Demos: Difference between revisions
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== ModuLearn == | == 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 | '''Authors:''' Quinn K. Wolter · '''Year:''' 2026 | ||
* [https://youtu.be/REPLACE_WITH_VIDEO_ID Watch demo] | * [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] | |||
|} | |||
= [[Reading Mirror|Intelligent Textbooks]] = | |||
== Recommend Wikipedia Articles to Understand Difficult Concepts == | |||
[[Image:Reading_mirror_original.png|thumb|left|250px|Reading Mirror is one of the several versions of Intelligent Textbooks developed at PAWSLab]] | |||
Our system applies the ideas of concept extraction from a digital textbook on topics in cognitive psychology and computer science for a graduate class in a large US-based university to generate search terms that can link with Wikipedia articles. Finally, we integrate these articles into the textbook reading interface, enabling students to quickly refer to Wikipedia articles in connection with the reading material of the course to understand a concept or topic that they struggle with or are interested in exploring further. With this demo, we present a system that can be utilized for data collection in a real-world classroom setup. Link to Paper Proceedings -- [https://educationaldatamining.org/edm2023/proceedings EDM23 Proceedings] | |||
'''Authors:''' [https://a2un.github.io/ Arun-Balajiee Lekshmi-Narayanan], [https://www.kthaker.com/ Khushboo Thaker], [https://eit.udp.cl/?persona=jordan-barria Jordan Barria-Pineda], [https://sites.pitt.edu/~peterb/ Peter Brusilovsky] | |||
'''Year:''' 2023 | |||
[https://www.youtube.com/watch?v=uY570Zyczpc Help me Read!] | |||
<div style="clear: both;"></div> | |||
<!-- TO DO | |||
== Example demo title == | == 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 | '''Authors:''' Author name(s) · '''Year:''' 2025 | ||
* [https://youtu.be/REPLACE_WITH_VIDEO_ID Watch demo] | * [https://youtu.be/REPLACE_WITH_VIDEO_ID Watch demo] | ||
* Related system: [[MasteryGrids]] (link to the associated system page if applicable) | * Related system: [[MasteryGrids]] (link to the associated system page if applicable) | ||
<div style="clear: both;"></div> | |||
== Another demo == | == 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 | '''Authors:''' Someone Else · '''Year:''' 2024 | ||
* [https://youtu.be/REPLACE_WITH_VIDEO_ID Watch demo] | * [https://youtu.be/REPLACE_WITH_VIDEO_ID Watch demo] | ||
<div style="clear: both;"></div> | |||
--> | |||
= Recommender Systems = | = Recommender Systems = | ||
== Placeholder demo == | == 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 | '''Authors:''' TBD · '''Year:''' TBD | ||
* [https://youtu.be/REPLACE_WITH_VIDEO_ID Watch demo] | * [https://youtu.be/REPLACE_WITH_VIDEO_ID Watch demo] | ||
<div style="clear: both;"></div> | |||
= Social Information Access = | = Social Information Access = | ||
| Line 49: | Line 104: | ||
''No demos in this group yet. Copy a block from another section to add one.'' | ''No demos in this group yet. Copy a block from another section to add one.'' | ||
= Adaptive Information Retrieval = | = Adaptive Information Retrieval = | ||
Latest revision as of 15:40, 4 August 2026
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
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
- Watch demo
- Visit system: ModuLearn
Program Construction Examples (PCEX)
 |
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.
|
WEAT
 |
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. |
Course Authoring
 |
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. |
Intelligent Textbooks
Recommend Wikipedia Articles to Understand Difficult Concepts
Our system applies the ideas of concept extraction from a digital textbook on topics in cognitive psychology and computer science for a graduate class in a large US-based university to generate search terms that can link with Wikipedia articles. Finally, we integrate these articles into the textbook reading interface, enabling students to quickly refer to Wikipedia articles in connection with the reading material of the course to understand a concept or topic that they struggle with or are interested in exploring further. With this demo, we present a system that can be utilized for data collection in a real-world classroom setup. Link to Paper Proceedings -- EDM23 Proceedings
Authors: Arun-Balajiee Lekshmi-Narayanan, Khushboo Thaker, Jordan Barria-Pineda, Peter Brusilovsky
Year: 2023
Recommender Systems
Placeholder demo
Placeholder description. Replace with real content when a demo is added.
Authors: TBD · Year: TBD
Social Information Access
No demos in this group yet. Copy a block from another section to add one.
Adaptive Information Retrieval
No demos in this group yet.