The document discusses the concept of adaptive learning, which personalizes education through technology and data analytics to enhance student engagement and achievement. It outlines a reference model for implementing adaptive learning and explores data flow, curriculum standards, and recommendations for its application in educational contexts. The findings suggest that students positively respond to adaptive learning technologies, highlighting their effectiveness in improving learning outcomes.

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

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>

10. Reference Model Design for
Implementation of Adaptive Learning

11. 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/

12. Resources
AnalyticsCurricula
Point of
Adaptive Learning

13. 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

14. 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

15. 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

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

17. xAPI
Transcript/learning data
can be delivered to LMSs, LRSs
or reporting tools
Experience data
LMS: Learning Management System
LRS: Learning Record Store

18. 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

20. One step further:
Exploring Curriculum Standards
- Get the topic and competency! -

21. 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

22. 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

23. 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

24. CURRICULUM
STANDARD
ACHIEVEMENT
STATEMENT
hasChild isPartOf
hasChild
isChildOf
Structural model for curricula
Source: ASN Framework & ISO/IEC JTC1 SC36 N2140

25. 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

26. Linked Open Data for achievement statement
Source: KS X 7004-2 Linked data profile for achievement in education – part 2: Application profile

27. •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

28. Conclusion & Recommendations

29. 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 …

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