This slide was used in ISO/IEC JTC1 SC36 Plenary Meeting in June 22, 2015. Title of this slide is 'Proof of Concept for Learning Analytics Interoperability and subtitle is 'Reference Model based on open source SW'.

1. Proof of Concept for Learning Analytics
- Reference Model based on open source SW -
Jaeho Lee, Ph.D (Professor, University of Seoul)
Yong-Sang CHO, Ph.D (Principal researcher, KERIS)
ISO/IEC JTC1 SC36 Plenary Meeting
2015-06-22, Rouen, France

2. Table of Contents
• What we know and what we don’t know
• Case study for data flows and exchange
- xAPI
- IMS Caliper
• Proof of Concept: reference model for learning analytics
- Reference architecture for learning analytics service
- System deployment using open source SW
• Future works by 2016
• But, keep in mind

3. What we know is …

4. Source: Smart Devices & Cloud Computing
http://www.exelanz.com/blogs/smart-devices-cloud-computing/

5. Learning applications
ebookeportfolio collaboration assessment
classroom
capture
creation/
delivery …
http://developers.imsglobal.org/catalog.html

6. Source: Big Data Sea Image by Venture Beat

7. What we don’t know is …

9. What to measure? How to collect?
Does it connected each other?
Source: http://tincanapi.com/overview/

10. Source: Collegestats.org
http://collegestats.org/2013/01/how-can-data-mining-analytics-enhance-education/
Questions expected from data mining & learning analytics

11. Case Study for
exploring what we don’t know
(Data flow and exchange)

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

13. 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

14. http://www.coursesmart.com/go/institutions/analytic

15. Proof of Concept:
reference model for learning analytics

16. We want to see iceberg below to understand
what we didn’t know before!!!
• What is a general process for analytics?
• Do we define workflows beyond xAPI or IMS Caliper?
• How do we prove the concept?
For exploring

17. Step 1. design reference architecture or learning analytics

18. Step 1. design reference architecture in terms of deployment (in 2014)
Cloudera Impala
HBase HDFS
Cloudera Impala
HBase HDFS
Cloudera Impala
HBase HDFS
JDBC / ODBC
Client
Cloudera Manager / HUE
HIVE
Metastore
Impala Shell
RImpala / RODBC
R
Rstudio
(Ploting Library)

19. Step 1. design reference architecture in terms of deployment (in 2015)
Cloudera Impala
Mong
o
DB
HDFS
Cloudera Impala
Mong
o
DB
HDFS
Cloudera Impala
Mong
o
DB
HDFS
JDBC / ODBC
Client
Cloudera Manager / HUE
HIVE
Metastore
Tools for Triple
i.e. GraphLab
RImpala / RODBC
R
Visualization Tool
(i.e. Chart.js)

20. Step 2. design deployment for the reference model

21. Step 2. design deployment for the reference model
- data collection process

22. Step 2. design deployment for the reference model
- data storing/filtering process

23. Step 2. design deployment for the reference model
- analyzing process

24. Step 2. design deployment for the reference model
- visualization process

25. Step 3. design test data and proof of concept for the model
(Basic analytics process)
1. Student open digital textbook on Readium-JS viewer
2. Data is generated through reading activities by student
3. Data capture API crawl the data and send to event store
4. On the analytics platform check collected data
5. See simple dashboard from collected data (without analysis algorithm)
(Advanced analytics process)
6. Design analysis algorithm with data filtering from collected data
7. See advanced dashboard pertaining to customized analysis
8. Calculate personal learning path connected to LOD for curriculum standard

26. DEMO

27. DEMO 1. open digital textbook on Readium-JS viewer
Open sample digital textbook via Readium-JS Viewer

28. DEMO 2. generate reading activities on Readium-JS viewer
Capture the event for page view

29. Call send() function from Sensor API from Endpoint
Captured data in JSON format by sensor API
Result for event data capturing by sensor API:
“result=SUCCESS”

30. DEMO 3. show reading activity data generated in RDF format
ViewEvent data in JSON format ViewEvent data in RDF format

31. DEMO 4. show collected data on event store

32. DEMO 5. show simple dashboard using engagement profile

33. Future works by 2017

34. •Complete development for data capture API (beta version)
- collaborate with IMS Global
* to improve efficiency of data sharing format
• Complete design and development for test-bed of reference model
- complete test for open source SWs to organize optimized composition
- design interface for analysis algorithm based on R
• 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
By February 2017

36. But, keep in mind!

37. <Gartner Hype Cycle, 2014>

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