NotesFAQContact Us
Search Tips
Peer reviewed Peer reviewed
Direct linkDirect link
ERIC Number: EJ1157661
Record Type: Journal
Publication Date: 2017
Pages: 13
Abstractor: As Provided
ISSN: ISSN-1049-4820
Exploring Online Students' Self-Regulated Learning with Self-Reported Surveys and Log Files: A Data Mining Approach
Cho, Moon-Heum; Yoo, Jin Soung
Interactive Learning Environments, v25 n8 p970-982 2017
Many researchers who are interested in studying students' online self-regulated learning (SRL) have heavily relied on self-reported surveys. Data mining is an alternative technique that can be used to discover students' SRL patterns from large data logs saved on a course management system. The purpose of this study was to identify students' online SRL patterns with the use of data mining techniques. We examined both self-reported self-regulation surveys and log files to predict online students' achievements and found using log files was more powerful in predicting students' achievements in an online course than self-reported survey data. Discussions to enhance teaching and learning practices with the use of data mining are provided.
Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site:
Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Assessments and Surveys: Motivated Strategies for Learning Questionnaire
Grant or Contract Numbers: N/A