The document discusses adaptive learning, which adjusts educational content to fit individual learner abilities, aiming to enhance student performance through data-driven approaches. It highlights the importance of learning analytics for implementing effective adaptive learning systems and addresses privacy concerns associated with student data usage. Additionally, it outlines ongoing initiatives and frameworks for improving learning analytics interoperability and governance.

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. 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. “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. What is an Adaptive Learning?

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. “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. Quality
Cost Access
Also adaptive learning may be a key (or hammer?)
to break “Iron Triangle”

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. Reference Model Design for
Implementation of Adaptive Learning

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. Resources
AnalyticsCurricula
Point of
Adaptive Learning

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. 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. 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. One step further:
Exploring Data Flow and Exchange
- Get the data! -

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. 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. Case Study: Video Lecture Capture and OpenLRS
<University of Michigan at LAIS- Asia 2016>
<source: http://www.lasi-

19. Case Study: xAPI and Caliper utility for single LRS
<UC Berkeley at LAIS- Asia 2016>
<source: http://www.lasi-

20. What will be happen?
“ISO/IEC TS 20748-3 Learning Analytics Interoperability
- Part 3: Guideline for data interoperability” will be come !!!

21. Issues and Concern:
Privacy and Data Protection

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. What is the Law?
<source: http://www.lasi-
<Tore Hoel at LAIS- Asia 2016 >

24. The LACE DELICAT
E
Checklist
to implement
trusted
Learning
Analytics
<source: http://www.lasi-
<Tore Hoel at LAIS- Asia 2016 >

25. What will be happen?
“ISO/IEC TS 20748-4 Learning Analytics Interoperability
- Part 4: Privacy and data protection policies ” will be come !!!

26. Topic List :
What JTC1 SC36/WG8 is doing now

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. Thank You !!!
Korea Education & Research Information Service
Yong-Sang CHO, Ph.D
zzosang@keris.or.kr
FB: /zzosang Twitter: @zzosang