Case study on the digital textbook-based learning analysis system in Korea
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Eui-Suk Jeong presents a case study on Korea's digital textbook-based learning analysis system. The system collects and analyzes data from digital textbooks and online learning communities to provide metrics on learning activities, diagnose learner competencies, and recommend personalized learning paths. It aims to optimize learning by understanding learner behaviors and contexts. A prototype was launched for pilot testing, with plans to expand algorithms, services, and metrics in subsequent years.
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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
- 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
- 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
- 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