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Prospect for learning analytics to achieve adaptive learning model

Slides for conference program at e-Learning Korea 2016. Also this slides contain ISO/IEC TR 20748-1 Learning Analytics Interoperability - Part 1: Reference model as well as curriculum standards. Mainly this slides was prepared for LASI-Asia 2016 #lasiasia16.

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Prospect for learning analytics to achieve adaptive learning model

  1. 1. Prospect for learning analytics to achieve adaptive learning model Research Fellow, KERIS Yong-Sang CHO, Ph.D zzosang@keris.or.kr FB: /zzosang Twitter: @zzosang e-Learning Conference September 21, 2016
  2. 2. Table of Contents • What is an Adaptive Learning • Reference Model Design for Implementation of Adaptive Learning • One step further: Exploring Data Flow and Exchange • One step further: Exploring Curriculum Standards • Conclusion and Recommendations
  3. 3. “Anyone who has ever been in a classroom – where as a student or instructor – knows that not all students procced at the same pace.” <Tyton Partners>
  4. 4. What is an Adaptive Learning?
  5. 5. Adaptive learning is “a more personalized, technology-enabled, and data-driven approach to learning that has the potential to deepen student engagement with learning materials, customize students’ pathways through curriculum, and permit instructors to use class time in more focused and productive ways." <Tyton Partners – Learning to Adapt> <Source: http://tytonpartners.com/tyton-wp/wp-content/uploads/2015/01/Learning-to-Adapt_Case-for-Accelerating-AL-in-Higher-Ed.pdf>
  6. 6. Quality Cost Access Also adaptive learning may be a key (or hammer?) to break “Iron Triangle”
  7. 7. Some students response … “The Impact of Technology on College Student Study Habits: 2,600 college students surveyed, 87% report that having access to data analytics concerning their academic performance has a positive impact on their learning. Adaptive learning technology is reported by 75% of students to be very helpful or extremely helpful in aiding their ability to retain new concepts, and 68% of students report that it is most helpful at making them better aware of new concepts." <McGraw-Hill Education and Hanover Research>
  8. 8. Reference Model Design for Implementation of Adaptive Learning
  9. 9. Two levels to adaptive learning technologies: • the first platform reacts to individual user data and adapts instructional material accordingly, • while the second leverages aggregated data across a large sample of users for insights into the design and adaptation of curricula. Source: Horizon Report 2015 – Higher Education Edition http://www.nmc.org/publication/nmc-horizon-report-2015-higher-education-edition/
  10. 10. Resources AnalyticsCurricula Point of Adaptive Learning
  11. 11. Conceptual relationship among curricula, resources and LA Source: Prospects for the application of learning analytics – Use cases and Service Model, Yong-SangCho, Journal of Information and Communication, 2014
  12. 12. Abstract layer of reference model for LA Input data items for learning analytics Data Collection Data Processing & Storing Visualization Analyzing Privacy Policy • lecture • material • learning tool • quiz/assessment • discussion forum • message • social network • homework • prior credit • achievement • system log …… personalization, intervention and prediction, etc Outcomes from learning analytics Dataprocessingandanalysis secured data exchange Learning & Teaching Activity • Reading • Lectures • Quiz • Projects • Homework • Media • Tutoring • Research • Assessment • Collaboration • Annotation • Gaming • Social Messaging • Scheduling • Discussion …… Feedback & Recommendation Source: ISO/IEC TR 20748-1:2016 Learning Analytics Interoperability – Part 1: Reference model
  13. 13. Analytics Data Store (Micro Data) Analytics Data Store (Analyzed Data) DataManipulation Data Analysis Analysis Interface Analysis Algorithm Analysis Processing Output Generation Statistic Analysis Topic Analysis Network Analysis Pattern Analysis Dynamic Modeling Association Analysis Constant Information (Curricula, Learning Resources, Preferences) DataControl Dashboard Integration Content Recommendation Learning Path Recommendation (Curriculum Support) Social Analysis Source: ISO/IEC TR 20748-1:2016 Learning Analytics Interoperability – Part 1: Reference model Zoom-in diagram for data analysis
  14. 14. One step further: Exploring Data Flow and Exchange - Get the data! -
  15. 15. xAPI Transcript/learning data can be delivered to LMSs, LRSs or reporting tools Experience data LMS: Learning Management System LRS: Learning Record Store
  16. 16. IMS Caliper Source: New Architect for Learning (Rob Abel, 2014) http://www.slideshare.net/JEPAslide/day3-edupub-tokyoims?qid=76ce5d4a-1ccf-468f-a428-c652584c395a&v=default&b=&from_search=4
  17. 17. One step further: Exploring Curriculum Standards - Get the topic and competency! -
  18. 18. Goal of achievement School level Second criteria of science subject (second level) Curriculum standard per school grade Achievement statement (third level) First criteria of science subject (top level) Curriculum standards – US case
  19. 19. Grade group Primary school 3-4 grade group Primary school 5-6 grade group Middle school 1-3 grade group Section Curriculum standards – Korean case Area of content
  20. 20. Achievement statement – Korean case Section of science subject (middle school) Content of curriculum Criteria of achievement Core achievement criteria Reason and explanation for core achievement
  21. 21. CURRICULUM STANDARD ACHIEVEMENT STATEMENT hasChild isPartOf hasChild isChildOf Structural model for curricula Source: ASN Framework & ISO/IEC JTC1 SC36 N2140
  22. 22. CURRICULUM STANDARD ACHIEVEMENT STATEMENT hasChild isPartOf alignFrom alignTo alignTo alignFrom hasChild isChildOf (derivedFrom) (crossSubjectReference) Semantic model for curricula Source: ASN Framework & ISO/IEC JTC1 SC36 N2140
  23. 23. Linked Open Data for achievement statement Source: KS X 7004-2 Linked data profile for achievement in education – part 2: Application profile
  24. 24. •Complete development for data capture API (beta version) - collaborate with IMS Global, ADL and ISO/IEC JTC1 SC36 * to improve efficiency of data sharing mechanism • Complete development and deployment for test-bed of reference model - complete test for open source SWs to organize optimized composition - design interface for analysis algorithm based on R * you may see on Github soon (github.com/KERISdev). • Complete design for LOD of achievement standards - to connect digital resources with specific topics of curriculum standards * connected digital resources will be used ISO/IEC 19788 MLR (KS X 7001) By February 2017
  25. 25. Conclusion & Recommendations
  26. 26. Source: Learning to Adapt 2.0 – Tyton Partners http://tytonpartners.com/tyton-wp/wp-content/uploads/2016/04/yton-Partners-Learning-to-Adapt-2.0-FINAL.pdf Consider This …
  27. 27. Thank You !!! Korea Education & Research Information Service Yong-Sang CHO, Ph.D zzosang@keris.or.kr FB: /zzosang Twitter: @zzosang

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  • luibin

    Nov. 28, 2016

Slides for conference program at e-Learning Korea 2016. Also this slides contain ISO/IEC TR 20748-1 Learning Analytics Interoperability - Part 1: Reference model as well as curriculum standards. Mainly this slides was prepared for LASI-Asia 2016 #lasiasia16.

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