PAWS Lab - User contributions [en]

Publications

2024-04-22T03:24:19Z

Behnam: /* Carousel-based Recommendation */

===Mastery Grids===

* Jordan Barria-Pineda, Kamil Akhuseyinoglu, and Peter Brusilovsky. '''Adaptive Navigational Support and Explainable Recommendations in a Personalized Programming Practice System'''. HyperText 2023

* Barria-Pineda, J., Akhuseyinoglu, K., Želem-Ćelap, S., Brusilovsky, P., Milicevic, A.K., Ivanovic, M. (2021). '''Explainable Recommendations in a Personalized Programming Practice System.''' AIED 2021.

* Akhuseyinoglu, K., Barria-Pineda, J., Sosnovsky, S., Lamprecht, AL., Guerra, J., Brusilovsky, P. (2020). '''Exploring Student-Controlled Social Comparison.''' EC-TEL 2020.

* Guerra, J., Hosseini, R., Somyurek, S., Brusilovsky, P. (2016). '''An Intelligent Interface for Learning Content: Combining an Open Learner Model and Social Comparison to Support Self-Regulated Learning and Engagement.''' I press (available [http://columbus.exp.sis.pitt.edu/jguerra/files/intelligent-interface-learning.pdf here]). IUI 2016.

* Loboda, T., Guerra, J., Hosseini, R., Brusilovsky, P. (2014). '''Mastery Grids: An Open Source Social Educational Progress Visualization.''' Paper accepted in ECTEL 2014

===Reading Mirror===
* A.-B. Lekshmi-Narayanan, K. Thaker, P. Brusilovsky, and J. Barria-Pineda. '''Help me read! expanding students’ reading with wikipedia articles.''' EDM 2023

* Barria-Pineda, Jordan, Arun Balajiee Lekshmi Narayanan and Peter Brusilovsky. '''Augmenting Digital Textbooks with Reusable Smart Learning Content: Solutions and Challenges.''' iTextbooks@AIED (2022).

* Javadian Sabet, Alireza, Isaac Alpizar Chacon, Jordan Barria-Pineda, Peter Brusilovsky and Sergey Sosnovsky. '''Enriching Intelligent Textbooks with Interactivity: When Smart Content Allocation Goes Wrong.''' iTextbooks@AIED (2022).

* Chacon, Isaac Alpizar, Jordan Barria-Pineda, Kamil Akhuseyinoglu, Sergey Sosnovsky and Peter Brusilovsky. '''Integrating Textbooks with Smart Interactive Content for Learning Programming.''' iTextbooks@AIED (2021).

* Rahdari, B., Brusilovsky, P., Thaker, K., Barria-Pineda, J. (2020). '''Knowledge-Driven Wikipedia Article Recommendation for Electronic Textbooks.''' EC-TEL 2020.

* Rahdari, B., Brusilovsky, P., Thaker, K., & Barria-Pineda, J. (2020, July). Using Knowledge Graph for Explainable Recommendation of External Content in Electronic Textbooks. In iTextbooks@ AIED (pp. 50-61).

===[[Grapevine]]===
* Rahdari, B., & Brusilovsky, P. (2019, August). '''Building a Knowledge Graph for Recommending Experts'''. In DI2KG@ KDD.
* Rahdari, B., Brusilovsky, P., Babichenko, D., Littleton, E. B., Patel, R., Fawcett, J., & Blum, Z. (2020). '''Grapevine: A profile‐based exploratory search and recommendation system for finding research advisors'''. Proceedings of the Association for Information Science and Technology, 57(1), e271.
* Rahdari, B., Brusilovsky, P., & Babichenko, D. (2020, July). '''Personalizing information exploration with an open user model'''. In Proceedings of the 31st ACM Conference on Hypertext and Social Media (pp. 167-176).
* Rahdari, B., Brusilovsky, P., & Sabet, A. J. (2021). '''Controlling Personalized Recommendations in Two Dimensions with a Carousel-Based Interface.''' In IntRS@ RecSys (pp. 112-122).

===HELPeR===
* Chi, Y., Thaker, K., He, D., Hui, V., Donovan, H., Brusilovsky, P., and Lee, Y. J. (2022) '''Knowledge Acquisition and Social Support in Online Health Communities: Analysis of an Online Ovarian Cancer Community.''' JMIR Cancer 8 (3), e39643.
* Rahdari, B., Brusilovsky, P., He, D., Thaker, K., Luo, Z., and Lee, Y. J. (2022) '''Helper: an interactive recommender system for ovarian cancer patients and caregivers.''' In: Proceedings of 16th ACM Conference on Recommender Systems, Seattle, WA, ACM, pp. 644-647.
* Thaker, K., Chi, Y., Birkhoff, S., He, D., Donovan, H., Rosenblum, L., Brusilovsky, P., Hui, V., and Lee, Y. J. (2022) '''Exploring Resource-Sharing Behaviors for Finding Relevant Health Resources: Analysis of an Online Ovarian Cancer Community.''' JMIR Cancer 8 (2), e33110.
* Chi, Y., Hui, V., Kunsak, H., Brusilovsky, P., Donovan, H., He, D., and Lee, Y. J. (2024) '''Women with ovarian cancer’s information seeking and avoidance behaviors: an interview study.''' JAMIA open 7 (1), ooae011.

===Carousel-based Recommendation===
* Rahdari, B., & Brusilovsky, P. (2024, March). '''CARE: An Infrastructure for Evaluation of Carousel-Based Recommender Interfaces. In Companion Proceedings of the 29th International Conference on Intelligent User Interfaces.''' (pp. 41-44).
* Rahdari, B., Brusilovsky, P., & Kveton, B. (2024). '''Towards Simulation-Based Evaluation of Recommender Systems with Carousel Interfaces.''' ACM Transactions on Recommender Systems.
* Rahdari, B., Kveton, B., & Brusilovsky, P. (2022). '''From Ranked Lists to Carousels: A Carousel Click Model.''' arXiv preprint arXiv:2209.13426.
* Rahdari, B., Kveton, B., & Brusilovsky, P. (2022, June). '''The magic of carousels: Single vs. multi-list recommender systems.''' In Proceedings of the 33rd ACM Conference on Hypertext and Social Media (pp. 166-174).
* Rahdari, B., Brusilovsky, P., & Kveton, B. (2022, May). '''Towards Increasing the Coverage of Interactive Recommendations.''' In The International FLAIRS Conference Proceedings (Vol. 35).

===ADVISE===
{{:Adaptive_VIBE}}

===[[AnnotEx]]===
* Hsiao, I. and Brusilvsky, P. (2011) '''The Role of Community Feedback in the Student Example Authoring Process: an Evaluation of AnnotEx''', British Journal of Educational Technology, Vol 42, Issue 3, Pages 482 - 499 [http://dx.doi.org/10.1111/j.1467-8535.2009.01030.x DOI]
* Hsiao, I. & Brusilovsky. P. (2008). '''Modeling Peer Review in Example Annotation'''. ICCE, The 16th International Conference on Computers in Education, Taipei, Taiwan, October 27- 31, 2008, ICCE [http://apsce.net/icce2008/contents/proceeding_0357.pdf URL] [http://www.sis.pitt.edu/~ihsiao/pub/5page_camera%20ready_ICCE_Modeling_Peer_Review_in_Example_Annotation.pdf PDF]
* Brusilovsky, P., Hsiao, I. & Yuldelson, M. (2008) '''Annotated Program Examples as First Class Objects in an Educational Digital Library''', JCDL 2008 [http://doi.acm.org/10.1145/1378889.1378946 DOI]
* Hsiao, I. & Brusilovsky, P. (2007) '''Collaborative Example Authoring System: The Value of Re-annotation based on Community Feedback''',In: J. Nall and R. Robson (eds.) Proceedings of World Conference on E-Learning, E-Learn 2007,Quebec City, Canada, October 15-19, 2007, AACE [http://www.editlib.org/p/26914 URL] [http://www.sis.pitt.edu/~ihsiao/pub/2007ELearn_Collaborative_Example_Authoring_System_final.pdf PDF]

===ASSIST===
* Freyne, J., Farzan, R., and Coyle, M. (2007). Toward the exploitation of social access patterns for recommendation. RecSys 2007.
* Farzan, R., Coyle, M., Freyne, J., Brusilovsky, P., and Smyth, P. (2007). ASSIST: Adaptive Social Support for Information Space Traversal. Hypertext 2007.
* Freyne J., Farzan R., Brusilovsky P., Smyth B., and Coyle M. (2007). Collecting Community Wisdom: Integrating Social Search & Social Navigation. In Proceedings of International Conference on Intelligent User Interfaces

===Conference Navigator===

*Rahdari, B., Tsai, C. H., & Brusilovsky, P. (2019, May). Expanding Controllability of Hybrid Recommender Systems: From Positive to Negative Relevance. In The Thirty-Second International Flairs Conference.
*Rahdari, B., & Brusilovsky, P. (2019, March). User-controlled hybrid recommendation for academic papers. In Proceedings of the 24th International Conference on Intelligent User Interfaces: Companion (pp. 99-100).
*Tsai, C. H., Rahdari, B., & Brusilovsky, P. (2019). Exploring User-Controlled Hybrid Recommendation in Conference Contexts. In IUI Workshops’ (Vol. 19).
* [[User:Clau|López C.]], Farzan R., Sahebi S., and Brusilovsky P. (2013). What Influences the Decision to Participate in Audience-bounded Online Communities. iConference 2013.
* [[User:Clau|López C.]], Farzan R., and Brusilovsky P. (2012). Personalized Incremental Users' Engagement: Driving Contributions One Step Forward. ACM GROUP 2012.
* Farzan R., and Brusilovsky P. Where did the Researchers Go? Supporting Social Navigation at a Large Academic Conference. Hypertext 2008.

===[[CourseAgent]]===
* Farzan, R. and Brusilovsky, P. (2011) Encouraging User Participation in a Course Recommender System: An Impact on User Behavior. Computers in Human Behavior 27 (1), 276-284.
*Farzan R. & Brusilovsky P. (2006). Social Navigation Support in a Course Recommendation System. In proceedings of 4th International Conference on Adaptive Hypermedia and Adaptive Web-based Systems.

===[[CUMULATE]]===
{{:CUMULATE}}

===[[Eventur]]===
* Lee, D. H. (2008) PITTCULT: Trust-based Cultural Event Recommender, Proceedings of Doctoral Symposium on the 2nd ACM International Conference on Recommender Systems, Lausanne, Switzerland, October 23 ~ 25, 2008
* Lee, D. H. (2008) PITTCULT: Recommender System using Trusted Human Network, Student Research Competition in Hypertext 2008, Pittsburgh PA., USA, June 19 ~ 21, 2008, Third Prize Winner of ACM Student Research Competition in Hypertext 2008 & Finalist for the Grand Prize of ACM SRC sponsored by Microsoft Research [http://pittcult.sis.pitt.edu/help.jsp Presentation], [http://www.sigweb.org/ht08/srcposters/lee.pdf pdf]

===[[Knowledge Sea II]] and [[AnnotatEd]]===
* Lin, Y., Brusilovsky, P., and He, D. (2011) Improving Self-Organizing Information Maps as Navigational Tools: A Semantic Approach. Online Information Review 35 (3), 401-424.
* Farzan, R. and Brusilovsky P. (2008). AnnotatEd: A Social Navigation and Annotation Service for Web-based Educational Resources. Journal of the New Review of Hypermedia and Multimedia (NRHM)
* Ahn, J., Farzan, R., and Brusilovsky, P. (2006) Social Search in the Context of Social Navigation. Journal of the Korean Society for Information Management 23(2):147-165.
* Bateman S., Farzan R., Brusilovsky P., and McCalla G. (2006) OATS: The Open Annotation and Tagging System. In Proceedings 3rd annual e-learning conference on Intelligent Interactive Learning Object Repositories
* Farzan R. & Brusilovsky P. (2006). AnnotatEd: A Social Navigation and Annotation Service for Web-based Educational Resources. In Proceedings of E-Learn 2006--World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education.Winner of outstanding paper award.
* Mertens R., Farzan R.,and Brusilovsky P. (2006) Social Navigation in Web Lectures. In proceedings of Seventeenth ACM Conference on Hypertext and Hypermedia(short paper).
* Farzan, R. & Brusilovsky P. (2005). Social Navigation Support through Annotation-Based Group Modeling. In proceedings of 10th International Conference on User Modeling.
* Brusilovsky P., Farzan R. & Ahn J. (2005). Comprehensive Personalized Information Access in an Educational Digital Library. In proceedings of Joint Conference on Digital Libraries.
* Brusilovsky, P., Chavan, G., Farzan, R. (2004). Social Adaptive Navigation Support for Open Corpus Electronic Textbooks - In: P.De Bra (ed.) Proceedings of the Third International Conference on Adaptive Hypermedia and Adaptive Web-based Systems (AH'2004), Eindhoven, the Netherlands

===Knowledge Zoom===
* Brusilovsky, P., Baishya, D., Hosseini, R., Guerra, J., & Liang, M. (2013, July). '''Knowledgezoom for java: A concept-based exam study tool with a zoomable open student model'''. In 2013 IEEE 13th International Conference on Advanced Learning Technologies (ICALT), (pp. 275-279). IEEE [http://ieeexplore.ieee.org/document/6601929/]. [Received Best Paper Award]

===[[NameSieve]]===
{{:NameSieve}}

===[[NavEx]]===
{{:NavEx}}

===[[PERSEUS]]===
{{:PERSEUS}}

===[[Proactive]]===
* Lee, D. H. & Brusilovsky, P. (2007) Fighting Information Overflow with Personalized Comprehensive Information Access: A Proactive Job Recommender, Proceedings of the Third International Conference on Autonomic and Autonomous Systems (ICAS '07), Athens, Greece, June 19 ~ 25, 2007
* Lee, D. and Brusilovsky, P. (2012) Proactive: Comprehensive Access to Job Information. Journal of Information Processing Systems 8 (4), 707-724.

===[[Progressor]]===
{{:Progressor}}
===[[ProgressorPlus]]===
{{:ProgressorPlus}}

===[[QuizJET]] and [[JavaGuide]]===
* Sahebi, S., Huang, Y., and Brusilovsky, P. (2014) '''Predicting Student Performance in Solving Parameterized Exercises''', ITS 2014.
* Sahebi, S., Huang, Y., and Brusilovsky, P. (2014) '''Parameterized Exercises in Java Programming: using Knowledge Structure for Performance Prediction''', The second Workshop on AI-supported Education for Computer Science (AIEDCS) 2014.
* Guerra, J., Sahebi, S., Lin, Y., Brusilovsky, P. The Problem Solving Genome: Analyzing Sequential Patterns of Student Work with Parameterized Exercises. Paper accepted in EDM 2014
{{:QuizJET}}

===[[TaskSieve]]===
{{:TaskSieve}}

===[[YourNews]]===
{{:YourNews}}

===[[Others]]===
* Sahebi, S. and Brusilovsky, P. (2013) '''Cross-Domain Recommendation in a Cold-Start Context: The impact of User Profile Size on the Quality of Recommendation''', UMAP 2013, Springer Berlin Heidelberg, p. 289-295.
* Brusilovsky, P., Hsiao, I-H. and Folajimi, Y., (2011) '''QuizMap: Open Social Student Modeling and Adaptive Navigation Support with TreeMaps''', In: Proceedings of 6th European Conference on Technology Enhanced Education (ECTEL), ECTEL 2011, Palermo, Italy, September 20-23, 2011, Springer-Verlag, Volume 6964/2011, pp.71-82

Publications

2024-04-22T03:23:49Z

Behnam:

===Mastery Grids===

* Jordan Barria-Pineda, Kamil Akhuseyinoglu, and Peter Brusilovsky. '''Adaptive Navigational Support and Explainable Recommendations in a Personalized Programming Practice System'''. HyperText 2023

* Barria-Pineda, J., Akhuseyinoglu, K., Želem-Ćelap, S., Brusilovsky, P., Milicevic, A.K., Ivanovic, M. (2021). '''Explainable Recommendations in a Personalized Programming Practice System.''' AIED 2021.

* Akhuseyinoglu, K., Barria-Pineda, J., Sosnovsky, S., Lamprecht, AL., Guerra, J., Brusilovsky, P. (2020). '''Exploring Student-Controlled Social Comparison.''' EC-TEL 2020.

* Guerra, J., Hosseini, R., Somyurek, S., Brusilovsky, P. (2016). '''An Intelligent Interface for Learning Content: Combining an Open Learner Model and Social Comparison to Support Self-Regulated Learning and Engagement.''' I press (available [http://columbus.exp.sis.pitt.edu/jguerra/files/intelligent-interface-learning.pdf here]). IUI 2016.

* Loboda, T., Guerra, J., Hosseini, R., Brusilovsky, P. (2014). '''Mastery Grids: An Open Source Social Educational Progress Visualization.''' Paper accepted in ECTEL 2014

===Reading Mirror===
* A.-B. Lekshmi-Narayanan, K. Thaker, P. Brusilovsky, and J. Barria-Pineda. '''Help me read! expanding students’ reading with wikipedia articles.''' EDM 2023

* Barria-Pineda, Jordan, Arun Balajiee Lekshmi Narayanan and Peter Brusilovsky. '''Augmenting Digital Textbooks with Reusable Smart Learning Content: Solutions and Challenges.''' iTextbooks@AIED (2022).

* Javadian Sabet, Alireza, Isaac Alpizar Chacon, Jordan Barria-Pineda, Peter Brusilovsky and Sergey Sosnovsky. '''Enriching Intelligent Textbooks with Interactivity: When Smart Content Allocation Goes Wrong.''' iTextbooks@AIED (2022).

* Chacon, Isaac Alpizar, Jordan Barria-Pineda, Kamil Akhuseyinoglu, Sergey Sosnovsky and Peter Brusilovsky. '''Integrating Textbooks with Smart Interactive Content for Learning Programming.''' iTextbooks@AIED (2021).

* Rahdari, B., Brusilovsky, P., Thaker, K., Barria-Pineda, J. (2020). '''Knowledge-Driven Wikipedia Article Recommendation for Electronic Textbooks.''' EC-TEL 2020.

* Rahdari, B., Brusilovsky, P., Thaker, K., & Barria-Pineda, J. (2020, July). Using Knowledge Graph for Explainable Recommendation of External Content in Electronic Textbooks. In iTextbooks@ AIED (pp. 50-61).

===[[Grapevine]]===
* Rahdari, B., & Brusilovsky, P. (2019, August). '''Building a Knowledge Graph for Recommending Experts'''. In DI2KG@ KDD.
* Rahdari, B., Brusilovsky, P., Babichenko, D., Littleton, E. B., Patel, R., Fawcett, J., & Blum, Z. (2020). '''Grapevine: A profile‐based exploratory search and recommendation system for finding research advisors'''. Proceedings of the Association for Information Science and Technology, 57(1), e271.
* Rahdari, B., Brusilovsky, P., & Babichenko, D. (2020, July). '''Personalizing information exploration with an open user model'''. In Proceedings of the 31st ACM Conference on Hypertext and Social Media (pp. 167-176).
* Rahdari, B., Brusilovsky, P., & Sabet, A. J. (2021). '''Controlling Personalized Recommendations in Two Dimensions with a Carousel-Based Interface.''' In IntRS@ RecSys (pp. 112-122).

===HELPeR===
* Chi, Y., Thaker, K., He, D., Hui, V., Donovan, H., Brusilovsky, P., and Lee, Y. J. (2022) '''Knowledge Acquisition and Social Support in Online Health Communities: Analysis of an Online Ovarian Cancer Community.''' JMIR Cancer 8 (3), e39643.
* Rahdari, B., Brusilovsky, P., He, D., Thaker, K., Luo, Z., and Lee, Y. J. (2022) '''Helper: an interactive recommender system for ovarian cancer patients and caregivers.''' In: Proceedings of 16th ACM Conference on Recommender Systems, Seattle, WA, ACM, pp. 644-647.
* Thaker, K., Chi, Y., Birkhoff, S., He, D., Donovan, H., Rosenblum, L., Brusilovsky, P., Hui, V., and Lee, Y. J. (2022) '''Exploring Resource-Sharing Behaviors for Finding Relevant Health Resources: Analysis of an Online Ovarian Cancer Community.''' JMIR Cancer 8 (2), e33110.
* Chi, Y., Hui, V., Kunsak, H., Brusilovsky, P., Donovan, H., He, D., and Lee, Y. J. (2024) '''Women with ovarian cancer’s information seeking and avoidance behaviors: an interview study.''' JAMIA open 7 (1), ooae011.

===[[Carousel-based Recommendation]]===
* Rahdari, B., & Brusilovsky, P. (2024, March). '''CARE: An Infrastructure for Evaluation of Carousel-Based Recommender Interfaces. In Companion Proceedings of the 29th International Conference on Intelligent User Interfaces.''' (pp. 41-44).
* Rahdari, B., Brusilovsky, P., & Kveton, B. (2024). '''Towards Simulation-Based Evaluation of Recommender Systems with Carousel Interfaces.''' ACM Transactions on Recommender Systems.
* Rahdari, B., Kveton, B., & Brusilovsky, P. (2022). '''From Ranked Lists to Carousels: A Carousel Click Model.''' arXiv preprint arXiv:2209.13426.
* Rahdari, B., Kveton, B., & Brusilovsky, P. (2022, June). '''The magic of carousels: Single vs. multi-list recommender systems.''' In Proceedings of the 33rd ACM Conference on Hypertext and Social Media (pp. 166-174).
* Rahdari, B., Brusilovsky, P., & Kveton, B. (2022, May). '''Towards Increasing the Coverage of Interactive Recommendations.''' In The International FLAIRS Conference Proceedings (Vol. 35).

===ADVISE===
{{:Adaptive_VIBE}}

===[[AnnotEx]]===
* Hsiao, I. and Brusilvsky, P. (2011) '''The Role of Community Feedback in the Student Example Authoring Process: an Evaluation of AnnotEx''', British Journal of Educational Technology, Vol 42, Issue 3, Pages 482 - 499 [http://dx.doi.org/10.1111/j.1467-8535.2009.01030.x DOI]
* Hsiao, I. & Brusilovsky. P. (2008). '''Modeling Peer Review in Example Annotation'''. ICCE, The 16th International Conference on Computers in Education, Taipei, Taiwan, October 27- 31, 2008, ICCE [http://apsce.net/icce2008/contents/proceeding_0357.pdf URL] [http://www.sis.pitt.edu/~ihsiao/pub/5page_camera%20ready_ICCE_Modeling_Peer_Review_in_Example_Annotation.pdf PDF]
* Brusilovsky, P., Hsiao, I. & Yuldelson, M. (2008) '''Annotated Program Examples as First Class Objects in an Educational Digital Library''', JCDL 2008 [http://doi.acm.org/10.1145/1378889.1378946 DOI]
* Hsiao, I. & Brusilovsky, P. (2007) '''Collaborative Example Authoring System: The Value of Re-annotation based on Community Feedback''',In: J. Nall and R. Robson (eds.) Proceedings of World Conference on E-Learning, E-Learn 2007,Quebec City, Canada, October 15-19, 2007, AACE [http://www.editlib.org/p/26914 URL] [http://www.sis.pitt.edu/~ihsiao/pub/2007ELearn_Collaborative_Example_Authoring_System_final.pdf PDF]

===ASSIST===
* Freyne, J., Farzan, R., and Coyle, M. (2007). Toward the exploitation of social access patterns for recommendation. RecSys 2007.
* Farzan, R., Coyle, M., Freyne, J., Brusilovsky, P., and Smyth, P. (2007). ASSIST: Adaptive Social Support for Information Space Traversal. Hypertext 2007.
* Freyne J., Farzan R., Brusilovsky P., Smyth B., and Coyle M. (2007). Collecting Community Wisdom: Integrating Social Search & Social Navigation. In Proceedings of International Conference on Intelligent User Interfaces

===Conference Navigator===

*Rahdari, B., Tsai, C. H., & Brusilovsky, P. (2019, May). Expanding Controllability of Hybrid Recommender Systems: From Positive to Negative Relevance. In The Thirty-Second International Flairs Conference.
*Rahdari, B., & Brusilovsky, P. (2019, March). User-controlled hybrid recommendation for academic papers. In Proceedings of the 24th International Conference on Intelligent User Interfaces: Companion (pp. 99-100).
*Tsai, C. H., Rahdari, B., & Brusilovsky, P. (2019). Exploring User-Controlled Hybrid Recommendation in Conference Contexts. In IUI Workshops’ (Vol. 19).
* [[User:Clau|López C.]], Farzan R., Sahebi S., and Brusilovsky P. (2013). What Influences the Decision to Participate in Audience-bounded Online Communities. iConference 2013.
* [[User:Clau|López C.]], Farzan R., and Brusilovsky P. (2012). Personalized Incremental Users' Engagement: Driving Contributions One Step Forward. ACM GROUP 2012.
* Farzan R., and Brusilovsky P. Where did the Researchers Go? Supporting Social Navigation at a Large Academic Conference. Hypertext 2008.

===[[CourseAgent]]===
* Farzan, R. and Brusilovsky, P. (2011) Encouraging User Participation in a Course Recommender System: An Impact on User Behavior. Computers in Human Behavior 27 (1), 276-284.
*Farzan R. & Brusilovsky P. (2006). Social Navigation Support in a Course Recommendation System. In proceedings of 4th International Conference on Adaptive Hypermedia and Adaptive Web-based Systems.

===[[CUMULATE]]===
{{:CUMULATE}}

===[[Eventur]]===
* Lee, D. H. (2008) PITTCULT: Trust-based Cultural Event Recommender, Proceedings of Doctoral Symposium on the 2nd ACM International Conference on Recommender Systems, Lausanne, Switzerland, October 23 ~ 25, 2008
* Lee, D. H. (2008) PITTCULT: Recommender System using Trusted Human Network, Student Research Competition in Hypertext 2008, Pittsburgh PA., USA, June 19 ~ 21, 2008, Third Prize Winner of ACM Student Research Competition in Hypertext 2008 & Finalist for the Grand Prize of ACM SRC sponsored by Microsoft Research [http://pittcult.sis.pitt.edu/help.jsp Presentation], [http://www.sigweb.org/ht08/srcposters/lee.pdf pdf]

===[[Knowledge Sea II]] and [[AnnotatEd]]===
* Lin, Y., Brusilovsky, P., and He, D. (2011) Improving Self-Organizing Information Maps as Navigational Tools: A Semantic Approach. Online Information Review 35 (3), 401-424.
* Farzan, R. and Brusilovsky P. (2008). AnnotatEd: A Social Navigation and Annotation Service for Web-based Educational Resources. Journal of the New Review of Hypermedia and Multimedia (NRHM)
* Ahn, J., Farzan, R., and Brusilovsky, P. (2006) Social Search in the Context of Social Navigation. Journal of the Korean Society for Information Management 23(2):147-165.
* Bateman S., Farzan R., Brusilovsky P., and McCalla G. (2006) OATS: The Open Annotation and Tagging System. In Proceedings 3rd annual e-learning conference on Intelligent Interactive Learning Object Repositories
* Farzan R. & Brusilovsky P. (2006). AnnotatEd: A Social Navigation and Annotation Service for Web-based Educational Resources. In Proceedings of E-Learn 2006--World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education.Winner of outstanding paper award.
* Mertens R., Farzan R.,and Brusilovsky P. (2006) Social Navigation in Web Lectures. In proceedings of Seventeenth ACM Conference on Hypertext and Hypermedia(short paper).
* Farzan, R. & Brusilovsky P. (2005). Social Navigation Support through Annotation-Based Group Modeling. In proceedings of 10th International Conference on User Modeling.
* Brusilovsky P., Farzan R. & Ahn J. (2005). Comprehensive Personalized Information Access in an Educational Digital Library. In proceedings of Joint Conference on Digital Libraries.
* Brusilovsky, P., Chavan, G., Farzan, R. (2004). Social Adaptive Navigation Support for Open Corpus Electronic Textbooks - In: P.De Bra (ed.) Proceedings of the Third International Conference on Adaptive Hypermedia and Adaptive Web-based Systems (AH'2004), Eindhoven, the Netherlands

===Knowledge Zoom===
* Brusilovsky, P., Baishya, D., Hosseini, R., Guerra, J., & Liang, M. (2013, July). '''Knowledgezoom for java: A concept-based exam study tool with a zoomable open student model'''. In 2013 IEEE 13th International Conference on Advanced Learning Technologies (ICALT), (pp. 275-279). IEEE [http://ieeexplore.ieee.org/document/6601929/]. [Received Best Paper Award]

===[[NameSieve]]===
{{:NameSieve}}

===[[NavEx]]===
{{:NavEx}}

===[[PERSEUS]]===
{{:PERSEUS}}

===[[Proactive]]===
* Lee, D. H. & Brusilovsky, P. (2007) Fighting Information Overflow with Personalized Comprehensive Information Access: A Proactive Job Recommender, Proceedings of the Third International Conference on Autonomic and Autonomous Systems (ICAS '07), Athens, Greece, June 19 ~ 25, 2007
* Lee, D. and Brusilovsky, P. (2012) Proactive: Comprehensive Access to Job Information. Journal of Information Processing Systems 8 (4), 707-724.

===[[Progressor]]===
{{:Progressor}}
===[[ProgressorPlus]]===
{{:ProgressorPlus}}

===[[QuizJET]] and [[JavaGuide]]===
* Sahebi, S., Huang, Y., and Brusilovsky, P. (2014) '''Predicting Student Performance in Solving Parameterized Exercises''', ITS 2014.
* Sahebi, S., Huang, Y., and Brusilovsky, P. (2014) '''Parameterized Exercises in Java Programming: using Knowledge Structure for Performance Prediction''', The second Workshop on AI-supported Education for Computer Science (AIEDCS) 2014.
* Guerra, J., Sahebi, S., Lin, Y., Brusilovsky, P. The Problem Solving Genome: Analyzing Sequential Patterns of Student Work with Parameterized Exercises. Paper accepted in EDM 2014
{{:QuizJET}}

===[[TaskSieve]]===
{{:TaskSieve}}

===[[YourNews]]===
{{:YourNews}}

===[[Others]]===
* Sahebi, S. and Brusilovsky, P. (2013) '''Cross-Domain Recommendation in a Cold-Start Context: The impact of User Profile Size on the Quality of Recommendation''', UMAP 2013, Springer Berlin Heidelberg, p. 289-295.
* Brusilovsky, P., Hsiao, I-H. and Folajimi, Y., (2011) '''QuizMap: Open Social Student Modeling and Adaptive Navigation Support with TreeMaps''', In: Proceedings of 6th European Conference on Technology Enhanced Education (ECTEL), ECTEL 2011, Palermo, Italy, September 20-23, 2011, Springer-Verlag, Volume 6964/2011, pp.71-82

HELPeR

2024-04-22T03:19:38Z

Behnam:

[http://adapt2.sis.pitt.edu/wiki/HELPeR_-_Health_e-Librarian_with_Personalized_Recommender HELPeR - Health e-Librarian with Personalized Recommender]]

PAWS

2024-04-22T03:14:53Z

Behnam:

== About PAWS ==
<div style="float: right; width: 50%">
[[Image:AIED_2019.jpg|class=img-responsive|EDM 2018 - Buffalo, NY]]
</div>
Personalized Adaptive Web Systems Lab was established in 2004 with support from National Science Foundation and the School of Information Sciences at the University of Pittsburgh. The goal of the PAWS Lab is development and evaluation of innovative user- and group-adaptive Web-based technologies, systems, and architectures. The Lab currently explores a range of user modeling, adaptation and personalization technologies. The work of the Lab is supported by NSF and DARPA funding.

== Most Recent News ==
* Sergey Sosnovsky has been promoted to Associate Professor at Utrecht University ([[News#2023-04-24|details]])
* Peter Brusilovsky receives 2023 Distinguished Research Award as a leader in the field of "cutting edge research"([[News#2023-03-14|details]])
* Peter Brusilovsky receives Amazon Research Award ([[News#2022-08-31|details]])
* Chun-Hua "Ronald" Tsai receives best paper award at ACM Learning at Scale 2022 ([[News#2022-06-08|details]])
* Peter Brusilovsky receives an NIH grant to work on the personalized treatment of aphasia ([[News#2022-04-05|details]])
* PAWS team receives Best Demo Award at ACM RecSys 2021 ([[News#2021-09-23|details]])
* Kamil Akhuseyinoglu and Peter Brusilovsky win Best Paper Award at UMAP 2021 ([[News#2021-06-23|details]])
* Chun-Hua "Ronald" Tsai starts as a tenure-track assistant professor at the University of Nebraska Omaha in Fall 2021! ([[News#2021-05-08|details]])
* Sherry Sahebi receives NSF CAREER award! ([[News#2021-05-06|details]])
* Rosta Farzan is appointed Associate Dean for diversity, equity and inclusion at the School of Computing and Information ([[News#2021-02-21|details]])
* Chun-Hua Tsai defends his PhD Thesis and joins Penn State as Assistant Research Professor ([[News#2019-08-25|details]])
* Hung Chau wins the Best Paper award at the ACM, Association for Computing Machinery Learning at Scale Conference!([[News#2019-06-25|details]])
* Prof. Peter Brusilovsky is awarded AMiner Most Influential Scholar Award in recognition of his contributions to the field of Recommender System by AMiner ([[News#2019-03-04|details]])
* Two PAWS graduates' papers were short-listed for the best paper award at EC-TEL 2018 ([[News#2018-08-13|details]])
* [[User:peterb | Peter Brusilovsky]] awarded NSF grant under the program Cyberlearn And Future Learn Tech. ([[News#2018-07-31|details]])
* Roya Hosseini defended her Ph. D. Thesis: 'Program Construction Examples in Computer Science Education: From Static Text to Adaptive and Engaging Learning Technology'.([[News#2018-07-24|details]])
* Yun Huang defended her Ph. D. Thesis: 'Learner Modeling for Integration Skills in Programming' ([[News#2018-07-05|details]])
* PAWS Lab scores thrice at IUI 2018 ([[News#2018-03-08|details]])

Go to [[News | News Page]] for all news.

== Check Demos of some of PAWS Lab systems Tools ==
* [https://dl.acm.org/doi/abs/10.1145/3640544.3645223 CARE: An Infrastructure for Evaluation of Carousel-Based Recommender Interfaces]
* [https://dl.acm.org/doi/10.1145/3279720.3279726 Programming Construction Examples] (2017) with [https://link.springer.com/chapter/10.1007/978-3-031-09680-8_4 Self Explanations] (2022)
* [https://dl.acm.org/doi/10.1145/3460231.3478879 Grapevine] (2021) - an interactive recommender system for finding research advisors
* [https://www.youtube.com/watch?v=GI9Fxo_FeRw&t=145s&ab_channel=RH Mastery Grids For Java] (2017) - a personalized Java Programming Practice System
* [https://www.youtube.com/watch?v=roCkp7QVfB8 Mastery Grids For Python] (2017) - a personalized Python Programming Practice System

== Try PAWS Lab systems Tools at our Community Portal ==

Go to [http://adapt2.sis.pitt.edu/kt Knowledge Tree] portal and [http://adapt2.sis.pitt.edu/kt/register.html create a trial account] to try our tools.

If you are a researcher and would like to quickly try our adaptation tools, create the trial account (link above) and proceed [http://adapt2.sis.pitt.edu/cbum/ here].

== PAWS Lab Contact Information ==
Information Sciences Building, Rm. 2A04<br/>
135 North Bellefield Avenue<br/>
Pittsburgh, PA 15260, USA<br/>
Tel: +1(412)624-9437<br/>

Systems

2024-04-22T02:59:42Z

Behnam:

Our group explores several kinds of information systems focused mostly on personalized systems (such as adaptive learning and recommender systems) and various kinds of systems that support human navigation in information space (such as adaptive hypermedia and social navigation). This page presents a brief overview of the types of systems we explore and follows with a quick overview of the systems and frameworks developed at [[Main Page|PAWS]] lab.
This page presents the main types of topics and technologies explored by PAWS Lab. Like other Wiki pages, it is permanently in construction.

= System Types =

== Personalized Learning Systems ==

Personalized learning technologies provide an alternative to the dominant “one-size-fits-all” approach to treating diverse student audiences. While having a relatively long history, this research direction moved to the forefront only recently when modern information technologies opened new learning opportunities for a wide range of students. Nowadays, personalized learning is considered to be a top priority research direction by many experts. For example, [http://www.engineeringchallenges.org/cms/8996/9127.aspx advanced personalized learning] was named among [http://www.engineeringchallenges.org/ 14 Grand Challenges for Engineering] along with preventing nuclear terror and making solar energy economical. It has also been listed among the highest funding priorities in [http://cacm.acm.org/magazines/2010/2/69358-assessing-the-changing-us-it-rd-ecosystem/fulltext Communications of the ACM].

Personalized learning technologies enable e-learning systems to maintain a model of the goals, preferences and knowledge of each student and apply this model to adapt the system performance to the student making the learning process more efficient and enjoyable. In so doing, various kinds of personalized e-learning systems demonstrated their ability to help students acquire knowledge faster, improve learning outcomes, reduce navigational overhead, and increase student engagement. Our team is interested a range of personalized learning technologies, focusing on modeling learner knowledge of the subject. Individual models of learner knowledge that our systems maintain are used to guide learners to the most appropriate learning content using course sequencing and adaptive navigation support technologies. Some systems also use the models to deliver adaptive visualization. Below is the list of personalized learning systems developed by our group. Most of these systems are open for anyone to use and explore online.

More at [[Personalized Learning Systems]]

Systems:
* [[QuizGuide]]
* [[NavEx]]
* [[Database Exploratorium]]
* [[KnowledgeZoom]]
* [[MasteryGrids]]
* [[JavaGuide]]
* [[Progressor]]
* [[ProgressorPlus]]

== Adaptive Information Retrieval Systems ==
Systems:

* [[TaskSieve]]
* [[YourNews]]

== Recommender Systems ==

* [[Grapevine]]
* [[HELPeR]]
* [[Proactive]]
* [[CourseAgent]]
* [[Cross-Domain Recommender Systems]]
* [[Social Recommender Systems]]

== Social Information Access Systems ==
Systems:
* [[ImageSieve]]
* [[NameSieve]]

== Personalized Social Systems for Local Communities ==
Systems:
* [[Conference Navigator 3]]
* [[Eventur]]
* [[CoMeT]]

= Currently Active Systems =

== ADAPT<sup>2</sup> Infrastructure==

{|
| valign="top" | [[Image:adapt2-arcitecture.gif|thumb|left|'''100'''|ADAPT<sup>2</sup> Architecture]]
| valign="top" | ADAPT<sup>2</sup> (read adapt-square) - Advanced Distributed Architecture for Personalized Teaching and Training - is a framework targeted at providing personalization and adaptation services for developers of content that lacks personalization. [[ADAPT2|more]])
|}

== CUMULATE ==
[[CUMULATE]] is a centralized user modeling server built for the [[ADAPT2|ADAPT<sup>2</sup>]] architecture. It is mainly targeted at providing user modeling support for adaptive educational hypermedia (AEH) systems. [[CUMULATE]] maintains a set of overlay models of students' knowledge. It uses several techniques for computing student models, including thresholded averaging, asymptotic user knowledge assessment, time-spent-reading.
([[CUMULATE|more]])

== Mastery Grids ==

{|
|valign="top" | [[Image:Mg_1.png|thumb|left|'''100'''|Mastery Grids Interface]]
|valign="top" | Mastery Grids is our latest implementation of Open Social Learner Modeling (OSLM). It 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]]
* [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]
|}

== Program Construction Examples ([[PCEX]])==
{|
| valign="top" | [[Image:Pcex_ex.PNG|thumb|left|'''100'''|Program Construction Examples]]
| valign="top" | PCEX is an interactive learning tool which demonstrates program construction examples to help students to develop program construction skills. It supports exploring the program construction examples freely and provide challenges to the students to help them self-assess their learning of program construction knowledge. It is now a component of [[ADAPT2]] Infrastructure.

* [[PCEX|More about PCEX]]

|}

==[[WEAT]]==
{|
| valign="top" | [[Image:weat.png|thumb|left|'''100'''|Worked Example Authoring Tool]]
| valign="top" | Worked Example Authoring Tool (WEAT) is an authoring tool for PCEX. The integrated ChatGPT support can be used to generate code explanations required for creating a program construction example. Created examples can be shared publicly with others, embed through iframes, or in an LMS like Canvas.

* [[WEAT|More about WEAT]]
* [[WEAT_Tutorial|WEAT's User Manual]]
* [https://youtu.be/IOfA0Ql3Zq0 WEAT Video Tutorial]

|}

== [[Grapevine]] ==
{|
|valign="top" | [[Image:Grapevine.png|thumb|left|'''100'''|Grapevine]]
|valign="top" | Grapevine is an interactive recommender system that assists students in finding advisors for their projects - from undergraduate capstone projects to PhD thesis work. It has been developed as a part of Personalized Education project sponsored by the University of Pittsburgh ([[Grapevine|more]])
|}

== QuizJET ==
{|
|valign="top" | [[Image:Quizjet.gif|thumb|left|'''100'''|QuizJET]]
|valign="top" | QuizJET is a system serves quizzes as a self-assessment Java Evaluation Tool. It's mainly used to assess students' knowledge in Java Programming Language. QuizJET randomly generates a question parameter, creates a presentation of the parameterized question in a Web-based quiz, compares student's input to the correct answer which QuizJET runs the parameterized code "behind the stage", and records the results into a server-side database. It is now a component of [[ADAPT2]] Infrastructure. ([[QuizJET|more]])
|}

== ReadingCircle ==
{|
| valign="top" | [[Image:Readingcircle1.png|left|thumb|200px|ReadingCircle interface.]]
| valign="top" | ReadingCircle is a system that explores approaches to encourage student reading using a social progress visualization interface. Click on the link to [[ReadingCircle]] to see more details.
|}

== [[HELPeR]] ==

{|
|valign="top" | [[Image:Helper.png|thumb|left|'''100'''|[[HELPeR]]]]
|valign="top" | Health e-Librarian with Personalized Recommender (HELPeR) is an interactive personalized search and recommender system designed to provide access to health information for cancer patients and their caregivers ([[HELPeR|-->more]])
|}

== [[WebEx]] ==
{|
| valign="top" | [[Image:AnnotatedExamples.jpg|left|thumb|200px|Screenshot of the WebEx interface.]]
| valign="top" | [[WebEx]] is a system that serves annotated code examples known also as dissections. Each dissection is a sequence of lines that have annotations associated with them. Dissections are grouped into collections - scopes. The natural domain of WebEx is programming. However, other applications are also possible, e.g. poetry. It is now a component of [[ADAPT2]] Infrastructure. It is one of the oldest PAWS systems, but WebEx is used in to provide access to examples in several domains. It is mostly superseded by [[PCX]] system which has more features.
|}

([[WebEx|more]])

= Earlier Systems =
== AdVisE (Adaptive Document Visualization for Education) ==
==== ADVISE 2D ====
Two dimensional document visualization based on inter-document similarities. The locations of the documents on the 2D space are determined by their similarities to another documents and users can visually see the relationships of the documents based on their contents.
==== ADVISE 3D ====
Three dimensional visualization of documents based on similarities. By adding one more dimension to 2D visualization, users are able to explore the document space more easily and access each document.

==== ADVISE VIBE ====

Relevance-based visualization of educational documents based on re-implementation of VIBE, a document visualization method based on similarities between documents and POIs (Points Of Interests) developed by Molde College and School of Information Sciences, University of Pittsburgh.

([http://ir.exp.sis.pitt.edu/advise more on ADVISE])

==Adaptive VIBE==

{|
| valign="top" | [[Image:AdaptiveVibe_part.png|thumb|left|'''100'''|Adaptive VIBE]]
| valign="top" | Two dimensional visualization based on POIs(Point Of Interest, or concepts) and document similarities. The position of the documents are calculated by their relationships with each POI.

* [http://amber.exp.sis.pitt.edu/~codex/tasksieve System Link] (Adaptive VIBE integrated into TaskSieve)
* [[Adaptive_VIBE | more on Adaptive VIBE]]
|}

== AnnotatEd ==
{|
| valign="top" | [[Image:ated.gif|thumb|left|'''100'''|AnnotatEd]]
| valign="top" | AnnotatEd is a system that enables learners to annotate online pages while keeping track of all activities of learners. AnnotatEd uses the learners' activity information to offer ''social navigation support'' for hyperlinks inside the AnnotatEd system. ([[AnnotatEd|more]])
|}

== AnnotEx ==
{|
| valign="top" | [[Image:AnnotEx.gif|thumb|left|'''100'''|AnnotEx]]
| valign="top" | AnnotEx - Example Annotator- is a web-based community based authoring tool for annotating programming examples.([[AnnotEx|more]])
|}

== CoMeT ==

{|
| valign="top" | [[Image:comet.gif|thumb|left|'''100'''|Comet]]
| valign="top" | COMET is a social system for sharing informaion about research talks. It allows to collaboratively collect, publish, and tag interesting research talks in Pittsburgh. COMET allows its users to schedule the talks they want to attend. It also automatically reminds about bookmarked talks and recommends other talks that fits isers' interests. ([http://halley.exp.sis.pitt.edu/comet/ visit COMET])
|}
== Conference Navigator 3==
{|
| valign="top" | [[Image:Cn3.jpg|thumb|left|'''100'''|CN3]]
| valign="top" | Conference Navigator 3 (CN3) is a personal conference scheduling tool with social linking and recommendation features. Users can control access to their information in the CN3 system and link their account with third party academic and non-academic social networks such as linkedIn, Facebook, citeulike, or Mendeley. Our main goal is to enhance attendees' experience at the conferences, and also investigate the mechanisms that drives attendees to engage in their research community. ([http://halley.exp.sis.pitt.edu/cn3/ visit Conference Navigator 3])
|}

== CoPE (Collaborative Paper Exchange) ==
{|
| valign="top" | [[Image:CoPE.1.overall.gif|thumb|left|'''100'''|CoPE]]
| valign="top" | CoPE - Collaborative Paper Exchange - is a system that provides community-based access to paper summaries via web. CoPE is currently an in-class tool for both teachers and students. ([[CoPE|more]])
|}

== CourseAgent ==
coming soon
([[CourseAgent|more]])

== Eventur ==
{|
| valign="top" | [[Image:Pittcult.gif|thumb|left|'''100'''|PittCult]]
| valign="top" | This project is to recommend interesting information using the combined technology of collaborative filtering and trust-based human network. This system is to overcome the emerging problems regarding collaborative filtering recommendations and to investigate how the information propagation is affected by trust among people. ([[Eventur|more]])
|}

== JavaGuide ==

{|
| valign="top" | [[Image:JavaGuide.png|thumb|left|'''100'''|JavaGuide]]
| valign="top" | JavaGuide is a personalized front-end for QuizJET developed by PAWS Lab (Hsiao, 2010). Java Guide collects student performance data sent by QuizJET to the activity storage, determines student current level of knowledge for multiple topics and concepts of Java programming language, and use it to provide adaptive guidance to the questions that are most appropriate for a specific student given the course goals and current state of knowledge.. ([[JavaGuide|more]])
|}

== Knowledge Sea II ==
{|
| valign="top" | [[Image:ks2.gif|thumb|left|'''100'''|Knowledge Sea II]]
| valign="top" | Knowledge Sea II is an extension of Knowledge Sea project that is designed to help users navigate from lectures to relevant online tutorials in a map-based horizontal navigation format. The most important feature of Knowledge Sea is facilitating the navigation through providing traffic and annotation based social navigation support. ([[Knowledge Sea II|more]])
|}

== Knowledge Tree ==
{|
| valign="top" | [[Image:KnowledgeTreeLogo.gif|thumb|left|'''100'''|Knowledge Tree]]
| valign="top" | Knowledge Tree is a link aggregating portal. It presents content structured according to the folder-document paradigm. Knowledge Tree provides authentication and authorization and implements a simplified form of access control. It supports collaborative authoring and social annotation. ([[Knowledge Tree|more]])
|}
== KnowledgeZoom ==
{|
| valign="top" | [[Image:KnowledgeZoom.png|thumb|left|'''100'''|KnowledgeZoom]]
| valign="top" | [[KnowledgeZoom]] is an exam preparation system with zoomable open student model showing student level of knowledge for hierarchy of Java programming concepts. KnowledgeZoom allows students to find gaps in their knowledge and access learning content that helps to bridge these gaps.

* [[KnowledgeZoom|More about KnowledgeZoom]]
|}

== MEMA ==
{|
|valign="top" | [[Image:MEMA.jpg|thumb|left|'''100'''|MEMA]]
|valign="top" | MEMA (Museum Exhibition MAnagement) ([[MEMA|more]])
* [http://halley.exp.sis.pitt.edu/mema/web/ Web System link]
* [http://halley.exp.sis.pitt.edu/mema Mobile System link]
|}

== NameSieve ==

{|
|valign="top" | [[Image:NameSieve-NEpanel.png|thumb|left|'''100'''|NameSieve Named-entity Navigator]]
|valign="top" | A name-entity based news exploration and filtering system. Important named-entities extracted from the search results are provided in the "cloud" form and helps further exploration. ([[NameSieve|more]])

* [http://amber.exp.sis.pitt.edu/namesieve System Link 1]
* [http://ir.exp.sis.pitt.edu/~jahn/cma/index.php System Link 2] (Carnegie Museum of Art version)
|}

== NavEx - Navigation to Examples ==
{|
| valign="top" | [[Image:NavEx.gif|thumb|left|'''100'''|NavEx]]
| valign="top" | NavEx provides adaptive guidance for accessing online interactive examples. Adaptation allows students to visualize both whether they are ready to explore certain examples and what is their progress with them. NavEx-SN (SN for social navigation) also allows students to relate their progress with the progress of the group. ([[NavEx|more]])
|}

== PERSEUS ==
{|
| valign="top" | [[Image:Perseus.gif|thumb|left|100px|PERSEUS]]
| valign="top" | [[PERSEUS]] is a Personalization Service Engine. It provides adaptive support for non-personalized (educational) hypermedia systems by abstracting content presentation/aggregation from user modeling. [[PERSEUS]] protocols are based on [http://en.wikipedia.org/wiki/Rdf RDF] and [http://en.wikipedia.org/wiki/RSS_(file_format)#RSS_1.0 RSS 1.0]. Although, [[PERSEUS]] was initially developed for [[ADAPT2|ADAPT<sup>2</sup>]] framework, its data model permits seamless support of any other hypermedia application. Currently [[PERSEUS]] provides social navigation, topic-based navigation, concept-based navigation, and adaptive filtering techniques. ([[PERSEUS|more]])
|}

== Proactive ==
{|
| valign="top" | [[Image:Proactive.gif|thumb|left|'''100'''|Proactive]]
| valign="top" | The Proactive is content-based job search and recommender system which is based on several knowledge engineering technology and personalized techniques. The system is adapts to each user by collecting various user's usage patterns. It integrates several approaches to provide access to job information ([[Proactive|more]])
|}

== Progressor ==
{|
| valign="top" | [[Image:Progressor.png|thumb|left|'''100'''|Progressor]]
| valign="top" | The Progressor is a system of personalized visual access to programming problems, which is based on open social user modeling technology and personalized techniques. ([[Progressor|more]])
|}

== Progressor+ ==
{|
| valign="top" | [[Image:progressorplus1.png|thumb|left|'''100'''|ProgressorPlus]]
| valign="top" | Progressor+ extends the benefits from Progressor and addresses the problems in personalized and social learning of how to help students to find the most appropriate educational resources and engage them into using these resources. Progressor+ adopts the same idea of open student modeling visualization and uses generic table representation for accessing and visualizing assorted educational content ([[ProgressorPlus|more]])
|}

== QuizGuide ==
{|
| valign="top" | [[Image:Quizguide.gif|thumb|left|'''100'''|QuizGuide]]
| valign="top" | QuizGuide, is an adaptive system that helps students in selecting most relevant quizzes for self-assessment of C knowledge. Quizzes are assigned to topics and adaptively annotated, to show which topics are currently important and which require further work. ([[QuizGuide|more]])
|}

== SetFusion ==
coming soon
([[SetFusion|more]])

== TalkExplorer ==
coming soon
([[TalkExplorer|more]])

== TaskSieve ==

{|
|valign="top" | [[Image:TaskSieve-surrogates.png|thumb|left|'''100'''|TaskSieve -- mediates query and user model]]
|valign="top" | An experimental personalized news search system based on task models and the interface to mediate between the query and the task model. Users can select three options (1) query only, (2) task model only, and (3) both. ([[TaskSieve|more]])
* [http://amber.exp.sis.pitt.edu/tasksieve System link 1]
* [http://amber.exp.sis.pitt.edu/~codex/tasksieve System link 2] (newer version integrated with Adaptive VIBE)
|}

== WADEIn (cWADEIn/jWADEIn) ==
coming soon
([[WADEIn|more]])

== YourNews ==

{|
|valign="top" | [[Image:YourNews-openUM.png|thumb|left|'''100'''|YourNews Open User Model UI]]
|valign="top" | YourNews is a news recommendation system based on the RSS feeds collected from various news sources. News articles are crawled every two hours, indexed, and then provided to users according to their specific needs. Users also can view and control their user profile with '''Open User Profile''' ([[YourNews|more]])

* [http://amber.exp.sis.pitt.edu/yournews System Link]
|}

HELPeR - Health e-Librarian with Personalized Recommender

2024-04-22T02:59:05Z

Behnam:

Ovarian cancer, a complex and challenging disease, requires patients and caregivers to carefully review vast array of medical information to make informed health decisions. To assist in this endeavor, the project 'HELPeR: Health e-Librarian with Personalized Recommender' has been developed. This innovative system aims to empower ovarian cancer patients and caregivers by providing personalized, accessible, and up-to-date information. Recognizing the diverse health literacy levels and specific informational needs of users, HELPeR is designed to streamline the process of finding reliable, relevant, and recent resources. This system could benefit patients in a landscape where patients and caregivers often face overwhelming amounts of data, varying in quality and relevance.

At the heart of HELPeR's utility is its user-friendly interface, which allows for intuitive navigation and interaction with a vast array of resources. As depicted in Figure 1, the interface features a straightforward layout with a search bar, filtering options, and a carousel display for search results. This design is deliberately chosen to ensure ease of use and familiarity for users of all backgrounds. The system's unique feature is its ability to adapt to the user's health literacy level, highlighted by color-coded document difficulty indicators and the option to select the desired level of complexity. This personalization ensures that users are not overwhelmed by overly technical information and can access resources that match their comprehension and needs.

<div style="width: 90%; align: center">
[[Image:Helper.jpeg|class=img-responsive|Grapevine System]]
</div>

Underlying HELPeR’s user-centric interface is a sophisticated architecture, encapsulated in Figure 2, which is composed of three primary pipelines: document collection, user profiling, and presentation. The document collection pipeline is dedicated to curating current and credible information, while the user profiling pipeline leverages data from health providers and patient activities to create detailed user profiles. These profiles then inform the presentation pipeline, ensuring that the system delivers the most relevant and personalized content. Built on a native graph database, HELPeR offers quick and responsive interactions, making it an efficient tool for users seeking specific information. This system not only bridges the information gap for ovarian cancer patients and caregivers but also sets a precedent in health informatics by providing a tailored, interactive, and adaptive platform for medical information dissemination.

<div style="width: 90%; align: center">
[[Image:Helper arch.jpeg|class=img-responsive|Grapevine System]]
</div>

This project is funded by NIH through the National Library of Medicine by grant [https://www.grantome.com/grant/NIH/R01-LM013038-02 R01-LM013038-02] (2019 - 2023).

==Research Team==
This project is a collaboration between the School of Nursing and the School of Computing and Information, University of Pittsburgh.
===School of Computing and Information===

* '''PI:''' Daqing He, [[User:Peterb|Peter Brusilovsky]]

* Graduate researchers: Behnam Rahdari, Khushboo Thaker, Zhenmin Hong, Mohammad Hassany

===School of Nursing===
* '''PI:''' Young Ji Lee, Heidi Donovan
* Postdoctoral researchers: Susan Birkhoff, Leah Rosenblum
* Graduate researchers: Yu Chi, Vivian Hui, Youjia Wang

===University Library System===
SP: Mary Lou Klem

==Systems==

* [[HELPeR]]

==Project Home Page==
For a detailed description of our vision and goals, see the [http://www.pitt.edu/~dah44/helper/ home page of the project ]

==Publications==

* Chi, Y., Thaker, K., He, D., Hui, V., Donovan, H., Brusilovsky, P., and Lee, Y. J. (2022) Knowledge Acquisition and Social Support in Online Health Communities: Analysis of an Online Ovarian Cancer Community. JMIR Cancer 8 (3), e39643.
* Rahdari, B., Brusilovsky, P., He, D., Thaker, K., Luo, Z., and Lee, Y. J. (2022) Helper: an interactive recommender system for ovarian cancer patients and caregivers. In: Proceedings of 16th ACM Conference on Recommender Systems, Seattle, WA, ACM, pp. 644-647.
* Thaker, K., Chi, Y., Birkhoff, S., He, D., Donovan, H., Rosenblum, L., Brusilovsky, P., Hui, V., and Lee, Y. J. (2022) Exploring Resource-Sharing Behaviors for Finding Relevant Health Resources: Analysis of an Online Ovarian Cancer Community. JMIR Cancer 8 (2), e33110.
* Chi, Y., Hui, V., Kunsak, H., Brusilovsky, P., Donovan, H., He, D., and Lee, Y. J. (2024) Women with ovarian cancer’s information seeking and avoidance behaviors: an interview study. JAMIA open 7 (1), ooae011.

File:Helper arch.jpeg

2024-04-22T02:55:23Z

Behnam: Three primary pipelines of HELPeR system; document collection, user profiling, and presentation.

== Summary ==
Three primary pipelines of HELPeR system; document collection, user profiling, and presentation.

File:Helper.jpeg

2024-04-22T02:54:07Z

Behnam: The HELPeR interface features a straightforward layout with a search bar, filtering options, and a carousel display for search results.

== Summary ==
The HELPeR interface features a straightforward layout with a search bar, filtering options, and a carousel display for search results.

Systems

2024-04-22T01:58:20Z

Behnam:

Our group explores several kinds of information systems focused mostly on personalized systems (such as adaptive learning and recommender systems) and various kinds of systems that support human navigation in information space (such as adaptive hypermedia and social navigation). This page presents a brief overview of the types of systems we explore and follows with a quick overview of the systems and frameworks developed at [[Main Page|PAWS]] lab.
This page presents the main types of topics and technologies explored by PAWS Lab. Like other Wiki pages, it is permanently in construction.

= System Types =

== Personalized Learning Systems ==

Personalized learning technologies provide an alternative to the dominant “one-size-fits-all” approach to treating diverse student audiences. While having a relatively long history, this research direction moved to the forefront only recently when modern information technologies opened new learning opportunities for a wide range of students. Nowadays, personalized learning is considered to be a top priority research direction by many experts. For example, [http://www.engineeringchallenges.org/cms/8996/9127.aspx advanced personalized learning] was named among [http://www.engineeringchallenges.org/ 14 Grand Challenges for Engineering] along with preventing nuclear terror and making solar energy economical. It has also been listed among the highest funding priorities in [http://cacm.acm.org/magazines/2010/2/69358-assessing-the-changing-us-it-rd-ecosystem/fulltext Communications of the ACM].

Personalized learning technologies enable e-learning systems to maintain a model of the goals, preferences and knowledge of each student and apply this model to adapt the system performance to the student making the learning process more efficient and enjoyable. In so doing, various kinds of personalized e-learning systems demonstrated their ability to help students acquire knowledge faster, improve learning outcomes, reduce navigational overhead, and increase student engagement. Our team is interested a range of personalized learning technologies, focusing on modeling learner knowledge of the subject. Individual models of learner knowledge that our systems maintain are used to guide learners to the most appropriate learning content using course sequencing and adaptive navigation support technologies. Some systems also use the models to deliver adaptive visualization. Below is the list of personalized learning systems developed by our group. Most of these systems are open for anyone to use and explore online.

More at [[Personalized Learning Systems]]

Systems:
* [[QuizGuide]]
* [[NavEx]]
* [[Database Exploratorium]]
* [[KnowledgeZoom]]
* [[MasteryGrids]]
* [[JavaGuide]]
* [[Progressor]]
* [[ProgressorPlus]]

== Adaptive Information Retrieval Systems ==
Systems:

* [[TaskSieve]]
* [[YourNews]]

== Recommender Systems ==

* [[Grapevine]]
* [[Proactive]]
* [[CourseAgent]]
* [[Cross-Domain Recommender Systems]]
* [[Social Recommender Systems]]

== Social Information Access Systems ==
Systems:
* [[ImageSieve]]
* [[NameSieve]]

== Personalized Social Systems for Local Communities ==
Systems:
* [[Conference Navigator 3]]
* [[Eventur]]
* [[CoMeT]]

= Currently Active Systems =

== ADAPT<sup>2</sup> Infrastructure==

{|
| valign="top" | [[Image:adapt2-arcitecture.gif|thumb|left|'''100'''|ADAPT<sup>2</sup> Architecture]]
| valign="top" | ADAPT<sup>2</sup> (read adapt-square) - Advanced Distributed Architecture for Personalized Teaching and Training - is a framework targeted at providing personalization and adaptation services for developers of content that lacks personalization. [[ADAPT2|more]])
|}

== CUMULATE ==
[[CUMULATE]] is a centralized user modeling server built for the [[ADAPT2|ADAPT<sup>2</sup>]] architecture. It is mainly targeted at providing user modeling support for adaptive educational hypermedia (AEH) systems. [[CUMULATE]] maintains a set of overlay models of students' knowledge. It uses several techniques for computing student models, including thresholded averaging, asymptotic user knowledge assessment, time-spent-reading.
([[CUMULATE|more]])

== Mastery Grids ==

{|
|valign="top" | [[Image:Mg_1.png|thumb|left|'''100'''|Mastery Grids Interface]]
|valign="top" | Mastery Grids is our latest implementation of Open Social Learner Modeling (OSLM). It 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]]
* [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]
|}

== Program Construction Examples ([[PCEX]])==
{|
| valign="top" | [[Image:Pcex_ex.PNG|thumb|left|'''100'''|Program Construction Examples]]
| valign="top" | PCEX is an interactive learning tool which demonstrates program construction examples to help students to develop program construction skills. It supports exploring the program construction examples freely and provide challenges to the students to help them self-assess their learning of program construction knowledge. It is now a component of [[ADAPT2]] Infrastructure.

* [[PCEX|More about PCEX]]

|}

==[[WEAT]]==
{|
| valign="top" | [[Image:weat.png|thumb|left|'''100'''|Worked Example Authoring Tool]]
| valign="top" | Worked Example Authoring Tool (WEAT) is an authoring tool for PCEX. The integrated ChatGPT support can be used to generate code explanations required for creating a program construction example. Created examples can be shared publicly with others, embed through iframes, or in an LMS like Canvas.

* [[WEAT|More about WEAT]]
* [[WEAT_Tutorial|WEAT's User Manual]]
* [https://youtu.be/IOfA0Ql3Zq0 WEAT Video Tutorial]

|}

== [[Grapevine]] ==
{|
|valign="top" | [[Image:Grapevine.png|thumb|left|'''100'''|Grapevine]]
|valign="top" | Grapevine is an interactive recommender system that assists students in finding advisors for their projects - from undergraduate capstone projects to PhD thesis work. It has been developed as a part of Personalized Education project sponsored by the University of Pittsburgh ([[Grapevine|more]])
|}

== QuizJET ==
{|
|valign="top" | [[Image:Quizjet.gif|thumb|left|'''100'''|QuizJET]]
|valign="top" | QuizJET is a system serves quizzes as a self-assessment Java Evaluation Tool. It's mainly used to assess students' knowledge in Java Programming Language. QuizJET randomly generates a question parameter, creates a presentation of the parameterized question in a Web-based quiz, compares student's input to the correct answer which QuizJET runs the parameterized code "behind the stage", and records the results into a server-side database. It is now a component of [[ADAPT2]] Infrastructure. ([[QuizJET|more]])
|}

== ReadingCircle ==
{|
| valign="top" | [[Image:Readingcircle1.png|left|thumb|200px|ReadingCircle interface.]]
| valign="top" | ReadingCircle is a system that explores approaches to encourage student reading using a social progress visualization interface. Click on the link to [[ReadingCircle]] to see more details.
|}

== [[HELPeR]] ==

{|
|valign="top" | [[Image:Helper.png|thumb|left|'''100'''|[[HELPeR]]]]
|valign="top" | Health e-Librarian with Personalized Recommender (HELPeR) is an interactive personalized search and recommender system designed to provide access to health information for cancer patients and their caregivers ([[HELPeR|-->more]])
|}

== [[WebEx]] ==
{|
| valign="top" | [[Image:AnnotatedExamples.jpg|left|thumb|200px|Screenshot of the WebEx interface.]]
| valign="top" | [[WebEx]] is a system that serves annotated code examples known also as dissections. Each dissection is a sequence of lines that have annotations associated with them. Dissections are grouped into collections - scopes. The natural domain of WebEx is programming. However, other applications are also possible, e.g. poetry. It is now a component of [[ADAPT2]] Infrastructure. It is one of the oldest PAWS systems, but WebEx is used in to provide access to examples in several domains. It is mostly superseded by [[PCX]] system which has more features.
|}

([[WebEx|more]])

= Earlier Systems =
== AdVisE (Adaptive Document Visualization for Education) ==
==== ADVISE 2D ====
Two dimensional document visualization based on inter-document similarities. The locations of the documents on the 2D space are determined by their similarities to another documents and users can visually see the relationships of the documents based on their contents.
==== ADVISE 3D ====
Three dimensional visualization of documents based on similarities. By adding one more dimension to 2D visualization, users are able to explore the document space more easily and access each document.

==== ADVISE VIBE ====

Relevance-based visualization of educational documents based on re-implementation of VIBE, a document visualization method based on similarities between documents and POIs (Points Of Interests) developed by Molde College and School of Information Sciences, University of Pittsburgh.

([http://ir.exp.sis.pitt.edu/advise more on ADVISE])

==Adaptive VIBE==

{|
| valign="top" | [[Image:AdaptiveVibe_part.png|thumb|left|'''100'''|Adaptive VIBE]]
| valign="top" | Two dimensional visualization based on POIs(Point Of Interest, or concepts) and document similarities. The position of the documents are calculated by their relationships with each POI.

* [http://amber.exp.sis.pitt.edu/~codex/tasksieve System Link] (Adaptive VIBE integrated into TaskSieve)
* [[Adaptive_VIBE | more on Adaptive VIBE]]
|}

== AnnotatEd ==
{|
| valign="top" | [[Image:ated.gif|thumb|left|'''100'''|AnnotatEd]]
| valign="top" | AnnotatEd is a system that enables learners to annotate online pages while keeping track of all activities of learners. AnnotatEd uses the learners' activity information to offer ''social navigation support'' for hyperlinks inside the AnnotatEd system. ([[AnnotatEd|more]])
|}

== AnnotEx ==
{|
| valign="top" | [[Image:AnnotEx.gif|thumb|left|'''100'''|AnnotEx]]
| valign="top" | AnnotEx - Example Annotator- is a web-based community based authoring tool for annotating programming examples.([[AnnotEx|more]])
|}

== CoMeT ==

{|
| valign="top" | [[Image:comet.gif|thumb|left|'''100'''|Comet]]
| valign="top" | COMET is a social system for sharing informaion about research talks. It allows to collaboratively collect, publish, and tag interesting research talks in Pittsburgh. COMET allows its users to schedule the talks they want to attend. It also automatically reminds about bookmarked talks and recommends other talks that fits isers' interests. ([http://halley.exp.sis.pitt.edu/comet/ visit COMET])
|}
== Conference Navigator 3==
{|
| valign="top" | [[Image:Cn3.jpg|thumb|left|'''100'''|CN3]]
| valign="top" | Conference Navigator 3 (CN3) is a personal conference scheduling tool with social linking and recommendation features. Users can control access to their information in the CN3 system and link their account with third party academic and non-academic social networks such as linkedIn, Facebook, citeulike, or Mendeley. Our main goal is to enhance attendees' experience at the conferences, and also investigate the mechanisms that drives attendees to engage in their research community. ([http://halley.exp.sis.pitt.edu/cn3/ visit Conference Navigator 3])
|}

== CoPE (Collaborative Paper Exchange) ==
{|
| valign="top" | [[Image:CoPE.1.overall.gif|thumb|left|'''100'''|CoPE]]
| valign="top" | CoPE - Collaborative Paper Exchange - is a system that provides community-based access to paper summaries via web. CoPE is currently an in-class tool for both teachers and students. ([[CoPE|more]])
|}

== CourseAgent ==
coming soon
([[CourseAgent|more]])

== Eventur ==
{|
| valign="top" | [[Image:Pittcult.gif|thumb|left|'''100'''|PittCult]]
| valign="top" | This project is to recommend interesting information using the combined technology of collaborative filtering and trust-based human network. This system is to overcome the emerging problems regarding collaborative filtering recommendations and to investigate how the information propagation is affected by trust among people. ([[Eventur|more]])
|}

== JavaGuide ==

{|
| valign="top" | [[Image:JavaGuide.png|thumb|left|'''100'''|JavaGuide]]
| valign="top" | JavaGuide is a personalized front-end for QuizJET developed by PAWS Lab (Hsiao, 2010). Java Guide collects student performance data sent by QuizJET to the activity storage, determines student current level of knowledge for multiple topics and concepts of Java programming language, and use it to provide adaptive guidance to the questions that are most appropriate for a specific student given the course goals and current state of knowledge.. ([[JavaGuide|more]])
|}

== Knowledge Sea II ==
{|
| valign="top" | [[Image:ks2.gif|thumb|left|'''100'''|Knowledge Sea II]]
| valign="top" | Knowledge Sea II is an extension of Knowledge Sea project that is designed to help users navigate from lectures to relevant online tutorials in a map-based horizontal navigation format. The most important feature of Knowledge Sea is facilitating the navigation through providing traffic and annotation based social navigation support. ([[Knowledge Sea II|more]])
|}

== Knowledge Tree ==
{|
| valign="top" | [[Image:KnowledgeTreeLogo.gif|thumb|left|'''100'''|Knowledge Tree]]
| valign="top" | Knowledge Tree is a link aggregating portal. It presents content structured according to the folder-document paradigm. Knowledge Tree provides authentication and authorization and implements a simplified form of access control. It supports collaborative authoring and social annotation. ([[Knowledge Tree|more]])
|}
== KnowledgeZoom ==
{|
| valign="top" | [[Image:KnowledgeZoom.png|thumb|left|'''100'''|KnowledgeZoom]]
| valign="top" | [[KnowledgeZoom]] is an exam preparation system with zoomable open student model showing student level of knowledge for hierarchy of Java programming concepts. KnowledgeZoom allows students to find gaps in their knowledge and access learning content that helps to bridge these gaps.

* [[KnowledgeZoom|More about KnowledgeZoom]]
|}

== MEMA ==
{|
|valign="top" | [[Image:MEMA.jpg|thumb|left|'''100'''|MEMA]]
|valign="top" | MEMA (Museum Exhibition MAnagement) ([[MEMA|more]])
* [http://halley.exp.sis.pitt.edu/mema/web/ Web System link]
* [http://halley.exp.sis.pitt.edu/mema Mobile System link]
|}

== NameSieve ==

{|
|valign="top" | [[Image:NameSieve-NEpanel.png|thumb|left|'''100'''|NameSieve Named-entity Navigator]]
|valign="top" | A name-entity based news exploration and filtering system. Important named-entities extracted from the search results are provided in the "cloud" form and helps further exploration. ([[NameSieve|more]])

* [http://amber.exp.sis.pitt.edu/namesieve System Link 1]
* [http://ir.exp.sis.pitt.edu/~jahn/cma/index.php System Link 2] (Carnegie Museum of Art version)
|}

== NavEx - Navigation to Examples ==
{|
| valign="top" | [[Image:NavEx.gif|thumb|left|'''100'''|NavEx]]
| valign="top" | NavEx provides adaptive guidance for accessing online interactive examples. Adaptation allows students to visualize both whether they are ready to explore certain examples and what is their progress with them. NavEx-SN (SN for social navigation) also allows students to relate their progress with the progress of the group. ([[NavEx|more]])
|}

== PERSEUS ==
{|
| valign="top" | [[Image:Perseus.gif|thumb|left|100px|PERSEUS]]
| valign="top" | [[PERSEUS]] is a Personalization Service Engine. It provides adaptive support for non-personalized (educational) hypermedia systems by abstracting content presentation/aggregation from user modeling. [[PERSEUS]] protocols are based on [http://en.wikipedia.org/wiki/Rdf RDF] and [http://en.wikipedia.org/wiki/RSS_(file_format)#RSS_1.0 RSS 1.0]. Although, [[PERSEUS]] was initially developed for [[ADAPT2|ADAPT<sup>2</sup>]] framework, its data model permits seamless support of any other hypermedia application. Currently [[PERSEUS]] provides social navigation, topic-based navigation, concept-based navigation, and adaptive filtering techniques. ([[PERSEUS|more]])
|}

== Proactive ==
{|
| valign="top" | [[Image:Proactive.gif|thumb|left|'''100'''|Proactive]]
| valign="top" | The Proactive is content-based job search and recommender system which is based on several knowledge engineering technology and personalized techniques. The system is adapts to each user by collecting various user's usage patterns. It integrates several approaches to provide access to job information ([[Proactive|more]])
|}

== Progressor ==
{|
| valign="top" | [[Image:Progressor.png|thumb|left|'''100'''|Progressor]]
| valign="top" | The Progressor is a system of personalized visual access to programming problems, which is based on open social user modeling technology and personalized techniques. ([[Progressor|more]])
|}

== Progressor+ ==
{|
| valign="top" | [[Image:progressorplus1.png|thumb|left|'''100'''|ProgressorPlus]]
| valign="top" | Progressor+ extends the benefits from Progressor and addresses the problems in personalized and social learning of how to help students to find the most appropriate educational resources and engage them into using these resources. Progressor+ adopts the same idea of open student modeling visualization and uses generic table representation for accessing and visualizing assorted educational content ([[ProgressorPlus|more]])
|}

== QuizGuide ==
{|
| valign="top" | [[Image:Quizguide.gif|thumb|left|'''100'''|QuizGuide]]
| valign="top" | QuizGuide, is an adaptive system that helps students in selecting most relevant quizzes for self-assessment of C knowledge. Quizzes are assigned to topics and adaptively annotated, to show which topics are currently important and which require further work. ([[QuizGuide|more]])
|}

== SetFusion ==
coming soon
([[SetFusion|more]])

== TalkExplorer ==
coming soon
([[TalkExplorer|more]])

== TaskSieve ==

{|
|valign="top" | [[Image:TaskSieve-surrogates.png|thumb|left|'''100'''|TaskSieve -- mediates query and user model]]
|valign="top" | An experimental personalized news search system based on task models and the interface to mediate between the query and the task model. Users can select three options (1) query only, (2) task model only, and (3) both. ([[TaskSieve|more]])
* [http://amber.exp.sis.pitt.edu/tasksieve System link 1]
* [http://amber.exp.sis.pitt.edu/~codex/tasksieve System link 2] (newer version integrated with Adaptive VIBE)
|}

== WADEIn (cWADEIn/jWADEIn) ==
coming soon
([[WADEIn|more]])

== YourNews ==

{|
|valign="top" | [[Image:YourNews-openUM.png|thumb|left|'''100'''|YourNews Open User Model UI]]
|valign="top" | YourNews is a news recommendation system based on the RSS feeds collected from various news sources. News articles are crawled every two hours, indexed, and then provided to users according to their specific needs. Users also can view and control their user profile with '''Open User Profile''' ([[YourNews|more]])

* [http://amber.exp.sis.pitt.edu/yournews System Link]
|}

Grapevine

2024-04-22T01:57:19Z

Behnam:

Finding a suitable research advisor is a significant challenge for undergraduate students, especially for those who are new to the intricacies of academic research. Grapevine addresses this challenge by offering a tailored system that simplifies the process of matching students with research advisors. It's particularly beneficial for first-generation college students who may not have prior exposure to navigating academic environments. Grapevine's aim is to facilitate these students' entry into the research domain, providing a supportive tool to identify advisors whose interests align with their own.

<div style="width: 90%; align: center">
[[Image:Grapevine systep.jpeg|class=img-responsive|Grapevine System]]
</div>

The interface of Grapevine, illustrated in Figure 1, is user-centric and intuitive. It features a search box, a list of suggested keywords, sliders for adjusting research interests, and a display of potential advisors. This design enables students to actively engage in refining their research preferences, helping them to clarify and articulate their interests more effectively. Detailed advisor profiles, as seen in Figure 2, offer in-depth information about each advisor’s research areas, assisting students in making informed decisions. The system's underlying knowledge graph, shown in Figure 3, links advisors, research topics, and keywords to generate personalized recommendations, adapting to each student's evolving research interests.

<div style="width: 50%; align: center">
[[Image:Helper advisor profile.jpeg|class=img-responsive|Grapevine System]]
</div>

In essence, Grapevine serves as a practical and accessible platform for undergraduate students embarking on their research journeys. It's designed to demystify the process of finding a research advisor, making it more approachable and aligned with individual student needs. By leveraging technology to connect students with compatible advisors, Grapevine hopes to contribute to creating a supportive environment for the next generation of researchers.

<div style="width: 50%; align: center">
[[Image:Grapevine graph.jpeg|class=img-responsive|Grapevine System]]
</div>

== Publications ==

* Rahdari, B., Brusilovsky, P., and Babichenko, D. (2020) Personalizing Information Exploration with an Open User Model. In: Proceedings of 31st ACM Conference on Hypertext and Social Media, July 13-15, 2020, ACM, pp. 167-176 ([https://doi.org/10.1145%2F3372923.3404797 paper])
* Rahdari, B., Brusilovsky, P., Babichenko, D., Littleton, E. B., Patel, R., Fawsett, J., and Blum, Z. (2020) Grapevine: A profile‐based exploratory search and recommendation system for finding research advisors. In: Proceedings of 83rd Annual Meeting of the Association for Information Science & Technology, October 25-29, 2020 ([https://doi.org/10.1002/pra2.271 paper])
* Rahdari, B., Brusilovsky, P., and Sabet, A. J. (2021) Controlling Personalized Recommendations in Two Dimensions with a Carousel-Based Interface. In: Proceedings of Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS’21) at 2021 ACM Conference on Recommender Systems (RecSys’21), September 25, 2021, CEUR, pp. 112-122. ([https://ceur-ws.org/Vol-2948/short3.pdf paper])
* Rahdari, B., Brusilovsky, P., and Sabet, A. J. (2021) Connecting Students with Research Advisors Through User-Controlled Recommendation. In: Proceedings of Fifteenth ACM Conference on Recommender Systems, Amsterdam, Netherlands, 27 September 2021 - 1 October 2021, ACM, pp. 745-748. ([https://doi.org/10.1145%2F3460231.3478879 paper])

File:Grapevine graph.jpeg

2024-04-22T01:54:15Z

Behnam: The system's underlying knowledge graph, shown in Figure 3, links advisors, research topics, and keywords to generate personalized recommendations, adapting to each student's evolving research interests.

== Summary ==
The system's underlying knowledge graph, shown in Figure 3, links advisors, research topics, and keywords to generate personalized recommendations, adapting to each student's evolving research interests.

File:Helper advisor profile.jpeg

2024-04-22T01:53:43Z

Behnam: Detailed advisor profiles, as seen in Figure 2, offer in-depth information about each advisor’s research areas, assisting students in making informed decisions.

== Summary ==
Detailed advisor profiles, as seen in Figure 2, offer in-depth information about each advisor’s research areas, assisting students in making informed decisions.

File:Grapevine systep.jpeg

2024-04-22T01:53:03Z

Behnam:

File:Grapevine systep.jpeg

2024-04-22T01:46:09Z

Behnam: The interface of Grapevine, illustrated in Figure 1, is user-centric and intuitive. It features a search box, a list of suggested keywords, sliders for adjusting research interests, and a display of potential advisors. This design enables students to a...

== Summary ==
The interface of Grapevine, illustrated in Figure 1, is user-centric and intuitive. It features a search box, a list of suggested keywords, sliders for adjusting research interests, and a display of potential advisors. This design enables students to actively engage in refining their research preferences, helping them to clarify and articulate their interests more effectively. Detailed advisor profiles, as seen in Figure 2, offer in-depth information about each advisor’s research areas, assisting students in making informed decisions. The system's underlying knowledge graph, shown in Figure 3, links advisors, research topics, and keywords to generate personalized recommendations, adapting to each student's evolving research interests.

Grapevine

2024-04-22T01:44:33Z

Behnam:

Finding a suitable research advisor is a significant challenge for undergraduate students, especially for those who are new to the intricacies of academic research. Grapevine addresses this challenge by offering a tailored system that simplifies the process of matching students with research advisors. It's particularly beneficial for first-generation college students who may not have prior exposure to navigating academic environments. Grapevine's aim is to facilitate these students' entry into the research domain, providing a supportive tool to identify advisors whose interests align with their own.

[add figure 1 here]

The interface of Grapevine, illustrated in Figure 1, is user-centric and intuitive. It features a search box, a list of suggested keywords, sliders for adjusting research interests, and a display of potential advisors. This design enables students to actively engage in refining their research preferences, helping them to clarify and articulate their interests more effectively. Detailed advisor profiles, as seen in Figure 2, offer in-depth information about each advisor’s research areas, assisting students in making informed decisions. The system's underlying knowledge graph, shown in Figure 3, links advisors, research topics, and keywords to generate personalized recommendations, adapting to each student's evolving research interests.

[add figure 2 here]

In essence, Grapevine serves as a practical and accessible platform for undergraduate students embarking on their research journeys. It's designed to demystify the process of finding a research advisor, making it more approachable and aligned with individual student needs. By leveraging technology to connect students with compatible advisors, Grapevine hopes to contribute to creating a supportive environment for the next generation of researchers.

== Publications ==

* Rahdari, B., Brusilovsky, P., and Babichenko, D. (2020) Personalizing Information Exploration with an Open User Model. In: Proceedings of 31st ACM Conference on Hypertext and Social Media, July 13-15, 2020, ACM, pp. 167-176 ([https://doi.org/10.1145%2F3372923.3404797 paper])
* Rahdari, B., Brusilovsky, P., Babichenko, D., Littleton, E. B., Patel, R., Fawsett, J., and Blum, Z. (2020) Grapevine: A profile‐based exploratory search and recommendation system for finding research advisors. In: Proceedings of 83rd Annual Meeting of the Association for Information Science & Technology, October 25-29, 2020 ([https://doi.org/10.1002/pra2.271 paper])
* Rahdari, B., Brusilovsky, P., and Sabet, A. J. (2021) Controlling Personalized Recommendations in Two Dimensions with a Carousel-Based Interface. In: Proceedings of Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS’21) at 2021 ACM Conference on Recommender Systems (RecSys’21), September 25, 2021, CEUR, pp. 112-122. ([https://ceur-ws.org/Vol-2948/short3.pdf paper])
* Rahdari, B., Brusilovsky, P., and Sabet, A. J. (2021) Connecting Students with Research Advisors Through User-Controlled Recommendation. In: Proceedings of Fifteenth ACM Conference on Recommender Systems, Amsterdam, Netherlands, 27 September 2021 - 1 October 2021, ACM, pp. 745-748. ([https://doi.org/10.1145%2F3460231.3478879 paper])

User:Behnam

2024-04-22T01:43:03Z

Behnam: My passion lies at the overlap of Human-Computer Interaction (HCI) and Artificial Intelligence (AI) and the challenge of integrating the two fields. Currently, I'm pursuing my Ph.D. at the University of Pittsburgh's School of Computing and Information, un

My passion lies at the overlap of Human-Computer Interaction (HCI) and Artificial Intelligence (AI) and the challenge of integrating the two fields. Currently, I'm pursuing my Ph.D. at the University of Pittsburgh's School of Computing and Information, under the guidance of Dr. Peter Brusilovsky, head of the PAWS Lab.

My research focuses on building and evaluating search and recommender systems that are user-centric and intelligent. I design solutions that integrate AI algorithms with user-friendly interfaces, effectively bridging the gap between high-level AI technologies and their practical applications. This approach underlines my commitment to making advanced technologies accessible and beneficial for everyone in their everyday contexts.

Having been fortunate to receive support from esteemed institutions such as National Institutes of Health (NIH), National Science Foundation (NSF), National Library of Medicine (NLM), and Amazon Research Awards (ARA) among others, my work is a testament to the potential of combining UX principles with the robustness of AI. These projects range from design and implementation of multiple online systems for healthcare (addressing IBD, ovarian cancer, and aphasia) and education (involving student advisement, e-readers, and the exploration of academic and medical documents), as well as more theoretical research on probabilistic models and simulation-based evaluation of interactive recommender systems.

The fusion of AI and HCI offers a promising horizon, and I am enthusiastic about the advancements we can achieve in this domain. With every research project and publication, I am committed to pushing this frontier, ensuring technology is more aligned with its users.