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![Try It!
• GitHub link Twitted
• Brusilovsky, P., Sosnovsky, S., and Yudelson, M. (2009)
Addictive links: The motivational value of adaptive link annotation.
New Review of Hypermedia and Multimedia 15 (1), 97-118.
• Hsiao, I.-H., Sosnovsky, S., and Brusilovsky, P. (2010) Guiding
students to the right questions: adaptive navigation support in an E-
Learning system for Java programming. Journal of Computer Assisted
Learning 26 (4), 270-283.
• Hsiao, I.-H., Bakalov, F., Brusilovsky, P., and König-Ries, B.
(2013) Progressor: social navigation support through open social
student modeling. New Review of Hypermedia and Multimedia [PDF]
Read About It!](https://image.slidesharecdn.com/ectel2014mg-150527224554-lva1-app6891/75/Mastery-Grids-An-Open-Source-Social-Educational-Progress-Visualization-41-2048.jpg)
Uploaded byPeter Brusilovsky
Mastery Grids: An Open Source Social Educational Progress Visualization
The document discusses Mastery Grids, an open-source visualization tool aimed at enhancing educational progress through adaptive navigation, topic-based adaptation, and social student modeling. It presents the project's past achievements, current developments, and future plans, including user engagement metrics and feedback on usability. The study highlights improved student performance and motivation through interaction with the visualization tool, alongside a call for collaboration in refining and expanding its application in educational environments.
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Mastery Grids: An Open Source Social Educational Progress Visualization
- 1. Mastery Grids: AnOpen Source Social Educational Progress Visualization Tomasz D. Loboda, Julio Guerra, Roya Hosseini, Peter Brusilovsky PAWS Lab, University of Pittsburgh
- 2. Overview • The past –Why we are doing it? • The paper – Mastery Grids and its evaluation • Today’s state – What we have done since submitting the paper? • The future – What are the plans and invitation to collaborate
- 3. The Past • Why? –Increaseuser performance –Increase motivation and retention • How? –Adaptive Navigation Support –Topic-based Adaptation –Open Social Student Modeling
- 4. Adaptive Link Annotation:InterBook 1. Concept role 2. Current concept state 3. Current section state 4. Linked sections state 4 3 2 1 √
- 5. Questions of the current quiz,served by QuizPACK List of annotated links to all quizzes available for a student in the current course Refresh and help icons QuizGuide = Topic-Based ANS
- 6. Topic-Based Adaptation Concept A Concept B Concept C Eachtopic is associated with a number of educational activities to learn about this topic Each activity classified under 1 topic
- 7. QuizGuide: Adaptive Annotations •Target-arrow abstraction: – Number of arrows – level of knowledge for the specific topic (from 0 to 3). Individual, event-based adaptation. – Color Intensity – learning goal (current, prerequisite for current, not-relevant, not-ready). Group, time- based adaptation. Topic–quiz organization:
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- 9. QuizGuide: Motivation Average activity 0 50 100 150 200 250 300 20022003 2004 Average num. of sessions 0 5 10 15 20 2002 2003 2004 Average course coverage 0% 10% 20% 30% 40% 50% 60% 2002 2003 2004 Within the same class QuizGuide session were much longer than QuizPACK sessions: 24 vs. 14 question attempts at average. Average Knowledge Gain for the class rose from 5.1 to 6.5
- 10. • Topic-Based interfaceorganization is familiar, matches the course organization, and provides a compromise between too-much and too-little • Two-way adaptive navigation support guides to the right topic • Open student model provides clear overview of the progress Topic-Based ANS: Success Recipes
- 11. Concept-based student modeling Example2 Example M Example 1 Problem 1 Problem 2 Problem K Concept 1 Concept 2 Concept 3 Concept 4 Concept 5 Concept N Examples Problems Concepts
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- 13. • To investigatepossible influence of concept-based adaptation in the present of topic-based adaptation we developed two versions of QuizGuide: Topic-based Topic-based+Concept-Based Concept-based vs Topic-based ANS
- 14. Social Guidance • Concept-basedand topic-based navigation support work well to increase success and motivation • Knowledge-based approaches require some knowledge engineering – concept/topic models, prerequisites, time schedule • In our past work we learned that social navigation – “wisdom” extracted from the work of a community of learners – might replace knowledge-based guidance • Social wisdom vs. knowledge engineering
- 15. Open Social StudentModeling • Key ideas – Assume simple topic-based design – Show topic- and content- level knowledge progress of a student in contrast to the same progress of the class • Main challenge – How to design the interface to show student and class progress over topics? – We went through several attempts…
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- 19. • Topic organizationshould follow the natural progress or topics in the course • Clear comparison between “me” and “group” • Ability to compare with individual peers, not only the group • Privacy management OSLM: Success Recipes
- 20. The Value ofOSLM 205.73 113.05 80.81 125.5 0 50 100 150 200 250 Attempts Progressor QuizJET+IV QuizJET+Portal JavaGuide 68.39% 71.35% 42.63% 58.31% 0.00% 20.00% 40.00% 60.00% 80.00% Success Rate Progressor QuizJET+IV QuizJET+Portal JavaGuide
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- 22. MasteryGrids • Adaptive NavigationSupport • Topic-based Adaptation • Open Social Student Modeling • Social Educational Progress Visualization • Multiple Content Types • Open Source • Concept-Based Recommendation • Multiple Groups
- 28. The Study • Fall2013, 3 classes • Java Programming – 19 topics,75 examples and 94 QiuzJet problems • Databases – 19 topics, 64 examples, 46 SQL-Knot questions. • Offered as an addition to the traditional portal access (called “Links” in the paper) • Incentive: 5 points out of 100 to solve 15+ problems – either Links or MG
- 29. Accessing MG interfacefrom Links
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- 31. Usage Groups inJava Class • (Z) 4 students did not use either of the tools • (L1) 2 students used Links only and never used visualization • (L2) 3 students used Links for content access, loaded the visualization, did not use it • (L.MG1) 16 students used Links for content access, interactively explored MG, did not use it for content access • (L.MG2) 6 students used both Links and Mastery Grids for content access • (MG) 5 students used exclusively Mastery Grids for content access.
- 32. Group-Based Analysis • 5(L1+L2) had little to no use of Mastery Grids • 26 (L.MG1+L.MG2+MG) used it considerably • Students who used the visualization seemed to be more engaged with self-study content – answered more questions – tried more examples – inspected more example line comments – got a higher correct question answer ratio.
- 34. Subjective Responses • Highlevel of “good” responses • A number of “poor” responses to specific features which guided our work in 2014
- 35. More MG Activity= Better Grade? • How? – Pooled data from all three courses – Only students who logged in at least once – Fitted four linear mixed models • Separately considered content and interface – Only content access through Links or MG – All activity with MG
- 36. What we aredoing now? • Enhanced Interface after two semesters of learning and student feedback • Easy authoring to define “your course” • Exploring more advanced guidance and modeling approaches based on large volume of social data • Interface and cultural studies in a wide variety of classes from US to Nigeria – Interested to be a pilot site? Write to [email protected]
- 37. MG flexibility • Parametersto set the visualization: – show hide toolbar or any of its elements – set the (sub) groups: top N, other sub groups – preset values (for example load individual view by default) – enable/disable recommendation • Parameters can be specified by group or by user
- 39. Course Authoring Interface Alabel showing that you are the creator of the course domain Institution code Course code Course title Number of Groups using this course Creator name
- 40. Acknowledgements • Past workon ANS and OSLM – Sergey Sosnovsky – Michael Yudelson – Sharon Hsiao • Pitt “Innovation in Education” grant • NSF Grants – EHR 0310576 – IIS 0426021 – CAREER 0447083 • ADL “PAL” grant to build MasteryGrids
- 41. Try It! • GitHublink Twitted • Brusilovsky, P., Sosnovsky, S., and Yudelson, M. (2009) Addictive links: The motivational value of adaptive link annotation. New Review of Hypermedia and Multimedia 15 (1), 97-118. • Hsiao, I.-H., Sosnovsky, S., and Brusilovsky, P. (2010) Guiding students to the right questions: adaptive navigation support in an E- Learning system for Java programming. Journal of Computer Assisted Learning 26 (4), 270-283. • Hsiao, I.-H., Bakalov, F., Brusilovsky, P., and König-Ries, B. (2013) Progressor: social navigation support through open social student modeling. New Review of Hypermedia and Multimedia [PDF] Read About It!