ERIC Number: EJ1141050
Record Type: Journal
Publication Date: 2017
Abstractor: As Provided
Combining University Student Self-Regulated Learning Indicators and Engagement with Online Learning Events to Predict Academic Performance
Pardo, Abelardo; Han, Feifei; Ellis, Robert A.
IEEE Transactions on Learning Technologies, v10 n1 p82-92 Jan-Mar 2017
Self-regulated learning theories are used to understand the reasons for different levels of university student academic performance. Similarly, learning analytics research proposes the combination of detailed data traces derived from technology-mediated tasks with a variety of algorithms to predict student academic performance. The former approach is designed to provide meaningful pedagogical guidance, while the latter is designed to identify event patterns and relations that can be translated into actionable remediation. The benefits of both approaches have motivated this study to investigate if a combination of the self-report data and data arising from an observation of the engagement of students with online learning events offers a deeper understanding and explanation of why some students achieve relatively higher levels of academic performance. In this paper we explore how to combine data about self-regulated learning skills with observable measures of online activity in a blended learning course to increase predictive capabilities of student academic performance for the purposes of informing teaching and task design. A case study in a course with 145 students showed that the variation of the students' final score for their course is better explained when factors from both approaches are considered. The results point to the potential of adopting a combined use of self-report and observed data to gain a more comprehensive understanding of successful university student learning.
Descriptors: Student Centered Learning, Learning Theories, College Students, Academic Achievement, Data Collection, Data Analysis, Blended Learning, Educational Technology, Case Studies, Grades (Scholastic), Learner Engagement, Predictor Variables, Computer Assisted Instruction, Self Management, Self Efficacy, Student Motivation, Correlation, Foreign Countries, Learning Strategies, Questionnaires, Factor Analysis, Multiple Regression Analysis
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Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Authoring Institution: N/A
Identifiers - Location: Australia
Identifiers - Assessments and Surveys: Motivated Strategies for Learning Questionnaire