ERIC Number: EJ1157661
Record Type: Journal
Publication Date: 2017
Pages: 13
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-1049-4820
EISSN: N/A
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.
Descriptors: Online Courses, Self Management, Active Learning, Data Analysis, Student Records, Student Surveys, Evaluation Methods, Predictive Validity, Academic Achievement, Likert Scales, Database Management Systems, Technology Uses in Education, Learning Strategies, Questionnaires, College Students
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: http://www.tandf.co.uk/journals
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