PAWS Lab - User contributions [en]

News

2018-09-29T14:02:32Z

Yuh43: /* 2018-07-05 :: Yun Huang defended her Ph. D. Thesis */

__NOTOC__

==== 2018-08-13 :: Two PAWS graduates' papers were short-listed for the best paper award at EC-TEL 2018. ====
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 inspiration for current PAWS students...

==== 2018-07-31 :: [[User:Peterb | Peter Brusilovsky]] awarded NSF grant under the program Cyberlearn And Future Learn Tech. ====
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. [https://www.nsf.gov/awardsearch/showAward?AWD_ID=1822752 Award Abstract].

==== 2018-07-24 :: [[User:R.hosseini | Roya Hosseini]] defended her Ph. D. Thesis ====
Roya Hosseini defended her Ph. D. Thesis: 'Program Construction Examples in Computer Science Education: From Static Text to Adaptive and Engaging Learning Technology'.

==== 2018-07-05 :: [[User:Yuh43 | Yun Huang]] defended her Ph. D. Thesis ====
Yun Huang defended her Ph. D. Thesis titled 'Learner Modeling for Integration Skills in Programming' ([http://d-scholarship.pitt.edu/35176/1/yunhuang_dissertation_v3_1.pdf])

==== 2018-03-08 :: PAWS Lab scores thrice at IUI 2018 ====
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.

==== 2017-10-24 :: [[User:Julio | Julio Guerra]] defended his Ph. D. Thesis ====
Julio Guerra defended his Ph. D. Thesis: 'Open Learner Models for Self-Regulated Learning: Exploring the Effects of Social Comparison and Granularity'.

==== 2017-08-17 :: [[User:Suleehs | Danielle H. Lee]] moves to Sangmyung University as an assistant professor ====
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.

==== 2017-07-11 :: Two PAWS papers were nominated and one received Best Paper Awards at UMAP 2017 ====
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.

==== 2017-02-21 :: [[User:peterb | Peter Brusilovsky]] received the 2017 Provost’s Award for Excellence in Mentoring ====
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]

==== 2016-11-23 :: [[User:Sergey | Sergey Sosnovsky]] moves to Utrecht University as a tenure-track professor ====
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.

==== 2016-08-15 :: Maria Harrington moves to University of Central Florida as an Assistant Professor ====
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.

==== 2016-07-21 :: Shaghayegh (Sherry) Sahebi defended her Ph. D. Thesis ====
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.

==== 2016-05-13 :: Dr. [[User:peterb | Peter Brusilovsky]] and Rosta Farzan are on new Association for Computing Machinery journal’s editorial board ====

Two SIS faculty members on new Association for Computing Machinery journal’s editorial board.

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.”

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.

==== 2016-03-04 :: Congratulations to [[User:Yuh43 | Yun Hung]] and [[User:R.hosseini | Roya Hosseini]] for receiving Andrew Mellon Pre-doctoral Fellowship ====
Yun Huang and Roya Hosseini received prestigious Andrew Mellon Pre-doctoral Fellowship Award for the academic year 2016-2017.

==== 2016-02-23 :: Dr. Ioanna Lykourentzou gave a talk on the topic of "Personalized Crowdsourcing" ====
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.

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.

==== 2015-08-15 :: [[User:peterb | Peter Brusilovsky]] and Daqing He awarded NSF grant to work on [[Open Corpus Personalized Learning]] ====
[[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.

==== 2015-04-15 :: Chirayu Wongchokprasitti defended his Ph. D. Thesis: 'Using External Sources To Improve Research Talk Recommendation In Small Communities'. ====
More details on the thesis can be accessed [http://d-scholarship.pitt.edu/25836/ here].

==== 2014-11-11 :: Dr.[[User:Dparra | Denis Parra]] won the contest for an invited talk at the "Chilean Computing Conference 2014". ====
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.
Please find the slides [http://www.slideshare.net/denisparra/keynote-at-chilean-week-of-computer-science here].
The abstract of the talk is also available at the home page of the event, JCC 2014. [http://www.jcc2014.ucm.cl/en/ (more)].

==== 2014-09-19 :: PAWS won the best paper award at the 9th European Conference on Technology Enhanced Learning (EC-TEL 2014). ====
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].

==== 2014-05-06 :: [[User: shoha99 | Sharon Hsiao]] is appointed as Assistant Professor in CIDSE @ ASU this Fall.====
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].

==== 2014-03-01 :: [[User:peterb | Peter Brusilovsky]] to deliver a keynote at a WWW 2014 workshop. ====
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+]].

==== 2014-03-13 :: [[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.====
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.

==== 2013-08-13 :: [[User:Dparra | Denis Parra]] started a new position as Assistant Professor in the Department of Computer Science at Pontifical Catholic University of Chile.====
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.

==== 2013-07-18 :: [[Systems#KnowledgeZoom | KnowledgeZoom]] paper receives ICALT 2013 Best full Paper Award ====
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.

==== 2013-07-09 :: [[User: Falakmasir | Mohammad Falakmasir]] won EDM 2013 Best Student Paper ====
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]

==== 2013-06-12 :: [[User:peterb | Peter Brusilovsky]] selected as a Fulbright-Nokia Distinguished Chair. ====
[[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]

==== 2013-05-01 :: [[User:peterb | Peter Brusilovsky]] and PAWS lab win Army Contract to develop social personalized learning architecture. ====
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]

==== 2013-04-01 :: Sergey Sosnovsky profiled in International Innovation ====
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.

==== 2013-01-01 :: [[User:peterb | Peter Brusilovsky]] appointed the Editor-In-Chief of [http://www.computer.org/portal/web/tlt IEEE Transactions on Learning Technologies]. ====
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]

==== 2012-08-26 :: [[User:Rostaf | Rosta Farzan]] started her new position as an Assistant Professor at the School of Information Sciences. ====
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]

==== 2012-07-09 :: [[User:shoha99 | Sharon Hsiao]] Thesis Defence: Navigation Support and Social Visualization for Personalized E-Learning ====
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.

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.

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.

download it from [http://d-scholarship.pitt.edu/13439/ here]

==== 2011-11-30 :: [[User:Sergey | Sergey Sosnovsky]] Thesis Defence: Ontology-Based Open-Corpus Personalization for E-Learning ====
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.

==== 2011-07-15 :: [[User:DParra | Denis Parra]] earns a James Chen Best Student paper award in UMAP 2011 ====
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.

==== 2011-06-12 :: [[User:peterb | Peter]] Promoted to Rank of Full Professor ====
Congratulations to our head of PAWS lab! [[http://www.ischool.pitt.edu/news/06-10-2011.php Read more ]]
==== 2011-05-01 :: [[User:shoha99 | Sharon]] received 2011 Allen Kent Award for Outstanding Contributions to the Graduate Program in Information Science ====
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.

==== 2010-12-15 :: [[User:peterb | Peter]] received Google grant to work on ''[[Personalized Social Systems for Local Communities]]'' ====
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.

==== 2010-09-17 :: [[User:Myudelson | Michael Yudelson's]] Thesis Defence: Providing Service-Based Personalization In An Adaptive Hypermedia System ====
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]]
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]).

==== 2010-09-15 :: [[User:peterb | Peter Brusilovsky]] received NSF grant to work on ''Modeling and Visualization of Latent Communities'' ====
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]]

==== 2010-09-08 :: [[User:jahn | Jae-wook's]] Thesis Defence: Adaptive Visualization for Focused Personalized Information Retrieval ====
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]]

==== 2010-09-01 :: [[User:peterb | Peter Brusilovsky]] and Jung Sun Oh received NSF grant to work on ''Personalization and social networking for short-term communities'' ====
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]]

==== 2010-08-15 :: SIGWeb Newsletter published an interview with [[User:peterb | Peter Brusilovsky]] ====
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.

==== 2010-07-01 :: [[User:jahn | Jae-wook]] has received Computing Inovation Fellowship ====
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.

==== 2010-05-01 :: Sergey has received EU Marie Curie International Incoming Fellowship ====
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.

==== 2009-06-27 :: Rosta receives her second James Chen Award ====
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!

==== 2009-06-26 :: PAWS Caught on UMAP 2009 Video ====
* 0:27 [[User:Myudelson|Michael]]
* 1:09 [[User:Rostaf|Rosta]]
* 1:18 [[User:Sergey|Sergey]]
* 1:51 [[User:Suleehs|Danielle]]
* 3:12 [[User:Myudelson|Michael]] and [[User:Sergey|Sergey]]
* 4:01 [[User:Peterb|Peter]]
<youtube>v_amf_zcLtQ</youtube>

==== 2009-05-28 :: [[User:peterb | Peter]] awarded honorary doctorate by the Slovak University of Technology in Bratislava ====
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".

==== 2008-07-31 :: [[User:peterb | Peter]] receives Best Paper Award at AH 2008 ====
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

==== 2007-06-28 :: [[User:Myudelson | Michael]] receives James Chen Best Student Paper Award at UM 2007 ====
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

2016-12-04T16:20:47Z

Yuh43:

Various e-learning systems that provide electronic textbooks are gathering data on large numbers of student reading interactions. This data can potentially be used to model student knowledge acquisition. However, reading activity is often overlooked in canonical student modeling. Prior studies modeling learning from reading either estimate student knowledge at the end of all reading activities, or use quiz performance data with expert-crafted knowledge components (KCs). In this work, we demonstrate that the dynamic modeling of student knowledge is feasible and that automatic text analysis can be applied to save expert effort. We propose a data-driven approach for dynamic student modeling in textbook-based learning. We formulate the problem of modeling learning from reading as a reading- time prediction problem, reconstruct existing popular student models (such as Knowledge Tracing) and explore two automatic text analysis approaches (bag-of-words-based and latent semantic-based) to build the KC model. We evaluate the proposed framework using a dataset collected from a Human-Computer Interaction course. Results show that our approach for reading modeling is plausible; the pro- posed Knowledge Tracing-based student model reliably outperforms baselines and the latent semantic-based approach can be a promising way to construct a KC model. Serving as the first step to model dynamic knowledge in textbook- based learning, our framework can be applied to a broader context of open-corpus personalized learning.

[[Image:ReadingLearningProcessExplain.png|300x300px]]
[[Image:TrainExplain.png|400x400px]]

== Publications ==
* Huang, Yun, Michael Yudelson, Shuguang Han, Daqing He, and Peter Brusilovsky. "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://columbus.exp.sis.pitt.edu/yunhuang/papers/UMAP16.pdf paper]) ([http://www.slideshare.net/huangyun/umap16-a-framework-for-dynamic-knowledge-modeling-in-textbookbased-learning presentation])

Yuh43: /* Open Corpus Personalized Learning */

This page presents a list of funded projects performed by PAWS Lab. Most recent projects are show on the top of the list.

==[[Open Corpus Personalized Learning]]==
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.

Supported by NSF grant IIS 1525186 (2015-2018). [[Open Corpus Personalized Learning|==> more]]

== [[Adaptive Navigation Support and Open Social Learner Modeling for PAL]] ==
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).

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]]

==[[Ensemble|Ensemble: Enriching Communities and Collections to Support Education in Computing]]==
[[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.

Supported by NSF (2008-2014). [[Ensemble|==> more]]
==[[Engaging Students in Online Reading Through Social Progress Visualization]]==

This project explores an alternative approach to encourage student online textbook reading using a social progress visualization interface.

Supported by Innovation in Education Award, University of Pittsburgh (2012-2013). [[Engaging Students in Online Reading Through Social Progress Visualization|==> more]]

== [[Personalized Social Systems for Local Communities]] ==
The project explored the use of personalization and mobile computing to increase user engagement in location-bound social systems.

Supported by Google (2010-2012). [[Personalized Social Systems for Local Communities|==> more]]

== [[Personalization and social networking for short-term communities]] ==
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.

Supported by NSF (2010-2011). In collaboration with Jung Sun Oh. [[Personalization and social networking for short-term communities|==> more]]

==Modeling and Visualization of Latent Communities==
The project focused on the problem of discovering latent communities from Social Web data and presenting this data in visual form.

Supported by the Institute for Defense Analysis and NSF (2010-2012). [[Modeling and Visualization of Latent Communities|==> more]]

== Personalized Exploratorium for Database Courses ==
The project focused on developing and evaluation of a personalized educational environment for teaching Database courses.

Supported by NSF (2007-2008). In collaboration with Vladimir Zadorozhny. [[Personalized Exploratorium for Database Courses|==> more]]

== GALE: Distillation with Utility-Optimized Transcription and Translation ==
Supported by DARPA (2005-2007) In collaboration with Carnegie Mellon University and IBM [[GALE: Distillation with Utility-Optimized Transcription and Translation|==> more]]

== Personalized Access to Open Corpus Educational Resources through Adaptive Navigation Support and Adaptive Visualization ==
The project focused on developing technologies for personalized access to information based on adaptive navigation support, collaborative filtering, and information visualization.

Supported by NSF (2005-2010). [[Personalized Access to Open Corpus Educational Resources through Adaptive Navigation Support and Adaptive Visualization|==> more]]

== Adaptive Explanatory Visualization for Learning Programming Concepts ==
The project focused on developing and studying adaptive explanatory visualization technologies for C and Java programming languages.

Supported by NSF (2004-2007). In collaboration with Michael Spring. [[Adaptive Explanatory Visualization for Learning Programming Concepts|==> more]]

== Individualized Exercises for Assessment and Self-Assessment of Programming Knowledge ==

The project focused developing and evaluating a personalized assessment technology for programming courses.

Supported by NSF (2003-2005). [[Individualized Exercises for Assessment and Self-Assessment of Programming Knowledge|==> more]]

== Educational Software for Teaching and Learning Information Retrieval ==

Supported by Innovation in Education Award, University of Pittsburgh (2003-2004). [[Educational Software for Teaching and Learning Information Retrieval| ==> more]]

== Supporting Learning from Examples in a Programming Course ==

Supported by Innovation in Education Award, University of Pittsburgh (2001-2002). [[Supporting Learning from Examples in a Programming Course|==> more]]

== Adaptive Electronic Textbooks for World Wide Web ==
Supported by NSF (1997-1998). In collaboration with John Anderson and Gerhard Weber [[Adaptive Electronic Textbooks for World Wide Web|==> more]]

Projects

2016-12-04T15:57:58Z

Yuh43: /* Open Corpus Personalized Learning */

This page presents a list of funded projects performed by PAWS Lab. Most recent projects are show on the top of the list.

==[[Open Corpus Personalized Learning]]==
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.

Supported by NSF grant IIS 1525186 (2015-2018). [[Open Corpus Personalized Learning|==> more]]

== [[Adaptive Navigation Support and Open Social Learner Modeling for PAL]] ==
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).

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]]

==[[Ensemble|Ensemble: Enriching Communities and Collections to Support Education in Computing]]==
[[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.

Supported by NSF (2008-2014). [[Ensemble|==> more]]
==[[Engaging Students in Online Reading Through Social Progress Visualization]]==

This project explores an alternative approach to encourage student online textbook reading using a social progress visualization interface.

Supported by Innovation in Education Award, University of Pittsburgh (2012-2013). [[Engaging Students in Online Reading Through Social Progress Visualization|==> more]]

== [[Personalized Social Systems for Local Communities]] ==
The project explored the use of personalization and mobile computing to increase user engagement in location-bound social systems.

Supported by Google (2010-2012). [[Personalized Social Systems for Local Communities|==> more]]

== [[Personalization and social networking for short-term communities]] ==
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.

Supported by NSF (2010-2011). In collaboration with Jung Sun Oh. [[Personalization and social networking for short-term communities|==> more]]

==Modeling and Visualization of Latent Communities==
The project focused on the problem of discovering latent communities from Social Web data and presenting this data in visual form.

Supported by the Institute for Defense Analysis and NSF (2010-2012). [[Modeling and Visualization of Latent Communities|==> more]]

== Personalized Exploratorium for Database Courses ==
The project focused on developing and evaluation of a personalized educational environment for teaching Database courses.

Supported by NSF (2007-2008). In collaboration with Vladimir Zadorozhny. [[Personalized Exploratorium for Database Courses|==> more]]

== GALE: Distillation with Utility-Optimized Transcription and Translation ==
Supported by DARPA (2005-2007) In collaboration with Carnegie Mellon University and IBM [[GALE: Distillation with Utility-Optimized Transcription and Translation|==> more]]

== Personalized Access to Open Corpus Educational Resources through Adaptive Navigation Support and Adaptive Visualization ==
The project focused on developing technologies for personalized access to information based on adaptive navigation support, collaborative filtering, and information visualization.

Supported by NSF (2005-2010). [[Personalized Access to Open Corpus Educational Resources through Adaptive Navigation Support and Adaptive Visualization|==> more]]

== Adaptive Explanatory Visualization for Learning Programming Concepts ==
The project focused on developing and studying adaptive explanatory visualization technologies for C and Java programming languages.

Supported by NSF (2004-2007). In collaboration with Michael Spring. [[Adaptive Explanatory Visualization for Learning Programming Concepts|==> more]]

== Individualized Exercises for Assessment and Self-Assessment of Programming Knowledge ==

The project focused developing and evaluating a personalized assessment technology for programming courses.

Supported by NSF (2003-2005). [[Individualized Exercises for Assessment and Self-Assessment of Programming Knowledge|==> more]]

== Educational Software for Teaching and Learning Information Retrieval ==

Supported by Innovation in Education Award, University of Pittsburgh (2003-2004). [[Educational Software for Teaching and Learning Information Retrieval| ==> more]]

== Supporting Learning from Examples in a Programming Course ==

Supported by Innovation in Education Award, University of Pittsburgh (2001-2002). [[Supporting Learning from Examples in a Programming Course|==> more]]

== Adaptive Electronic Textbooks for World Wide Web ==
Supported by NSF (1997-1998). In collaboration with John Anderson and Gerhard Weber [[Adaptive Electronic Textbooks for World Wide Web|==> more]]

File:ReadingLearningProcess.png

2016-12-04T15:56:46Z

Yuh43:

Open Corpus Personalized Learning

2016-12-04T15:56:31Z

Yuh43: /* Learner Modeling */

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.

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.

==The Project Team==
<gallery>
Image:Peter.jpg|'''[[User:Peterb | Peter Brusilovsky]]''' Director
Image:Daqing_He.png|'''[http://www.pitt.edu/~dah44/ Daqing He]''' Co-director
Image:yunhuang.png|[http://columbus.exp.sis.pitt.edu/yunhuang/index.htm Yun Huang]
Image:shuguang.png|[http://www.pitt.edu/~shh69/ Shuguang Han]
Image:rui.png|[http://memray.me/ Rui Meng]
Image:Sanqiang.png|[http://pitt.edu/~sanqiang Sanqiang Zhao]
Image:Jordan.jpeg|[http://pitt.edu/~jab464 Jordan Barria-Pineda]
Image:Hungavatar.jpg|[http://pitt.edu/~hkc6 Hung Chau]
</gallery>

==Knowledge Extraction==

== Learner Modeling ==
{|
|valign="top" | [[Image:ReadingLearningProcess.png|thumb|left|'''100'''|Dynamic Knowledge Modeling in Textbook Reading]]
|valign="center" | We have developed [[CUMULATE]], a centralized user modeling server built for the [[ADAPT2]] architecture, to provide user modeling support for adaptive educational hypermedia (AEH) systems. CUMULATE allows flexible learner models to infer learner knowledge. Mastery Grids's architecture is supported by CUMULATE and thus it also supports flexible learner models. The explanation of the communication between the interface and learner model can be found in [[Aggregate]]. We have proposed and implemented different learner models over past years, including [[CUMULATE asymptotic knowledge assessment|asymptotic assessment of user knowledge]] which is the main one currently deployed in our systems, and [[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. We have also explored different aspects to improve learner modeling, including reducing the content model, better evaluation for practitioners and applying network (graph) analysis.

* [[Learner Modeling|More about Learner Modeling]]
* [[CUMULATE|More about CUMULATE]]
* [[CUMULATE asymptotic knowledge assessment|More about asymptotic assessment of user knowledge]]
* [[Feature-Aware Student knowledge Tracing (FAST)|More about Feature-Aware Student knowledge Tracing (FAST)]]
|}

==The Experimental Platform==

==Publications==

Open Corpus Personalized Learning

2016-12-04T15:50:57Z

Yuh43: /* Student Modeling */

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.

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.

==The Project Team==
<gallery>
Image:Peter.jpg|'''[[User:Peterb | Peter Brusilovsky]]''' Director
Image:Daqing_He.png|'''[http://www.pitt.edu/~dah44/ Daqing He]''' Co-director
Image:yunhuang.png|[http://columbus.exp.sis.pitt.edu/yunhuang/index.htm Yun Huang]
Image:shuguang.png|[http://www.pitt.edu/~shh69/ Shuguang Han]
Image:rui.png|[http://memray.me/ Rui Meng]
Image:Sanqiang.png|[http://pitt.edu/~sanqiang Sanqiang Zhao]
Image:Jordan.jpeg|[http://pitt.edu/~jab464 Jordan Barria-Pineda]
Image:Hungavatar.jpg|[http://pitt.edu/~hkc6 Hung Chau]
</gallery>

==Knowledge Extraction==

== Learner Modeling ==
{|
|valign="top" | [[Image:CUMULATE.evidence propagation.png|thumb|left|'''100'''|CUMULATE]]
|valign="center" | We have developed [[CUMULATE]], a centralized user modeling server built for the [[ADAPT2]] architecture, to provide user modeling support for adaptive educational hypermedia (AEH) systems. CUMULATE allows flexible learner models to infer learner knowledge. Mastery Grids's architecture is supported by CUMULATE and thus it also supports flexible learner models. The explanation of the communication between the interface and learner model can be found in [[Aggregate]]. We have proposed and implemented different learner models over past years, including [[CUMULATE asymptotic knowledge assessment|asymptotic assessment of user knowledge]] which is the main one currently deployed in our systems, and [[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. We have also explored different aspects to improve learner modeling, including reducing the content model, better evaluation for practitioners and applying network (graph) analysis.

* [[Learner Modeling|More about Learner Modeling]]
* [[CUMULATE|More about CUMULATE]]
* [[CUMULATE asymptotic knowledge assessment|More about asymptotic assessment of user knowledge]]
* [[Feature-Aware Student knowledge Tracing (FAST)|More about Feature-Aware Student knowledge Tracing (FAST)]]
|}

==The Experimental Platform==

==Publications==

Open Corpus Personalized Learning

2016-12-03T15:00:38Z

Yuh43: /* The Project Team */

Open Corpus Personalized Learning

2016-12-03T15:00:14Z

Yuh43: /* The Project Team */

Data and Ontology

2016-07-29T14:18:40Z

Yuh43: /* Ontology */

== Data ==
== Ontology ==
Please cite PAWs lab if you use following resources.
* [https://www.sis.pitt.edu/~paws/ont/sql.rdf SQL ontology link].
* [http://www.pitt.edu/~paws//ont/java.owl Java ontology link]. You can also query KT um2 tables to get the nodes (not the structure). The latest version is with domain id 11 (java ontology v.2).

2016-04-27T16:15:58Z

Yuh43:

File:MasteryGridsFlierPythonNew.pdf

2016-04-27T16:14:47Z

Yuh43:

Adaptive Navigation Support and Open Social Learner Modeling for PAL

2016-04-27T16:14:37Z

Yuh43: /* Overview */

== Overview ==

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). The project is supported by the [http://adlnet.gov Advanced Distributed Learning Initiative] contract W911QY13C0032. This is a joint project with [http://cs.aalto.fi/en/research/ Learning + Technology] research group at Aalto University. The LeTech group at Aalto University focuses on developing several kinds of [http://acos.cs.hut.fi/ smart learning content for Java and Python programming] that are compatible with the project architecture [[Aggregate]].

The project focuses on both exploration and implementation of adaptive navigation support and open social learner modeling and pursues three directions of work:

* Exploring open social learner modeling interface for diverse learning content
* Enhancing algorithms for personalized guidance using knowledge-based and social approaches
* Developing architectural solutions and authoring tools to support open social learner modeling

We have prepared fliers for quickly getting to know our systems:
* For researchers or system developers (designers), please check [[Media:MGFlier.pdf|here]].
* For teachers or educators, please check the flier for your interested domain: [[Media:MasteryGridsFlierJava.pdf|Java]], [[Media:MasteryGridsFlierPythonNew.pdf|Python]], or [[Media:MasteryGridsFlierSQL.pdf|SQL]].

== Open Social Learner Model Interface: Mastery Grids ==

{|
|valign="top" | [[Image:Mg_1.png|thumb|left|'''100'''|Mastery Grids Interface]]
|valign="top" | Our latest implementation of Open Social Learner Modeling (OSLM) is [[Mastery Grids Interface]]. Mastery Grids is both an innovative Open Social Learner Model Interface and an adaptive E-learning platform with integrated functionalities enabling multi-facet social comparison, open learner modeling, and adaptive navigation support to access multiple kinds of smart learning content. Mastery Grids is supported by adaptive social learning framework [[Aggregate]]. This framework supports several kinds of open student modeling, social comparison, and recommendation. In detail, Mastery Grids presents and compares user learning progress and knowledge level using colored grids, tracks user activities with learning content, and provides flexible user-centered navigation across different content levels (e.g. topic, question) and different content types (e.g. problem, example). Our past research shows that open student modeling and social comparison effectively increases students’ performance, motivation, engagement and retention.

* [[Mastery Grids Interface|More about Mastery Grids interface]]
* [https://www.youtube.com/watch?v=76YLR2VY2QE YouTube demo of Mastery Grids interface]
* [http://adapt2.sis.pitt.edu/um-vis-adl/index.html?usr=adl01&grp=ADL&sid=test&cid=13&data-top-n-grp=5&def-val-rep-lvl-id=p&def-val-res-id=AVG&ui-tbar-rep-lvl-vis=0&ui-tbar-topic-size-vis=0 An interactive demo of Mastery Grids interface]
|}

== Architecture: Aggregate==
{|
|valign="top" | [[Image:arch_v2.png|thumb|left|'''100'''|Aggregate Architecture]]
|valign="top" | We developed an adaptive social learning architecture [[Aggregate]] to support Mastery Grids interface. [[Aggregate]] is an extension of our original [[ADAPT2]] architecture. On the top of [[ADAPT2]] , [[Aggregate]] architecture supports several kinds of open student modeling, social comparison, content brokering, and recommendation services. The architecture fulfills a major objective, portability, which is the ability to be integrated to other systems with little set up and modification. The architecture is modular and includes different software components.

* [[ADAPT2| More about ADAPT2]]
* [[Aggregate| More about Aggregate]]
|}

== Learner Modeling ==
{|
|valign="top" | [[Image:CUMULATE.evidence propagation.png|thumb|left|'''100'''|CUMULATE]]
|valign="center" | We have developed [[CUMULATE]], a centralized user modeling server built for the [[ADAPT2]] architecture, to provide user modeling support for adaptive educational hypermedia (AEH) systems. CUMULATE allows flexible learner models to infer learner knowledge. Mastery Grids's architecture is supported by CUMULATE and thus it also supports flexible learner models. The explanation of the communication between the interface and learner model can be found in [[Aggregate]]. We have proposed and implemented different learner models over past years, including [[CUMULATE asymptotic knowledge assessment|asymptotic assessment of user knowledge]] which is the main one currently deployed in our systems, and [[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. We have also explored different aspects to improve learner modeling, including reducing the content model, better evaluation for practitioners and applying network (graph) analysis.

* [[Learner Modeling|More about Learner Modeling]]
* [[CUMULATE|More about CUMULATE]]
* [[CUMULATE asymptotic knowledge assessment|More about asymptotic assessment of user knowledge]]
* [[Feature-Aware Student knowledge Tracing (FAST)|More about Feature-Aware Student knowledge Tracing (FAST)]]
|}

== Recommendation ==
{|
|valign="top" | [[Image:starRecommendation.png|thumb|left|'''100'''|Personalization]]
|valign="top" | There are multiple personalization approaches, that are developed and researched in our system. In the form of recommendations, we have various methods in different levels for recommending learning material to students. Two major approached for recommending resources are reactive and proactive recommendations. In the reactive approach, the recommender system activates in reaction to the student's activity, e.g. if the student fails in solving a quiz, the reactive recommender system recommends related examples to this student to help her understand the skills required to solve that quiz. The pro-active recommender system, proactively suggests learning materials to the students. [[Learning Recommendation|==> more]]

|}

== Educational Data Mining ==
{|
|valign="top" | [[Image:TensorFactorization.png|thumb|left|'''100'''|Educational Data Mining]]
|valign="top" | In this part of the project, we aim to make sense of data from Mastery Grids system, including logs of student attempts. The goal in this part includes understanding students' learning patterns and its relationship with students' behavioral traits, predicting students' performance, modeling student knowledge, and discovering the content model. These tasks eventually help us in providing a better service to both instructors and students. [[Educational Data Mining|==> more]]

|}

== Smart Content ==
{|
|valign="top" | [[Image:AnimatedExamples.jpg|thumb|left|'''100'''|Smart Content (Animated Examples)]]
|valign="top" | Mastery Grids supports and provides multiple types of learning materials. It has been applied in three domains (Java, SQL, and Python) as a supplementary E-learning system for undergraduate and graduate level programming and database classes since 2013. We have developed different content applications (e.g., [[QuizJET]], [[QuizPET]], [[WebEx]]) and authoring tools (e.g., [[Content Authoring Tools]], [[Course Authoring Tool]], [[Group Authoring Tool]]) for accessing and authoring such contents. In each learning domain, courses are organized by topics and different types of learning contents are arranged under each topic. Learning contents contain problems (quizzes), parson problems, annotated examples, and animated examples collected from experienced course teachers, textbooks or domain experts. [[Smart Content|==> more]]
|}

== Authoring Tools ==
{|
|valign="top" | [[Image:ExampleAuthoringModify.jpg|thumb|left|'''100'''|Annotated Example Content Authoring]]
|valign="top" | We have developed four major types of authoring tools for the project: 1) Content Authoring Tools for creating different kinds of smart learning content, 2) Course Authoring Tool for creating adaptive courses that use the content, 3) Group Authoring Tool for managing users and groups, as well as 4) the portal to access different authoring tools.

* [[Authoring Tools|More about Authoring Tools]]
* [[Content Authoring Tools|More about Content Authoring Tools]]
* [[Course Authoring Tool|More about Course Authoring Tool]]
* [[Group Authoring Tool|More about Group Authoring Tool]]
* [[Authoring Tool Portal|More about Authoring Tool Portal]]
|}

== Open Source ==
Software sources and documentations are in GitHub [https://github.com/PAWSLabUniversityOfPittsburgh PAWSLabUniversityOfPittsburgh organization], and [https://github.com/acos-server/ acos-server organization].
* The Mastery Grids Interface, back-end Aggregate and documentation can be found [https://github.com/PAWSLabUniversityOfPittsburgh/mastery-grids here].
* User model services can be found in [https://github.com/PAWSLabUniversityOfPittsburgh/AggregateUMServices here].
* QuizJET Interface, Authoring Tool, Content Brokering and documentations can be found [https://github.com/PAWSLabUniversityOfPittsburgh/quizjet here].
* QuizPET Interface, Authoring Tool, Content Brokering and documentations can be found [https://github.com/PAWSLabUniversityOfPittsburgh/quizpet here].
* Parson Problem Authoring Tool can be found [https://github.com/acos-server/acos-jsparsons-generator here].
* Annotated Examples Interface, Authoring Tool, Content Brokering and documentations can be found [https://github.com/PAWSLabUniversityOfPittsburgh/annotated-examples here].
* Animated Examples Authoring Tool can be found [https://github.com/acos-server/acos-jsvee-transpiler-python here].
* Videos User Interface, Authoring Tool, Content Brokering and documentations can be found [https://github.com/PAWSLabUniversityOfPittsburgh/educvideos here]

== Publications ==
* Gonzalez-Brenes, J. P., Huang, Y., and Brusilovsky, P. (2013) FAST: Feature-Aware Student Knowledge Tracing. In: Proceedings of NIPS 2013 Workshop on Data Driven Education, Lake Tahoe, NV, December 10, 2013, ([http://d-scholarship.pitt.edu/20353/ paper])
* Hosseini, R. and Brusilovsky, P. (2013) JavaParser: A Fine-Grain Concept Indexing Tool for Java Problems. In: Proceedings of The First Workshop on AI-supported Education for Computer Science (AIEDCS) at the 16th Annual Conference on Artificial Intelligence in Education, AIED 2013, Memphis, TN, USA, July 13, 2013, pp. 60-63. ([https://d-scholarship.pitt.edu/secure/26270/1/AIED2013-workshop-camera_ready_version.pdf paper]) ([http://www.slideshare.net/RoyaHosseini1/java-parser-a-fine-grained-indexing-tool-and-its-application presentation])
* Hosseini, R., Brusilovsky, P., and Guerra, J. (2013) Knowledge Maximizer: Concept-based Adaptive Problem Sequencing for Exam Preparation. In: Proceedings of the 16th International Conference on Artificial Intelligence in Education (AIED 2013), Memphis, USA, pp. 848-851. ([https://d-scholarship.pitt.edu/secure/26271/4/AIED2013-camera-ready-Knowledge_maximizer_.pdf paper]) ([http://www.slideshare.net/RoyaHosseini1/aied-2013 poster])
* Brusilovsky, P., Baishya, D., Hosseini, R., Guerra, J., and Liang, M. (2013) KnowledgeZoom for Java: A Concept-Based Exam Study Tool with a Zoomable Open Student Model. In: Proceedings of 2013 IEEE 13th International Conference on Advanced Learning Technologies, Beijing, China, July 15-18, 2013, pp. 275-279. ([http://dx.doi.org/10.1109/ICALT.2013.86 paper]) ([http://www.slideshare.net/RoyaHosseini1/kowledge-zoom-michelle-48735584 presentation])
* Brusilovsky, P. (2014) Addictive Links: Engaging Students through Adaptive Navigation Support and Open Social Student Modeling (Keynote talk). In: Proceedings of WWW 2014 Workshop on Web-based Education Technologies, Seoul, Korea, April 8, 2014. ([http://www.slideshare.net/pbrusilovsky/addictive-links-keynote-talk-at-www-2014-workshop presentation])
* Huang, Y., Xu, Y., and Brusilovsky, P. (2014) Doing More with Less: Student Modeling and Performance Prediction with Reduced Content Models. In: V. Dimitrova, et al. (eds.) Proceedings of 22nd Conference on User Modeling, Adaptation and Personalization (UMAP 2014), Aalborg, Denmark, July 7-11, 2014, Springer Verlag, pp. 338-349. ([http://www.slideshare.net/pbrusilovsky/umap-v1 presentation][http://link.springer.com/chapter/10.1007%2F978-3-319-08786-3_30 paper])
* Hosseini, R. and Brusilovsky, P. (2014) Example-Based Problem Solving Support Using concept Analysis of Programming Content. In: S. Trausan-Matu, K. Boyer, M. Crosby and K. Panourgia (eds.) Proceedings of 12th International Conference on Intelligent Tutoring Systems (ITS 2014), Honolulu, HI, USA, June 5-9, 2014, Springer International Publishing, pp. 683-685. ([https://d-scholarship.pitt.edu/secure/26268/1/CameraReady_ITS2014_paper.pdf paper]) ([http://www.slideshare.net/RoyaHosseini1/presentation-48735557 presentation])
* Hosseini, R., Vihavainen, A., and Brusilovsky, P. (2014) Exploring Problem Solving Paths in a Java Programming Course. In: Proceedings of Psychology of Programming Interest Group Annual Conference, PPIG 2014, Brighton, UK, June 25-27, 2014, pp. 65-76. ([https://d-scholarship.pitt.edu/secure/26272/1/PPIG_2014_camera_ready.pdf paper]) ([http://www.slideshare.net/RoyaHosseini1/ppig2014-problem-solvingpaths presentation])
* Huang, Y., González-Brenes, J. P., and Brusilovsky, P. (2014) General Features in Knowledge Tracing to Model Multiple Subskills, Temporal Item Response Theory, and Expert Knowledge. In: J. Stamper, Z. Pardos, M. Mavrikis and B. M. McLaren (eds.) Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014), London, UK, July 4-7, 2014, pp. 84-91. (First two authors contributed equally. Nominated for Best Paper Award) ([http://www.slideshare.net/huangyun/fast-presentation-48711687 presentation][http://educationaldatamining.org/EDM2014/uploads/procs2014/long%20papers/84_EDM-2014-Full.pdf paper][http://www.slideshare.net/huangyun/2015edm-featureaware-student-knowledge-tracing-tutorial tutorial] [http://ml-smores.github.io/fast/ code])
* Khajah, M. M., Huang, Y., González-Brenes, J. P., Mozer, M. C., and Brusilovsky, P. (2014) Integrating Knowledge Tracing and Item Response Theory: A Tale of Two Frameworks. In: I. Cantador, M. Chi, R. Farzan and R. Jäschke (eds.) Proceedings of Workshop on Personalization Approaches in Learning Environments (PALE 2014) at the 22th International Conference on User Modeling, Adaptation, and Personalization, UMAP 2014, Aalborg, Denmark, July 11, 2014, CEUR, pp. 7-12. (First three authors contributed equally) ([http://www.slideshare.net/huangyun/pale-public-slideshare presentation][http://ceur-ws.org/Vol-1181/pale2014_paper_01.pdf paper]).
* Yudelson, M., Hosseini, R., Vihavainen, A., and Brusilovsky, P. (2014) Investigating Automated Student Modeling in a Java MOOC. In: J. Stamper, Z. Pardos, M. Mavrikis and B. M. McLaren (eds.) Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014), London, UK, July 4-7, 2014, pp. 261-264. ([https://d-scholarship.pitt.edu/secure/26273/1/EDM2014YudelsonHVB_camready.pdf paper]) ([http://www.slideshare.net/RoyaHosseini1/edm2014-investigating-automated-student-modeling-in-a-java-mooc presentation])
* Sahebi, S., Huang, Y., and Brusilovsky, P. (2014) Parameterized Exercises in Java Programming: Using Knowledge Structure for Performance Prediction. In: Proceedings of The second Workshop on AI-supported Education for Computer Science (AIEDCS) at 12th International Conference on Intelligent Tutoring Systems ITS 2014, Honolulu, Hawaii, June 6 2014. ([http://d-scholarship.pitt.edu/21915/ paper])([http://www.slideshare.net/chagh/parameterized-exercises-in-java-programming-using-knowledge-structure-for-performance-prediction presentation])
* Sahebi, S., Huang, Y., and Brusilovsky, P. (2014) Predicting Student Performance in Solving Parameterized Exercises. In: S. Trausan-Matu, K. Boyer, M. Crosby and K. Panourgia (eds.) Proceedings of 12th International Conference on Intelligent Tutoring Systems (ITS 2014), Honolulu, HI, USA, June 5-9, 2014, Springer International Publishing, pp. 496-503, ([http://d-scholarship.pitt.edu/21916/ paper]) ([http://www.slideshare.net/chagh/its14-pitttemplate presentation])
* Guerra, J., Sahebi, S., Lin, Y.-R., and Brusilovsky, P. (2014) The Problem Solving Genome: Analyzing Sequential Patterns of Student Work with Parameterized Exercises. In: J. Stamper, Z. Pardos, M. Mavrikis and B. M. McLaren (eds.) Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014), London, UK, July 4-7, 2014, pp. 153-160 ([http://www.slideshare.net/huangyun/guerra-the-problemsolvinggenome presentation][http://educationaldatamining.org/EDM2014/uploads/procs2014/long%20papers/153_EDM-2014-Full.pdf paper])
* Loboda, T., Guerra, J., Hosseini, R., and Brusilovsky, P. (2014) Mastery Grids: An Open Source Social Educational Progress Visualization. In: S. de Freitas, C. Rensing, P. J. Muñoz Merino and T. Ley (eds.) Proceedings of 9th European Conference on Technology Enhanced Learning (EC-TEL 2014), Graz, Austria, September 16-19, 2014 (Best paper award). ([http://link.springer.com/chapter/10.1007%2F978-3-319-11200-8_18#page-1 paper]) ([http://www.slideshare.net/pbrusilovsky/ectel2014-mg presentation])
* Brusilovsky, P., Edwards, S., Kumar, A., Malmi, L., Benotti, L., Buck, D., Ihantola, P., Prince, R., Sirkiä, T., Sosnovsky, S., Urquiza, J., Vihavainen, A., and Wollowski, M. (2014) Increasing Adoption of Smart Learning Content for Computer Science Education. In: Proceedings of Proceedings of the Working Group Reports of the 2014 on Innovation & Technology in Computer Science Education Conference, Uppsala, Sweden, ACM, pp. 31-57. ([http://dx.doi.org/10.1145/2713609.2713611 paper])
* Gonzalez-Brenes, J. P., Huang, Y. (2014) The White Method: Towards Automatic Evaluation Metrics for Adaptive Tutoring Systems. In: Proceedings of NIPS 2014 Workshop on Human Propelled Machine Learning, Montreal, Canada, December 13, 2014 ([http://d-scholarship.pitt.edu/26061/ paper])
* Huang, Y., González-Brenes, J. P., and Brusilovsky, P. (2015) The FAST toolkit for Unsupervised Learning of HMMs with Features. In: The Machine Learning Open Source Software workshop at the 32nd International Conference on Machine Learning (ICML-MLOSS 2015), Lille, France July 10, 2015. ([http://d-scholarship.pitt.edu/26043/ paper][http://mloss.org/software/view/609/ code])
* Huang, Y., González-Brenes, J. P., Kumar, R., Brusilovsky, P. (2015) A Framework for Multifaceted Evaluation of Student Models. In: Proceedings of the 8th International Conference on Educational Data Mining (EDM 2015), Madrid, Spain, pp. 203-210. ([http://www.educationaldatamining.org/EDM2015/uploads/papers/paper_164.pdf paper]) ([http://www.slideshare.net/huangyun/2015edm-a-framework-for-multifaceted-evaluation-of-student-models-polygon presentation])
* Huang, Y., González-Brenes, J. P., Brusilovsky, P. (2015) Challenges of Using Observational Data to Determine the Importance of Example Usage. In: Proceedings of the 17th International Conference on Artificial Intelligence in Education (AIED 2015), Madrid, Spain, pp. 633-637. ([http://d-scholarship.pitt.edu/26056/ paper])
* Gonzalez-Brenes, J. P., Huang, Y. (2015) Your model is predictive— but is it useful? Theoretical and Empirical Considerations of a New Paradigm for Adaptive Tutoring Evaluation. In: Proceedings of the 8th International Conference on Educational Data Mining (EDM 2015), Madrid, Spain, pp. 187-194. ([http://d-scholarship.pitt.edu/26046/ paper] [http://www.slideshare.net/huangyun/2015-edm-leopard-for-adaptive-tutoring-evaluation presentation])
* Gonzalez-Brenes, J. P., Huang, Y. The Leopard Framework: Towards understanding educational technology interventions with a Pareto Efficiency Perspective. In: The ICML 2015 Workshop on Machine Learning for Education (ICML 2015), Lille, France, 2015. ([https://dsp.rice.edu/sites/dsp.rice.edu/files/leopard_evaluation(1).pdf paper])
* Gonzalez-Brenes, J. P., Huang, Y. Using Data from Real and Simulated Learners to Evaluate Adaptive Tutoring Systems. In: 2nd AIED Workshop on Simulated Learners at the 17th Intl. Conf. on Artificial Intelligence in Education (AIED 2015), Madrid, Spain, 2015. ([http://ceur-ws.org/Vol-1432/sl_pap4.pdf paper])
* Brusilovsky, P., Somyurek, S., Guerra, J., Hosseini, R., and Zadorozhny, V. (2015) The Value of Social: Comparing Open Student Modeling and Open Social Student Modeling. In: F. Ricci, K. Bontcheva, O. Conlan and S. Lawless (eds.) Proceedings of 23nd Conference on User Modeling, Adaptation and Personalization (UMAP 2015), Dublin, Ireland, , June 29 - July 3, 2015, Springer Verlag, pp. 44-55 ([https://www.researchgate.net/publication/280805929_The_Value_of_Social_Comparing_Open_Student_Modeling_and_Open_Social_Student_Modeling paper] [http://www.slideshare.net/pbrusilovsky/umap2015-mg presentation])
* Guerra, J., Huang, Y., Hosseini, R., & Brusilovsky, P. (2015, June). Graph Analysis of Student Model Networks. In Proceedings of the Second International Workshop on Graph-Based Educational Data Mining (GEDM 2015). CEUR-WS. ([https://d-scholarship.pitt.edu/secure/25933/1/graph_analysis.pdf paper]) ([http://www.slideshare.net/mallium/graph-analysis-of-student-model-networks presentation])
* Guerra, J., Huang, Y., Hosseini, R., & Brusilovsky, P. Exploring the Effects of Open Social Student Model Beyond Social Comparison. In ISLG 2015 Fourth Workshop on Intelligent Support for Learning in Groups (p. 19). ([https://d-scholarship.pitt.edu/secure/25931/1/islg_pap4.pdf paper]) ([http://www.slideshare.net/mallium/exploring-the-effects-of-open-social-student-model-beyond-social-comparison poster])
* Hosseini, R., Hsiao, I.-H., Guerra, J., Brusilovsky, P. (2015) Off the Beaten Path: The Impact of Adaptive Content Sequencing on Student Navigation in an Open Social Student Modeling Interface. In: Proceedings of the 17th International Conference on Artificial Intelligence in Education (AIED 2015), Madrid, Spain, pp. 624-628. ([https://d-scholarship.pitt.edu/secure/25938/1/paper_183.pdf paper]) ([http://www.slideshare.net/RoyaHosseini1/aied-2015-poster-off-the-beaten-path-the-impact-of-adaptive-content-sequencing-on-student-navigation-in-an-open-social-student-modeling-interface poster])
* Hosseini, R., Hsiao, I.-H., Guerra, J., Brusilovsky, P. (2015) What Should I Do Next? Adaptive Sequencing in the Context of Open Social Student Modeling. Proceedings of 10th European Conference on Technology Enhanced Learning (EC-TEL 2015), Toledo, Spain, September 15-18, 2015, pp. 155-168. ([https://d-scholarship.pitt.edu/secure/26266/1/camera_ready.pdf paper])([http://www.slideshare.net/RoyaHosseini1/ectel-2015 presentation]).
* Somyürek, S. & Brusilovsky, P. (2015). Impact of Open Social Student Modeling on Self-Assessment of Performance. Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2009 (E-Learn 2015). Kona, Hawaii, United States, October 19-22, 2015
* Hosseini, R., Sirkiä, T., Guerra, J., Brusilovsky, P., Malmi, L. (2016) Animated Examples as Practice Content in a Java Programming Course. Proceedings of the 47th ACM technical symposium on Computer Science Education (SIGCSE), Memphis, Tennessee, March 2-5, 2016. ([https://d-scholarship.pitt.edu/secure/27083/1/sigcse2016.pdf paper]) ([http://www.slideshare.net/RoyaHosseini1/sigcse-2016 presentation])
* Guerra, J., Hosseini, R., Somyurek, S., and Brusilovsky, P. (2016) An Intelligent Interface for Learning Content: Combining an Open Learner Model and Social Comparison to Support Self-Regulated Learning and Engagement. In: Proceedings of Proceedings of the 21st International Conference on Intelligent User Interfaces (IUI '16), Sonoma, California, USA, ACM, pp. 152-163. ([https://d-scholarship.pitt.edu/secure/27083/1/sigcse2016.pdf paper]) ([http://www.slideshare.net/RoyaHosseini1/sigcse-2016 presentation])

File:MasteryGridsFlierPython.pdf

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Yuh43:

File:MasteryGridsFlierJava.pdf

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Yuh43:

Adaptive Navigation Support and Open Social Learner Modeling for PAL

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Yuh43: /* Overview */

== Overview ==

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). The project is supported by the [http://adlnet.gov Advanced Distributed Learning Initiative] contract W911QY13C0032. This is a joint project with [http://cs.aalto.fi/en/research/ Learning + Technology] research group at Aalto University. The LeTech group at Aalto University focuses on developing several kinds of [http://acos.cs.hut.fi/ smart learning content for Java and Python programming] that are compatible with the project architecture [[Aggregate]].

The project focuses on both exploration and implementation of adaptive navigation support and open social learner modeling and pursues three directions of work:

* Exploring open social learner modeling interface for diverse learning content
* Enhancing algorithms for personalized guidance using knowledge-based and social approaches
* Developing architectural solutions and authoring tools to support open social learner modeling

We have prepared fliers for quickly getting to know our systems:
* For researchers or system developers (designers), please check [[Media:MGFlier.pdf|here]].
* For teachers or educators, please check the flier for your interested domain: [[Media:MasteryGridsFlierJava.pdf|Java]], [[Media:MasteryGridsFlierPython.pdf|Python]], or [[Media:MasteryGridsFlierSQL.pdf|SQL]].

== Open Social Learner Model Interface: Mastery Grids ==

{|
|valign="top" | [[Image:Mg_1.png|thumb|left|'''100'''|Mastery Grids Interface]]
|valign="top" | Our latest implementation of Open Social Learner Modeling (OSLM) is [[Mastery Grids Interface]]. Mastery Grids is both an innovative Open Social Learner Model Interface and an adaptive E-learning platform with integrated functionalities enabling multi-facet social comparison, open learner modeling, and adaptive navigation support to access multiple kinds of smart learning content. Mastery Grids is supported by adaptive social learning framework [[Aggregate]]. This framework supports several kinds of open student modeling, social comparison, and recommendation. In detail, Mastery Grids presents and compares user learning progress and knowledge level using colored grids, tracks user activities with learning content, and provides flexible user-centered navigation across different content levels (e.g. topic, question) and different content types (e.g. problem, example). Our past research shows that open student modeling and social comparison effectively increases students’ performance, motivation, engagement and retention.

* [[Mastery Grids Interface|More about Mastery Grids interface]]
* [https://www.youtube.com/watch?v=76YLR2VY2QE YouTube demo of Mastery Grids interface]
* [http://adapt2.sis.pitt.edu/um-vis-adl/index.html?usr=adl01&grp=ADL&sid=test&cid=13&data-top-n-grp=5&def-val-rep-lvl-id=p&def-val-res-id=AVG&ui-tbar-rep-lvl-vis=0&ui-tbar-topic-size-vis=0 An interactive demo of Mastery Grids interface]
|}

== Architecture: Aggregate==
{|
|valign="top" | [[Image:arch_v2.png|thumb|left|'''100'''|Aggregate Architecture]]
|valign="top" | We developed an adaptive social learning architecture [[Aggregate]] to support Mastery Grids interface. [[Aggregate]] is an extension of our original [[ADAPT2]] architecture. On the top of [[ADAPT2]] , [[Aggregate]] architecture supports several kinds of open student modeling, social comparison, content brokering, and recommendation services. The architecture fulfills a major objective, portability, which is the ability to be integrated to other systems with little set up and modification. The architecture is modular and includes different software components.

* [[ADAPT2| More about ADAPT2]]
* [[Aggregate| More about Aggregate]]
|}

== Learner Modeling ==
{|
|valign="top" | [[Image:CUMULATE.evidence propagation.png|thumb|left|'''100'''|CUMULATE]]
|valign="center" | We have developed [[CUMULATE]], a centralized user modeling server built for the [[ADAPT2]] architecture, to provide user modeling support for adaptive educational hypermedia (AEH) systems. CUMULATE allows flexible learner models to infer learner knowledge. Mastery Grids's architecture is supported by CUMULATE and thus it also supports flexible learner models. The explanation of the communication between the interface and learner model can be found in [[Aggregate]]. We have proposed and implemented different learner models over past years, including [[CUMULATE asymptotic knowledge assessment|asymptotic assessment of user knowledge]] which is the main one currently deployed in our systems, and [[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. We have also explored different aspects to improve learner modeling, including reducing the content model, better evaluation for practitioners and applying network (graph) analysis.

* [[Learner Modeling|More about Learner Modeling]]
* [[CUMULATE|More about CUMULATE]]
* [[CUMULATE asymptotic knowledge assessment|More about asymptotic assessment of user knowledge]]
* [[Feature-Aware Student knowledge Tracing (FAST)|More about Feature-Aware Student knowledge Tracing (FAST)]]
|}

== Recommendation ==
{|
|valign="top" | [[Image:starRecommendation.png|thumb|left|'''100'''|Personalization]]
|valign="top" | There are multiple personalization approaches, that are developed and researched in our system. In the form of recommendations, we have various methods in different levels for recommending learning material to students. Two major approached for recommending resources are reactive and proactive recommendations. In the reactive approach, the recommender system activates in reaction to the student's activity, e.g. if the student fails in solving a quiz, the reactive recommender system recommends related examples to this student to help her understand the skills required to solve that quiz. The pro-active recommender system, proactively suggests learning materials to the students. [[Learning Recommendation|==> more]]

|}

== Educational Data Mining ==
{|
|valign="top" | [[Image:TensorFactorization.png|thumb|left|'''100'''|Educational Data Mining]]
|valign="top" | In this part of the project, we aim to make sense of data from Mastery Grids system, including logs of student attempts. The goal in this part includes understanding students' learning patterns and its relationship with students' behavioral traits, predicting students' performance, modeling student knowledge, and discovering the content model. These tasks eventually help us in providing a better service to both instructors and students. [[Educational Data Mining|==> more]]

|}

== Smart Content ==
{|
|valign="top" | [[Image:AnimatedExamples.jpg|thumb|left|'''100'''|Smart Content (Animated Examples)]]
|valign="top" | Mastery Grids supports and provides multiple types of learning materials. It has been applied in three domains (Java, SQL, and Python) as a supplementary E-learning system for undergraduate and graduate level programming and database classes since 2013. We have developed different content applications (e.g., [[QuizJET]], [[QuizPET]], [[WebEx]]) and authoring tools (e.g., [[Content Authoring Tools]], [[Course Authoring Tool]], [[Group Authoring Tool]]) for accessing and authoring such contents. In each learning domain, courses are organized by topics and different types of learning contents are arranged under each topic. Learning contents contain problems (quizzes), parson problems, annotated examples, and animated examples collected from experienced course teachers, textbooks or domain experts. [[Smart Content|==> more]]
|}

== Authoring Tools ==
{|
|valign="top" | [[Image:ExampleAuthoringModify.jpg|thumb|left|'''100'''|Annotated Example Content Authoring]]
|valign="top" | We have developed four major types of authoring tools for the project: 1) Content Authoring Tools for creating different kinds of smart learning content, 2) Course Authoring Tool for creating adaptive courses that use the content, 3) Group Authoring Tool for managing users and groups, as well as 4) the portal to access different authoring tools.

* [[Authoring Tools|More about Authoring Tools]]
* [[Content Authoring Tools|More about Content Authoring Tools]]
* [[Course Authoring Tool|More about Course Authoring Tool]]
* [[Group Authoring Tool|More about Group Authoring Tool]]
* [[Authoring Tool Portal|More about Authoring Tool Portal]]
|}

== Open Source ==
Software sources and documentations are in GitHub [https://github.com/PAWSLabUniversityOfPittsburgh PAWSLabUniversityOfPittsburgh organization], and [https://github.com/acos-server/ acos-server organization].
* The Mastery Grids Interface, back-end Aggregate and documentation can be found [https://github.com/PAWSLabUniversityOfPittsburgh/mastery-grids here].
* User model services can be found in [https://github.com/PAWSLabUniversityOfPittsburgh/AggregateUMServices here].
* QuizJET Interface, Authoring Tool, Content Brokering and documentations can be found [https://github.com/PAWSLabUniversityOfPittsburgh/quizjet here].
* QuizPET Interface, Authoring Tool, Content Brokering and documentations can be found [https://github.com/PAWSLabUniversityOfPittsburgh/quizpet here].
* Parson Problem Authoring Tool can be found [https://github.com/acos-server/acos-jsparsons-generator here].
* Annotated Examples Interface, Authoring Tool, Content Brokering and documentations can be found [https://github.com/PAWSLabUniversityOfPittsburgh/annotated-examples here].
* Animated Examples Authoring Tool can be found [https://github.com/acos-server/acos-jsvee-transpiler-python here].
* Videos User Interface, Authoring Tool, Content Brokering and documentations can be found [https://github.com/PAWSLabUniversityOfPittsburgh/educvideos here]

== Publications ==
* Gonzalez-Brenes, J. P., Huang, Y., and Brusilovsky, P. (2013) FAST: Feature-Aware Student Knowledge Tracing. In: Proceedings of NIPS 2013 Workshop on Data Driven Education, Lake Tahoe, NV, December 10, 2013, ([http://d-scholarship.pitt.edu/20353/ paper])
* Hosseini, R. and Brusilovsky, P. (2013) JavaParser: A Fine-Grain Concept Indexing Tool for Java Problems. In: Proceedings of The First Workshop on AI-supported Education for Computer Science (AIEDCS) at the 16th Annual Conference on Artificial Intelligence in Education, AIED 2013, Memphis, TN, USA, July 13, 2013, pp. 60-63. ([https://d-scholarship.pitt.edu/secure/26270/1/AIED2013-workshop-camera_ready_version.pdf paper]) ([http://www.slideshare.net/RoyaHosseini1/java-parser-a-fine-grained-indexing-tool-and-its-application presentation])
* Hosseini, R., Brusilovsky, P., and Guerra, J. (2013) Knowledge Maximizer: Concept-based Adaptive Problem Sequencing for Exam Preparation. In: Proceedings of the 16th International Conference on Artificial Intelligence in Education (AIED 2013), Memphis, USA, pp. 848-851. ([https://d-scholarship.pitt.edu/secure/26271/4/AIED2013-camera-ready-Knowledge_maximizer_.pdf paper]) ([http://www.slideshare.net/RoyaHosseini1/aied-2013 poster])
* Brusilovsky, P., Baishya, D., Hosseini, R., Guerra, J., and Liang, M. (2013) KnowledgeZoom for Java: A Concept-Based Exam Study Tool with a Zoomable Open Student Model. In: Proceedings of 2013 IEEE 13th International Conference on Advanced Learning Technologies, Beijing, China, July 15-18, 2013, pp. 275-279. ([http://dx.doi.org/10.1109/ICALT.2013.86 paper]) ([http://www.slideshare.net/RoyaHosseini1/kowledge-zoom-michelle-48735584 presentation])
* Brusilovsky, P. (2014) Addictive Links: Engaging Students through Adaptive Navigation Support and Open Social Student Modeling (Keynote talk). In: Proceedings of WWW 2014 Workshop on Web-based Education Technologies, Seoul, Korea, April 8, 2014. ([http://www.slideshare.net/pbrusilovsky/addictive-links-keynote-talk-at-www-2014-workshop presentation])
* Huang, Y., Xu, Y., and Brusilovsky, P. (2014) Doing More with Less: Student Modeling and Performance Prediction with Reduced Content Models. In: V. Dimitrova, et al. (eds.) Proceedings of 22nd Conference on User Modeling, Adaptation and Personalization (UMAP 2014), Aalborg, Denmark, July 7-11, 2014, Springer Verlag, pp. 338-349. ([http://www.slideshare.net/pbrusilovsky/umap-v1 presentation][http://link.springer.com/chapter/10.1007%2F978-3-319-08786-3_30 paper])
* Hosseini, R. and Brusilovsky, P. (2014) Example-Based Problem Solving Support Using concept Analysis of Programming Content. In: S. Trausan-Matu, K. Boyer, M. Crosby and K. Panourgia (eds.) Proceedings of 12th International Conference on Intelligent Tutoring Systems (ITS 2014), Honolulu, HI, USA, June 5-9, 2014, Springer International Publishing, pp. 683-685. ([https://d-scholarship.pitt.edu/secure/26268/1/CameraReady_ITS2014_paper.pdf paper]) ([http://www.slideshare.net/RoyaHosseini1/presentation-48735557 presentation])
* Hosseini, R., Vihavainen, A., and Brusilovsky, P. (2014) Exploring Problem Solving Paths in a Java Programming Course. In: Proceedings of Psychology of Programming Interest Group Annual Conference, PPIG 2014, Brighton, UK, June 25-27, 2014, pp. 65-76. ([https://d-scholarship.pitt.edu/secure/26272/1/PPIG_2014_camera_ready.pdf paper]) ([http://www.slideshare.net/RoyaHosseini1/ppig2014-problem-solvingpaths presentation])
* Huang, Y., González-Brenes, J. P., and Brusilovsky, P. (2014) General Features in Knowledge Tracing to Model Multiple Subskills, Temporal Item Response Theory, and Expert Knowledge. In: J. Stamper, Z. Pardos, M. Mavrikis and B. M. McLaren (eds.) Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014), London, UK, July 4-7, 2014, pp. 84-91. (First two authors contributed equally. Nominated for Best Paper Award) ([http://www.slideshare.net/huangyun/fast-presentation-48711687 presentation][http://educationaldatamining.org/EDM2014/uploads/procs2014/long%20papers/84_EDM-2014-Full.pdf paper][http://www.slideshare.net/huangyun/2015edm-featureaware-student-knowledge-tracing-tutorial tutorial] [http://ml-smores.github.io/fast/ code])
* Khajah, M. M., Huang, Y., González-Brenes, J. P., Mozer, M. C., and Brusilovsky, P. (2014) Integrating Knowledge Tracing and Item Response Theory: A Tale of Two Frameworks. In: I. Cantador, M. Chi, R. Farzan and R. Jäschke (eds.) Proceedings of Workshop on Personalization Approaches in Learning Environments (PALE 2014) at the 22th International Conference on User Modeling, Adaptation, and Personalization, UMAP 2014, Aalborg, Denmark, July 11, 2014, CEUR, pp. 7-12. (First three authors contributed equally) ([http://www.slideshare.net/huangyun/pale-public-slideshare presentation][http://ceur-ws.org/Vol-1181/pale2014_paper_01.pdf paper]).
* Yudelson, M., Hosseini, R., Vihavainen, A., and Brusilovsky, P. (2014) Investigating Automated Student Modeling in a Java MOOC. In: J. Stamper, Z. Pardos, M. Mavrikis and B. M. McLaren (eds.) Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014), London, UK, July 4-7, 2014, pp. 261-264. ([https://d-scholarship.pitt.edu/secure/26273/1/EDM2014YudelsonHVB_camready.pdf paper]) ([http://www.slideshare.net/RoyaHosseini1/edm2014-investigating-automated-student-modeling-in-a-java-mooc presentation])
* Sahebi, S., Huang, Y., and Brusilovsky, P. (2014) Parameterized Exercises in Java Programming: Using Knowledge Structure for Performance Prediction. In: Proceedings of The second Workshop on AI-supported Education for Computer Science (AIEDCS) at 12th International Conference on Intelligent Tutoring Systems ITS 2014, Honolulu, Hawaii, June 6 2014. ([http://d-scholarship.pitt.edu/21915/ paper])([http://www.slideshare.net/chagh/parameterized-exercises-in-java-programming-using-knowledge-structure-for-performance-prediction presentation])
* Sahebi, S., Huang, Y., and Brusilovsky, P. (2014) Predicting Student Performance in Solving Parameterized Exercises. In: S. Trausan-Matu, K. Boyer, M. Crosby and K. Panourgia (eds.) Proceedings of 12th International Conference on Intelligent Tutoring Systems (ITS 2014), Honolulu, HI, USA, June 5-9, 2014, Springer International Publishing, pp. 496-503, ([http://d-scholarship.pitt.edu/21916/ paper]) ([http://www.slideshare.net/chagh/its14-pitttemplate presentation])
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* Huang, Y., González-Brenes, J. P., Brusilovsky, P. (2015) Challenges of Using Observational Data to Determine the Importance of Example Usage. In: Proceedings of the 17th International Conference on Artificial Intelligence in Education (AIED 2015), Madrid, Spain, pp. 633-637. ([http://d-scholarship.pitt.edu/26056/ paper])
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* Gonzalez-Brenes, J. P., Huang, Y. Using Data from Real and Simulated Learners to Evaluate Adaptive Tutoring Systems. In: 2nd AIED Workshop on Simulated Learners at the 17th Intl. Conf. on Artificial Intelligence in Education (AIED 2015), Madrid, Spain, 2015. ([http://ceur-ws.org/Vol-1432/sl_pap4.pdf paper])
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* Hosseini, R., Sirkiä, T., Guerra, J., Brusilovsky, P., Malmi, L. (2016) Animated Examples as Practice Content in a Java Programming Course. Proceedings of the 47th ACM technical symposium on Computer Science Education (SIGCSE), Memphis, Tennessee, March 2-5, 2016. ([https://d-scholarship.pitt.edu/secure/27083/1/sigcse2016.pdf paper]) ([http://www.slideshare.net/RoyaHosseini1/sigcse-2016 presentation])
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