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Learning Analytics for Adaptive Learning And Standardization

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Learning Analytics for Adaptive Learning And Standardization

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Materials for introduction to adaptive learning and learning analytics as well as efforts of interoperability standardization. This slides treats brief concept of adaptive learning, reference model of learning analytics, data APIs for learning analytics, and topic list of standardization community (ISO/IEC JTC1 SC36).

Materials for introduction to adaptive learning and learning analytics as well as efforts of interoperability standardization. This slides treats brief concept of adaptive learning, reference model of learning analytics, data APIs for learning analytics, and topic list of standardization community (ISO/IEC JTC1 SC36).

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Learning Analytics for Adaptive Learning And Standardization

  1. 1. Learning Analytics for Adaptive Learning And Standardization Research Fellow, KERIS Yong-Sang CHO, Ph.D zzosang@keris.or.kr FB: /zzosang Twitter: @zzosang LASI – Asia 2017 August 26, 2017
  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 • Issues and Concern: Privacy and Data Protection • Topic List: What JTC1/SC36 WG8 is doing now
  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 “dynamically adjust to the level or type of course content based on an individual’s abilities or skill attainment, in ways that accelerate a learner’s performance with both automated and instructor interventions." <Horizon Report 2017 – HE edition > <Source: http://http://er.educause.edu/articles/2016/10/adaptive-learning-systems-surviving-the-storm>
  6. 6. “Enabled by machine learning, adaptive learning technologies can adapt to a student in real time, providing both instructors and students with actionable data. The goal is to accurately and logically move students through a learning path, empowering active learning, targeting at-risk student populations, and assessing factors affecting completion and student success. " <Horizon Report 2017 – HE edition > <Source: http://http://er.educause.edu/articles/2016/10/adaptive-learning-systems-surviving-the-storm>
  7. 7. Quality Cost Access Also adaptive learning may be a key (or hammer?) to break “Iron Triangle”
  8. 8. Case Study: CogBook “After using CogBooks for one semester, student success rates rose from 76% to 94% and the dropout rate reduced from 15% to 1.5%. " <Source: https://www.cogbooks.com/2016/02/04/improve-student-success-and-retention-with-adaptive-courseware>
  9. 9. Reference Model Design for Implementation of Adaptive Learning
  10. 10. 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/>
  11. 11. Resources AnalyticsCurricula Point of Adaptive Learning
  12. 12. 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>
  13. 13. 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>
  14. 14. 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
  15. 15. One step further: Exploring Data Flow and Exchange - Get the data! -
  16. 16. xAPI Transcript/learning data can be delivered to LMSs, LRSs or reporting tools Experience data LMS: Learning Management System LRS: Learning Record Store
  17. 17. 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
  18. 18. Case Study: Video Lecture Capture and OpenLRS <University of Michigan at LAIS- Asia 2016> <source: http://www.lasi-
  19. 19. Case Study: xAPI and Caliper utility for single LRS <UC Berkeley at LAIS- Asia 2016> <source: http://www.lasi-
  20. 20. What will be happen? “ISO/IEC TS 20748-3 Learning Analytics Interoperability - Part 3: Guideline for data interoperability” will be come !!!
  21. 21. Issues and Concern: Privacy and Data Protection
  22. 22. Unlawful Learning Analytics? «learning analytics will be unlawful and the sch ool owner will not be able to maintain the most vita l data protection principle: the data principal (the student) will not have control of and a say concerning the use of his or her own informatio Lawfulness, Purpose limitation, Data minimisation, Consent, etc. <source: http://www.lasi- <Tore Hoel at LAIS- Asia 2016 >
  23. 23. What is the Law? <source: http://www.lasi- <Tore Hoel at LAIS- Asia 2016 >
  24. 24. The LACE DELICAT E Checklist to implement trusted Learning Analytics <source: http://www.lasi- <Tore Hoel at LAIS- Asia 2016 >
  25. 25. What will be happen? “ISO/IEC TS 20748-4 Learning Analytics Interoperability - Part 4: Privacy and data protection policies ” will be come !!!
  26. 26. Topic List : What JTC1 SC36/WG8 is doing now
  27. 27. (2017 Melbourne) Resolution 3: Reaffirm of study group's topics for learning analytics interoperability and encourage participation to NBLOs SC36/WG8 reaffirms the study group's topics for learning analytics interoperability: • Systems governance for learning analytics (assigned in June 2015) • Data framework for learning analytics interoperability (assigned in June 2015) • Principles of data design for learning analytics (assigned in November 2015) • Privacy and data protection (assigned in April 2016) • Learning analytics model or profile (assigned in June 2016) • Ethics guideline for learning analytics providers and systems (assigned in November 2016) • Intelligence methods for learning analytics using machine learning technologies (assigned in November 2016) • Multi-Modal Learning Analytics (assigned in August 2017) Note: SC36/WG8 informs for study group leaders, Yong-Sang Cho (leader), Tore Hoel, Yasuhisa Tamura, Jaeho Lee and Jerry Leeson.
  28. 28. Thank You !!! Korea Education & Research Information Service Yong-Sang CHO, Ph.D zzosang@keris.or.kr FB: /zzosang Twitter: @zzosang

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