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Learning Analytics on Student Engagement to Enhance Students’ Learning Performance: A Systematic Review
Open accessAbstract
The study of learning analytics provides statistical analysis and extract insights from data, particularly in education. Various studies regarding student engagement in online learning have been conducted at tertiary institutions to verify its effects on students’ learning performance. However, there exists a knowledge gap whereby the types of student-engagement issues derived from learning analytics have not been collectively studied thus far. In order to bridge the knowledge gap, this paper engages a new systematic literature review (SLR) that analysed 42 articles using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The existing research on student engagement in online learning does not extensively integrate the five types of online engagement proposed by Redmond et al., and the use of learning analytics on the subject matter is also limited. Thus, this review sheds light on the types of student engagement indicated by using learning analytics, hoping to enhance students’ learning performance in online learning. As revealed in the findings, some studies measured multifaceted engagement to enhance students’ learning performance, but they are limited in number. Thus, it is recommended that future research incorporate multifaceted engagement such as social, cognitive, collaborative, behavioural, and emotional engagement in online learning and utilise learning analytics to improve students’ learning performance. This review could serve as the basis for future research in online higher education.
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“While the unidimensional approach has potential, it has been criticised for using easy-to-count engagement indicators in LA research rather than considering differences in indicator value and distinguishing between meaningful and less meaningful ones (e.g., Johar et al., 2023).”
From Unpacking student engagement in higher education learning analytics: a systematic review (2024) · cites this paper as Johar et al., 2023
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“calls have been made for future LA research to explore all dimensions of engagement beyond purely behavioural measures (Johar et al., 2023).”
From Unpacking student engagement in higher education learning analytics: a systematic review (2024) · cites this paper as Johar et al., 2023
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“with Johar et al. (2023) emphasising the need for studies that consider all dimensions of student engagement more robustly rather than purely focusing on one in isolation, echoing calls from wider engagement literature (e.g., Henrie et al., 2015).”
From Unpacking student engagement in higher education learning analytics: a systematic review (2024) · cites this paper as Johar et al. (2023)
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“However, that review only focused on research published between 2011 and 2021 and was limited to online learning in higher education studies indexed in Scopus or within four publisher repositories.”
From Unpacking student engagement in higher education learning analytics: a systematic review (2024) · cites this paper as Johar et al. (2023)
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Metadata
- Authors
- Johar, N. A., Kew, S. N., Tasir, Z., & Koh, E.
- Affiliations
- University of Technology Malaysia (MY)
- Nanyang Technological University (SG)
- Corresponding author
- Kew, Si Na
- Venue
- Sustainability
- Publisher
- MDPI AG
- Published
- 2023-05-10
- Volume
- 15
- Issue
- 10
- Pages
- 7849
- ISSN
- 2071-1050 (ISSN-L)
- OpenAlex
- W4376137941
- Cited by
- 108
- References
- 84
- Citations by year
- 2026: 48 · 2025: 28 · 2024: 26 · 2023: 6
- FWCI
- 4.57
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sustainability (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- NIE Digital Repository (National Institute of Education, Nanyang Technological University) (submittedVersion)
- Funders
- Malaysian Ministry of Higher Education (Q.J130000.3853.19J71)
- Universiti Teknologi Malaysia (UTM) (Q.J130000.3853.19J71)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Education › Online and Blended Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- learning analytics, student engagement, online learning, learning performance, social engagement, cognitive engagement, collaborative engagement, behavioral engagement, emotional engagement, higher education
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