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Using Learning Analytics to Measure Self-Regulated Learning: A Systematic Review of Empirical Studies in Higher Education

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Title: Using Learning Analytics to Measure Self-Regulated Learning: A Systematic Review of Empirical Studies in Higher Education
Language: English
Authors: Saleh Alhazbi (ORCID 0000-0001-9985-9429), Afnan Al-ali, Aliya Tabassum, Abdulla Al-Ali, Ahmed Al-Emadi, Tamer Khattab, Mahmood A. Hasan
Source: Journal of Computer Assisted Learning. 2024 40(4):1658-1674.
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: 17
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Learning Analytics, Independent Study, Higher Education, College Students, Self Management, Time Management, Measurement Techniques, Learning Processes
DOI: 10.1111/jcal.12982
ISSN: 0266-4909
1365-2729
Abstract: Background: Measuring students' self-regulation skills is essential to understand how they approach their learning tasks in order to identify areas where they might need additional support. Traditionally, self-report questionnaires and think aloud protocols have been used to measure self-regulated learning skills (SRL). However, these methods are based on students' interpretation, so they are prone to potential inaccuracy. Recently, there has been a growing interest in utilizing learning analytics (LA) to capture students' self-regulated learning (SRL) by extracting indicators from their online trace data. Objectives: This paper aims to identify the indicators and metrics employed by previous studies to measure SRL in higher education. Additionally, the study examined how these measurements were validated. Methods: Following the protocol of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), this study conducted an analysis of 25 articles, published between 2015 and 2022, and sourced from major databases. Results and Conclusions: The results showed that previous research used a variety of indicators to capture learners' SRL. Most of these indicators are related to time management skills, such as indicators of engagement, regularity, and anti-procrastination. Furthermore, the study found that the majority of the reviewed studies did not validate the proposed measurements based on any theoretical models. This highlights the importance of fostering closer collaboration between learning analytics and learning science to ensure the extracted indicators accurately represent students' learning processes. Moreover, this collaboration can enhance the validity and reliability of data-driven approaches, ultimately leading to more meaningful and impactful educational interventions.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1431977
Database: ERIC
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  Data: Using Learning Analytics to Measure Self-Regulated Learning: A Systematic Review of Empirical Studies in Higher Education
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  Data: <searchLink fieldCode="AR" term="%22Saleh+Alhazbi%22">Saleh Alhazbi</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-9985-9429">0000-0001-9985-9429</externalLink>)<br /><searchLink fieldCode="AR" term="%22Afnan+Al-ali%22">Afnan Al-ali</searchLink><br /><searchLink fieldCode="AR" term="%22Aliya+Tabassum%22">Aliya Tabassum</searchLink><br /><searchLink fieldCode="AR" term="%22Abdulla+Al-Ali%22">Abdulla Al-Ali</searchLink><br /><searchLink fieldCode="AR" term="%22Ahmed+Al-Emadi%22">Ahmed Al-Emadi</searchLink><br /><searchLink fieldCode="AR" term="%22Tamer+Khattab%22">Tamer Khattab</searchLink><br /><searchLink fieldCode="AR" term="%22Mahmood+A%2E+Hasan%22">Mahmood A. Hasan</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Computer+Assisted+Learning%22"><i>Journal of Computer Assisted Learning</i></searchLink>. 2024 40(4):1658-1674.
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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: 17
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  Data: Journal Articles<br />Reports - Research
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  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Learning+Analytics%22">Learning Analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Independent+Study%22">Independent Study</searchLink><br /><searchLink fieldCode="DE" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="DE" term="%22College+Students%22">College Students</searchLink><br /><searchLink fieldCode="DE" term="%22Self+Management%22">Self Management</searchLink><br /><searchLink fieldCode="DE" term="%22Time+Management%22">Time Management</searchLink><br /><searchLink fieldCode="DE" term="%22Measurement+Techniques%22">Measurement Techniques</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Processes%22">Learning Processes</searchLink>
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  Data: 10.1111/jcal.12982
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  Data: 0266-4909<br />1365-2729
– Name: Abstract
  Label: Abstract
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  Data: Background: Measuring students' self-regulation skills is essential to understand how they approach their learning tasks in order to identify areas where they might need additional support. Traditionally, self-report questionnaires and think aloud protocols have been used to measure self-regulated learning skills (SRL). However, these methods are based on students' interpretation, so they are prone to potential inaccuracy. Recently, there has been a growing interest in utilizing learning analytics (LA) to capture students' self-regulated learning (SRL) by extracting indicators from their online trace data. Objectives: This paper aims to identify the indicators and metrics employed by previous studies to measure SRL in higher education. Additionally, the study examined how these measurements were validated. Methods: Following the protocol of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), this study conducted an analysis of 25 articles, published between 2015 and 2022, and sourced from major databases. Results and Conclusions: The results showed that previous research used a variety of indicators to capture learners' SRL. Most of these indicators are related to time management skills, such as indicators of engagement, regularity, and anti-procrastination. Furthermore, the study found that the majority of the reviewed studies did not validate the proposed measurements based on any theoretical models. This highlights the importance of fostering closer collaboration between learning analytics and learning science to ensure the extracted indicators accurately represent students' learning processes. Moreover, this collaboration can enhance the validity and reliability of data-driven approaches, ultimately leading to more meaningful and impactful educational interventions.
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      – SubjectFull: Higher Education
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