1. Learning Analytics and IMS standards Using JMOOC Case
The 8th TCU International e-Learning Conference
BITEC, Bangkok, Thailand
20 July 2017
Learning Analytics and IMS standards Using JMOOC Case
Tsuneo Yamada
The Open University of Japan (OUJ)
JMOOC, JOCW, AXIES, IMS Japan, AAOU MOOC Portal SC, GONGOVA
All rights reserved by NIME and Tsuneo Yamada

2. Learning analytics expected Use case 1: Improving MOOC quality
　　　　　　　　　　
All rights reserved by NIME and Tsuneo Yamada

3. MOOC MOOC Not only scalable LMSs
but a total solution to realize Openness
and Quality Assurance at once in sustainable way using technologies
Innovations
Scalability
Sustainability
MOOC
Quality of Education
　　　　　　　　　　

4. 2013 was the first year of MOOC in Japan
　　　　　　　　　　

5. JMOOC Establishment （October, 2013）

6. Multiple Platforms NTT Docomo Platform “Gacco” (97 courses)
Open edX-based/Video Lecture
From April 2014
OUJ-TIES Platform (4 courses)
Multimedia e-textbook (iBOOK/epub3)+LMS
Video Lecture/CAI/SNS
Net-Learning Platform “Open Learning” (22 courses)
Domestic Integrated Learning Support Platform
From October 2014
Fujitsu Platform “Fisdom” (1 course)
Web-based
From March 2016

7. OUJ MOOC Platform: Concept
LMS
Social Apps
eTextbook
+ Big DATA Collection &
Learning Metrics and Analytics

8. Mashup

9. Japanese Language Starter A1 level
Standard Curriculum (CEFR)
Competency model
Japanese Language Starter A1 level

10. 講座の構成 Ｃｏｕｒｓｅ Ｐｌａｎ 10 Lessons 27 Can-dos 1) Hello Konnichiwa
講座の構成　Ｃｏｕｒｓｅ　Ｐｌａｎ
10 Lessons
27 Can-dos
1) Hello　
　　　Konnichiwa
1) Exchange greetings
2) Recognise Japanese characters
2) Would you say that again?　
　　　Moo ichido onegaishimasu
3) Use basic classroom expressions
4) Write your name and country in Japanese
3) Nice to meet you.
　　　Doozo yoroshiku
5) Give a simple self-introduction
6) Recognise the parts of a business card
4) There are three people in my family Kazoku wa san-nin desu
7) Talk briefly about your family
8) Tell someone about your family, using a family photo
5) What kind of food do you like?　
　　　Nani ga suki desu ka
9) Talk about your favorite foods 10) Offer someone a drink
11) Talk about your breakfast
6) Where are you going to have lunch today?　
　　　Doko de tabemasu ka
12) Say what your favorite dish is 13) Talk with a friend about where to go for lunch 14) Read a menu 15) Order food and drinks at a hamburger shop
7) There are three rooms in my home.　Heya ga mittsu arimasu
16) Say what kind of home you live in 17) Say what you have in your home 18) Write an inviting someone to your home
8) It's a nice room.
　　　Ii heya desu ne
19) Ask/Say where to put things in the room 20) Visit / Welcome a friend 21) Show someone around your home
22) Recognise the name and address on signs
9) What time do you get up?　
　　　Nan-ji ni okimasu ka
23) Say the time you do something
24) Talk about your daily routine
10) When is convenient for you?　
　　　Itsu ga ii desu ka
25) Talk about your schedule for this week 26) Talk about when to have a party 27) Write a birthday card

11. Number of Registrants/Graduates
CLS
Period
Pace-making
No. of Registrants [Moodle/Facebo ok]
No. of Certifica te holders 1
14th Apr- 18th May 2014
2 lessons /wk
[ - /467]
262* 2
2nd Jun – 7th Jul 2014
[ - /882] 3
4th Aug –15th Oct 2014
Self-paced
[ - /1475] 4
3rd Nov- 23rd Dec 2014
602 [353/249] 5
12 Jan – 23 Mar 2015
1 lesson / wk
[120/96] 6
11 Jun -15 Jul 2015
[200/0] 7
9 Nov 2015 – 20 Dec, 2015
[256/0] 8
20 Jun – 31 Jul 2016
[0/941*]
Total
5039* [929/4110 *]
262* (5.2%)
* As of 1st August 2016

12. New MOOC concepts Adapting and evolving in each context,
Developed countries (like USA),
Micro credential-oriented
Combination of open and proprietary education (blended w/MOOC, SPOC and F2F) – Smaller differences on Cloud
Testbed for NGDLE and LA
Developing countries
Regional MOOC consortia
OER-oriented MOOC
Delivery system of high-quality learning content
　　　　　　　　　　

13. Problems High drop-out rates Learner supports insufficient
5-20 percent (strongly-motivated and well –prepared) can finish
show more autonomous, flexible and independent learners (non credit-oriented)
Learner supports insufficient
Technologies for supporting personalized learning are not matured
Practices utilizing learner community are not accumulated
　　　　　　　　　　

16. Learning Analytics: Pro & Con
Evidence-based Instruction
Improvement of course modules (Learning Objects)
Improvements of learner supports
Road blocks
Technical side
Quality of learning activity data in LRS
Operation side
Agreements on data collection and usages among stakeholders
　　　　　　　　　　

17. Learning analytics Expected Use case 2: Personalization
　　　　　　　　　　
All rights reserved by NIME and Tsuneo Yamada

18. NIHONGO Starter: Localization
Bhutan:
Sherubtse College, Royal University Of Bhutan
* As of 18 July 2016

19. Personalization of Courses and Tools
Learning Analytics
Detection of weak points
Optimization
by Machines
Materials
Repository
Personalization of Courses and Tools
　　　　　　　　　　

20. Why smaller granular materials?
Adaptive Learning process
Localization and Personalization
Development process
Limited financial and human resources
Multiple media delivery (broadcasting, digital textbook, Internet, ….)
Reuse and sharing of quality materials in component/module level
　　　　　　　　　　

21. Metadata for Searchability
No.
Metadata element
Corresponding element to LOM (IEEE ) 1.
ID of the metadata
3.1 Meta-Metadata - Identifier 2.
ID of the LO1
1.1 General - Identifier 3.
Title
1.2 General - Title 4.
Language(s) used within the LO
1.3 General - Language 5.
Description
1.4 General - Description 6.
Keyword(s)
1.5 General - Keyword 7.
Aggregation level
1.8 General - Aggregation Level 8.
Contributor to the LO
2.3 Life Cycle - Contribute 9.
Language of the metadata
3.4 Meta-Metadata - Language
10.
Technical features of LO
4.1 Technical - Format
11.
URL
4.3 Technical - Location
12.
Technical requirements to use the LO
4.4 Technical - Requirement
13.
Educational stages2
5.6 Educational - Context
14.
Intended learning time
5.9 Educational - Typical Learning Time
15.
Academic contexts with taxonomies
5.10 Educational - Description
16.
Paid-for or free
6.1 Rights - Cost
17.
Restriction of usage
6.3 Rights - Description
18.
Classification3
9. Classification
19.
Copyright4 -
20.
Quality5
21.
Permission to Harvesting
(for GLOBE Harvesting)
22.
Permission to Federated Search
(for GLOBE Federated search)
Under Restructuring
　　　　　　　　　　

22. The Metadata Database System at NII JAIRO Cloud
　　　　　　　　　　

23. GLOBE: An international consortium for reuse and sharing (from 2004)
Cross-Institutional search system of quality learning content and information beyond borders
Exchange and Sharing of METADATA
Federated search and Harvesting
Movement of global coverage, all school level
International level
Nation-wide level
Institution level
University repository level
　　　　　　　　　　

24. Learning Analytics Customization in various level
Curriculum (Localization)
Learning environments (Device and Media, F2F/Online/Blended)
Course design
Course materials (Learning objects)
Assessment (Adaptive testing)
From Localization to Personalization
Courseware… Personalized
Materials… Shared, distributed, remixed and reused
　　　　　　　　　　

25. Learning analytics Expected Use case 3: Resource management
　　　　　　　　　　
All rights reserved by NIME and Tsuneo Yamada

26. MOOCs showed new styles of education/ learning
　　　　　　　　　　

27. Flipped Classroom Flipped Classroom じゅう Knowledge Knowledge
4/25/2018
Flipped Classroom
じゅう
Traditional
Class room
Lecture/Teaching
Preparation, HW
Knowledge
Transfer
Knowledge
Integration
Flipped
Classroom
Online Courses
Q/A, Program/Group
Learning
OUJ & JMOOC

28. Common elements (@Cloud)
MOOC and SPOC
“MOOC” Test-beds for late-majority/adopters of e-Learning and for technologies like LA Free samples for PR using primer courses Social contribution to global LLL Community
“SPOC”
Regular (traditional ) online courses
Accredited
Proprietary
Common elements
Infrastructure and Platform, services and tools, course materials
Standard Curriculum, Learning outcome Records (badges and certificates, e-portfolio, )
Learning Activity Records (Learning log)
　　　　　　　　　　

29. Our view 1: the nature
“MOOC” is an aspect / a phenotype of the next-generation ICT-enhanced learning /education system for humans toward singularity. Depending on ICT environments and learners’ readiness, the system(s) reorganizes the elements, architecture (structure) and functions in adaptive and scalable ways ( “Paradigm EX”, tentatively, and maybe same as NGDLE).
　　　　　　　　　　

30. IMS Global:　Concept

31. IMS Global:　Concept

32. MOOC is not replaced with conventional HE but “big wave” is transforming HE in broader and more invisible way
　　　　　　　　　　

33. Our view 2: the historical roles
“MOOC” is a catalyst of the next-generation ICT-enhanced learning /education system(s), which is applicable to developing areas and late-majority (thoughtful!) institutions. “MOOC” is the common test-beds for late-majority/adopters.
　　　　　　　　　　

34. Learning analytics Use case 4: Paradigm shift in Learning Science
　　　　　　　　　　
All rights reserved by NIME and Tsuneo Yamada

35. Scientific Paradigm（cf. Kitagawa）
Approach
Scientist（Human）
Machine
（Computer）
Deductive
Theoretical Science
Computational Science
Inductive
Empiric Science
Data Science
ビッグデータ工学は，現在でもペタバイト級の散在する多様なデータを処理するた
めに必要な情報処理技術であり，データ可視化は，次元圧縮，特徴抽出，パターン認識な
ど，膨大な高次元データそのものや解析結果を人間が的確に把握できるようにするための
技術である．データ解析法はビッグデータからの Deep Knowledge 獲得のための方法であり，
統計数理，機械学習，情報検索，自然言語処理，最適化などの方法が主要な役割を果たす（北川）
cf. Exploratory data
analysis (Tukey)

36. Hypothesis Testing vs. Exploratory data analysis
Data based on experiments and observations
“Big Data”
・High-precision, well –structured and high quality data based with Experimental Design
・No-reuse without follow-up studies
・Large-scale data which are generated from various“sensors” and stored thorough the Internet and so on
・Reuse in the different contexts from the original purposes and goals→Sharing and “Open Data”
・Sometimes sparsed

37. Learning Analytics Reconsidered
　　　　　　　　　　

38. 1) Separate Metrics and Analytics
　　　　　　　　　　

39. Spatial and Temporal structure of LAs
Individual
Social
Analytics
LA Granuality
Time
Layer of Sensors
Learner １
Learning
Activity A
Activity
Store
LMS/CMS
e-Portfolio
Other LLS
LA tool
collections
SIS
Dashboard
t=0
t=1
t=2
Metrics
Activity X
　　　　　　　　　　

40. Spatial and Temporal structure of LAs
Individual
Social
LA Granuality
Time
Layer of Sensors
Learner １
Learning
Activity A
Activity
Store
t=0
t=1
t=2
Metrics
Activity X
　　　　　　　　　　

41. Spatial and Temporal structure of LAs
Analytics
Learning
Activity
Store
LMS/CMS
e-Portfolio
Other LLS
LA tool
collections
SIS
Dashboard
t=1

42. Spatial and Temporal structure of LAs
Individual
Social
Analytics
LA Granuality
Time
Layer of Sensors
Learner １
Learning
Activity A
Activity
Store
LMS/CMS
e-Portfolio
Other LLS
LA tool
collections
SIS
Dashboard
t=0
t=1
t=2
Metrics
Activity X
　　　　　　　　　　

43. 2) Define clearly “Learning Log data” and “Educational Big Data” because the characteristics and management of personal data and more are entirely different.
　　　　　　　　　　

44. Learning Activity Log and Educational Information
Data of Learning Processes
Use for Personalization,Optimization
Learning Metrics and Analytics
Sensor (LMS, Biometrics)
Learning Log Store
Spatial and temporal structure
Scalability, Anomalization,
Educational Information Data
e-Portfolio
SIS
Digital Badge / Extended Transcript
OneRoster

45. 3) Is the holistic observation possible?
　　　　　　　　　　

46. Spatial and Temporal structure of LAs
Indivisual
Social
Analytics
LA Granuality
Time
Layer of Sensors
Learner １
Learning
Activity A
Activity
Store
LMS/CMS
e-Portfolio
Other LLS
LA tool
collections
SIS
Dashboard
t=0
t=1
t=2
Metrics
Activity X

47. 4) What is the major differences between the recent LA and the previous Machine Learning studies?
　　　　　　　　　　

48. 5) LA policy is a promising process to reach the agreement among stakeholders at each institution
　　　　　　　　　　

49. Consensus formation in institutions
Privacy Policy
Personal Information Policy
Learning Analytics（LA）Policy

50. Learning Analytics Policy

51. 6) MOOC is a testbed to realize NGDLE
　　　　　　　　　　

52. Innovations Innovations expected
Learning Management supported by machines
Personalization: Optimization of learning processes
We have
1) Simple materials repository
2) Sequential course management
3) Assessment for teachers/learners
We do not have
1) Semantic content repository
2) AI engines for learners and educators
3) Metrics and analytics for machine and humans
….. Learning Analytics is the missing link
　　　　　　　　　　

53. IMS Global:　Concept
NEXT of LMS

54. IMS Global:　Concept

55. 7) IMS Standards will be a total solution in the near future
　　　　　　　　　　

56. (Rob Abel, 2016) EDTECH ECOSYSTEM COMPONENTS Learning Resources &
Applications
Educational &
Learning
Resources/
Applications
Catalogs
Data
Warehouse
& Analytics
Processing
Learning Environments & Platforms
Student Information/Advising/Relationship Management
Systems
EDTECH
ECOSYSTEM
COMPONENTS
Student Learning Objectives
Student Transcript Reporting

57. (Rob Abel, 2016) IMS ARCHITECTURE FOR EDUCATIONAL INNOVATION
CURRICULUM
APPS &
TOOLS
ASSESSMENT
PARTNER
APP
CATALOG
SUPPLIER/
DEVELOPER
ANALYTICS
Seamless Enterprise Integration via IMS Standards
Peer-to-peer network
LEARNING
PLATFORM/
PORTAL/LOR
Data
Interventions
Click
Stream
INSTITUTION
ANALYTICS
Metadata
Apps
INSTITUTION
APP
CATALOG
STUDENT
INFO/ERP
SYSTEM
IMS Caliper
Analytics
Framework
IMS App Sharing
& Integration
Objectives/Pathways
Achievements
IMS ARCHITECTURE
FOR EDUCATIONAL
INNOVATION
IMS Competency Services & Learning Stds Framework
IMS Educational Badges & Extended Transcript

58. Plug & Play

59. 25+ Standards for e-Learning and ICT-enhanced education

60. IMS Global:　LTI

61. A Learning Architecture
Learning Tools Interoperability
Student Information System
(SIS)
Learning Management System
(LMS)
OneRoster
Learning Information Services
OneRoster (Gradebook)
LIS (Gradebook)
QTI (Results)
LTI (Outcomes)
Caliper
Caliper
Common Cartridge
Thin Common Cartridge
Analytics
System
Authoring
Tool
Assessment System
Question & Test Interoperability

63. OUJ MOOC: Learning Data and Analytics
“Big Data” Database and the Federation
Toward to a Compliant of some International Standards
e.g. IMS Caliper (Version 1.1
will be released soon)
Description of multi-layer learning processes
Population/Individual/Mental Function
/Sensory-Motor/Brain Activity!!

65. IMS Metric Profile

66. Our cases
　　　　　　　　　　

67. IMS/JS - JMOOC joint project
JMOOC Official platforms
Open Learning, Japan
OUJ MOOC
Fisdom
OUJ Learning Log Store
@ NII Academic Cloud
　　　　　　　　　　

68. AAOU Asia MOOC and OER Asia Portals: Concept
Other Regions
AAOU
Asia MOOC
Global Harvesting
OAI-PMH
Target
for GLOBE
Harvester
OAI-PMH
Harvesters
Central
repository
Central
repository
OAI-PMH
Harvesting
Service and
Target
OAI-PMH
Targets
for GLOBE
members
OAI-PMH
Harvester
AAOU
AAOU Harvestor
OER Asia
Central
repository
　　　　　　　　　　
AAOU member universities

69. Thank you very much!! Contact Information
　　　　　　　　　　

70. DEMO
OUJ-MOOC
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71. DEMO Chilo Producer An epub3 authoring tool Text, Video, Audio..
JAVA-based
How to mash-up
Introduction Manual (E)
Install Manual (E)
Other resources