypermedia is known for its ability to improve learning outcomes and engagement maximizing educational opportunity for learners with different levels of knowledge. The development of this more automatic, open-corpus approach to adaptive educational hypermedia will increase the volume and the variety of resources available for meaningful online learning, especially for individuals learning on their own. Automatic knowledge indexing of educational content makes the system easy to maintain and update over time. These new open corpus user modeling techniques automatically adapt user models and personalized guidance to new materials as they are acquired. The ability to automatically organize, index, and adaptively recommend distributed educational content without the need of manual processing by system developers, enables new material to be integrated dynamically and with minimal effort in response to student needs.<br />
<br />
This project merges research on text analysis, human learning, and personalization to enable open corpus personalized learning. It develops its models of the domain and human learning from an initial set of well-organized, manually selected materials. Automatic text analysis creates an ensemble of domain models with different characteristics. Each individual model may be flawed or incomplete, however collectively they provide comprehensive coverage of the topic from several perspectives, thus reducing the manual effort required to create adaptive educational hypermedia. Multiple perspectives also give the system more flexibility in how to guide each student. These domain models are used as a foundation for building and maintaining dynamic models of user knowledge. The ensemble of domain and user models is used to deliver reactive and proactive adaptive guidance in an open corpus context. The growth of a person's knowledge is inferred by observing learner behavior and obtaining occasional feedback. This exploratory research opens the way to open corpus personalized learning. The domain modeling, user modeling, and personalization techniques developed in this research will be evaluated using a multi-layer framework that includes assessment by subject experts, performance prediction, cross-validation, and user studies.<br />
<br />
==The Project Team==<br />
<gallery><br />
Image:Peter.jpg|'''[[User:Peterb | Peter Brusilovsky]]'''<br/>Director<br />
Image:Daqing_He.png|'''[http://www.pitt.edu/~dah44/ Daqing He]'''<br/>Co-director<br />
Image:Yun_Acadamic.png|[http://columbus.exp.sis.pitt.edu/yunhuang/index.htm Yun Huang]<br />
Image:shuguang.png|[http://www.pitt.edu/~shh69/ Shuguang Han]<br />
Image:rui.png|[http://memray.me/ Rui Meng]<br />
Image:Sanqiang.png|[http://pitt.edu/~sanqiang Sanqiang Zhao]<br />
Image:Jordan.jpeg|[http://pitt.edu/~jab464 Jordan Barria-Pineda]<br />
Image:Avatar.jpg|[http://pitt.edu/~hkc6 Hung Chau]<br />
Image:k.thaker.png|[http://pitt.edu/~kmt81 Khushboo Thaker]<br />
<br />
</gallery><br />
<br />
==Knowledge Extraction==<br />
{|<br />
|valign="top" | [[ Image:Knowledge-linking-Illustration.png|thumb|left|'''100'''|Dynamic Linking Textbooks]]<br />
|valign="center" |<br />
The Internet has dramatically increased both the volume and variety of online educational resources, such as online textbooks, online courses, and tutorials. The development of modern search techniques has further promoted the quick access of these resources. However, most of these educational resources are not well-structured, which imposed an important challenge -- readers without sufficient background knowledge may be difficult to understand its content. To achieve the goal of recommending ''the right content'' that matches individuals' knowledge levels, the first critical step is to provide a better organization for educational resources. The project visions two important components when organizing educational resources: (1) knowledge concept extraction; and (2) concept hierarchy extraction. Traditional solutions for these two problems heavily rely on experts' manual efforts which are time-consuming and unscalable. <br />
<br />
Our goal for knowledge extraction is to provide a scalable solution for the above two problems. We pilot our study with extracting knowledge structures from textbooks since they provide a comprehensive list of concepts and are often used as major educational resources in schools, colleges and universities. In addition, textbooks are also equipped with structural information such as table of contents and glossaries, which are very helpful in identifying concepts and their relationships. <br />
<br />
<br />
However, the automated extraction of knowledge concepts. Accurately extracting knowledge concepts from educational content is a challenge since the miss of a large-scale knowledge concept labels for building reliable machine learning algorithms. Considering the high time cost for expert-based labeling, we explore an alternative crowdsourcing-based, with restricted quality control, approach. That is, we distribute our knowledge concept labeling work to massive crowdsourcing workers, and further aggregate the obtained labels based on well-developed quality control methods in crowdsourcing. So far, we have built our annotation system and conducted several pilot studies. In the future, we would like to conduct a live experiment to examine the validity of this approach.<br />
|}<br />
<br />
== Learner Modeling ==<br />
{|<br />
|valign="top" | [[Image:ReadingLearningProcess.png|thumb|left|'''100'''|Dynamic Knowledge Modeling in Textbook Reading]]<br />
|valign="center" | We have recently proposed a data-driven framework for [[Dynamic Knowledge Modeling in Textbook-Based Learning| dynamic knowledge modeling in textbook-based learning]] (UMAP 2016). We formulated the problem of modeling learning from reading as a reading-time prediction problem, reconstructed existing popular student models (such as Knowledge Tracing) and explored two automatic text analysis approaches (bag-of-words-based and latent semantic-based) to build the KC model. This framework can be applied to a broader context of open-corpus personalized learning, empowering learners with the ability to access the right reading content at the right moment, despite the huge volume of online educational content. We are also working on applying [[Feature-Aware Student knowledge Tracing (FAST)|Feature-Aware Student knowledge Tracing (FAST)]] which is our new learner model proposed in 2014 with state-of-the-art predictive performance into textbook-based learning environment. <br />
<br />
Over past years, our lab has developed [[CUMULATE]], a centralized user modeling server built for the [[ADAPT2]] architecture, to provide user modeling support for adaptive educational hypermedia (AEH) systems. We have proposed and implemented different learner models , including [[CUMULATE asymptotic knowledge assessment|asymptotic assessment of user knowledge]]. We have explored different aspects to improve learner modeling, including reducing the content model, better evaluation for practitioners and applying network (graph) analysis.<br />
<br />
* [[Dynamic Knowledge Modeling in Textbook-Based Learning|More about dynamic knowledge modeling in textbook-based learning]]<br />
* [[Learner Modeling|More about learner modeling]]<br />
* [[Feature-Aware Student knowledge Tracing (FAST)|More about Feature-Aware Student knowledge Tracing (FAST)]]<br />
* [[CUMULATE|More about CUMULATE]]<br />
* [[CUMULATE asymptotic knowledge assessment|More about asymptotic assessment of user knowledge]]<br />
|}<br />
<br />
==The Experimental Platform==<br />
<br />
In order to support students' learning in the classroom environment, we have implemented a web platform for students to access class materials including textbooks, research publications, web tutorials, etc. More importantly, the system automatically records users' reading behaviors in order to be able to build their student models based on this data. By now, the reading system is able to render material in two formats:<br />
* Image-based<br />
* HTML-based (not tested yet in classroom studies)<br />
We plan to support pdf document rendering soon.<br />
<br />
[[Image:ReadingSystem_122016.jpg|thumb|left|alt=Current reading platform.|The experimental Reading System.]]<br />
<br />
The reading system is basically formed by 2 main parts:<br />
* The reader itself (see right side of the figure)<br />
* The student reading data section (see left side of the figure)<br />
<br />
In the student reading data section, the users can have access to two information sources. The first one is a sunburst hierarchical visualization tool (see upper section) that allows them to know their progress in the reading of the contents that are associated with the course using a color scale encoding from red (non-read) to green (totally read). The former version of this visualization tool is called [[ReadingCircle]]. <br />
The second one (see lower section) is the hierarchical index of the group, where each section<br />
The system is created for including learning material following a hierarchical structure in a similar way as books are structured (chapter, subchapter, section, etc.). In addition to this, the system allows the inclusion of multiple choice questions at the end of each section with the aim of test the acquired knowledge of the students.<br />
<br />
==Publications==<br />
* Huang, Yun and Yudelson, Michael and Han, Shuguang and He, Daqing and Brusilovsky, Peter. "A Framework for Dynamic Knowledge Modeling in Textbook-Based Learning." In Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization, pp. 141-150. ACM, 2016 ([http://d-scholarship.pitt.edu/28248/ paper]).<br />
* Meng, Rui and Han, Shuguang and Huang, Yun and He, Daqing and Brusilovsky, Peter. "Knowledge-based Content Linking for Online Textbooks." In Proceeding of the 2016 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 13-16. IEEE Computer Society, 2016. ([http://d-scholarship.pitt.edu/30486/1/wi16-knowledge-linking.pdf paper]).<br />
* Meng, R., Zhao, S., Han, S., He, D., Brusilovsky, P., and Chi, Y. (2017) Deep Keyphrase Generation. In: Proceedings of ACL2017, Annual Meeting of the Association for Computational Linguistics, Vancouver, Canada, July 30-August 4, 2017 pp. 582-592.<br />
* Labutov, I., Huang, Y., Brusilovsky, P., and He, D. (2017) Semi-Supervised Techniques for Mining Learning Outcomes and Prerequisites. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, ACM, pp. 907-915.<br />
* Thaker, K., Huang, Y., Brusilovsky, P., and He, D. (2018) Dynamic Knowledge Modeling with Heterogeneous Activities for Adaptive Textbooks. In: Proceedings of the 11th International Conference on Educational Data Mining, Buffalo, USA, July 15-18, 2018, pp. 592-595. [http://educationaldatamining.org/files/conferences/EDM2018/papers/EDM2018_paper_199.pdf paper]<br />
* Thaker, K. M., Brusilovsky, P., and He, D. (2018) Concept Enhanced Content Representation for Linking Educational Resources. In: Proceedings of 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), Santiago, Chile, December 3-6, 2018, IEEE, pp. 413-420.<br />
* Barria-Pineda, J., Brusilovsky, P., and He, D. (2019) Reading Mirror: Social Navigation and Social Comparison for Electronic Textbooks. In: Proceedings of First Workshop on Intelligent Textbooks at 20th International Conference on Artificial Intelligence in Education (AIED 2019), Chicago, USA, June 25, 2019, CEUR, pp. 30-37.<br />
* Thaker, K., Brusilovsky, P., and He, D. (2019) Student Modeling with Automatic Knowledge Component Extraction for Adaptive Textbooks. In: Proceedings of First Workshop on Intelligent Textbooks at 20th International Conference on Artificial Intelligence in Education (AIED 2019), Chicago, USA, June 25, 2019, CEUR, pp. 95-102.<br />
* Thaker, K., Zhang, L., He, D., and Brusilovsky, P. (2020) Recommending Remedial Readings Using Student’s Knowledge State. In: Proceedings of 13th International Conference on Educational Data Mining, July 10-13, 2020, pp. 233-244.<br />
* Yuan, X., Wang, T., Meng, R., Thaker, K., Brusilovsky, P., He, D., and Trischler, A. (2020) One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases. In: D. Jurafsky, J. Chai, N. Schluter and J. R. Tetreault (eds.) Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online, July 5-10, 2020, Association for Computational Linguistics, pp. 7961-7975.<br />
* Chau, H., Labutov, I., Thaker, K., He, D., and Brusilovsky, P. (2021) Automatic Concept Extraction for Domain and Student Modeling in Adaptive Textbooks. International Journal of Artificial Intelligence in Education 31 (4), 820–846.<br />
* Wang, M., Chau, H., Thaker, K., Brusilovsky, P., and He, D. (2022) Knowledge Annotation for Intelligent Textbooks. Technology, Knowledge and Learning, in press.</div>K.thakerhttps://adapt2.sis.pitt.edu/w/index.php?title=Open_Corpus_Personalized_Learning&diff=4175Open Corpus Personalized Learning2023-01-11T15:25:31Z<p>K.thaker: </p>
<hr />
<div>Goal: This project challenges the assumption that adaptive hypermedia systems require expensive knowledge engineering for domain and content modeling. It replaces carefully-crafted domain model with automatically-created domain models, lowering the cost of developing adaptive educational hypermedia software while also providing a wider range of instructional paths through the content. Adaptive educational hypermedia is known for its ability to improve learning outcomes and engagement maximizing educational opportunity for learners with different levels of knowledge. The development of this more automatic, open-corpus approach to adaptive educational hypermedia will increase the volume and the variety of resources available for meaningful online learning, especially for individuals learning on their own. Automatic knowledge indexing of educational content makes the system easy to maintain and update over time. These new open corpus user modeling techniques automatically adapt user models and personalized guidance to new materials as they are acquired. The ability to automatically organize, index, and adaptively recommend distributed educational content without the need of manual processing by system developers, enables new material to be integrated dynamically and with minimal effort in response to student needs.<br />
<br />
This project merges research on text analysis, human learning, and personalization to enable open corpus personalized learning. It develops its models of the domain and human learning from an initial set of well-organized, manually selected materials. Automatic text analysis creates an ensemble of domain models with different characteristics. Each individual model may be flawed or incomplete, however collectively they provide comprehensive coverage of the topic from several perspectives, thus reducing the manual effort required to create adaptive educational hypermedia. Multiple perspectives also give the system more flexibility in how to guide each student. These domain models are used as a foundation for building and maintaining dynamic models of user knowledge. The ensemble of domain and user models is used to deliver reactive and proactive adaptive guidance in an open corpus context. The growth of a person's knowledge is inferred by observing learner behavior and obtaining occasional feedback. This exploratory research opens the way to open corpus personalized learning. The domain modeling, user modeling, and personalization techniques developed in this research will be evaluated using a multi-layer framework that includes assessment by subject experts, performance prediction, cross-validation, and user studies.<br />
<br />
==The Project Team==<br />
<gallery><br />
Image:Peter.jpg|'''[[User:Peterb | Peter Brusilovsky]]'''<br/>Director<br />
Image:Daqing_He.png|'''[http://www.pitt.edu/~dah44/ Daqing He]'''<br/>Co-director<br />
Image:Yun_Acadamic.png|[http://columbus.exp.sis.pitt.edu/yunhuang/index.htm Yun Huang]<br />
Image:shuguang.png|[http://www.pitt.edu/~shh69/ Shuguang Han]<br />
Image:rui.png|[http://memray.me/ Rui Meng]<br />
Image:Sanqiang.png|[http://pitt.edu/~sanqiang Sanqiang Zhao]<br />
Image:Jordan.jpeg|[http://pitt.edu/~jab464 Jordan Barria-Pineda]<br />
Image:Avatar.jpg|[http://pitt.edu/~hkc6 Hung Chau]<br />
Image:k.thaker.png|[http://pitt.edu/~kmt81 Khushboo Thaker]<br />
<br />
</gallery><br />
<br />
==Knowledge Extraction==<br />
{|<br />
|valign="top" | [[ Image:Knowledge-linking-Illustration.png|thumb|left|'''100'''|Dynamic Knowledge Modeling in Textbook Reading]]<br />
|valign="center" |<br />
The Internet has dramatically increased both the volume and variety of online educational resources, such as online textbooks, online courses, and tutorials. The development of modern search techniques has further promoted the quick access of these resources. However, most of these educational resources are not well-structured, which imposed an important challenge -- readers without sufficient background knowledge may be difficult to understand its content. To achieve the goal of recommending ''the right content'' that matches individuals' knowledge levels, the first critical step is to provide a better organization for educational resources. The project visions two important components when organizing educational resources: (1) knowledge concept extraction; and (2) concept hierarchy extraction. Traditional solutions for these two problems heavily rely on experts' manual efforts which are time-consuming and unscalable. <br />
<br />
Our goal for knowledge extraction is to provide a scalable solution for the above two problems. We pilot our study with extracting knowledge structures from textbooks since they provide a comprehensive list of concepts and are often used as major educational resources in schools, colleges and universities. In addition, textbooks are also equipped with structural information such as table of contents and glossaries, which are very helpful in identifying concepts and their relationships. <br />
<br />
<br />
However, the automated extraction of knowledge concepts. Accurately extracting knowledge concepts from educational content is a challenge since the miss of a large-scale knowledge concept labels for building reliable machine learning algorithms. Considering the high time cost for expert-based labeling, we explore an alternative crowdsourcing-based, with restricted quality control, approach. That is, we distribute our knowledge concept labeling work to massive crowdsourcing workers, and further aggregate the obtained labels based on well-developed quality control methods in crowdsourcing. So far, we have built our annotation system and conducted several pilot studies. In the future, we would like to conduct a live experiment to examine the validity of this approach.<br />
|}<br />
<br />
== Learner Modeling ==<br />
{|<br />
|valign="top" | [[Image:ReadingLearningProcess.png|thumb|left|'''100'''|Dynamic Knowledge Modeling in Textbook Reading]]<br />
|valign="center" | We have recently proposed a data-driven framework for [[Dynamic Knowledge Modeling in Textbook-Based Learning| dynamic knowledge modeling in textbook-based learning]] (UMAP 2016). We formulated the problem of modeling learning from reading as a reading-time prediction problem, reconstructed existing popular student models (such as Knowledge Tracing) and explored two automatic text analysis approaches (bag-of-words-based and latent semantic-based) to build the KC model. This framework can be applied to a broader context of open-corpus personalized learning, empowering learners with the ability to access the right reading content at the right moment, despite the huge volume of online educational content. We are also working on applying [[Feature-Aware Student knowledge Tracing (FAST)|Feature-Aware Student knowledge Tracing (FAST)]] which is our new learner model proposed in 2014 with state-of-the-art predictive performance into textbook-based learning environment. <br />
<br />
Over past years, our lab has developed [[CUMULATE]], a centralized user modeling server built for the [[ADAPT2]] architecture, to provide user modeling support for adaptive educational hypermedia (AEH) systems. We have proposed and implemented different learner models , including [[CUMULATE asymptotic knowledge assessment|asymptotic assessment of user knowledge]]. We have explored different aspects to improve learner modeling, including reducing the content model, better evaluation for practitioners and applying network (graph) analysis.<br />
<br />
* [[Dynamic Knowledge Modeling in Textbook-Based Learning|More about dynamic knowledge modeling in textbook-based learning]]<br />
* [[Learner Modeling|More about learner modeling]]<br />
* [[Feature-Aware Student knowledge Tracing (FAST)|More about Feature-Aware Student knowledge Tracing (FAST)]]<br />
* [[CUMULATE|More about CUMULATE]]<br />
* [[CUMULATE asymptotic knowledge assessment|More about asymptotic assessment of user knowledge]]<br />
|}<br />
<br />
==The Experimental Platform==<br />
<br />
In order to support students' learning in the classroom environment, we have implemented a web platform for students to access class materials including textbooks, research publications, web tutorials, etc. More importantly, the system automatically records users' reading behaviors in order to be able to build their student models based on this data. By now, the reading system is able to render material in two formats:<br />
* Image-based<br />
* HTML-based (not tested yet in classroom studies)<br />
We plan to support pdf document rendering soon.<br />
<br />
[[Image:ReadingSystem_122016.jpg|thumb|left|alt=Current reading platform.|The experimental Reading System.]]<br />
<br />
The reading system is basically formed by 2 main parts:<br />
* The reader itself (see right side of the figure)<br />
* The student reading data section (see left side of the figure)<br />
<br />
In the student reading data section, the users can have access to two information sources. The first one is a sunburst hierarchical visualization tool (see upper section) that allows them to know their progress in the reading of the contents that are associated with the course using a color scale encoding from red (non-read) to green (totally read). The former version of this visualization tool is called [[ReadingCircle]]. <br />
The second one (see lower section) is the hierarchical index of the group, where each section<br />
The system is created for including learning material following a hierarchical structure in a similar way as books are structured (chapter, subchapter, section, etc.). In addition to this, the system allows the inclusion of multiple choice questions at the end of each section with the aim of test the acquired knowledge of the students.<br />
<br />
==Publications==<br />
* Huang, Yun and Yudelson, Michael and Han, Shuguang and He, Daqing and Brusilovsky, Peter. "A Framework for Dynamic Knowledge Modeling in Textbook-Based Learning." In Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization, pp. 141-150. ACM, 2016 ([http://d-scholarship.pitt.edu/28248/ paper]).<br />
* Meng, Rui and Han, Shuguang and Huang, Yun and He, Daqing and Brusilovsky, Peter. "Knowledge-based Content Linking for Online Textbooks." In Proceeding of the 2016 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 13-16. IEEE Computer Society, 2016. ([http://d-scholarship.pitt.edu/30486/1/wi16-knowledge-linking.pdf paper]).<br />
* Meng, R., Zhao, S., Han, S., He, D., Brusilovsky, P., and Chi, Y. (2017) Deep Keyphrase Generation. In: Proceedings of ACL2017, Annual Meeting of the Association for Computational Linguistics, Vancouver, Canada, July 30-August 4, 2017 pp. 582-592.<br />
* Labutov, I., Huang, Y., Brusilovsky, P., and He, D. (2017) Semi-Supervised Techniques for Mining Learning Outcomes and Prerequisites. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, ACM, pp. 907-915.<br />
* Thaker, K., Huang, Y., Brusilovsky, P., and He, D. (2018) Dynamic Knowledge Modeling with Heterogeneous Activities for Adaptive Textbooks. In: Proceedings of the 11th International Conference on Educational Data Mining, Buffalo, USA, July 15-18, 2018, pp. 592-595. [http://educationaldatamining.org/files/conferences/EDM2018/papers/EDM2018_paper_199.pdf paper]<br />
* Thaker, K. M., Brusilovsky, P., and He, D. (2018) Concept Enhanced Content Representation for Linking Educational Resources. In: Proceedings of 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), Santiago, Chile, December 3-6, 2018, IEEE, pp. 413-420.<br />
* Barria-Pineda, J., Brusilovsky, P., and He, D. (2019) Reading Mirror: Social Navigation and Social Comparison for Electronic Textbooks. In: Proceedings of First Workshop on Intelligent Textbooks at 20th International Conference on Artificial Intelligence in Education (AIED 2019), Chicago, USA, June 25, 2019, CEUR, pp. 30-37.<br />
* Thaker, K., Brusilovsky, P., and He, D. (2019) Student Modeling with Automatic Knowledge Component Extraction for Adaptive Textbooks. In: Proceedings of First Workshop on Intelligent Textbooks at 20th International Conference on Artificial Intelligence in Education (AIED 2019), Chicago, USA, June 25, 2019, CEUR, pp. 95-102.<br />
* Thaker, K., Zhang, L., He, D., and Brusilovsky, P. (2020) Recommending Remedial Readings Using Student’s Knowledge State. In: Proceedings of 13th International Conference on Educational Data Mining, July 10-13, 2020, pp. 233-244.<br />
* Yuan, X., Wang, T., Meng, R., Thaker, K., Brusilovsky, P., He, D., and Trischler, A. (2020) One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases. In: D. Jurafsky, J. Chai, N. Schluter and J. R. Tetreault (eds.) Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online, July 5-10, 2020, Association for Computational Linguistics, pp. 7961-7975.<br />
* Chau, H., Labutov, I., Thaker, K., He, D., and Brusilovsky, P. (2021) Automatic Concept Extraction for Domain and Student Modeling in Adaptive Textbooks. International Journal of Artificial Intelligence in Education 31 (4), 820–846.<br />
* Wang, M., Chau, H., Thaker, K., Brusilovsky, P., and He, D. (2022) Knowledge Annotation for Intelligent Textbooks. Technology, Knowledge and Learning, in press.</div>K.thakerhttps://adapt2.sis.pitt.edu/w/index.php?title=Projects&diff=4077Projects2021-01-21T14:26:22Z<p>K.thaker: </p>
<hr />
<div>This page presents a list of funded projects performed by PAWS Lab. The most recent projects are shown at the top of the list.<br />
<br />
==[[HELPeR - Health e-Librarian with Personalized Recommender]]==<br />
As the Internet has become a prominent source of health information to guide patients’ decision-making and self-management activities, patients strongly indicate they need navigational support to locate appropriate information on the Internet. The overall goal of our project is to build and implement a “Health E-Librarian with Personalized Recommendations (HELPeR)”, a personalized digital librarian that provides individualized, reliable online information relevant to the patient’s needs, interests and knowledge across the disease trajectory.<br />
<br />
For a fuller description of our vision and goals, see the [home page of the project http://www.pitt.edu/~dah44/helper/]<br />
<br />
Supported by NIH grant R01-LM013038-02 (2019 - 2022). [https://www.grantome.com/grant/NIH/R01-LM013038-02 more]<br />
<br />
==[[Community-Building and Infrastructure Design for Data-Intensive Research in Computer Science Education]]==<br />
The mission of this collaborative project is to support the CS Education community by supplying documentation and infrastructure to help with adopting shared standards, protocols, and tools. In this way we hope to promote<br />
* development and broader re-use of innovative learning content that is instrumented for rich data collection;<br />
* formats and tools for analysis of learner data; and<br />
* best practices to make large collections of learner data and associated analytics available to researchers in the CSE, data science, and learner science communities.<br />
For a fuller description of our vision and goals, see the [home page of the project https://cssplice.github.io/]<br />
<br />
Supported by NSF grant EHR 1740775 (2017-2021). [[Community-Building and Infrastructure Design for Data-Intensive Research in Computer Science Education|==> more]]<br />
<br />
==[[Open Corpus Personalized Learning]]==<br />
This project challenges the assumption that adaptive hypermedia systems require expensive knowledge engineering for domain and content modeling. It replaces the carefully-crafted domain model with automatically-created domain models, lowering the cost of developing such systems while also providing a wider range of instructional paths through the content. <br />
<br />
Supported by NSF grant IIS 1525186 (2015-2018). [[Open Corpus Personalized Learning|==> more]]<br />
<br />
== [[Adaptive Navigation Support and Open Social Learner Modeling for PAL]] ==<br />
The goal of this project is to leverage the power of [[open social learner modeling]] and [[adaptive navigation support]] in the context of the envisioned Personalized Assistant for Learning (PAL). <br />
<br />
Supported by the [http://adlnet.gov|Advanced Distributed Learning Initiative] contract W911QY13C0032 (2013-2016). [[Adaptive Navigation Support and Open Social Learner Modeling for PAL|==> more]]<br />
<br />
==[[Ensemble|Ensemble: Enriching Communities and Collections to Support Education in Computing]]==<br />
[[Ensemble]] is a cross-university collaborative effort that aims to bring together the global community of computing educators around a growing set of content collections with high-quality educational resources.<br />
<br />
Supported by NSF (2008-2014). [[Ensemble|==> more]]<br />
==[[Engaging Students in Online Reading Through Social Progress Visualization]]==<br />
<br />
This project explores an alternative approach to encourage student online textbook reading using a social progress visualization interface.<br />
<br />
Supported by Innovation in Education Award, University of Pittsburgh (2012-2013). [[Engaging Students in Online Reading Through Social Progress Visualization|==> more]]<br />
<br />
== [[Personalized Social Systems for Local Communities]] ==<br />
The project explored the use of personalization and mobile computing to increase user engagement in location-bound social systems. <br />
<br />
Supported by Google (2010-2012). [[Personalized Social Systems for Local Communities|==> more]]<br />
<br />
== [[Personalization and social networking for short-term communities]] ==<br />
The project explored a range of approaches, which can enable reliable social networking and personalization in communities, which exist for short period of time, like researchers attending a specific conference. <br />
<br />
Supported by NSF (2010-2011). In collaboration with Jung Sun Oh. [[Personalization and social networking for short-term communities|==> more]]<br />
<br />
==Modeling and Visualization of Latent Communities==<br />
The project focused on the problem of discovering latent communities from Social Web data and presenting this data in visual form. <br />
<br />
Supported by the Institute for Defense Analysis and NSF (2010-2012). [[Modeling and Visualization of Latent Communities|==> more]]<br />
<br />
== Personalized Exploratorium for Database Courses ==<br />
The project focused on developing and evaluation of a personalized educational environment for teaching Database courses. <br />
<br />
Supported by NSF (2007-2008). In collaboration with Vladimir Zadorozhny. [[Personalized Exploratorium for Database Courses|==> more]]<br />
<br />
== GALE: Distillation with Utility-Optimized Transcription and Translation ==<br />
Supported by DARPA (2005-2007) In collaboration with Carnegie Mellon University and IBM [[GALE: Distillation with Utility-Optimized Transcription and Translation|==> more]]<br />
<br />
== Personalized Access to Open Corpus Educational Resources through Adaptive Navigation Support and Adaptive Visualization ==<br />
The project focused on developing technologies for personalized access to information based on adaptive navigation support, collaborative filtering, and information visualization.<br />
<br />
Supported by NSF (2005-2010). [[Personalized Access to Open Corpus Educational Resources through Adaptive Navigation Support and Adaptive Visualization|==> more]]<br />
<br />
== Adaptive Explanatory Visualization for Learning Programming Concepts ==<br />
The project focused on developing and studying adaptive explanatory visualization technologies for C and Java programming languages. <br />
<br />
Supported by NSF (2004-2007). In collaboration with Michael Spring. [[Adaptive Explanatory Visualization for Learning Programming Concepts|==> more]]<br />
<br />
== Individualized Exercises for Assessment and Self-Assessment of Programming Knowledge ==<br />
<br />
The project focused developing and evaluating a personalized assessment technology for programming courses.<br />
<br />
Supported by NSF (2003-2005). [[Individualized Exercises for Assessment and Self-Assessment of Programming Knowledge|==> more]]<br />
<br />
== Educational Software for Teaching and Learning Information Retrieval ==<br />
<br />
Supported by Innovation in Education Award, University of Pittsburgh (2003-2004). [[Educational Software for Teaching and Learning Information Retrieval| ==> more]]<br />
<br />
== Supporting Learning from Examples in a Programming Course ==<br />
<br />
Supported by Innovation in Education Award, University of Pittsburgh (2001-2002). [[Supporting Learning from Examples in a Programming Course|==> more]]<br />
<br />
== Adaptive Electronic Textbooks for World Wide Web == <br />
Supported by NSF (1997-1998). In collaboration with John Anderson and Gerhard Weber [[Adaptive Electronic Textbooks for World Wide Web|==> more]]</div>K.thakerhttps://adapt2.sis.pitt.edu/w/index.php?title=Projects&diff=4076Projects2021-01-21T14:25:43Z<p>K.thaker: </p>
<hr />
<div>This page presents a list of funded projects performed by PAWS Lab. The most recent projects are shown at the top of the list.<br />
<br />
==[[ HELPeR - Health e-Librarian with Personalized Recommender]]==<br />
As the Internet has become a prominent source of health information to guide patients’ decision-making and self-management activities, patients strongly indicate they need navigational support to locate appropriate information on the Internet. The overall goal of our project is to build and implement a “Health E-Librarian with Personalized Recommendations (HELPeR)”, a personalized digital librarian that provides individualized, reliable online information relevant to the patient’s needs, interests and knowledge across the disease trajectory.<br />
<br />
For a fuller description of our vision and goals, see the [home page of the project http://www.pitt.edu/~dah44/helper/]<br />
<br />
Supported by NIH grant R01-LM013038-02 (2019 - 2022). [https://www.grantome.com/grant/NIH/R01-LM013038-02 more]<br />
<br />
==[[Community-Building and Infrastructure Design for Data-Intensive Research in Computer Science Education]]==<br />
The mission of this collaborative project is to support the CS Education community by supplying documentation and infrastructure to help with adopting shared standards, protocols, and tools. In this way we hope to promote<br />
* development and broader re-use of innovative learning content that is instrumented for rich data collection;<br />
* formats and tools for analysis of learner data; and<br />
* best practices to make large collections of learner data and associated analytics available to researchers in the CSE, data science, and learner science communities.<br />
For a fuller description of our vision and goals, see the [home page of the project https://cssplice.github.io/]<br />
<br />
Supported by NSF grant EHR 1740775 (2017-2021). [[Community-Building and Infrastructure Design for Data-Intensive Research in Computer Science Education|==> more]]<br />
<br />
==[[Open Corpus Personalized Learning]]==<br />
This project challenges the assumption that adaptive hypermedia systems require expensive knowledge engineering for domain and content modeling. It replaces the carefully-crafted domain model with automatically-created domain models, lowering the cost of developing such systems while also providing a wider range of instructional paths through the content. <br />
<br />
Supported by NSF grant IIS 1525186 (2015-2018). [[Open Corpus Personalized Learning|==> more]]<br />
<br />
== [[Adaptive Navigation Support and Open Social Learner Modeling for PAL]] ==<br />
The goal of this project is to leverage the power of [[open social learner modeling]] and [[adaptive navigation support]] in the context of the envisioned Personalized Assistant for Learning (PAL). <br />
<br />
Supported by the [http://adlnet.gov|Advanced Distributed Learning Initiative] contract W911QY13C0032 (2013-2016). [[Adaptive Navigation Support and Open Social Learner Modeling for PAL|==> more]]<br />
<br />
==[[Ensemble|Ensemble: Enriching Communities and Collections to Support Education in Computing]]==<br />
[[Ensemble]] is a cross-university collaborative effort that aims to bring together the global community of computing educators around a growing set of content collections with high-quality educational resources.<br />
<br />
Supported by NSF (2008-2014). [[Ensemble|==> more]]<br />
==[[Engaging Students in Online Reading Through Social Progress Visualization]]==<br />
<br />
This project explores an alternative approach to encourage student online textbook reading using a social progress visualization interface.<br />
<br />
Supported by Innovation in Education Award, University of Pittsburgh (2012-2013). [[Engaging Students in Online Reading Through Social Progress Visualization|==> more]]<br />
<br />
== [[Personalized Social Systems for Local Communities]] ==<br />
The project explored the use of personalization and mobile computing to increase user engagement in location-bound social systems. <br />
<br />
Supported by Google (2010-2012). [[Personalized Social Systems for Local Communities|==> more]]<br />
<br />
== [[Personalization and social networking for short-term communities]] ==<br />
The project explored a range of approaches, which can enable reliable social networking and personalization in communities, which exist for short period of time, like researchers attending a specific conference. <br />
<br />
Supported by NSF (2010-2011). In collaboration with Jung Sun Oh. [[Personalization and social networking for short-term communities|==> more]]<br />
<br />
==Modeling and Visualization of Latent Communities==<br />
The project focused on the problem of discovering latent communities from Social Web data and presenting this data in visual form. <br />
<br />
Supported by the Institute for Defense Analysis and NSF (2010-2012). [[Modeling and Visualization of Latent Communities|==> more]]<br />
<br />
== Personalized Exploratorium for Database Courses ==<br />
The project focused on developing and evaluation of a personalized educational environment for teaching Database courses. <br />
<br />
Supported by NSF (2007-2008). In collaboration with Vladimir Zadorozhny. [[Personalized Exploratorium for Database Courses|==> more]]<br />
<br />
== GALE: Distillation with Utility-Optimized Transcription and Translation ==<br />
Supported by DARPA (2005-2007) In collaboration with Carnegie Mellon University and IBM [[GALE: Distillation with Utility-Optimized Transcription and Translation|==> more]]<br />
<br />
== Personalized Access to Open Corpus Educational Resources through Adaptive Navigation Support and Adaptive Visualization ==<br />
The project focused on developing technologies for personalized access to information based on adaptive navigation support, collaborative filtering, and information visualization.<br />
<br />
Supported by NSF (2005-2010). [[Personalized Access to Open Corpus Educational Resources through Adaptive Navigation Support and Adaptive Visualization|==> more]]<br />
<br />
== Adaptive Explanatory Visualization for Learning Programming Concepts ==<br />
The project focused on developing and studying adaptive explanatory visualization technologies for C and Java programming languages. <br />
<br />
Supported by NSF (2004-2007). In collaboration with Michael Spring. [[Adaptive Explanatory Visualization for Learning Programming Concepts|==> more]]<br />
<br />
== Individualized Exercises for Assessment and Self-Assessment of Programming Knowledge ==<br />
<br />
The project focused developing and evaluating a personalized assessment technology for programming courses.<br />
<br />
Supported by NSF (2003-2005). [[Individualized Exercises for Assessment and Self-Assessment of Programming Knowledge|==> more]]<br />
<br />
== Educational Software for Teaching and Learning Information Retrieval ==<br />
<br />
Supported by Innovation in Education Award, University of Pittsburgh (2003-2004). [[Educational Software for Teaching and Learning Information Retrieval| ==> more]]<br />
<br />
== Supporting Learning from Examples in a Programming Course ==<br />
<br />
Supported by Innovation in Education Award, University of Pittsburgh (2001-2002). [[Supporting Learning from Examples in a Programming Course|==> more]]<br />
<br />
== Adaptive Electronic Textbooks for World Wide Web == <br />
Supported by NSF (1997-1998). In collaboration with John Anderson and Gerhard Weber [[Adaptive Electronic Textbooks for World Wide Web|==> more]]</div>K.thakerhttps://adapt2.sis.pitt.edu/w/index.php?title=Projects&diff=4075Projects2021-01-21T14:23:37Z<p>K.thaker: </p>
<hr />
<div>This page presents a list of funded projects performed by PAWS Lab. The most recent projects are shown at the top of the list.<br />
<br />
<br />
==[[Community-Building and Infrastructure Design for Data-Intensive Research in Computer Science Education]]==<br />
The mission of this collaborative project is to support the CS Education community by supplying documentation and infrastructure to help with adopting shared standards, protocols, and tools. In this way we hope to promote<br />
* development and broader re-use of innovative learning content that is instrumented for rich data collection;<br />
* formats and tools for analysis of learner data; and<br />
* best practices to make large collections of learner data and associated analytics available to researchers in the CSE, data science, and learner science communities.<br />
For a fuller description of our vision and goals, see the [home page of the project https://cssplice.github.io/]<br />
<br />
Supported by NSF grant EHR 1740775 (2017-2021). [[Community-Building and Infrastructure Design for Data-Intensive Research in Computer Science Education|==> more]]<br />
<br />
==[[Open Corpus Personalized Learning]]==<br />
This project challenges the assumption that adaptive hypermedia systems require expensive knowledge engineering for domain and content modeling. It replaces the carefully-crafted domain model with automatically-created domain models, lowering the cost of developing such systems while also providing a wider range of instructional paths through the content. <br />
<br />
Supported by NSF grant IIS 1525186 (2015-2018). [[Open Corpus Personalized Learning|==> more]]<br />
<br />
== [[Adaptive Navigation Support and Open Social Learner Modeling for PAL]] ==<br />
The goal of this project is to leverage the power of [[open social learner modeling]] and [[adaptive navigation support]] in the context of the envisioned Personalized Assistant for Learning (PAL). <br />
<br />
Supported by the [http://adlnet.gov|Advanced Distributed Learning Initiative] contract W911QY13C0032 (2013-2016). [[Adaptive Navigation Support and Open Social Learner Modeling for PAL|==> more]]<br />
<br />
==[[Ensemble|Ensemble: Enriching Communities and Collections to Support Education in Computing]]==<br />
[[Ensemble]] is a cross-university collaborative effort that aims to bring together the global community of computing educators around a growing set of content collections with high-quality educational resources.<br />
<br />
Supported by NSF (2008-2014). [[Ensemble|==> more]]<br />
==[[Engaging Students in Online Reading Through Social Progress Visualization]]==<br />
<br />
This project explores an alternative approach to encourage student online textbook reading using a social progress visualization interface.<br />
<br />
Supported by Innovation in Education Award, University of Pittsburgh (2012-2013). [[Engaging Students in Online Reading Through Social Progress Visualization|==> more]]<br />
<br />
== [[Personalized Social Systems for Local Communities]] ==<br />
The project explored the use of personalization and mobile computing to increase user engagement in location-bound social systems. <br />
<br />
Supported by Google (2010-2012). [[Personalized Social Systems for Local Communities|==> more]]<br />
<br />
== [[Personalization and social networking for short-term communities]] ==<br />
The project explored a range of approaches, which can enable reliable social networking and personalization in communities, which exist for short period of time, like researchers attending a specific conference. <br />
<br />
Supported by NSF (2010-2011). In collaboration with Jung Sun Oh. [[Personalization and social networking for short-term communities|==> more]]<br />
<br />
==Modeling and Visualization of Latent Communities==<br />
The project focused on the problem of discovering latent communities from Social Web data and presenting this data in visual form. <br />
<br />
Supported by the Institute for Defense Analysis and NSF (2010-2012). [[Modeling and Visualization of Latent Communities|==> more]]<br />
<br />
== Personalized Exploratorium for Database Courses ==<br />
The project focused on developing and evaluation of a personalized educational environment for teaching Database courses. <br />
<br />
Supported by NSF (2007-2008). In collaboration with Vladimir Zadorozhny. [[Personalized Exploratorium for Database Courses|==> more]]<br />
<br />
== GALE: Distillation with Utility-Optimized Transcription and Translation ==<br />
Supported by DARPA (2005-2007) In collaboration with Carnegie Mellon University and IBM [[GALE: Distillation with Utility-Optimized Transcription and Translation|==> more]]<br />
<br />
== Personalized Access to Open Corpus Educational Resources through Adaptive Navigation Support and Adaptive Visualization ==<br />
The project focused on developing technologies for personalized access to information based on adaptive navigation support, collaborative filtering, and information visualization.<br />
<br />
Supported by NSF (2005-2010). [[Personalized Access to Open Corpus Educational Resources through Adaptive Navigation Support and Adaptive Visualization|==> more]]<br />
<br />
== Adaptive Explanatory Visualization for Learning Programming Concepts ==<br />
The project focused on developing and studying adaptive explanatory visualization technologies for C and Java programming languages. <br />
<br />
Supported by NSF (2004-2007). In collaboration with Michael Spring. [[Adaptive Explanatory Visualization for Learning Programming Concepts|==> more]]<br />
<br />
== Individualized Exercises for Assessment and Self-Assessment of Programming Knowledge ==<br />
<br />
The project focused developing and evaluating a personalized assessment technology for programming courses.<br />
<br />
Supported by NSF (2003-2005). [[Individualized Exercises for Assessment and Self-Assessment of Programming Knowledge|==> more]]<br />
<br />
== Educational Software for Teaching and Learning Information Retrieval ==<br />
<br />
Supported by Innovation in Education Award, University of Pittsburgh (2003-2004). [[Educational Software for Teaching and Learning Information Retrieval| ==> more]]<br />
<br />
== Supporting Learning from Examples in a Programming Course ==<br />
<br />
Supported by Innovation in Education Award, University of Pittsburgh (2001-2002). [[Supporting Learning from Examples in a Programming Course|==> more]]<br />
<br />
== Adaptive Electronic Textbooks for World Wide Web == <br />
Supported by NSF (1997-1998). In collaboration with John Anderson and Gerhard Weber [[Adaptive Electronic Textbooks for World Wide Web|==> more]]</div>K.thakerhttps://adapt2.sis.pitt.edu/w/index.php?title=Projects&diff=4074Projects2021-01-21T14:21:40Z<p>K.thaker: </p>
<hr />
<div>This page presents a list of funded projects performed by PAWS Lab. The most recent projects are shown at the top of the list.<br />
<br />
==[[ HELPeR - Health e-Librarian with Personalized Recommender]]==<br />
As the Internet has become a prominent source of health information to guide patients’ decision-making and self-management activities, patients strongly indicate they need navigational support to locate appropriate information on the Internet. The overall goal of our project is to build and implement a “Health E-Librarian with Personalized Recommendations (HELPeR)”, a personalized digital librarian that provides individualized, reliable online information relevant to the patient’s needs, interests, and knowledge across the disease trajectory.<br />
<br />
For a fuller description of our vision and goals, see the [home page of the project http://www.pitt.edu/~dah44/helper/]<br />
<br />
Supported by NIH grant R01-LM013038-02 (2019-2022). [[https://www.grantome.com/grant/NIH/R01-LM013038-02|more]]<br />
<br />
==[[Community-Building and Infrastructure Design for Data-Intensive Research in Computer Science Education]]==<br />
The mission of this collaborative project is to support the CS Education community by supplying documentation and infrastructure to help with adopting shared standards, protocols, and tools. In this way we hope to promote<br />
* development and broader re-use of innovative learning content that is instrumented for rich data collection;<br />
* formats and tools for analysis of learner data; and<br />
* best practices to make large collections of learner data and associated analytics available to researchers in the CSE, data science, and learner science communities.<br />
For a fuller description of our vision and goals, see the [home page of the project https://cssplice.github.io/]<br />
<br />
Supported by NSF grant EHR 1740775 (2017-2021). [[Community-Building and Infrastructure Design for Data-Intensive Research in Computer Science Education|==> more]]<br />
<br />
==[[Open Corpus Personalized Learning]]==<br />
This project challenges the assumption that adaptive hypermedia systems require expensive knowledge engineering for domain and content modeling. It replaces the carefully-crafted domain model with automatically-created domain models, lowering the cost of developing such systems while also providing a wider range of instructional paths through the content. <br />
<br />
Supported by NSF grant IIS 1525186 (2015-2018). [[Open Corpus Personalized Learning|==> more]]<br />
<br />
== [[Adaptive Navigation Support and Open Social Learner Modeling for PAL]] ==<br />
The goal of this project is to leverage the power of [[open social learner modeling]] and [[adaptive navigation support]] in the context of the envisioned Personalized Assistant for Learning (PAL). <br />
<br />
Supported by the [http://adlnet.gov|Advanced Distributed Learning Initiative] contract W911QY13C0032 (2013-2016). [[Adaptive Navigation Support and Open Social Learner Modeling for PAL|==> more]]<br />
<br />
==[[Ensemble|Ensemble: Enriching Communities and Collections to Support Education in Computing]]==<br />
[[Ensemble]] is a cross-university collaborative effort that aims to bring together the global community of computing educators around a growing set of content collections with high-quality educational resources.<br />
<br />
Supported by NSF (2008-2014). [[Ensemble|==> more]]<br />
==[[Engaging Students in Online Reading Through Social Progress Visualization]]==<br />
<br />
This project explores an alternative approach to encourage student online textbook reading using a social progress visualization interface.<br />
<br />
Supported by Innovation in Education Award, University of Pittsburgh (2012-2013). [[Engaging Students in Online Reading Through Social Progress Visualization|==> more]]<br />
<br />
== [[Personalized Social Systems for Local Communities]] ==<br />
The project explored the use of personalization and mobile computing to increase user engagement in location-bound social systems. <br />
<br />
Supported by Google (2010-2012). [[Personalized Social Systems for Local Communities|==> more]]<br />
<br />
== [[Personalization and social networking for short-term communities]] ==<br />
The project explored a range of approaches, which can enable reliable social networking and personalization in communities, which exist for short period of time, like researchers attending a specific conference. <br />
<br />
Supported by NSF (2010-2011). In collaboration with Jung Sun Oh. [[Personalization and social networking for short-term communities|==> more]]<br />
<br />
==Modeling and Visualization of Latent Communities==<br />
The project focused on the problem of discovering latent communities from Social Web data and presenting this data in visual form. <br />
<br />
Supported by the Institute for Defense Analysis and NSF (2010-2012). [[Modeling and Visualization of Latent Communities|==> more]]<br />
<br />
== Personalized Exploratorium for Database Courses ==<br />
The project focused on developing and evaluation of a personalized educational environment for teaching Database courses. <br />
<br />
Supported by NSF (2007-2008). In collaboration with Vladimir Zadorozhny. [[Personalized Exploratorium for Database Courses|==> more]]<br />
<br />
== GALE: Distillation with Utility-Optimized Transcription and Translation ==<br />
Supported by DARPA (2005-2007) In collaboration with Carnegie Mellon University and IBM [[GALE: Distillation with Utility-Optimized Transcription and Translation|==> more]]<br />
<br />
== Personalized Access to Open Corpus Educational Resources through Adaptive Navigation Support and Adaptive Visualization ==<br />
The project focused on developing technologies for personalized access to information based on adaptive navigation support, collaborative filtering, and information visualization.<br />
<br />
Supported by NSF (2005-2010). [[Personalized Access to Open Corpus Educational Resources through Adaptive Navigation Support and Adaptive Visualization|==> more]]<br />
<br />
== Adaptive Explanatory Visualization for Learning Programming Concepts ==<br />
The project focused on developing and studying adaptive explanatory visualization technologies for C and Java programming languages. <br />
<br />
Supported by NSF (2004-2007). In collaboration with Michael Spring. [[Adaptive Explanatory Visualization for Learning Programming Concepts|==> more]]<br />
<br />
== Individualized Exercises for Assessment and Self-Assessment of Programming Knowledge ==<br />
<br />
The project focused developing and evaluating a personalized assessment technology for programming courses.<br />
<br />
Supported by NSF (2003-2005). [[Individualized Exercises for Assessment and Self-Assessment of Programming Knowledge|==> more]]<br />
<br />
== Educational Software for Teaching and Learning Information Retrieval ==<br />
<br />
Supported by Innovation in Education Award, University of Pittsburgh (2003-2004). [[Educational Software for Teaching and Learning Information Retrieval| ==> more]]<br />
<br />
== Supporting Learning from Examples in a Programming Course ==<br />
<br />
Supported by Innovation in Education Award, University of Pittsburgh (2001-2002). [[Supporting Learning from Examples in a Programming Course|==> more]]<br />
<br />
== Adaptive Electronic Textbooks for World Wide Web == <br />
Supported by NSF (1997-1998). In collaboration with John Anderson and Gerhard Weber [[Adaptive Electronic Textbooks for World Wide Web|==> more]]</div>K.thakerhttps://adapt2.sis.pitt.edu/w/index.php?title=Projects&diff=4073Projects2021-01-21T14:19:37Z<p>K.thaker: </p>
<hr />
<div>This page presents a list of funded projects performed by PAWS Lab. The most recent projects are shown at the top of the list.<br />
<br />
==[[ HELPeR - Health e-Librarian with Personalized Recommender]]==<br />
As the Internet has become a prominent source of health information to guide patients’ decision-making and self-management activities, patients strongly indicate they need navigational support to locate appropriate information on the Internet. The overall goal of our project is to build and implement a “Health E-Librarian with Personalized Recommendations (HELPeR)”, a personalized digital librarian that provides individualized, reliable online information relevant to the patient’s needs, interests, and knowledge across the disease trajectory.<br />
<br />
For a fuller description of our vision and goals, see the [home page of the project http://www.pitt.edu/~dah44/helper/]<br />
<br />
Supported by NIH grant R01-LM013038-02 (2019-2022). [[https://www.grantome.com/grant/NIH/R01-LM013038-02|==> more]]<br />
<br />
==[[Community-Building and Infrastructure Design for Data-Intensive Research in Computer Science Education]]==<br />
The mission of this collaborative project is to support the CS Education community by supplying documentation and infrastructure to help with adopting shared standards, protocols, and tools. In this way we hope to promote<br />
* development and broader re-use of innovative learning content that is instrumented for rich data collection;<br />
* formats and tools for analysis of learner data; and<br />
* best practices to make large collections of learner data and associated analytics available to researchers in the CSE, data science, and learner science communities.<br />
For a fuller description of our vision and goals, see the [home page of the project https://cssplice.github.io/]<br />
<br />
Supported by NSF grant EHR 1740775 (2017-2021). [[Community-Building and Infrastructure Design for Data-Intensive Research in Computer Science Education|==> more]]<br />
<br />
==[[Open Corpus Personalized Learning]]==<br />
This project challenges the assumption that adaptive hypermedia systems require expensive knowledge engineering for domain and content modeling. It replaces the carefully-crafted domain model with automatically-created domain models, lowering the cost of developing such systems while also providing a wider range of instructional paths through the content. <br />
<br />
Supported by NSF grant IIS 1525186 (2015-2018). [[Open Corpus Personalized Learning|==> more]]<br />
<br />
== [[Adaptive Navigation Support and Open Social Learner Modeling for PAL]] ==<br />
The goal of this project is to leverage the power of [[open social learner modeling]] and [[adaptive navigation support]] in the context of the envisioned Personalized Assistant for Learning (PAL). <br />
<br />
Supported by the [http://adlnet.gov|Advanced Distributed Learning Initiative] contract W911QY13C0032 (2013-2016). [[Adaptive Navigation Support and Open Social Learner Modeling for PAL|==> more]]<br />
<br />
==[[Ensemble|Ensemble: Enriching Communities and Collections to Support Education in Computing]]==<br />
[[Ensemble]] is a cross-university collaborative effort that aims to bring together the global community of computing educators around a growing set of content collections with high-quality educational resources.<br />
<br />
Supported by NSF (2008-2014). [[Ensemble|==> more]]<br />
==[[Engaging Students in Online Reading Through Social Progress Visualization]]==<br />
<br />
This project explores an alternative approach to encourage student online textbook reading using a social progress visualization interface.<br />
<br />
Supported by Innovation in Education Award, University of Pittsburgh (2012-2013). [[Engaging Students in Online Reading Through Social Progress Visualization|==> more]]<br />
<br />
== [[Personalized Social Systems for Local Communities]] ==<br />
The project explored the use of personalization and mobile computing to increase user engagement in location-bound social systems. <br />
<br />
Supported by Google (2010-2012). [[Personalized Social Systems for Local Communities|==> more]]<br />
<br />
== [[Personalization and social networking for short-term communities]] ==<br />
The project explored a range of approaches, which can enable reliable social networking and personalization in communities, which exist for short period of time, like researchers attending a specific conference. <br />
<br />
Supported by NSF (2010-2011). In collaboration with Jung Sun Oh. [[Personalization and social networking for short-term communities|==> more]]<br />
<br />
==Modeling and Visualization of Latent Communities==<br />
The project focused on the problem of discovering latent communities from Social Web data and presenting this data in visual form. <br />
<br />
Supported by the Institute for Defense Analysis and NSF (2010-2012). [[Modeling and Visualization of Latent Communities|==> more]]<br />
<br />
== Personalized Exploratorium for Database Courses ==<br />
The project focused on developing and evaluation of a personalized educational environment for teaching Database courses. <br />
<br />
Supported by NSF (2007-2008). In collaboration with Vladimir Zadorozhny. [[Personalized Exploratorium for Database Courses|==> more]]<br />
<br />
== GALE: Distillation with Utility-Optimized Transcription and Translation ==<br />
Supported by DARPA (2005-2007) In collaboration with Carnegie Mellon University and IBM [[GALE: Distillation with Utility-Optimized Transcription and Translation|==> more]]<br />
<br />
== Personalized Access to Open Corpus Educational Resources through Adaptive Navigation Support and Adaptive Visualization ==<br />
The project focused on developing technologies for personalized access to information based on adaptive navigation support, collaborative filtering, and information visualization.<br />
<br />
Supported by NSF (2005-2010). [[Personalized Access to Open Corpus Educational Resources through Adaptive Navigation Support and Adaptive Visualization|==> more]]<br />
<br />
== Adaptive Explanatory Visualization for Learning Programming Concepts ==<br />
The project focused on developing and studying adaptive explanatory visualization technologies for C and Java programming languages. <br />
<br />
Supported by NSF (2004-2007). In collaboration with Michael Spring. [[Adaptive Explanatory Visualization for Learning Programming Concepts|==> more]]<br />
<br />
== Individualized Exercises for Assessment and Self-Assessment of Programming Knowledge ==<br />
<br />
The project focused developing and evaluating a personalized assessment technology for programming courses.<br />
<br />
Supported by NSF (2003-2005). [[Individualized Exercises for Assessment and Self-Assessment of Programming Knowledge|==> more]]<br />
<br />
== Educational Software for Teaching and Learning Information Retrieval ==<br />
<br />
Supported by Innovation in Education Award, University of Pittsburgh (2003-2004). [[Educational Software for Teaching and Learning Information Retrieval| ==> more]]<br />
<br />
== Supporting Learning from Examples in a Programming Course ==<br />
<br />
Supported by Innovation in Education Award, University of Pittsburgh (2001-2002). [[Supporting Learning from Examples in a Programming Course|==> more]]<br />
<br />
== Adaptive Electronic Textbooks for World Wide Web == <br />
Supported by NSF (1997-1998). In collaboration with John Anderson and Gerhard Weber [[Adaptive Electronic Textbooks for World Wide Web|==> more]]</div>K.thakerhttps://adapt2.sis.pitt.edu/w/index.php?title=News&diff=4051News2019-03-11T22:34:18Z<p>K.thaker: </p>
<hr />
<div>__NOTOC__<br />
<br />
==== <span id="2019-03-04">2019-03-04</span> :: [[User:peterb | Peter Brusilovsky]] is awarded AMiner Most Influential Scholar Award in recognition of his outstanding and vibrant contributions to the field of Recommender System ====<br />
[https://www.aminer.cn/ai10/recommendation]. Congratulations! Your contribution is an inspiration for all PAWS students...<br />
<br />
==== <span id="2018-08-13">2018-08-13</span> :: Two PAWS graduates' papers were short-listed for the best paper award at EC-TEL 2018. ====<br />
Paper titled Learning by Reviewing Paper-based Programming Assessments co-authored by [[User:Shoha99 | Sharon Hsiao]] and paper titled Detection of Student Modelling Anomalies by [[User:Sergey | Sergey Sosnovsky]] short-listed for the best paper award at EC-TEL 2018 [http://www.ec-tel.eu/index.php?id=805]. Congratulations to PAWS alumni Sharon and Sergey for having nominations for best paper award. Your contribution is an inspiration for current PAWS students...<br />
<br />
==== <span id="2018-07-31">2018-07-31</span> :: [[User:Peterb | Peter Brusilovsky]] awarded NSF grant under the program Cyberlearn And Future Learn Tech. ====<br />
Congratulations Peter Brusilovsky for NSF Award for Project: Collaborative Research: CSEdPad: Investigating and Scaffolding Students' Mental Models during Computer Programming Tasks to Improve Learning, Engagement, and Retention. See more at [http://sci.pitt.edu/news/professor-peter-brusilovsky-receives-nsf-grant/ SCI News] or check [https://www.nsf.gov/awardsearch/showAward?AWD_ID=1822752 Award Abstract].<br />
<br />
==== <span id="2018-07-24">2018-07-24</span> :: [[User:R.hosseini | Roya Hosseini]] defended her Ph. D. Thesis ====<br />
Roya Hosseini defended her Ph. D. Thesis: [http://d-scholarship.pitt.edu/35280/ Program Construction Examples in Computer Science Education: From Static Text to Adaptive and Engaging Learning Technology].<br />
<br />
==== <span id="2018-07-05">2018-07-05</span> :: [[User:Yuh43 | Yun Huang]] defended her Ph. D. Thesis ====<br />
Yun Huang defended her Ph. D. Thesis titled [http://d-scholarship.pitt.edu/35176/1/yunhuang_dissertation_v3_1.pdf Learner Modeling for Integration Skills in Programming].<br />
<br />
==== <span id="2018-03-08">2018-03-08</span> :: PAWS Lab scores thrice at IUI 2018 ====<br />
PAWS Lab took almost the whole podium at [https://iui.acm.org/2018/ IUI 2018] award ceremony. Hyman Tsai receives an honorable mention for the best student paper award. [[User:Dparra | Denis Parra]] with now his own advisee Ivania Donoso also got an honorable mention. And finally, our recent visitor Cecilia di Sciascio did win the best student paper award for a paper "A Study on User-Controllable Social Exploratory Search" with [[User:peterb | Peter Brusilovsky]] and Eduardo Veas. See more at [http://sci.pitt.edu/news/school-of-computing-and-information-received-recognition-at-acm-iui-2018/ SCI News].<br />
<br />
==== <span id="2017-10-24">2017-10-24</span> :: [[User:Julio | Julio Guerra]] defended his Ph. D. Thesis ====<br />
Julio Guerra defended his Ph. D. Thesis: [http://d-scholarship.pitt.edu/28980/ Open Learner Models for Self-Regulated Learning: Exploring the Effects of Social Comparison and Granularity].<br />
<br />
==== <span id="2017-08-17">2017-08-17</span> :: [[User:Suleehs | Danielle H. Lee]] moves to Sangmyung University as an assistant professor ====<br />
Dr. [[User:Suleehs | Danielle H. Lee]] has joined [http://www.smuc.ac.kr/mbs/eng/index.jsp Sangmyung University] (Korea) as an assistant professor at the Department of Software on August 17, 2017. Previously, Danielle was an Assistant Professor at the University of Washington, Bothel.<br />
<br />
==== <span id="2017-07-11">2017-07-11</span> :: Two PAWS papers were nominated and one received Best Paper Awards at UMAP 2017 ====<br />
Two papers - [https://doi.org/10.1145/3079628.3079672 Stereotype Modeling for Problem-Solving Performance Predictions in MOOCs and Traditional Courses] and [https://doi.org/10.1145/3079628.3079682 Fine-Grained Open Learner Models: Complexity Versus Support] were nominated for the best paper award at [http://www.um.org/umap2017/ UMAP2017] – 25th Conference on User Modeling, Adaptation and Personalization. The second paper lead by Julio Guerra and Jordan Barria-Pineda received [http://www.um.org/awards/james-chen-best-student-paper-awards James Chen Best Student Paper award]. Congratulations to Julio and Jordan who now joined former PAWS lab members Rosta Farzan, [[User:Myudelson | Michael Yudelson's]], and [[User:Dparra | Denis Parra]] as recipients of this prestigious award. This is 6th James Chen award won by PAWS lab members! Rosta Farzan received this award twice as a student and once more as a senior co-author.<br />
<br />
==== <span id="2017-02-21">2017-02-21</span> :: [[User:peterb | Peter Brusilovsky]] received the 2017 Provost’s Award for Excellence in Mentoring ====<br />
Congratulations to our mentor at PAWS, Dr. Peter Brusilovsky, on receiving the 2017 Provost’s Award for Excellence in Mentoring! Read more on [http://www.utimes.pitt.edu/?p=42853 University Times]<br />
<br />
==== <span id="2016-11-23">2016-11-23</span> :: [[User:Sergey | Sergey Sosnovsky]] moves to Utrecht University as a tenure-track professor ====<br />
Dr. [[User:Sergey | Sergey Sosnovsky]] has joined [http://www.uu.nl/en/organisation/department-of-information-and-computing-sciences the Department of Information and Computing Sciences] at Utrecht University (the Netherlands). The tenure-track position in computer science with the focus on educational technology started on November 1st, 2016.<br />
<br />
==== <span id="2016-08-15">2016-08-15</span> :: Maria Harrington moves to University of Central Florida as an Assistant Professor ====<br />
Dr. [http://svad.cah.ucf.edu/staff.php?id=1350 Maria Harrington] has joined [https://www.ucf.edu/ University of Central Florida] as an Assistant Professor at the [http://svad.cah.ucf.edu/ School of Visual Arts and Design]. This is a great place to continue her work on educational virtual reality. <br />
<br />
==== <span id="2016-07-21">2016-07-21</span> :: Shaghayegh (Sherry) Sahebi defended her Ph. D. Thesis ====<br />
Shaghayegh (Sherry) Sahebi defended her Ph. D. Thesis: 'Canonical Correlation Analysis in Cross-Domain Recommendation'. More details on the thesis can be accessed [http://d-scholarship.pitt.edu/29220/ here]. She will join the University of Albany as an Assistant Professor in September 2016.<br />
<br />
==== <span id="2016-05-13">2016-05-13</span> :: Dr. [[User:peterb | Peter Brusilovsky]] and Rosta Farzan are on new Association for Computing Machinery journal’s editorial board ====<br />
<br />
Two SIS faculty members on new Association for Computing Machinery journal’s editorial board. <br />
<br />
Peter Brusilovsky, professor and current chair of the Information Sciences program, and Rosta Farzan, assistant professor of Information Sciences & Technology, have been selected as associate editors of the Association for Computing Machinery’s (ACM) new journal titled “Transactions on Social Computing.”<br />
<br />
This quarterly journal will publish works encompassing theoretical, empirical, systems, and design research on social computing. It will be part of the ACM Digital Library, which is the most comprehensive collection of full-text articles and bibliographic records covering the fields of computing and information technology.<br />
<br />
==== <span id="2016-03-04">2016-03-04</span> :: Congratulations to [[User:Yuh43 | Yun Hung]] and [[User:R.hosseini | Roya Hosseini]] for receiving Andrew Mellon Pre-doctoral Fellowship ====<br />
Yun Huang and Roya Hosseini received prestigious Andrew Mellon Pre-doctoral Fellowship Award for the academic year 2016-2017.<br />
<br />
==== <span id="2016-02-23">2016-02-23</span> :: Dr. Ioanna Lykourentzou gave a talk on the topic of "Personalized Crowdsourcing" ====<br />
Dr. Lykourentzou illustrated the potential that personalization has for the improvement of crowdsourcing systems, through two example applications. The first application was about making personalized task recommendations to crowd workers, and the second application was about personalizing team building, i.e. a method that brings people to work together on a collaborating task taking into account their individual personalities. <br />
<br />
At the time, Dr. Ioanna Lykourentzou was a researcher at the Luxembourg Institute of Science and Technology and was collaborating with the Human-Computer Interaction Institute at Carnegie Mellon University, as Visiting Researcher.<br />
<br />
==== <span id="2015-08-15">2015-08-15</span> :: [[User:peterb | Peter Brusilovsky]] and Daqing He awarded NSF grant to work on [[Open Corpus Personalized Learning]] ====<br />
[[User:peterb | Peter Brusilovsky]] and Daqing He (project lead and co-lead respectively) has been awarded a Nation Science Foundation Information and Intelligent Systems grant for their project titled “[[Open Corpus Personalized Learning]].” The project will focus on streamlining and expanding the reach of effective adaptive educational hypermedia, which allows students and independent learners without access to traditional classrooms to gain a personalized education.<br />
<br />
==== <span id="2015-04-15">2015-04-15</span> :: Chirayu Wongchokprasitti defended his Ph. D. Thesis: 'Using External Sources To Improve Research Talk Recommendation In Small Communities'. ====<br />
More details on the thesis can be accessed [http://d-scholarship.pitt.edu/25836/ here].<br />
<br />
==== <span id="2014-11-11">2014-11-11</span> :: Dr.[[User:Dparra | Denis Parra]] won the contest for an invited talk at the "Chilean Computing Conference 2014". ====<br />
Dr.[[User:Dparra | Denis Parra]] won the contest for an invited talk at the "Chilean Computing Conference 2014", and presented his research on Recommender Systems. <br />
Please find the slides [http://www.slideshare.net/denisparra/keynote-at-chilean-week-of-computer-science here].<br />
The abstract of the talk is also available at the home page of the event, JCC 2014. [http://www.jcc2014.ucm.cl/en/ (more)].<br />
<br />
==== <span id="2014-09-19">2014-09-19</span> :: PAWS won the best paper award at the 9th European Conference on Technology Enhanced Learning (EC-TEL 2014). ====<br />
Congratulations to Tomek, [[User:Julio | Julio]], [[User:R.hosseini | Roya]], and [[User:peterb| Peter]]! Their paper entitled "[https://www.researchgate.net/publication/266656951_Mastery_Grids_An_Open_Source_Social_Educational_Progress_Visualization Mastery Grids: An Open Source Social Educational Progress Visualization]" has won the best paper award of EC-TEL 2014 conference. More details on the paper can be accessed [http://link.springer.com/chapter/10.1007/978-3-319-11200-8_18 here].<br />
<br />
==== <span id="2014-05-06">2014-05-06</span> :: [[User: shoha99 | Sharon Hsiao]] is appointed as Assistant Professor in CIDSE @ ASU this Fall.====<br />
On completing 2 years successful post-doctoral innovation fellow position in [http://edlab.tc.columbia.edu EdLab], Teachers College @ Columbia University, [[User: shoha99 | Sharon]] is moving onto a tenure-track position in [http://cidse.engineering.asu.edu School of Computing, Informatics, Decision Systems Engineering] (AKA: Engineering school) in [http://www.asu.edu Arizona State University], Phoenix, Arizona. She anticipates to continue working on the emerging topics of computational technologies in learning. Meanwhile, persistently dedicate to the course - [http://www.columbia.edu/~ih2240/dataviz/index.htm Data Visualization], which she established in [http://www.qmss.columbia.edu QMSS (Quantitative Methods in the Social Sciences)], School of Arts & Sciences in [http://www.columbia.edu Columbia University]. For more details see [http://www.ischool.pitt.edu/news/05-14-2014.php SIS news].<br />
<br />
==== <span id="2014-03-01">2014-03-01</span> :: [[User:peterb | Peter Brusilovsky]] to deliver a keynote at a WWW 2014 workshop. ====<br />
Peter will deliver a keynote titled "Addictive Links: Engaging Students through Adaptive Navigation Support and Open Social Student Modeling" at [http://www2014.kr/program/webet-2014/ WebET 2014] - Workshop on Web-based Education Technologies at the Word Wide Web Conference in Seoul, Korea. The talk will present PAWS work on such systems as [[QuizGuide]], [[NavEx]], JavaGuide, [[Progressor]], and [[ProgressorPlus|Progressor+]].<br />
<br />
==== <span id="2014-03-13">2014-03-13</span> :: [[User: Jennifer | Yi-Ling(Jennifer) Lin]] started her new position as an Assistant Professor in the Department of Information Management at the National Sun Yat-Sen University in Taiwan.====<br />
Yi-Ling (Jennifer) joined the Information Management faculty at the National Sun Yat-Sen University [http://http://epage.mis.nsysu.edu.tw/files/11-1100-4988-1.php?Lang=en] where she is an Assistant Professor. She is teaching Java course and continues to cooperate with Paws Lab to explore the social comparison in cyberlearning.<br />
<br />
==== <span id="2013-08-13">2013-08-13</span> :: [[User:Dparra | Denis Parra]] started a new position as Assistant Professor in the Department of Computer Science at Pontifical Catholic University of Chile.====<br />
Denis joined the faculty at the School of Engineering in Pontificia Universidad Catolica de Chile, [http://www.topuniversities.com/node/2261/ranking-details/latin-american-university-rankings/2013 ranked 2nd among Latinamerican Universities], where he is an [http://www.ing.puc.cl/cuerpo-docente/parra-santander/ Assistant Professor at the Department of Computer Science]. At the undergraduate level, he is teaching a course that explores several topics for the major in Computer Science, and at the graduate level he teaches Data Mining in the [http://mpgi.ing.puc.cl/profesores.html Master for Information Processing and Management]. He continues doing research on Recommender Systems, the topic he investigated while a student at the PAWS lab, and is also Analyzing Social Media, collaborating with PAWS lab student Xidao Wen, studying [http://www.christophtrattner.info/pubs/ht2014.pdf how Twitter is used in academic Conferences]. In addition, he is fostering collaboration between professor Brusilovsky and some areas of teaching such as Databases in the CS department at Catholic University.<br />
<br />
==== <span id="2013-07-18">2013-07-18</span> :: [[Systems#KnowledgeZoom | KnowledgeZoom]] paper receives ICALT 2013 Best full Paper Award ====<br />
Congratulations to paws! "[https://www.researchgate.net/publication/256524709_KnowledgeZoom_for_Java_A_Concept-Based_Exam_Study_Tool_with_a_Zoomable_Open_Student_Model KnowledgeZoom for Java: A Concept Based Exam Study Tool with a Zoomable Open Student Model]" won the best full paper award of ICALT 2013 conference.<br />
<br />
==== <span id="2013-07-09">2013-07-09</span> :: [[User: Falakmasir | Mohammad Falakmasir]] won EDM 2013 Best Student Paper ====<br />
Congratulations to Mohammad! His paper with Zachary A. Pardos, Geoffrey J. Gordon, and Peter Brusilovsky entitled "A Spectral Learning Approach to Knowledge Tracing" won the best student paper award of EDM 2013 conference. In his paper, he proposed using Spectral Learning (SL) to learn the BKT parameters. Results of his study showed that SL can improve knowledge tracing parameter fitting time significantly while maintaining the same prediction accuracy. For more details on his paper please refer to his article [http://halley.exp.sis.pitt.edu/cn3/presentation2.php?presentationID=5323&conferenceID=115]<br />
<br />
==== <span id="2013-06-12">2013-06-12</span> :: [[User:peterb | Peter Brusilovsky]] selected as a Fulbright-Nokia Distinguished Chair. ====<br />
[[User:peterb | Peter Brusilovsky]], has been selected as a Fulbright-Nokia Distinguished Chair by the Fulbright Commission. With this Award Peter will spend 5 month in FInland collaborating with researchers from University of Helsinki, Aalto University, and Helsinki Institute of Information Technology. Read the news story on SIS Web site: [http://www.ischool.pitt.edu/news/08-01-2013.php]<br />
<br />
==== <span id="2013-05-01">2013-05-01</span> :: [[User:peterb | Peter Brusilovsky]] and PAWS lab win Army Contract to develop social personalized learning architecture. ====<br />
Peter Brusilovsky, Professor at the iSchool, has been awarded a contract by the United States Army Contracting Command to participate in the Advanced Distributed Learning (ADL) Initiative. Brusilovsky’ s contract, for $623,005 over a three-year period, will support the project [[Adaptive Navigation Support and Open Social Learner Modeling for PAL]] that will focus on the architecture, algorithms, and interfaces for a Personal Assistant for Learning (PAL), one of the major endeavors undertaken by the ADL Initiative. Through a PAL, the Initiative will provide state of the art education and training -- using technology and innovative learning methodologies -- for workforce members in the Department of Defense and the federal government. Read the news story on SIS Web site: [http://www.ischool.pitt.edu/news/05-01-2013.php]<br />
<br />
==== <span id="2013-04-01">2013-04-01</span> :: Sergey Sosnovsky profiled in International Innovation ====<br />
Sergey Sosnovsky, who earned his PhD in Information Science in 2012, was recently profiled in International Innovation Magazine about his work on eLearning systems research and tools. Sosnovsky is the Principal Researcher and Head of the Intelligent e-Learning Technology Lab at the German Research Center for Artificial Intelligence in Saarbruken, Germany. The article explored his work on the Intelligent Support for Authoring Semantic Learning Content project funded by the European Commission’s Community Research and Development Information Service. The magazine article (published March 2013) discussed how Sosnovsky’s project will enhance adaptive e-Learning by making it possible to develop smart instructional material for a broader audience of content authors. The article can be viewed at [[http://www.research-europe.com/magazine/REGIONAL/EX8/index.html]] , beginning on page 71.<br />
<br />
==== <span id="2013-01-01">2013-01-01</span> :: [[User:peterb | Peter Brusilovsky]] appointed the Editor-In-Chief of [http://www.computer.org/portal/web/tlt IEEE Transactions on Learning Technologies]. ====<br />
Peter is appointed the Editor-In-Chief of IEEE Transactions on Learning Technologies, a journal dedicated to advancing the state of the art in technology-enhanced learning. This quarterly publication covers leading edge research on topics such as educational software applications, online learning systems, and simulation systems for education and training. Read the news story on SIS Web site: [http://www.ischool.pitt.edu/news/3-11-2013.php]<br />
<br />
==== <span id="2012-07-09">2012-08-26</span> :: [[User:Rostaf | Rosta Farzan]] started her new position as an Assistant Professor at the School of Information Sciences. ====<br />
Congratulations to Rosta! After 3 years as a postdoc at CMU, she is now back to Pitt as an Assistant Professor. During her first semester at SIS she is teaching IS2430 Social Computing course. Read the news story on SIS Web site: [http://www.ischool.pitt.edu/news/08-02-2012.php]<br />
<br />
==== <span id="2012-07-09">2012-07-09</span> :: [[User:shoha99 | Sharon Hsiao]] Thesis Defence: [http://d-scholarship.pitt.edu/13439/ Navigation Support and Social Visualization for Personalized E-Learning] ====<br />
A large number of educational resources is now made available on the Web to support both regular classroom learning and online learning. However, the abundance of available content produced at least two problems: how to help students to find the most appropriate resources and how to engage them into using these resources and benefit from them. Personalized and social learning have been suggested as potential ways to address these problems.<br />
<br />
This work attempts to combine the ideas of personalized and social learning by providing navigation support through an open social student modeling visualization. A series of classroom studies exploited the idea of the approach and revealed promising results, which demonstrated the personalized guidance and social visualization combined helped students to find the most relevant resources of parameterized self-assessment questions for Java programming. Thus, this dissertation extend the approach to a larger collection of learning objects for cross content navigation and verify its capability of supporting social visualization for personalized E-Learning.<br />
<br />
The study results confirm that working with the non-mandatory system, students enhanced the learning quality in increasing their motivation and engagement. They successfully achieved better learning results. Meanwhile, incorporating a mixed collection of content in the open social student modeling visualizations effectively led the students to work at the right level of questions. Both strong and weak student worked with the appropriate levels of questions for their readiness accordingly and yielded a consistent performance across all three levels of complexities. Additionally, providing a more realistic content collection on the navigation supported open social student modeling visualizations results in a uniform performance in the group. The classroom study revealed a clear pattern of social guidance, where the stronger students left the traces for weaker ones to follow. The subjective evaluation confirms the design of the interface in terms of the content organization. Students’ positive responses also compliment the objective system usage data.<br />
<br />
download it from [http://d-scholarship.pitt.edu/13439/ here]<br />
<br />
==== <span id="2011-11-30">2011-11-30</span> :: [[User:Sergey | Sergey Sosnovsky]] Thesis Defence: Ontology-Based Open-Corpus Personalization for E-Learning ====<br />
Conventional closed-corpus adaptive information systems control limited sets of documents in fixed subject domains and cannot provide access to the content outside the system. Such restrictions contradict the requirements of today, when most of the information systems are implemented in the open document space of WWW and are expected to operate on the open-corpus content. In order to maintain personalized access to open-corpus documents, an adaptive system should be able to model the documents and the relations between the documents and the domain knowledge automatically and dynamically. This dissertation explores the problem of open-corpus personalization and semantic modeling of open-corpus content in the context of e-Learning. Information on WWW is not without structure. Many collections of online instructional material (tutorials, electronic books, digital libraries, etc.) have been provided with implicit knowledge models encoded in form of tables of content, indexes, headers of chapters, links between pages, and different styles of text fragments. The main dissertation approach tries to leverage this layer of hidden semantics by extracting and representing it as coarse-grained models of collections. A central domain ontology is used to maintain overlay modeling of studentsÕ knowledge and serves as a reference point for multiple collections of external instructional material. In order to establish the link between the ontology and the open-corpus content models a special ontology mapping algorithm has been developed. The proposed approach has been applied in the Ontology-based Open-corpus Personalization Service (OOPS) that recommends and adaptively annotates online reading material. The domain of Java programming has been chosen for the proof-of-concept implementation. A controlled experiment has been organized to evaluate the developed adaptive system and the proposed approach overall. The results of the evaluation have demonstrated several significant learning effects of the implemented open-corpus personalization. The analysis of log-based data has also shown that the open-corpus version of the system is capable of providing personalization of similar quality to the close-corpus one. Such results indicate that the proposed approach supports fully-scale open-corpus personalization for e-Learning. Further research is required to verify if the approach remains effective in other subject domains and with other types of instructional content.<br />
<br />
==== <span id="2011-07-15">2011-07-15</span> :: [[User:DParra | Denis Parra]] earns a James Chen Best Student paper award in UMAP 2011 ====<br />
In the last conference of User Modeling, Adaptation and Personalization (UMAP 2011) Denis Parra won one of the 2 [http://www.umap2011.org/program/best-paper-awards James Chen Best Student paper awards] for his paper '''Walk The Talk: Analyzing the relation between implicit and explicit feedback for preference elicitation''' that he co-authored with Dr. Xavier Amatriain. In this paper, the authors present a study on the music domain with last.fm users, which results leads them to create a regression model that maps implicit information (such as playcounts and how recently a user listened to albums) with explicit information in the form of ratings. More details in the [http://www.springerlink.com/content/645721483544r815/ conference proceedings in Springer]. Denis is the third PAWS Lab member to receive this prestigious award. Prior to that, Rosta Farzan and Michael Yudelson won this award at earlier User Modeling conferences. Rosta also won another James Chen award at Adaptive Hypermedia conference.<br />
<br />
==== <span id="2011-06-12">2011-06-12</span> :: [[User:peterb | Peter]] Promoted to Rank of Full Professor ====<br />
Congratulations to our head of PAWS lab! [[http://www.ischool.pitt.edu/news/06-10-2011.php Read more ]]<br />
==== <span id="2011-05-01">2011-05-01</span> :: [[User:shoha99 | Sharon]] received 2011 Allen Kent Award for Outstanding Contributions to the Graduate Program in Information Science ==== <br />
She has worked with [http://www.sis.pitt.edu/~gray/ Dr. Glenn Ray] several years in designing and teaching undergraduate courses. She's affiliated as teaching fellow and teaches in our school now.<br />
<br />
==== <span id="2010-12-15">2010-12-15</span> :: [[User:peterb | Peter]] received Google grant to work on ''[[Personalized Social Systems for Local Communities]]'' ====<br />
The grant will support our efforts to increase user participation in social systems designed for local communities. In the course of the project will explore two innovative ideas for increasing participation. The first idea is to provide access to information “beyond the desktop,” by adding a mobile location-based interface to access information. This will increase both the number of active users and the volume of their contributions. The second idea is to provide personalized access to information to increase the chance to gather relevant information. This work will be based on two existing social systems that were developed and maintained by PAWs lab: the [[Systems#CoMeT | CoMeT]] system for sharing information about research talks at Carnegie Mellon and University of Pittsburgh and [[Systems#Eventur | Eventur]], a social system for recommending cultural events in the Pittsburgh area.<br />
<br />
==== <span id="2010-09-17">2010-09-17</span> :: [[User:Myudelson | Michael Yudelson's]] Thesis Defence: Providing Service-Based Personalization In An Adaptive Hypermedia System ====<br />
The dissertation proposes a novel way of speeding the development of new adaptive hypermedia systems. The gist of the approach is to extract the adaptation functionality out of the adaptive hypermedia system, encapsulate it into a standalone system, and offer adaptation as a service to the client applications. Such a standalone adaptation provider reduces the development of adaptation functionality to configuration and compliance and as a result creates new adaptive systems faster and helps serve larger user populations with adaptively accessible content. [[http://washington.sis.pitt.edu/comet/presentColloquium.do?col_id=777 details]]<br />
The electronic version of [[User:Myudelson | Michael Yudelson's]] dissertation has been approved by the School of Information Sciences. ETD is accessible worldwide from the online library catalog of the University of Pittsburgh ([http://etd.library.pitt.edu/ETD/available/etd-10132010-092137/ link]).<br />
<br />
==== <span id="2010-09-15">2010-09-15</span> :: [[User:peterb | Peter Brusilovsky]] received NSF grant to work on ''Modeling and Visualization of Latent Communities'' ====<br />
This EAGER grant will allow us to investigate how to model and visualize latent communities – those groups of people who form communities based on their similar interests. This work will consider how to elicit latent communities from various kinds of data about individuals available in the modern social Web and deliver the results in a manner suitable for interactive exploration through interactive visualizations. This will be one of the first attempts to use a variety of social Web data and approaches to community modeling. [[http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1059577 details]]<br />
<br />
==== <span id="2010-09-08">2010-09-08</span> :: [[User:jahn | Jae-wook's]] Thesis Defence: Adaptive Visualization for Focused Personalized Information Retrieval ====<br />
Jae-wook Ahn's dissertation proposes to incorporate interactive visualization into personalized search in order to overcome the limitation. By combining the personalized search and the interactive visualization, we expect our approach will be able to help users to better explore the information space and locate relevant information more efficiently. [[http://washington.sis.pitt.edu/comet/presentColloquium.do?col_id=764 details]]<br />
<br />
==== <span id="2010-09-01">2010-09-01</span> :: [[User:peterb | Peter Brusilovsky]] and Jung Sun Oh received NSF grant to work on ''Personalization and social networking for short-term communities'' ====<br />
This one-year grant will support a project exploring personalization and social networking for short-term communities. Using academic research conferences as a test bed, our team will explore new methods to leverage information about user interests (available from multiple external resources) and develop techniques to facilitate use of existing social technologies. [[http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1052768 details]]<br />
<br />
==== 2010-08-15 :: SIGWeb Newsletter published an interview with [[User:peterb | Peter Brusilovsky]] ====<br />
The Summer 2010 issue of SIGWeb Newsletter (a magazine of ACM Special Interest Group on Hypertext and the Web) published an [http://dx.doi.org/10.1145/1796390.1796393 interview with Peter Brusilovsky]. The interview provides some personal view on research project performed at PAWS.<br />
<br />
==== 2010-07-01 :: [[User:jahn | Jae-wook]] has received Computing Inovation Fellowship ====<br />
Jae-wook Ahn was chosen as a [http://cifellows.org CIFellow] (Computing Innovation Fellow) supported by the Computing Community Consortium (CCC), the Computing Research Association (CRA), and the National Science Foundation. Starting from the fall 2010, he is going to work with [http://www.cs.umd.edu/~ben/ Dr. Ben Shneiderman] at the [http://www.cs.umd.edu/hcil Human Computer Interaction Lab], University of Maryland.<br />
<br />
==== 2010-05-01 :: Sergey has received EU Marie Curie International Incoming Fellowship ====<br />
Sergey Sosnovsky's proposal for EU [http://cordis.europa.eu/improving/fellowships/home.htm Marie Curie Fellowship] is approved by the EU Research Executive Agency. The funding starts in July, 2010 and will last until July 2012. The project "Intelligent Support for Authoring Semantic Learning Content" will focus on implementation of author-friendly technologies for learning content development, including collaborative authoring support, metadata authoring support, open-corpus content discovery, interactivity authoring, and gap detection.<br />
<br />
==== 2009-06-27 :: Rosta receives her second James Chen Award ====<br />
Rosta Farzan received James Chen Best Student Paper Award at the 12th International Conference on User Modelling, Adaptation and Personalization, UMAP2009, in Trento, Italy for the paper ''Social Navigation Support for Information Seeking: If You Build It, Will They Come?'' by Rosta Farzan and Peter Brusilovsky. This is her second James Chen Award, congratulations!<br />
<br />
==== 2009-06-26 :: PAWS Caught on UMAP 2009 Video ====<br />
* 0:27 [[User:Myudelson|Michael]]<br />
* 1:09 [[User:Rostaf|Rosta]]<br />
* 1:18 [[User:Sergey|Sergey]]<br />
* 1:51 [[User:Suleehs|Danielle]]<br />
* 3:12 [[User:Myudelson|Michael]] and [[User:Sergey|Sergey]]<br />
* 4:01 [[User:Peterb|Peter]]<br />
<youtube>v_amf_zcLtQ</youtube><br />
<br />
==== 2009-05-28 :: [[User:peterb | Peter]] awarded honorary doctorate by the Slovak University of Technology in Bratislava ====<br />
At a ceremony in Bratislava today, Peter Brusilovsky was honored by the [http://www.stuba.sk/ Slovak University of Technology in Bratislava] with the degree of Doctor honoris causa. The university, founded in 1937 in Bratislava, is one of the most significant institutions of higher education in Slovakia. Peter was selected for this recognition for "his contributions to the fields of Informatics and Information Technologies".<br />
<br />
==== 2008-07-31 :: [[User:peterb | Peter]] receives Best Paper Award at AH 2008 ====<br />
Peter Brusilovsky received Best Paper Award at the 5th International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, AH 2008, in Hannover, Germany for the paper [http://www.springerlink.com/content/6h410u3w4836v866/ ''Social Information Access for the Rest of Us: An Exploration of Social YouTube''] by Maurice Coyle, Jill Freyne, Peter Brusilovsky, and Barry Smyth<br />
<br />
==== 2007-06-28 :: [[User:Myudelson | Michael]] receives James Chen Best Student Paper Award at UM 2007 ====<br />
Michael Yudelson received James Chen Best Student Paper Award at the 11th International Conference on User Modelling, UM07, in Corfu, Greece for the paper [http://www.springerlink.com/content/c723060442701737/ ''A User Modeling Server for Contemporary Adaptive Hypermedia: an Evaluation of Push Approach to Evidence Propagation''] by Michael Yudelson, Peter Brusilovsky, and Vladimir Zadorozhny</div>K.thakerhttps://adapt2.sis.pitt.edu/w/index.php?title=News&diff=4050News2019-03-11T22:32:35Z<p>K.thaker: </p>
<hr />
<div>__NOTOC__<br />
<br />
==== <span id="2019-03-04">2019-03-04</span> :: Prof. Peter Brusilovksy is awarded AMiner Most Influential Scholar Award in recognition of his outstanding and vibrant contributions to the field of Recommender System ====<br />
[https://www.aminer.cn/ai10/recommendation]. Congratulations! Your contribution is an inspiration for all PAWS students...<br />
<br />
==== <span id="2018-08-13">2018-08-13</span> :: Two PAWS graduates' papers were short-listed for the best paper award at EC-TEL 2018. ====<br />
Paper titled Learning by Reviewing Paper-based Programming Assessments co-authored by [[User:Shoha99 | Sharon Hsiao]] and paper titled Detection of Student Modelling Anomalies by [[User:Sergey | Sergey Sosnovsky]] short-listed for the best paper award at EC-TEL 2018 [http://www.ec-tel.eu/index.php?id=805]. Congratulations to PAWS alumni Sharon and Sergey for having nominations for best paper award. Your contribution is an inspiration for current PAWS students...<br />
<br />
==== <span id="2018-07-31">2018-07-31</span> :: [[User:Peterb | Peter Brusilovsky]] awarded NSF grant under the program Cyberlearn And Future Learn Tech. ====<br />
Congratulations Peter Brusilovsky for NSF Award for Project: Collaborative Research: CSEdPad: Investigating and Scaffolding Students' Mental Models during Computer Programming Tasks to Improve Learning, Engagement, and Retention. See more at [http://sci.pitt.edu/news/professor-peter-brusilovsky-receives-nsf-grant/ SCI News] or check [https://www.nsf.gov/awardsearch/showAward?AWD_ID=1822752 Award Abstract].<br />
<br />
==== <span id="2018-07-24">2018-07-24</span> :: [[User:R.hosseini | Roya Hosseini]] defended her Ph. D. Thesis ====<br />
Roya Hosseini defended her Ph. D. Thesis: [http://d-scholarship.pitt.edu/35280/ Program Construction Examples in Computer Science Education: From Static Text to Adaptive and Engaging Learning Technology].<br />
<br />
==== <span id="2018-07-05">2018-07-05</span> :: [[User:Yuh43 | Yun Huang]] defended her Ph. D. Thesis ====<br />
Yun Huang defended her Ph. D. Thesis titled [http://d-scholarship.pitt.edu/35176/1/yunhuang_dissertation_v3_1.pdf Learner Modeling for Integration Skills in Programming].<br />
<br />
==== <span id="2018-03-08">2018-03-08</span> :: PAWS Lab scores thrice at IUI 2018 ====<br />
PAWS Lab took almost the whole podium at [https://iui.acm.org/2018/ IUI 2018] award ceremony. Hyman Tsai receives an honorable mention for the best student paper award. [[User:Dparra | Denis Parra]] with now his own advisee Ivania Donoso also got an honorable mention. And finally, our recent visitor Cecilia di Sciascio did win the best student paper award for a paper "A Study on User-Controllable Social Exploratory Search" with [[User:peterb | Peter Brusilovsky]] and Eduardo Veas. See more at [http://sci.pitt.edu/news/school-of-computing-and-information-received-recognition-at-acm-iui-2018/ SCI News].<br />
<br />
==== <span id="2017-10-24">2017-10-24</span> :: [[User:Julio | Julio Guerra]] defended his Ph. D. Thesis ====<br />
Julio Guerra defended his Ph. D. Thesis: [http://d-scholarship.pitt.edu/28980/ Open Learner Models for Self-Regulated Learning: Exploring the Effects of Social Comparison and Granularity].<br />
<br />
==== <span id="2017-08-17">2017-08-17</span> :: [[User:Suleehs | Danielle H. Lee]] moves to Sangmyung University as an assistant professor ====<br />
Dr. [[User:Suleehs | Danielle H. Lee]] has joined [http://www.smuc.ac.kr/mbs/eng/index.jsp Sangmyung University] (Korea) as an assistant professor at the Department of Software on August 17, 2017. Previously, Danielle was an Assistant Professor at the University of Washington, Bothel.<br />
<br />
==== <span id="2017-07-11">2017-07-11</span> :: Two PAWS papers were nominated and one received Best Paper Awards at UMAP 2017 ====<br />
Two papers - [https://doi.org/10.1145/3079628.3079672 Stereotype Modeling for Problem-Solving Performance Predictions in MOOCs and Traditional Courses] and [https://doi.org/10.1145/3079628.3079682 Fine-Grained Open Learner Models: Complexity Versus Support] were nominated for the best paper award at [http://www.um.org/umap2017/ UMAP2017] – 25th Conference on User Modeling, Adaptation and Personalization. The second paper lead by Julio Guerra and Jordan Barria-Pineda received [http://www.um.org/awards/james-chen-best-student-paper-awards James Chen Best Student Paper award]. Congratulations to Julio and Jordan who now joined former PAWS lab members Rosta Farzan, [[User:Myudelson | Michael Yudelson's]], and [[User:Dparra | Denis Parra]] as recipients of this prestigious award. This is 6th James Chen award won by PAWS lab members! Rosta Farzan received this award twice as a student and once more as a senior co-author.<br />
<br />
==== <span id="2017-02-21">2017-02-21</span> :: [[User:peterb | Peter Brusilovsky]] received the 2017 Provost’s Award for Excellence in Mentoring ====<br />
Congratulations to our mentor at PAWS, Dr. Peter Brusilovsky, on receiving the 2017 Provost’s Award for Excellence in Mentoring! Read more on [http://www.utimes.pitt.edu/?p=42853 University Times]<br />
<br />
==== <span id="2016-11-23">2016-11-23</span> :: [[User:Sergey | Sergey Sosnovsky]] moves to Utrecht University as a tenure-track professor ====<br />
Dr. [[User:Sergey | Sergey Sosnovsky]] has joined [http://www.uu.nl/en/organisation/department-of-information-and-computing-sciences the Department of Information and Computing Sciences] at Utrecht University (the Netherlands). The tenure-track position in computer science with the focus on educational technology started on November 1st, 2016.<br />
<br />
==== <span id="2016-08-15">2016-08-15</span> :: Maria Harrington moves to University of Central Florida as an Assistant Professor ====<br />
Dr. [http://svad.cah.ucf.edu/staff.php?id=1350 Maria Harrington] has joined [https://www.ucf.edu/ University of Central Florida] as an Assistant Professor at the [http://svad.cah.ucf.edu/ School of Visual Arts and Design]. This is a great place to continue her work on educational virtual reality. <br />
<br />
==== <span id="2016-07-21">2016-07-21</span> :: Shaghayegh (Sherry) Sahebi defended her Ph. D. Thesis ====<br />
Shaghayegh (Sherry) Sahebi defended her Ph. D. Thesis: 'Canonical Correlation Analysis in Cross-Domain Recommendation'. More details on the thesis can be accessed [http://d-scholarship.pitt.edu/29220/ here]. She will join the University of Albany as an Assistant Professor in September 2016.<br />
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==== <span id="2016-05-13">2016-05-13</span> :: Dr. [[User:peterb | Peter Brusilovsky]] and Rosta Farzan are on new Association for Computing Machinery journal’s editorial board ====<br />
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Two SIS faculty members on new Association for Computing Machinery journal’s editorial board. <br />
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Peter Brusilovsky, professor and current chair of the Information Sciences program, and Rosta Farzan, assistant professor of Information Sciences & Technology, have been selected as associate editors of the Association for Computing Machinery’s (ACM) new journal titled “Transactions on Social Computing.”<br />
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This quarterly journal will publish works encompassing theoretical, empirical, systems, and design research on social computing. It will be part of the ACM Digital Library, which is the most comprehensive collection of full-text articles and bibliographic records covering the fields of computing and information technology.<br />
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==== <span id="2016-03-04">2016-03-04</span> :: Congratulations to [[User:Yuh43 | Yun Hung]] and [[User:R.hosseini | Roya Hosseini]] for receiving Andrew Mellon Pre-doctoral Fellowship ====<br />
Yun Huang and Roya Hosseini received prestigious Andrew Mellon Pre-doctoral Fellowship Award for the academic year 2016-2017.<br />
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==== <span id="2016-02-23">2016-02-23</span> :: Dr. Ioanna Lykourentzou gave a talk on the topic of "Personalized Crowdsourcing" ====<br />
Dr. Lykourentzou illustrated the potential that personalization has for the improvement of crowdsourcing systems, through two example applications. The first application was about making personalized task recommendations to crowd workers, and the second application was about personalizing team building, i.e. a method that brings people to work together on a collaborating task taking into account their individual personalities. <br />
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At the time, Dr. Ioanna Lykourentzou was a researcher at the Luxembourg Institute of Science and Technology and was collaborating with the Human-Computer Interaction Institute at Carnegie Mellon University, as Visiting Researcher.<br />
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==== <span id="2015-08-15">2015-08-15</span> :: [[User:peterb | Peter Brusilovsky]] and Daqing He awarded NSF grant to work on [[Open Corpus Personalized Learning]] ====<br />
[[User:peterb | Peter Brusilovsky]] and Daqing He (project lead and co-lead respectively) has been awarded a Nation Science Foundation Information and Intelligent Systems grant for their project titled “[[Open Corpus Personalized Learning]].” The project will focus on streamlining and expanding the reach of effective adaptive educational hypermedia, which allows students and independent learners without access to traditional classrooms to gain a personalized education.<br />
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==== <span id="2015-04-15">2015-04-15</span> :: Chirayu Wongchokprasitti defended his Ph. D. Thesis: 'Using External Sources To Improve Research Talk Recommendation In Small Communities'. ====<br />
More details on the thesis can be accessed [http://d-scholarship.pitt.edu/25836/ here].<br />
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==== <span id="2014-11-11">2014-11-11</span> :: Dr.[[User:Dparra | Denis Parra]] won the contest for an invited talk at the "Chilean Computing Conference 2014". ====<br />
Dr.[[User:Dparra | Denis Parra]] won the contest for an invited talk at the "Chilean Computing Conference 2014", and presented his research on Recommender Systems. <br />
Please find the slides [http://www.slideshare.net/denisparra/keynote-at-chilean-week-of-computer-science here].<br />
The abstract of the talk is also available at the home page of the event, JCC 2014. [http://www.jcc2014.ucm.cl/en/ (more)].<br />
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==== <span id="2014-09-19">2014-09-19</span> :: PAWS won the best paper award at the 9th European Conference on Technology Enhanced Learning (EC-TEL 2014). ====<br />
Congratulations to Tomek, [[User:Julio | Julio]], [[User:R.hosseini | Roya]], and [[User:peterb| Peter]]! Their paper entitled "[https://www.researchgate.net/publication/266656951_Mastery_Grids_An_Open_Source_Social_Educational_Progress_Visualization Mastery Grids: An Open Source Social Educational Progress Visualization]" has won the best paper award of EC-TEL 2014 conference. More details on the paper can be accessed [http://link.springer.com/chapter/10.1007/978-3-319-11200-8_18 here].<br />
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==== <span id="2014-05-06">2014-05-06</span> :: [[User: shoha99 | Sharon Hsiao]] is appointed as Assistant Professor in CIDSE @ ASU this Fall.====<br />
On completing 2 years successful post-doctoral innovation fellow position in [http://edlab.tc.columbia.edu EdLab], Teachers College @ Columbia University, [[User: shoha99 | Sharon]] is moving onto a tenure-track position in [http://cidse.engineering.asu.edu School of Computing, Informatics, Decision Systems Engineering] (AKA: Engineering school) in [http://www.asu.edu Arizona State University], Phoenix, Arizona. She anticipates to continue working on the emerging topics of computational technologies in learning. Meanwhile, persistently dedicate to the course - [http://www.columbia.edu/~ih2240/dataviz/index.htm Data Visualization], which she established in [http://www.qmss.columbia.edu QMSS (Quantitative Methods in the Social Sciences)], School of Arts & Sciences in [http://www.columbia.edu Columbia University]. For more details see [http://www.ischool.pitt.edu/news/05-14-2014.php SIS news].<br />
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==== <span id="2014-03-01">2014-03-01</span> :: [[User:peterb | Peter Brusilovsky]] to deliver a keynote at a WWW 2014 workshop. ====<br />
Peter will deliver a keynote titled "Addictive Links: Engaging Students through Adaptive Navigation Support and Open Social Student Modeling" at [http://www2014.kr/program/webet-2014/ WebET 2014] - Workshop on Web-based Education Technologies at the Word Wide Web Conference in Seoul, Korea. The talk will present PAWS work on such systems as [[QuizGuide]], [[NavEx]], JavaGuide, [[Progressor]], and [[ProgressorPlus|Progressor+]].<br />
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==== <span id="2014-03-13">2014-03-13</span> :: [[User: Jennifer | Yi-Ling(Jennifer) Lin]] started her new position as an Assistant Professor in the Department of Information Management at the National Sun Yat-Sen University in Taiwan.====<br />
Yi-Ling (Jennifer) joined the Information Management faculty at the National Sun Yat-Sen University [http://http://epage.mis.nsysu.edu.tw/files/11-1100-4988-1.php?Lang=en] where she is an Assistant Professor. She is teaching Java course and continues to cooperate with Paws Lab to explore the social comparison in cyberlearning.<br />
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==== <span id="2013-08-13">2013-08-13</span> :: [[User:Dparra | Denis Parra]] started a new position as Assistant Professor in the Department of Computer Science at Pontifical Catholic University of Chile.====<br />
Denis joined the faculty at the School of Engineering in Pontificia Universidad Catolica de Chile, [http://www.topuniversities.com/node/2261/ranking-details/latin-american-university-rankings/2013 ranked 2nd among Latinamerican Universities], where he is an [http://www.ing.puc.cl/cuerpo-docente/parra-santander/ Assistant Professor at the Department of Computer Science]. At the undergraduate level, he is teaching a course that explores several topics for the major in Computer Science, and at the graduate level he teaches Data Mining in the [http://mpgi.ing.puc.cl/profesores.html Master for Information Processing and Management]. He continues doing research on Recommender Systems, the topic he investigated while a student at the PAWS lab, and is also Analyzing Social Media, collaborating with PAWS lab student Xidao Wen, studying [http://www.christophtrattner.info/pubs/ht2014.pdf how Twitter is used in academic Conferences]. In addition, he is fostering collaboration between professor Brusilovsky and some areas of teaching such as Databases in the CS department at Catholic University.<br />
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==== <span id="2013-07-18">2013-07-18</span> :: [[Systems#KnowledgeZoom | KnowledgeZoom]] paper receives ICALT 2013 Best full Paper Award ====<br />
Congratulations to paws! "[https://www.researchgate.net/publication/256524709_KnowledgeZoom_for_Java_A_Concept-Based_Exam_Study_Tool_with_a_Zoomable_Open_Student_Model KnowledgeZoom for Java: A Concept Based Exam Study Tool with a Zoomable Open Student Model]" won the best full paper award of ICALT 2013 conference.<br />
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==== <span id="2013-07-09">2013-07-09</span> :: [[User: Falakmasir | Mohammad Falakmasir]] won EDM 2013 Best Student Paper ====<br />
Congratulations to Mohammad! His paper with Zachary A. Pardos, Geoffrey J. Gordon, and Peter Brusilovsky entitled "A Spectral Learning Approach to Knowledge Tracing" won the best student paper award of EDM 2013 conference. In his paper, he proposed using Spectral Learning (SL) to learn the BKT parameters. Results of his study showed that SL can improve knowledge tracing parameter fitting time significantly while maintaining the same prediction accuracy. For more details on his paper please refer to his article [http://halley.exp.sis.pitt.edu/cn3/presentation2.php?presentationID=5323&conferenceID=115]<br />
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==== <span id="2013-06-12">2013-06-12</span> :: [[User:peterb | Peter Brusilovsky]] selected as a Fulbright-Nokia Distinguished Chair. ====<br />
[[User:peterb | Peter Brusilovsky]], has been selected as a Fulbright-Nokia Distinguished Chair by the Fulbright Commission. With this Award Peter will spend 5 month in FInland collaborating with researchers from University of Helsinki, Aalto University, and Helsinki Institute of Information Technology. Read the news story on SIS Web site: [http://www.ischool.pitt.edu/news/08-01-2013.php]<br />
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==== <span id="2013-05-01">2013-05-01</span> :: [[User:peterb | Peter Brusilovsky]] and PAWS lab win Army Contract to develop social personalized learning architecture. ====<br />
Peter Brusilovsky, Professor at the iSchool, has been awarded a contract by the United States Army Contracting Command to participate in the Advanced Distributed Learning (ADL) Initiative. Brusilovsky’ s contract, for $623,005 over a three-year period, will support the project [[Adaptive Navigation Support and Open Social Learner Modeling for PAL]] that will focus on the architecture, algorithms, and interfaces for a Personal Assistant for Learning (PAL), one of the major endeavors undertaken by the ADL Initiative. Through a PAL, the Initiative will provide state of the art education and training -- using technology and innovative learning methodologies -- for workforce members in the Department of Defense and the federal government. Read the news story on SIS Web site: [http://www.ischool.pitt.edu/news/05-01-2013.php]<br />
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==== <span id="2013-04-01">2013-04-01</span> :: Sergey Sosnovsky profiled in International Innovation ====<br />
Sergey Sosnovsky, who earned his PhD in Information Science in 2012, was recently profiled in International Innovation Magazine about his work on eLearning systems research and tools. Sosnovsky is the Principal Researcher and Head of the Intelligent e-Learning Technology Lab at the German Research Center for Artificial Intelligence in Saarbruken, Germany. The article explored his work on the Intelligent Support for Authoring Semantic Learning Content project funded by the European Commission’s Community Research and Development Information Service. The magazine article (published March 2013) discussed how Sosnovsky’s project will enhance adaptive e-Learning by making it possible to develop smart instructional material for a broader audience of content authors. The article can be viewed at [[http://www.research-europe.com/magazine/REGIONAL/EX8/index.html]] , beginning on page 71.<br />
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==== <span id="2013-01-01">2013-01-01</span> :: [[User:peterb | Peter Brusilovsky]] appointed the Editor-In-Chief of [http://www.computer.org/portal/web/tlt IEEE Transactions on Learning Technologies]. ====<br />
Peter is appointed the Editor-In-Chief of IEEE Transactions on Learning Technologies, a journal dedicated to advancing the state of the art in technology-enhanced learning. This quarterly publication covers leading edge research on topics such as educational software applications, online learning systems, and simulation systems for education and training. Read the news story on SIS Web site: [http://www.ischool.pitt.edu/news/3-11-2013.php]<br />
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==== <span id="2012-07-09">2012-08-26</span> :: [[User:Rostaf | Rosta Farzan]] started her new position as an Assistant Professor at the School of Information Sciences. ====<br />
Congratulations to Rosta! After 3 years as a postdoc at CMU, she is now back to Pitt as an Assistant Professor. During her first semester at SIS she is teaching IS2430 Social Computing course. Read the news story on SIS Web site: [http://www.ischool.pitt.edu/news/08-02-2012.php]<br />
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==== <span id="2012-07-09">2012-07-09</span> :: [[User:shoha99 | Sharon Hsiao]] Thesis Defence: [http://d-scholarship.pitt.edu/13439/ Navigation Support and Social Visualization for Personalized E-Learning] ====<br />
A large number of educational resources is now made available on the Web to support both regular classroom learning and online learning. However, the abundance of available content produced at least two problems: how to help students to find the most appropriate resources and how to engage them into using these resources and benefit from them. Personalized and social learning have been suggested as potential ways to address these problems.<br />
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This work attempts to combine the ideas of personalized and social learning by providing navigation support through an open social student modeling visualization. A series of classroom studies exploited the idea of the approach and revealed promising results, which demonstrated the personalized guidance and social visualization combined helped students to find the most relevant resources of parameterized self-assessment questions for Java programming. Thus, this dissertation extend the approach to a larger collection of learning objects for cross content navigation and verify its capability of supporting social visualization for personalized E-Learning.<br />
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The study results confirm that working with the non-mandatory system, students enhanced the learning quality in increasing their motivation and engagement. They successfully achieved better learning results. Meanwhile, incorporating a mixed collection of content in the open social student modeling visualizations effectively led the students to work at the right level of questions. Both strong and weak student worked with the appropriate levels of questions for their readiness accordingly and yielded a consistent performance across all three levels of complexities. Additionally, providing a more realistic content collection on the navigation supported open social student modeling visualizations results in a uniform performance in the group. The classroom study revealed a clear pattern of social guidance, where the stronger students left the traces for weaker ones to follow. The subjective evaluation confirms the design of the interface in terms of the content organization. Students’ positive responses also compliment the objective system usage data.<br />
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download it from [http://d-scholarship.pitt.edu/13439/ here]<br />
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==== <span id="2011-11-30">2011-11-30</span> :: [[User:Sergey | Sergey Sosnovsky]] Thesis Defence: Ontology-Based Open-Corpus Personalization for E-Learning ====<br />
Conventional closed-corpus adaptive information systems control limited sets of documents in fixed subject domains and cannot provide access to the content outside the system. Such restrictions contradict the requirements of today, when most of the information systems are implemented in the open document space of WWW and are expected to operate on the open-corpus content. In order to maintain personalized access to open-corpus documents, an adaptive system should be able to model the documents and the relations between the documents and the domain knowledge automatically and dynamically. This dissertation explores the problem of open-corpus personalization and semantic modeling of open-corpus content in the context of e-Learning. Information on WWW is not without structure. Many collections of online instructional material (tutorials, electronic books, digital libraries, etc.) have been provided with implicit knowledge models encoded in form of tables of content, indexes, headers of chapters, links between pages, and different styles of text fragments. The main dissertation approach tries to leverage this layer of hidden semantics by extracting and representing it as coarse-grained models of collections. A central domain ontology is used to maintain overlay modeling of studentsÕ knowledge and serves as a reference point for multiple collections of external instructional material. In order to establish the link between the ontology and the open-corpus content models a special ontology mapping algorithm has been developed. The proposed approach has been applied in the Ontology-based Open-corpus Personalization Service (OOPS) that recommends and adaptively annotates online reading material. The domain of Java programming has been chosen for the proof-of-concept implementation. A controlled experiment has been organized to evaluate the developed adaptive system and the proposed approach overall. The results of the evaluation have demonstrated several significant learning effects of the implemented open-corpus personalization. The analysis of log-based data has also shown that the open-corpus version of the system is capable of providing personalization of similar quality to the close-corpus one. Such results indicate that the proposed approach supports fully-scale open-corpus personalization for e-Learning. Further research is required to verify if the approach remains effective in other subject domains and with other types of instructional content.<br />
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==== <span id="2011-07-15">2011-07-15</span> :: [[User:DParra | Denis Parra]] earns a James Chen Best Student paper award in UMAP 2011 ====<br />
In the last conference of User Modeling, Adaptation and Personalization (UMAP 2011) Denis Parra won one of the 2 [http://www.umap2011.org/program/best-paper-awards James Chen Best Student paper awards] for his paper '''Walk The Talk: Analyzing the relation between implicit and explicit feedback for preference elicitation''' that he co-authored with Dr. Xavier Amatriain. In this paper, the authors present a study on the music domain with last.fm users, which results leads them to create a regression model that maps implicit information (such as playcounts and how recently a user listened to albums) with explicit information in the form of ratings. More details in the [http://www.springerlink.com/content/645721483544r815/ conference proceedings in Springer]. Denis is the third PAWS Lab member to receive this prestigious award. Prior to that, Rosta Farzan and Michael Yudelson won this award at earlier User Modeling conferences. Rosta also won another James Chen award at Adaptive Hypermedia conference.<br />
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==== <span id="2011-06-12">2011-06-12</span> :: [[User:peterb | Peter]] Promoted to Rank of Full Professor ====<br />
Congratulations to our head of PAWS lab! [[http://www.ischool.pitt.edu/news/06-10-2011.php Read more ]]<br />
==== <span id="2011-05-01">2011-05-01</span> :: [[User:shoha99 | Sharon]] received 2011 Allen Kent Award for Outstanding Contributions to the Graduate Program in Information Science ==== <br />
She has worked with [http://www.sis.pitt.edu/~gray/ Dr. Glenn Ray] several years in designing and teaching undergraduate courses. She's affiliated as teaching fellow and teaches in our school now.<br />
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==== <span id="2010-12-15">2010-12-15</span> :: [[User:peterb | Peter]] received Google grant to work on ''[[Personalized Social Systems for Local Communities]]'' ====<br />
The grant will support our efforts to increase user participation in social systems designed for local communities. In the course of the project will explore two innovative ideas for increasing participation. The first idea is to provide access to information “beyond the desktop,” by adding a mobile location-based interface to access information. This will increase both the number of active users and the volume of their contributions. The second idea is to provide personalized access to information to increase the chance to gather relevant information. This work will be based on two existing social systems that were developed and maintained by PAWs lab: the [[Systems#CoMeT | CoMeT]] system for sharing information about research talks at Carnegie Mellon and University of Pittsburgh and [[Systems#Eventur | Eventur]], a social system for recommending cultural events in the Pittsburgh area.<br />
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==== <span id="2010-09-17">2010-09-17</span> :: [[User:Myudelson | Michael Yudelson's]] Thesis Defence: Providing Service-Based Personalization In An Adaptive Hypermedia System ====<br />
The dissertation proposes a novel way of speeding the development of new adaptive hypermedia systems. The gist of the approach is to extract the adaptation functionality out of the adaptive hypermedia system, encapsulate it into a standalone system, and offer adaptation as a service to the client applications. Such a standalone adaptation provider reduces the development of adaptation functionality to configuration and compliance and as a result creates new adaptive systems faster and helps serve larger user populations with adaptively accessible content. [[http://washington.sis.pitt.edu/comet/presentColloquium.do?col_id=777 details]]<br />
The electronic version of [[User:Myudelson | Michael Yudelson's]] dissertation has been approved by the School of Information Sciences. ETD is accessible worldwide from the online library catalog of the University of Pittsburgh ([http://etd.library.pitt.edu/ETD/available/etd-10132010-092137/ link]).<br />
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==== <span id="2010-09-15">2010-09-15</span> :: [[User:peterb | Peter Brusilovsky]] received NSF grant to work on ''Modeling and Visualization of Latent Communities'' ====<br />
This EAGER grant will allow us to investigate how to model and visualize latent communities – those groups of people who form communities based on their similar interests. This work will consider how to elicit latent communities from various kinds of data about individuals available in the modern social Web and deliver the results in a manner suitable for interactive exploration through interactive visualizations. This will be one of the first attempts to use a variety of social Web data and approaches to community modeling. [[http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1059577 details]]<br />
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==== <span id="2010-09-08">2010-09-08</span> :: [[User:jahn | Jae-wook's]] Thesis Defence: Adaptive Visualization for Focused Personalized Information Retrieval ====<br />
Jae-wook Ahn's dissertation proposes to incorporate interactive visualization into personalized search in order to overcome the limitation. By combining the personalized search and the interactive visualization, we expect our approach will be able to help users to better explore the information space and locate relevant information more efficiently. [[http://washington.sis.pitt.edu/comet/presentColloquium.do?col_id=764 details]]<br />
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==== <span id="2010-09-01">2010-09-01</span> :: [[User:peterb | Peter Brusilovsky]] and Jung Sun Oh received NSF grant to work on ''Personalization and social networking for short-term communities'' ====<br />
This one-year grant will support a project exploring personalization and social networking for short-term communities. Using academic research conferences as a test bed, our team will explore new methods to leverage information about user interests (available from multiple external resources) and develop techniques to facilitate use of existing social technologies. [[http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1052768 details]]<br />
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==== 2010-08-15 :: SIGWeb Newsletter published an interview with [[User:peterb | Peter Brusilovsky]] ====<br />
The Summer 2010 issue of SIGWeb Newsletter (a magazine of ACM Special Interest Group on Hypertext and the Web) published an [http://dx.doi.org/10.1145/1796390.1796393 interview with Peter Brusilovsky]. The interview provides some personal view on research project performed at PAWS.<br />
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==== 2010-07-01 :: [[User:jahn | Jae-wook]] has received Computing Inovation Fellowship ====<br />
Jae-wook Ahn was chosen as a [http://cifellows.org CIFellow] (Computing Innovation Fellow) supported by the Computing Community Consortium (CCC), the Computing Research Association (CRA), and the National Science Foundation. Starting from the fall 2010, he is going to work with [http://www.cs.umd.edu/~ben/ Dr. Ben Shneiderman] at the [http://www.cs.umd.edu/hcil Human Computer Interaction Lab], University of Maryland.<br />
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==== 2010-05-01 :: Sergey has received EU Marie Curie International Incoming Fellowship ====<br />
Sergey Sosnovsky's proposal for EU [http://cordis.europa.eu/improving/fellowships/home.htm Marie Curie Fellowship] is approved by the EU Research Executive Agency. The funding starts in July, 2010 and will last until July 2012. The project "Intelligent Support for Authoring Semantic Learning Content" will focus on implementation of author-friendly technologies for learning content development, including collaborative authoring support, metadata authoring support, open-corpus content discovery, interactivity authoring, and gap detection.<br />
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==== 2009-06-27 :: Rosta receives her second James Chen Award ====<br />
Rosta Farzan received James Chen Best Student Paper Award at the 12th International Conference on User Modelling, Adaptation and Personalization, UMAP2009, in Trento, Italy for the paper ''Social Navigation Support for Information Seeking: If You Build It, Will They Come?'' by Rosta Farzan and Peter Brusilovsky. This is her second James Chen Award, congratulations!<br />
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==== 2009-06-26 :: PAWS Caught on UMAP 2009 Video ====<br />
* 0:27 [[User:Myudelson|Michael]]<br />
* 1:09 [[User:Rostaf|Rosta]]<br />
* 1:18 [[User:Sergey|Sergey]]<br />
* 1:51 [[User:Suleehs|Danielle]]<br />
* 3:12 [[User:Myudelson|Michael]] and [[User:Sergey|Sergey]]<br />
* 4:01 [[User:Peterb|Peter]]<br />
<youtube>v_amf_zcLtQ</youtube><br />
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==== 2009-05-28 :: [[User:peterb | Peter]] awarded honorary doctorate by the Slovak University of Technology in Bratislava ====<br />
At a ceremony in Bratislava today, Peter Brusilovsky was honored by the [http://www.stuba.sk/ Slovak University of Technology in Bratislava] with the degree of Doctor honoris causa. The university, founded in 1937 in Bratislava, is one of the most significant institutions of higher education in Slovakia. Peter was selected for this recognition for "his contributions to the fields of Informatics and Information Technologies".<br />
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==== 2008-07-31 :: [[User:peterb | Peter]] receives Best Paper Award at AH 2008 ====<br />
Peter Brusilovsky received Best Paper Award at the 5th International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, AH 2008, in Hannover, Germany for the paper [http://www.springerlink.com/content/6h410u3w4836v866/ ''Social Information Access for the Rest of Us: An Exploration of Social YouTube''] by Maurice Coyle, Jill Freyne, Peter Brusilovsky, and Barry Smyth<br />
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==== 2007-06-28 :: [[User:Myudelson | Michael]] receives James Chen Best Student Paper Award at UM 2007 ====<br />
Michael Yudelson received James Chen Best Student Paper Award at the 11th International Conference on User Modelling, UM07, in Corfu, Greece for the paper [http://www.springerlink.com/content/c723060442701737/ ''A User Modeling Server for Contemporary Adaptive Hypermedia: an Evaluation of Push Approach to Evidence Propagation''] by Michael Yudelson, Peter Brusilovsky, and Vladimir Zadorozhny</div>K.thakerhttps://adapt2.sis.pitt.edu/w/index.php?title=PAWS&diff=4049PAWS2019-03-11T22:24:18Z<p>K.thaker: /* Most Recent News */</p>
<hr />
<div>== About PAWS ==<br />
<div style="float: right; width: 50%"><br />
[[Image:Edm_2018.jpg|class=img-responsive|EDM 2018 - Buffalo, NY]]<br />
</div><br />
Personalized Adaptive Web Systems Lab was established in 2004 with support from National Science Foundation and the School of Information Sciences at the University of Pittsburgh. The goal of the PAWS Lab is development and evaluation of innovative user- and group-adaptive Web-based technologies, systems, and architectures. The Lab currently explores a range of user modeling, adaptation and personalization technologies. The work of the Lab is supported by NSF and DARPA funding.<br />
<br />
== Most Recent News ==<br />
* Prof. Peter Brusilovksy is awarded AMiner Most Influential Scholar Award in recognition of his outstanding and vibrant contributions to the field of Recommender System by AMiner.([[News#2019-03-04|details]])<br />
* Two PAWS graduates' papers were short-listed for the best paper award at EC-TEL 2018.([[News#2018-08-13|details]])<br />
* [[User:peterb | Peter Brusilovsky]] awarded NSF grant under the program Cyberlearn And Future Learn Tech. ([[News#2018-07-31|details]])<br />
* Roya Hosseini defended her Ph. D. Thesis: 'Program Construction Examples in Computer Science Education: From Static Text to Adaptive and Engaging Learning Technology'.([[News#2018-07-24|details]])<br />
* Yun Huang defended her Ph. D. Thesis: 'Learner Modeling for Integration Skills in Programming' ([[News#2018-07-05|details]])<br />
* PAWS Lab scores thrice at IUI 2018 ([[News#2018-03-08|details]])<br />
* Julio Guerra defended his Ph. D. Thesis: 'Open Learner Models for Self-Regulated Learning: Exploring the Effects of Social Comparison and Granularity'.([[News#2017-10-24|details]])<br />
* [[User:Suleehs | Danielle Lee]] moves to Sangmyung University as an assistant professor ([[News#2017-08-17|details]])<br />
* Two PAWS papers were nominated and one received Best Paper Awards at UMAP 2017 ([[News#2017-07-11|details]])<br />
* [[User:peterb | Peter Brusilovsky]] received the 2017 Provost’s Award for Excellence in Mentoring. ([[News#2017-02-21|details]])<br />
* [[User:Sergey | Sergey Sosnovsky]] moves to Utrecht University as a tenure-track professor ([[News#2016-11-23|details]])<br />
* Maria Harrington moves to University of Central Florida as an Assistant Professor ([[News#2016-08-15 |details]])<br />
* Shaghayegh (Sherry) Sahebi defended her Ph. D. Thesis: ''Canonical Correlation Analysis in Cross-Domain Recommendation'', will start as an Assistant Professor in University of Albany ([[News#2016-07-21|details]])<br />
* Dr. [[User:peterb | Peter Brusilovsky]] and Rosta Farzan are on new Association for Computing Machinery journal’s editorial board. ([[News#2016-05-13|details]])<br />
* [[User:Yuh43 | Yun Hung]] and [[User:R.hosseini | Roya Hosseini]] received Andrew Mellon Pre-doctoral Fellowship for the academic year 2016-2017. ([[News#2016-03-04|details]])<br />
<br />
* [[User:peterb | Peter Brusilovsky]] and Daqing He awarded NSF grant to work on [[Open Corpus Personalized Learning]] ([[News#2016-02-23|details]])<br />
* Chirayu Wongchokprasitti defended his Ph. D. Thesis: ''Using External Sources To Improve Research Talk Recommendation In Small Communities''. ([[News#2015-04-15|details]])<br />
* Dr.[[User:Dparra | Denis Parra]] won the contest for an invited talk at the "Chilean Computing Conference 2014". ([[News#2014-11-11|details]])<br />
* Tomek Loboda, [[User:Julio | Julio Guerra]], [[User:R.hosseini | Roya Hosseini]] , and [[User:peterb | Peter Brusilovsky]] won the best paper award at the 9th European Conference on Technology Enhanced Learning (EC-TEL 2014). ([[News#2014-09-19|details]])<br />
* [[User: Yuh43 | Yun Huang]] has been nominated for the best paper award at the 7th International Conference on Educational Data Mining (EDM 2014). ([[News#2014-07-06|details]])<br />
* [[User:shoha99 | Sharon Hsiao]] finished her 2nd year postdoc in Columbia University and starts as Assistant Professor in CIDSE @ ASU this Fall ([[News#2014-05-06|details]])<br />
<br />
Go to [[News | News Page]] for all news.<br />
<br />
== Try PAWS Tools at our Community Portal == <br />
<br />
Go to [http://adapt2.sis.pitt.edu/kt Knowledge Tree] portal and [http://adapt2.sis.pitt.edu/kt/register.html create trial account] to try our tools.<br />
<br />
[http://www.sis.pitt.edu/~cagent/ CourseAgent]: Share evaluations of IS graduate courses at Pitt and plan your career.<br />
<br />
[http://pittsburgh.comettalks.com/ Comet]: Share, tag, recommend and schedule interesting talks in Pittsburgh.<br />
<br />
[http://eventur.us/ Eventur]: Share and schedule cultural events in Pittsburgh.<br />
<br />
[http://www.computingportal.org/ Ensemble]: This cross-university collaborative effort aims to bring together the global community of computing educators around a growing set of content collections with high-quality educational resources.<br />
<br />
[http://amber.exp.sis.pitt.edu/yournews/ YourNews]: This is a personalized RSS news access portal with several levels of user modeling and feed recommendation. Read your news in a personalized way!<br />
<br />
If you are a researcher and would like to quickly try our adaptation tools, create the trial account (link above) and proceed [http://adapt2.sis.pitt.edu/cbum/ here].<br />
<br />
== PAWS Lab Contact Information ==<br />
Information Sciences Building, Rm. 2A04<br/><br />
135 North Bellefield Avenue<br/><br />
Pittsburgh, PA 15260, USA<br/><br />
Tel: +1(412)624-9437<br/></div>K.thakerhttps://adapt2.sis.pitt.edu/w/index.php?title=People&diff=4034People2019-02-22T02:25:32Z<p>K.thaker: </p>
<hr />
<div>== Faculty ==<br />
<gallery><br />
Image:Peter.jpg|'''[[User:Peterb | Peter Brusilovsky]]'''<br/>Director<br />
</gallery><br />
<br />
== Doctoral Students ==<br />
<gallery perrow="4"><br />
Image:Tsai.jpg|[http://www.cht77.com/ Chun-Hua Tsai]<br />
Image:Avatar.jpg|[http://pitt.edu/~hkc6 Hung Chau]<br />
Image:Jordan.jpeg|[http://pitt.edu/~jab464 Jordan Barria-Pineda]<br />
Image:Kamil.jpg|[http://pitt.edu/~kaa108 Kamil Akhuseyinoglu]<br />
Image:zrisha.png|[https://zakrisha.com Zak Risha]<br />
Image:behnam.jpg|[http://pitt.edu/~ber58 Behnam Rahdari]<br />
Image:k.thaker.png|[http://pitt.edu/~kmt81 Khushboo Thaker]<br />
</gallery><br />
<br />
== Visiting Scholars ==<br />
<gallery perrow="4"><br />
<br />
</gallery><br />
<br />
== Past Doctoral Students ==<br />
<gallery perrow="4"><br />
Image:Roya.jpg|[[User:R.hosseini | Roya Hosseini]]<br />
Image:yunhuang.png|[http://columbus.exp.sis.pitt.edu/yunhuang/index.htm Yun Huang]<br />
Image:Julio.jpg|[[User:Julio | Julio Guerra]]<br />
Image:xidao.jpg|[[User:Xidao| Xidao Wen]]<br />
Image:Shaghayeghsahebi.jpg|[[User:Sherry | Shaghayegh Sahebi (Sherry)]] <br/>Currently Assistant Professor in the Computer Science Department at State University of New York (SUNY) at Albany<br />
Image:Clau.JPG|[[User:Clau | Claudia López]] <br/>Currently Assistant Professor in the Departamento de Informática, Universidad Técnica Federico Santa María, Chile<br />
Image:Kong.png|[[User:Chirayu | Chirayu Wongchokprasitti]] <br/> Currently at the Department of Biomedical Informatics, University of Pittsburgh<br />
Image:jennifer.jpg|[[User:Jennifer | Jennifer (Yiling) Lin]]<br/>Currently Assistant Professor in the department of Information Management at the National Sun Yat-Sen University.<br />
Image:Denis_PAWS_blog.jpg|[[User:Dparra | Denis Parra]]<br/>Currently Assistant Professor at the Computer Science Department, School of Engineering at PUC Chile.<br />
Image:jaewook-1.jpg|[[User:Jahn | Jae-wook Ahn]]<br/>Projects: [[ADVISE]], [[Adaptive VIBE]], [[YourNews]], [[YourSports]], [[TaskSieve]], [[NameSieve]]<br/>Research Staff Member, Cognitive Sciences and Education,IBM Research<br />
Image:RostaFarzan.jpg|[[User:Rostaf | Rosta Farzan]]<br/>Currently Assistant Professor at School of Computing and Information, University of Pittsburgh.<br />
Image:Michael_V_Yudelson.gif|'''[[User:Myudelson | Michael V. Yudelson]]'''<br/>Projects: [[Knowledge Tree]], [[CUMULATE]], [[PERSEUS]], [[NavEx]], [[CoPE]], [[WebEx]]<br/>Currently Postdoctoral Fellow at Carnegie Mellon University<br />
Image:Sergey.jpg|[[User:Sergey | Sergey Sosnovsky]]<br> Currently Assistant Professor at Utrecht University (the Netherlands)<br />
Image:Hsiao.jpg|[[User:Shoha99 | Sharon (I-Han) Hsiao]]<br/>Projects: [[AnnotEx]], [[QuizJET]], [[Progressor]], [[ProgressorPlus]]<br/>Currently Assistant Professor @ CIDSE, Arizona State University<br />
Image:Danielle.gif|[[User:Suleehs | Danielle H. Lee]]<br/>Projects: [[Eventur]], [[Proactive]]<br/>Currently Assistant Professor at the Department of Software, Sangmyung University, Korea<br />
</gallery><br />
<br />
== Past Visiting Faculty ==<br />
<gallery perrow="4"><br />
Image:Susan_bull.png|[https://www.researchgate.net/profile/Susan_Bull2 Susan Bull]<br />
Image:Jaakko peltonen 215x296.jpg|[http://users.ics.aalto.fi/jtpelto// Jaakko Peltonen]<br />
Image:IMG_0611.JPG|[http://www.dcs.warwick.ac.uk/~acristea/ Alexandra I. Cristea]<br />
Image:Sibel.jpg|[http://sibelsomyurek.com/ Sibel Somyürek]<br />
Image:KatrienVerbert.jpg|[http://people.cs.kuleuven.be/~katrien.verbert/KatrienVerbert/Katrien_Verbert.html Katrien Verbert]<br />
Image:Roman bednarik.png|[http://cs.uef.fi/~rbednari/ Roman Bednarik]<br />
Image:TanjaMitrovic.jpg|[http://www.cosc.canterbury.ac.nz/tanja.mitrovic/ Tanja Mitrovic]<br />
Image:Eva millan.gif|[http://www.lcc.uma.es/~eva/ Eva Millán Valldeperas]<br />
Image:Julita vasilleva.gif|[http://www.cs.usask.ca/faculty/julita/ Julita Vassileva]<br />
Image:NicolaHenze.gif|[http://www.kbs.uni-hannover.de/~henze/ Nicola Henze]<br />
<br />
</gallery><br />
<br />
== Past Master Students ==<br />
<gallery perrow="4"><br />
Image:Vikrant Khenat.jpg|[http://www.sis.pitt.edu/~vkhenat/ Vikrant Khenat]<br />
Image:Tibor Dumitriu.gif|[http://www.sis.pitt.edu/~dumitriu/ Tibor Dumitriu]<br/>Projects: [http://ir.exp.sis.pitt.edu/advise/ AdVisE]<br />
</gallery><br />
<br />
== Past Visiting Scholars == <br />
<gallery perrow="4"><br />
Image:huhtamaki-jukka-300.jpg|[http://www.linkedin.com/in/jukkahuhtamaki Jukka Huhtamäki]<br/>Postdoc Researcher, DSc (Tech), University of Tampere<br />
Image:Andrew.jpg|Shuchen Li (Andrew) <br/>From Beijing University of Posts and Telecommunications<br />
Image:Rafael.png|[https://rafaelrda.wordpress.com Rafael Dias Araújo]<br />
Image:Liping_wang.jpg|Liping Wang<br/>From JiLin University<br />
Image:Ayca cebi.jpg|[https://ktu.academia.edu/aycacebi Ayça ÇEBİ] <br/>From Karadeniz Technical University<br />
Image:File crop1 1183908 y 384.jpg|[https://people.aalto.fi/index.html?profilepage=isfor#!teemu_sirkia Teemu Sirkiä]<br />
Image:Michelle_liang.JPG|[http://www.tcs.fudan.edu.cn/~michelle/index.html Michelle Liang]<br />
Image:Pkraker.jpg|[http://science20.wordpress.com Peter Kraker] <br/>Marshall Plan Scholar <br />
Image:Kim.jpg|Jaekyung Kim<br />
Image:Jbravo.gif|[http://www.eps.uam.es/esp/personal/ficha.php?empid=367 Javier Bravo Agapito]<br />
Image:MarkusKetterl1.jpg|[http://studip.serv.uni-osnabrueck.de/extern.php?username=mketterl&page_url=http://www.virtuos.uni-osnabrueck.de/VirtUOS/TemplStudipMitarbDetails&global_id=4c8fb9ddd4dde83366119b2031d39ab3 Markus Ketterl]<br />
Image:Jillfreyne.jpg|[http://www.csi.ucd.ie/users/jill-freyne Jill Freyne]<br />
Image:Robert.jpg|Robert Mertens<br />
Image:Roman bednarik.png|[http://www.cs.joensuu.fi/~rbednari/ Roman bednarik]<br />
Image:Ewald ramp.jpg|Ewald W. A. Ramp<br />
Image:Jacopo.jpg|Jacopo Armani<br />
Image:Yetunde.JPG|Yetunde Folajimi<br />
Image:Chris_face.jpg|[http://www.austria-lexikon.at/af/User/Trattner%20Christoph Christof Trattner]<br />
</gallery></div>K.thakerhttps://adapt2.sis.pitt.edu/w/index.php?title=File:K.thaker.png&diff=4033File:K.thaker.png2019-02-22T02:24:54Z<p>K.thaker: </p>
<hr />
<div></div>K.thakerhttps://adapt2.sis.pitt.edu/w/index.php?title=People&diff=4032People2019-02-20T21:54:02Z<p>K.thaker: </p>
<hr />
<div>== Faculty ==<br />
<gallery><br />
Image:Peter.jpg|'''[[User:Peterb | Peter Brusilovsky]]'''<br/>Director<br />
</gallery><br />
<br />
== Doctoral Students ==<br />
<gallery perrow="4"><br />
Image:Tsai.jpg|[http://www.cht77.com/ Chun-Hua Tsai]<br />
Image:Avatar.jpg|[http://pitt.edu/~hkc6 Hung Chau]<br />
Image:Jordan.jpeg|[http://pitt.edu/~jab464 Jordan Barria-Pineda]<br />
Image:Kamil.jpg|[http://pitt.edu/~kaa108 Kamil Akhuseyinoglu]<br />
Image:zrisha.png|[https://zakrisha.com Zak Risha]<br />
Image:behnam.jpg|[http://pitt.edu/~ber58 Behnam Rahdari]<br />
Image:khushboo.jpg|[http://pitt.edu/~kmt81 Khushboo Thaker]<br />
</gallery><br />
<br />
== Visiting Scholars ==<br />
<gallery perrow="4"><br />
<br />
</gallery><br />
<br />
== Past Doctoral Students ==<br />
<gallery perrow="4"><br />
Image:Roya.jpg|[[User:R.hosseini | Roya Hosseini]]<br />
Image:yunhuang.png|[http://columbus.exp.sis.pitt.edu/yunhuang/index.htm Yun Huang]<br />
Image:Julio.jpg|[[User:Julio | Julio Guerra]]<br />
Image:xidao.jpg|[[User:Xidao| Xidao Wen]]<br />
Image:Shaghayeghsahebi.jpg|[[User:Sherry | Shaghayegh Sahebi (Sherry)]] <br/>Currently Assistant Professor in the Computer Science Department at State University of New York (SUNY) at Albany<br />
Image:Clau.JPG|[[User:Clau | Claudia López]] <br/>Currently Assistant Professor in the Departamento de Informática, Universidad Técnica Federico Santa María, Chile<br />
Image:Kong.png|[[User:Chirayu | Chirayu Wongchokprasitti]] <br/> Currently at the Department of Biomedical Informatics, University of Pittsburgh<br />
Image:jennifer.jpg|[[User:Jennifer | Jennifer (Yiling) Lin]]<br/>Currently Assistant Professor in the department of Information Management at the National Sun Yat-Sen University.<br />
Image:Denis_PAWS_blog.jpg|[[User:Dparra | Denis Parra]]<br/>Currently Assistant Professor at the Computer Science Department, School of Engineering at PUC Chile.<br />
Image:jaewook-1.jpg|[[User:Jahn | Jae-wook Ahn]]<br/>Projects: [[ADVISE]], [[Adaptive VIBE]], [[YourNews]], [[YourSports]], [[TaskSieve]], [[NameSieve]]<br/>Research Staff Member, Cognitive Sciences and Education,IBM Research<br />
Image:RostaFarzan.jpg|[[User:Rostaf | Rosta Farzan]]<br/>Currently Assistant Professor at School of Computing and Information, University of Pittsburgh.<br />
Image:Michael_V_Yudelson.gif|'''[[User:Myudelson | Michael V. Yudelson]]'''<br/>Projects: [[Knowledge Tree]], [[CUMULATE]], [[PERSEUS]], [[NavEx]], [[CoPE]], [[WebEx]]<br/>Currently Postdoctoral Fellow at Carnegie Mellon University<br />
Image:Sergey.jpg|[[User:Sergey | Sergey Sosnovsky]]<br> Currently Assistant Professor at Utrecht University (the Netherlands)<br />
Image:Hsiao.jpg|[[User:Shoha99 | Sharon (I-Han) Hsiao]]<br/>Projects: [[AnnotEx]], [[QuizJET]], [[Progressor]], [[ProgressorPlus]]<br/>Currently Assistant Professor @ CIDSE, Arizona State University<br />
Image:Danielle.gif|[[User:Suleehs | Danielle H. Lee]]<br/>Projects: [[Eventur]], [[Proactive]]<br/>Currently Assistant Professor at the Department of Software, Sangmyung University, Korea<br />
</gallery><br />
<br />
== Past Visiting Faculty ==<br />
<gallery perrow="4"><br />
Image:Susan_bull.png|[https://www.researchgate.net/profile/Susan_Bull2 Susan Bull]<br />
Image:Jaakko peltonen 215x296.jpg|[http://users.ics.aalto.fi/jtpelto// Jaakko Peltonen]<br />
Image:IMG_0611.JPG|[http://www.dcs.warwick.ac.uk/~acristea/ Alexandra I. Cristea]<br />
Image:Sibel.jpg|[http://sibelsomyurek.com/ Sibel Somyürek]<br />
Image:KatrienVerbert.jpg|[http://people.cs.kuleuven.be/~katrien.verbert/KatrienVerbert/Katrien_Verbert.html Katrien Verbert]<br />
Image:Roman bednarik.png|[http://cs.uef.fi/~rbednari/ Roman Bednarik]<br />
Image:TanjaMitrovic.jpg|[http://www.cosc.canterbury.ac.nz/tanja.mitrovic/ Tanja Mitrovic]<br />
Image:Eva millan.gif|[http://www.lcc.uma.es/~eva/ Eva Millán Valldeperas]<br />
Image:Julita vasilleva.gif|[http://www.cs.usask.ca/faculty/julita/ Julita Vassileva]<br />
Image:NicolaHenze.gif|[http://www.kbs.uni-hannover.de/~henze/ Nicola Henze]<br />
<br />
</gallery><br />
<br />
== Past Master Students ==<br />
<gallery perrow="4"><br />
Image:Vikrant Khenat.jpg|[http://www.sis.pitt.edu/~vkhenat/ Vikrant Khenat]<br />
Image:Tibor Dumitriu.gif|[http://www.sis.pitt.edu/~dumitriu/ Tibor Dumitriu]<br/>Projects: [http://ir.exp.sis.pitt.edu/advise/ AdVisE]<br />
</gallery><br />
<br />
== Past Visiting Scholars == <br />
<gallery perrow="4"><br />
Image:huhtamaki-jukka-300.jpg|[http://www.linkedin.com/in/jukkahuhtamaki Jukka Huhtamäki]<br/>Postdoc Researcher, DSc (Tech), University of Tampere<br />
Image:Andrew.jpg|Shuchen Li (Andrew) <br/>From Beijing University of Posts and Telecommunications<br />
Image:Rafael.png|[https://rafaelrda.wordpress.com Rafael Dias Araújo]<br />
Image:Liping_wang.jpg|Liping Wang<br/>From JiLin University<br />
Image:Ayca cebi.jpg|[https://ktu.academia.edu/aycacebi Ayça ÇEBİ] <br/>From Karadeniz Technical University<br />
Image:File crop1 1183908 y 384.jpg|[https://people.aalto.fi/index.html?profilepage=isfor#!teemu_sirkia Teemu Sirkiä]<br />
Image:Michelle_liang.JPG|[http://www.tcs.fudan.edu.cn/~michelle/index.html Michelle Liang]<br />
Image:Pkraker.jpg|[http://science20.wordpress.com Peter Kraker] <br/>Marshall Plan Scholar <br />
Image:Kim.jpg|Jaekyung Kim<br />
Image:Jbravo.gif|[http://www.eps.uam.es/esp/personal/ficha.php?empid=367 Javier Bravo Agapito]<br />
Image:MarkusKetterl1.jpg|[http://studip.serv.uni-osnabrueck.de/extern.php?username=mketterl&page_url=http://www.virtuos.uni-osnabrueck.de/VirtUOS/TemplStudipMitarbDetails&global_id=4c8fb9ddd4dde83366119b2031d39ab3 Markus Ketterl]<br />
Image:Jillfreyne.jpg|[http://www.csi.ucd.ie/users/jill-freyne Jill Freyne]<br />
Image:Robert.jpg|Robert Mertens<br />
Image:Roman bednarik.png|[http://www.cs.joensuu.fi/~rbednari/ Roman bednarik]<br />
Image:Ewald ramp.jpg|Ewald W. A. Ramp<br />
Image:Jacopo.jpg|Jacopo Armani<br />
Image:Yetunde.JPG|Yetunde Folajimi<br />
Image:Chris_face.jpg|[http://www.austria-lexikon.at/af/User/Trattner%20Christoph Christof Trattner]<br />
</gallery></div>K.thakerhttps://adapt2.sis.pitt.edu/w/index.php?title=File:Khushboo.jpg&diff=4031File:Khushboo.jpg2019-02-20T21:52:36Z<p>K.thaker: </p>
<hr />
<div></div>K.thaker