Eui-Suk Jeong(goodguy@keris.or.kr)

Senior Researcher, KERIS

CASE STUDY ON THE DIGITAL TEXTBOOK-

BASED LEARNING ANALYSIS SYSTEM

IN KOREA

Table of Contents

Background

Related Works

Learning activity Metrics

Learning Analytics System

Conclusion and Future works

Background (1/4)

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

Background (3/4)

6

Background (4/4)

Digital

Textbook

On-line

Learning

Community

Class room

Activity

Rich media

contents

Class room

AnalyticData

Collaborative

Learning

• Self-diagnosis

by learner

• Assessment

and Prescription

by teacher

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

Related Works(2/3)

Class room

Online Learning

Environment

Blended Learning

Environment

IMS Global Caliper Framework Digital textbook-based K12 classes

9

Related Works(3/3)

9

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,

- quantitative scores

• patterns• correlation

s

• activity/usagetime ontask

• session timelast access

• activity affinity

• content affinity

• task patterns

correlations

Tool

• associated context• post objectives• Enter targets• Event patterns• frequency

Basic a

ctivity

Com

ple

x

activ

ity

(Source: Study of Learning Analytics Model and Extension Plans(Seoul National Univ., Seoul Metropolitan Office of Education)

11

Learning Analysis System(1/4)

Learning analytics result

Current state of learning activity

Learning relations analysis

Learning activity patterns

Learning data recommendation

Learning competency diagnosis

Data on usingdigital textbooks

Login (count)highlights/notes (# of times)

Keyword (lookups, # of times)

note-taking (# of times)

Data on usinglearning community

Writing (# of times)

Replies (# of times)

data registration (cases)

homework submission (cases)

Learning diagnosis data

learner self-diagnosis

teacher diagnosis

teacher assessment

teacher/

student

Learning tendencies

Learner Self Directedness

Learning Competencies- Interest in subject matter

- self-regulated learning- meta cognition - collaboration

Learning activity data Learning analyticsalgorithm

interest insubject matter

meta cognition

self-regulatedlearning

collaboration

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

Le

arn

ing

An

aly

tic

s

Clustering

Classification

Sentimental Analysis

Indexing

Network Analysis

React

Progress

Statistics

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

mongoDB aggregation functions and map-reduce

ApachMahout

Learning Analysis System(4/4)

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

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

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

Thank you