PAWS Lab - User contributions [en]

Demos

2026-07-31T16:47:45Z

Moh70: fixed borken demo link

Video demos of PAWS Lab systems and research projects, organized by group. Slide-based demos should be added to the associated [[Systems]] page rather than here.

To add a new demo, copy an existing block below and fill in the details. Each demo needs: a video link (YouTube, Vimeo, or hosted file), a short description, author(s), and optionally a thumbnail image.

= Adaptive Learning =

== ModuLearn ==
[[Image:ModuLearn01.png|thumb|left|250px|ModuLearn Prototype.]]
ModuLearn is an open-source eLearning platform and smart-learning content dashboard for organizing, delivering, and studying interactive educational modules across an open learning ecosystem. Developed alongside the broader SPLICE infrastructure, ModuLearn supports reusable course structures, configurable course sessions, smart-content launch flows, role-specific student and instructor workspaces, and research-oriented study workflows. Its roadmap emphasizes flexible integration with multiple Learning Management Systems through standards-based and platform-specific connectors, while preserving a modular architecture for analytics, adaptive sequencing, content replacement, and experimental learning interventions.

'''Authors:''' Quinn K. Wolter · '''Year:''' 2026

* [https://youtu.be/REPLACE_WITH_VIDEO_ID Watch demo]
* Visit system: [https://proxy.personalized-learning.org/modulearn/ ModuLearn]

<div style="clear: both;"></div>

== 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]]
* A [https://www.youtube.com/watch?v=gv46knva1Lo demo of PCEX for Python], as a part of PCLab, a [[MasteryGrids]] setup to practice construction knowledge for Python
* A [https://www.youtube.com/watch?v=EGTkrTJ7YaM demo of PCEX for Java], as a part of PCLab, a [[MasteryGrids]] setup to practice construction knowledge for Java
|}

<div style="clear: both;"></div>

==[[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]
|}

<div style="clear: both;"></div>

==[[Course Authoring]]==
{|
| valign="top" | [[Image:CourseAuthoring.png|thumb|left|'''100'''|Course Authoring Tool]]
| valign="top" | The Course Authoring Tool is designed to simplify the process of course creation for instructors. This tool allows instructors to efficiently organize and bundle smart content from multiple providers into structured units or modules, which can be seamlessly integrated into LMS such as Canvas. Additional features include the ability to explore and clone publicly shared course structures, create new courses from scratch, and facilitate sharing and reuse of course materials.

* [[CourseAuthoring|More about Course Authoring Tool]]
* [https://youtu.be/9ozfFszmZGk Course Authoring Video Tutorial]
|}

== Example demo title ==
[[Image:PAWS_logo.png|thumb|left|250px|Replace with a screenshot from the video]]
Short description of what the demo shows. 2-3 sentences works well. Include the context (course used in, target audience, or research question being explored).

'''Authors:''' Author name(s) · '''Year:''' 2025

* [https://youtu.be/REPLACE_WITH_VIDEO_ID Watch demo]
* Related system: [[MasteryGrids]] (link to the associated system page if applicable)

<div style="clear: both;"></div>

== Another demo ==
[[Image:PAWS_logo.png|thumb|left|250px|Replace with a screenshot from the video]]
Another placeholder description. Replace with real content.

'''Authors:''' Someone Else · '''Year:''' 2024

* [https://youtu.be/REPLACE_WITH_VIDEO_ID Watch demo]

<div style="clear: both;"></div>

= Recommender Systems =

== Placeholder demo ==
[[Image:PAWS_logo.png|thumb|left|250px|Placeholder thumbnail]]
Placeholder description. Replace with real content when a demo is added.

'''Authors:''' TBD · '''Year:''' TBD

* [https://youtu.be/REPLACE_WITH_VIDEO_ID Watch demo]

<div style="clear: both;"></div>

= Social Information Access =

''No demos in this group yet. Copy a block from another section to add one.''

= Intelligent Textbooks =

''No demos in this group yet.''

= Adaptive Information Retrieval =

''No demos in this group yet.''

[[Category:Demos]]

Systems

2026-07-31T16:47:07Z

Moh70: fixed broken tutorial link

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]]
* [[Mastery_Grids_Interface]]
* [[JavaGuide]]
* [[Progressor]]
* [[ProgressorPlus]]

== Smart Learning Content for Computing Education ==

To support our research on personalized learning, we developed several types of [[Smart Content]] for Computing Education. Smart content engages students in various kinds of interaction - exploration, simulation, problem solving - and use a rich trace of learning data generated by students interacting with this content to better model learner's knowledge. For some types of smart content activities we have authoring and delivery systems, other types are just collection of items, which you could use but can't edit. An overview of our smart content types can found on the [[Smart Content]] page. Systems supporting delivery and authoring of smart content are listed below.

* [[Smart Content]] types supported by [[ADAPT2]] infrastructure and [[MasteryGrids]] interface
* [[PCEX]] - advanced code worked examples that could be created with [[WEAT]]
* [[QuizJET]] - code tracing problems for Java that could be created with [[QuizJET Authoring System]]
* [[QuizPET]] - code tracing problems for Python that could be created with [[QuizPET Authoring System]]
* [[WebEx]] - simple code worked examples that could be created with [[AnnotEx]]

== Adaptive Information Retrieval Systems ==
Systems:

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

== Recommender Systems ==

* [[Grapevine]]
* [[ArtEx]]
* [[Grapevine2]]
* [[HELPeR]]
* [[Proactive]]
* [[CourseAgent]]
* [[Conference Navigator 3]]
* [[Eventur]]
* [[CoMeT]]
* [[Cross-Domain Recommender Systems]]
* [[Social Recommender Systems]]

== Social Information Access Systems ==
Systems:
* [[ImageSieve]]
* [[Knowledge Sea II]]
* [[NameSieve]]

== Intelligent Textbooks ==

* [[PDF Documents]]
* [[Reading Mirror]]
* [[Reading Circle]]

= 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, and time-spent-reading.
([[CUMULATE|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.
* More about [[Knowledge Tree]]
|}

== 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]]
* A [https://www.youtube.com/watch?v=gv46knva1Lo demo of PCEX for Python], as a part of PCLab, a [[MasteryGrids]] setup to practice construction knowledge for Python
* A [https://www.youtube.com/watch?v=EGTkrTJ7YaM demo of PCEX for Java], as a part of PCLab, a [[MasteryGrids]] setup to practice construction knowledge for Java

|}

==[[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]

|}

==[[Course Authoring]]==
{|
| valign="top" | [[Image:CourseAuthoring.png|thumb|left|'''100'''|Course Authoring Tool]]
| valign="top" | The Course Authoring Tool is designed to simplify the process of course creation for instructors. This tool allows instructors to efficiently organize and bundle smart content from multiple providers into structured units or modules, which can be seamlessly integrated into LMS such as Canvas. Additional features include the ability to explore and clone publicly shared course structures, create new courses from scratch, and facilitate sharing and reuse of course materials.

* [[CourseAuthoring|More about Course Authoring Tool]]
* [https://youtu.be/9ozfFszmZGk Course Authoring 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
* More about ([[Grapevine]])
|}

== QuizJET ==
{|
|valign="top" | [[Image:Quizjet.gif|thumb|left|'''100'''|QuizJET]]
|valign="top" | QuizJet is an educational system that delivers and automatically assesses parameterized quiz-style code-tracing problems for Java. It's mainly used to practice code-tracing knowledge (program semantics) of Java programming language, but it can also be used for assessment needs. QuizJET randomly generates a question parameter, creates a presentation of the parameterized question as a Web-based quiz question, compares the student's input to the correct answer, which QuizJET produces by executing the parameterized code "behind the stage", and records the results into a server-side database. It is now a component of [[ADAPT2]] Infrastructure.
* More about [[QuizJET]]
* A silent [https://www.youtube.com/watch?v=dWRdAm7mUbk demo] of QuizJET, QuizPET and its authoring tools
|}

== QuizPET ==
{|
|valign="top" | [[Image:QuizPET.jpg|thumb|left|'''80'''|QuizPET]]
|valign="top" | QuizPET delivers and automatically assesses parameterized quiz-style code-tracing problems for Python. It is using the same approach to generate problems and assess student answers as [[QuizJET]]. It is now a component of [[ADAPT2]] Infrastructure.
* More about [[QuizPET]]
* A silent [https://www.youtube.com/watch?v=dWRdAm7mUbk demo] of QuizJET, QuizPET and its authoring tools
|}

== 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.
|}

== [[CN3|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. [[CN3|(more)]]
|}

== 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]])
|}

== 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]
|}

Demos

2026-07-31T16:45:14Z

Moh70: added pcex, weat, cat

Video demos of PAWS Lab systems and research projects, organized by group. Slide-based demos should be added to the associated [[Systems]] page rather than here.

To add a new demo, copy an existing block below and fill in the details. Each demo needs: a video link (YouTube, Vimeo, or hosted file), a short description, author(s), and optionally a thumbnail image.

= Adaptive Learning =

== ModuLearn ==
[[Image:ModuLearn01.png|thumb|left|250px|ModuLearn Prototype.]]
ModuLearn is an open-source eLearning platform and smart-learning content dashboard for organizing, delivering, and studying interactive educational modules across an open learning ecosystem. Developed alongside the broader SPLICE infrastructure, ModuLearn supports reusable course structures, configurable course sessions, smart-content launch flows, role-specific student and instructor workspaces, and research-oriented study workflows. Its roadmap emphasizes flexible integration with multiple Learning Management Systems through standards-based and platform-specific connectors, while preserving a modular architecture for analytics, adaptive sequencing, content replacement, and experimental learning interventions.

'''Authors:''' Quinn K. Wolter · '''Year:''' 2026

* [https://youtu.be/REPLACE_WITH_VIDEO_ID Watch demo]
* Visit system: [https://proxy.personalized-learning.org/modulearn/ ModuLearn]

<div style="clear: both;"></div>

== 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]]
* A [https://www.youtube.com/watch?v=gv46knva1Lo demo of PCEX for Python], as a part of PCLab, a [[MasteryGrids]] setup to practice construction knowledge for Python
* A [https://www.youtube.com/watch?v=EGTkrTJ7YaM demo of PCEX for Java], as a part of PCLab, a [[MasteryGrids]] setup to practice construction knowledge for Java
|}

<div style="clear: both;"></div>

==[[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]
|}

<div style="clear: both;"></div>

==[[Course Authoring]]==
{|
| valign="top" | [[Image:CourseAuthoring.png|thumb|left|'''100'''|Course Authoring Tool]]
| valign="top" | The Course Authoring Tool is designed to simplify the process of course creation for instructors. This tool allows instructors to efficiently organize and bundle smart content from multiple providers into structured units or modules, which can be seamlessly integrated into LMS such as Canvas. Additional features include the ability to explore and clone publicly shared course structures, create new courses from scratch, and facilitate sharing and reuse of course materials.

* [[CourseAuthoring|More about Course Authoring Tool]]
* [https://youtu.be/2Nm6yhlTu10 Course Authoring Video Tutorial]
|}

== Example demo title ==
[[Image:PAWS_logo.png|thumb|left|250px|Replace with a screenshot from the video]]
Short description of what the demo shows. 2-3 sentences works well. Include the context (course used in, target audience, or research question being explored).

'''Authors:''' Author name(s) · '''Year:''' 2025

* [https://youtu.be/REPLACE_WITH_VIDEO_ID Watch demo]
* Related system: [[MasteryGrids]] (link to the associated system page if applicable)

<div style="clear: both;"></div>

== Another demo ==
[[Image:PAWS_logo.png|thumb|left|250px|Replace with a screenshot from the video]]
Another placeholder description. Replace with real content.

'''Authors:''' Someone Else · '''Year:''' 2024

* [https://youtu.be/REPLACE_WITH_VIDEO_ID Watch demo]

<div style="clear: both;"></div>

= Recommender Systems =

== Placeholder demo ==
[[Image:PAWS_logo.png|thumb|left|250px|Placeholder thumbnail]]
Placeholder description. Replace with real content when a demo is added.

'''Authors:''' TBD · '''Year:''' TBD

* [https://youtu.be/REPLACE_WITH_VIDEO_ID Watch demo]

<div style="clear: both;"></div>

= Social Information Access =

''No demos in this group yet. Copy a block from another section to add one.''

= Intelligent Textbooks =

''No demos in this group yet.''

= Adaptive Information Retrieval =

''No demos in this group yet.''

[[Category:Demos]]

Publications

2026-07-28T02:07:14Z

Moh70: added a section for WEAT

===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}}

I need the URLs — they aren't in the BibTeX you provided (only titles/venues). Let me search for the two 2026 papers to find their links.I found the SIGCSE 2026 link. Let me find the AIED 2026 case study paper.Found SIGCSE 2026 and SAC 2024 DOIs. Let me find the AIED 2026 case study link.I have links for four of the five. The AIED 2026 case study doesn't surface a public DOI yet. Let me check Brusilovsky's publications page, which should have it.Links added. I found DOIs for the SIGCSE 2026, SAC 2024, IUI, and EDM papers. The AIED 2026 case study doesn't have a public DOI or PDF indexed yet (the Springer LNAI proceedings are recent), so I've flagged it.

===WEAT===

* Hassany, M., Brusilovsky, P., Barria-Pineda, J., Alpizar-Chacon, I. (2026). '''Translating Smart Content for Learning Python through Human-AI Collaboration.''' SIGCSE 2026 (available [https://doi.org/10.1145/3770761.3777318 here]).
* Hassany, M., Ke, J., Brusilovsky, P., Lekshmi Narayanan, A.B., Akhuseyinoglu, K. (2024). '''Authoring Worked Examples for JAVA Programming with Human-AI Collaboration.''' SAC 2024 (available [https://doi.org/10.1145/3605098.3636160 here]).
* Hassany, M., Brusilovsky, P., Ke, J., Akhuseyinoglu, K., Lekshmi Narayanan, A.B. (2024). '''Human-AI Co-Creation of Worked Examples for Programming Classes.''' IUI Workshops 2024 (available [https://ceur-ws.org/Vol-3660/paper16.pdf here]).
* Hassany, M., Brusilovsky, P., Ke, J., Akhuseyinoglu, K., Lekshmi-Narayanan, A.B. (2024). '''Engaging an LLM to Explain Worked Examples for Java Programming: Prompt Engineering and a Feasibility Study.''' HEXED/L3MNGET Workshop at EDM 2024 (available [https://ceur-ws.org/Vol-3840/L3MNGET24_paper1.pdf here]).

===[[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