Case study on the digital textbook-based learning analysis system in Korea

1. Eui-Suk Jeong(goodguy@keris.or.kr)
Senior Researcher, KERIS
CASE STUDY ON THE DIGITAL TEXTBOOK-
BASED LEARNING ANALYSIS SYSTEM
IN KOREA

2. Table of Contents
Background
Related Works
Learning activity Metrics
Learning Analytics System
Conclusion and Future works

3. Background (1/4)

4. Background (2/4)
Education Normalization
to Grow Dream & Talents
Reduction of School
Expenses to Secure Equal
Educational Opportunities
Establishment of Foundations for Ability Oriented
Society to Develop Future Human Resources
• Operate curricula for growing
students dream & talents
• Support designing personalized career
development plans
• Promote physical education
• Create environments to eradicate
violence and threatening harm
• Simplify college admission process
• Encourage teachers to dedicate
themselves to education
• Expand after-school care service
• Reduce education expenses from
kindergarten to high school
• Reduce college expenses
• Offer extended education opportunities
to handicapped, multicultural and
North Korean defectors’ children
• Establish National Competency Standards
• Enhance occupational education to nurture
professionals
• Promote universities characterization &
improve competitiveness of colleges
• Support university financially and improve
transparency of colleges finance
• Construct life-long education system
• Promote technical colleges to higher
vocational education(V/E) institutions
• Expand support for local colleges

5. 5
Background (3/4)

6. 6
Background (4/4)
Digital
Textbook
On-line
Learning
Community
Class room
Activity
Rich media
contents
Class room
Analytic
Data
Collaborative
Learning
• Self-diagnosis
by learner
• Assessment
and Prescription
by teacher

7. 7
• Learning analytics is the measurement, collection, analysis, and reporting of data about
learners and their contexts, for purposes of understanding and optimizing learning and the
environments in which learning occurs (Hawksey, 2014, Google Apps for Education European
User Group Meeting)
• Learning analytics does not merely take interest in student’s performance, but it can also be
used for the assessment of curricula, educational programs, and educational institutions. In
other words, learning analytics can be used for educational innovation through in-depth
analysis. This will give opportunities not only for learner,s but also for putting comprehensively
together all activities, including formal and non-formal learning (Johnson, Smith, Willis, Levien,
& Haywood, 2011, p.28)
• Learning analytics is a series of process that collects, measures, and analyzes data on learners
and their learning contexts to understand learning and the environments in which it occurs and
to provide optimized learning environments including instructions and forecast (Redefinition)
Related Works(1/3)

8. 8
Related Works(2/3)
Class room
Online Learning
Environment
Blended Learning
Environment
IMS Global Caliper Framework Digital textbook-based K12 classes

9. 9
Related Works(3/3)
9

10. 10
Learning activity Metrics
• annotations
• page/block
use
• media use
• lookups
• frameset use
• scrub marks
• view time
• Web link refs
• scores
• attempts
• remediation
• associated refs
• deliverables
• structure
• milestone
performance
• group profile
• scores
• attempts
• remediation
• associated
refs
• media type
• frameset use
• scrub marks
• view time
• usage context
• topics
• associated
context
• frequency
• feedback
• Searches
• patterns
• citations
• topics
• Scores
• patterns (item)
• Time utilization
• attempts
• completion
• connections
• associated
context
• message
profile
• frequency
• Highlights
• notes
• marks
• tags
• attachments
• progress
• Cognition
• attempts
• hints
• collaboration
• connections
• associated
context
• message profile
• frequency
• associated
context
• Outbound pool
• Inbound pool
• attachments
• associated
context
• event patterns
• event profile
• Time utilization
• post marks
• frequency
• participation
• collaboration
• associated
context
• entering
• writing content
• attachments
• drawing up
• frequency
• participation
• collaboration
• associated
context
• frequency
• participation
• collaboration
Institution, course/section, learner profile, course context, path/sequence, usage context
• grades
• Progress
• rubrics
– course
goals
- topic
objectives
- Qualitative
evaluation,
- quantitativ
e scores
• patterns
• correlation
s
• activity/
usage
time on
task
• session
time
last access
• activity
affinity
• content
affinity
• task
patterns
correlations
Tool
• associated context
• post objectives
• Enter targets
• Event patterns
• frequency
Basicactivity
Complex
activity
(Source: Study of Learning Analytics Model and Extension Plans(Seoul National Univ., Seoul Metropolitan Office of
Education)

11. 11
Learning Analysis System(1/4)
Learning
analytics result
Current state
of learning activity
Learning relations
analysis
Learning activity
patterns
Learning data
recommendation
Learningcompetency
diagnosis
Data on using
digital textbooks
Login(count)
highlights/notes(#oftimes)
Keyword(lookups,#oftimes)
note-taking(#oftimes)
Data on using
learning community
Writing(#oftimes)
Replies(#oftimes)
dataregistration(cases)
homeworksubmission(cases)
Learning diagnosis
data
learnerself-diagnosis
teacherdiagnosis
teacherassessment
teacher/
student
Learning tendencies
Learner Self
Directedness
Learning
Competencies
- Interest in subject
matter
- self-regulated learning
- meta cognition
- collaboration
Learning activity data Learning analytics
algorithm
interest in
subject matter
meta
cognition
self-regulated
learning
collaboration

12. 12
Learning Analysis System(2/4)
Data
Collection
Dashboard to
support teaching &
Learning
Connected learning devices
(ex. Som Note)
Learning Activity Data
Digital Textbook/
Wedorang(online community)
Volume of posts
Memos
Sharing/
Responses
Quizzes, Discussion
Note, Highlighting
Access Info.
SNS,
Blog,
Facebook,
Twitter,
etc.
Outside Info.
Learning
Activity Data
Transformation
Data Storage &
Management
Data
Analysis
Data
Visualization
S P O
… … …
… … …
… … …
Ontology for
Learning
Informal
date
Semi-
formal
Formal
Data
Triple
Data
Formal
Data for analyze
Triple
Store
Search/Inquiry
Extracting
Transforming
Refining/ Integrating
Ontology for
Learning
Analysis
Results
Research
Index
Ontology for
Learning
Repository for
Info. Analysis
LearningAnalytics
Clustering
Classification
Sentimental Analysis
Indexing
Network Analysis
React
Progress
Statistics

13. 13
Learning Analysis System(3/4)
The first level metric The second level metric The third level metric
Definition  Metric data extractable
through queries only of
learning action log data
 Metric data extractable
through simple statistics,
conversion, and filtering
 Metric data extractable
through data similarity
analysis and pattern
analysis
Implemen
-tation
methods
 Data extraction through
queries of learning action
log data repository (ex:
mongoDB)
 Calculation of the first
level metric, including total
sum, count,
average/median value, and
min/max value
 Raw data conversion
through implementation of
conversion functions
 Data similarity analysis:
similarity analysis of
attribute value in raw data
modeling (similarity
between binary vectors,
Cosine similarity)
 Extraction of time-series
data pattern (Periodic
Pattern Analysis in Time
Series Databases)
Login log data for a
students in a
particular school
How many times
logged in?
How many times
logged in after
school?
What is the login
(relative)pattern?

14. 14
mongoDB aggregation
functions and map-reduce
Apach
Mahout
Learning Analysis System(4/4)

15. 15
Conclusion and Future works
• Design&Implementation
of Learning Analysis
System
• Prototyping Learning
Analytics Algorithms
• Pilot service launching
Year 1
• Pilot Service
• Expand Learning
Analytics Algorithm
Year 2
• Expand Service target
• Verify/Expand Learning
Activity Metrics
Year 3

16. 16
References
• Inception report material for Study of Learning Analytics Model and Extension Plans (Seoul
National Univ., 2014)
•Inception report material for Vitamin L-Task (KERIS,2014)
• Strategies for Using Big Data in Smart Education Environment (Eui-suk Jeong,2014)
• Presentation material for the Big Data Analysis Forum for Promoting Learning (KERIS, Seoul
National Univ., Seoul Metropolitan Office of Education,2014)
• Presentation material for KERIS Symposium(Eui-suk Jeong, 2014)
• Final report material for Study of Learning Analytics Model and Extension Plans
• (KERIS, Seoul National Univ., Seoul Metropolitan Office of Education,2015)
• Presentation material for the final completion of Vitamin L-Task (KERIS, Daou-incube, 2015)
• http://www.elearnspace.org/blog/2010/08/25/what-are-learning-analytics/
• http://imsglobal.org

17. • goodguy@keris.or.kr
• facebook.com/euisuk.jeong.98
Thank you