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Dynamic Evolution of Self-Regulated Learning Profiles in Blended Learning: A Longitudinal Study of Freshmen and Upper-Level Students

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Title: Dynamic Evolution of Self-Regulated Learning Profiles in Blended Learning: A Longitudinal Study of Freshmen and Upper-Level Students
Language: English
Authors: Shadi Esnaashari (ORCID 0000-0001-9522-9657), Lesley Gardner, Michael Rehm, Tiru Arthanari, Olga Filippova
Source: Journal of Computer Assisted Learning. 2025 41(5).
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 19
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Blended Learning, Learning Strategies, College Freshmen, Undergraduate Students, Profiles, Student Motivation
Assessment and Survey Identifiers: Motivated Strategies for Learning Questionnaire
DOI: 10.1111/jcal.70119
ISSN: 0266-4909
1365-2729
Abstract: Background: Self-Regulated Learning (SRL) plays a crucial role in student success, particularly in blended learning (BL) environments where learners must take greater ownership of their educational journey. Whilst prior research has extensively examined SRL, there remains a gap in understanding how students' SRL profiles evolve over time and how motivation and learning strategies dynamically interact within these profiles. Objectives: This study investigates the dynamic nature of SRL by identifying distinct learner profiles and tracking their evolution throughout a semester in a BL setting. By adopting a person-centred clustering approach, the research provides insights into how students' motivation and strategy use shift over time. Methods: Data were collected from 314 tertiary-level students enrolled in two BL courses, with responses from the Motivated Strategies for Learning Questionnaire (MSLQ) captured at three time points. K-Means clustering was used to classify students into SRL profiles, and longitudinal analysis was conducted to track transitions between profiles over time. Results: The findings revealed three distinct SRL profiles--highly self-regulated, moderately self-regulated, and minimally self-regulated learners--suggesting that students adapt their motivation and strategies in response to course feedback and assessments. The study highlights the fluid and iterative nature of SRL development. Conclusions: This research enhances the theoretical understanding of SRL by empirically illustrating how students' motivation and learning strategies evolve within a semester. Additionally, it offers practical insights for designing interventions to support students with varying levels of SRL, ultimately contributing to more adaptive and effective BL environments.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1484335
Database: ERIC
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  Data: Dynamic Evolution of Self-Regulated Learning Profiles in Blended Learning: A Longitudinal Study of Freshmen and Upper-Level Students
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  Data: <searchLink fieldCode="AR" term="%22Shadi+Esnaashari%22">Shadi Esnaashari</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-9522-9657">0000-0001-9522-9657</externalLink>)<br /><searchLink fieldCode="AR" term="%22Lesley+Gardner%22">Lesley Gardner</searchLink><br /><searchLink fieldCode="AR" term="%22Michael+Rehm%22">Michael Rehm</searchLink><br /><searchLink fieldCode="AR" term="%22Tiru+Arthanari%22">Tiru Arthanari</searchLink><br /><searchLink fieldCode="AR" term="%22Olga+Filippova%22">Olga Filippova</searchLink>
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  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
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  Data: <searchLink fieldCode="DE" term="%22Blended+Learning%22">Blended Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Strategies%22">Learning Strategies</searchLink><br /><searchLink fieldCode="DE" term="%22College+Freshmen%22">College Freshmen</searchLink><br /><searchLink fieldCode="DE" term="%22Undergraduate+Students%22">Undergraduate Students</searchLink><br /><searchLink fieldCode="DE" term="%22Profiles%22">Profiles</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Motivation%22">Student Motivation</searchLink>
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  Data: 10.1111/jcal.70119
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– Name: Abstract
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  Data: Background: Self-Regulated Learning (SRL) plays a crucial role in student success, particularly in blended learning (BL) environments where learners must take greater ownership of their educational journey. Whilst prior research has extensively examined SRL, there remains a gap in understanding how students' SRL profiles evolve over time and how motivation and learning strategies dynamically interact within these profiles. Objectives: This study investigates the dynamic nature of SRL by identifying distinct learner profiles and tracking their evolution throughout a semester in a BL setting. By adopting a person-centred clustering approach, the research provides insights into how students' motivation and strategy use shift over time. Methods: Data were collected from 314 tertiary-level students enrolled in two BL courses, with responses from the Motivated Strategies for Learning Questionnaire (MSLQ) captured at three time points. K-Means clustering was used to classify students into SRL profiles, and longitudinal analysis was conducted to track transitions between profiles over time. Results: The findings revealed three distinct SRL profiles--highly self-regulated, moderately self-regulated, and minimally self-regulated learners--suggesting that students adapt their motivation and strategies in response to course feedback and assessments. The study highlights the fluid and iterative nature of SRL development. Conclusions: This research enhances the theoretical understanding of SRL by empirically illustrating how students' motivation and learning strategies evolve within a semester. Additionally, it offers practical insights for designing interventions to support students with varying levels of SRL, ultimately contributing to more adaptive and effective BL environments.
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      – Text: English
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