Learning analytics on student engagement to enhance students' learning performance: a systematic review

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, ther...

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Main Authors: Johar, Nurul Atiqah, Kew, Si Na, Tasir, Zaidatun, Koh, Elizabeth
Format: Article
Language:English
Published: MDPI 2023
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Online Access:http://eprints.utm.my/107320/1/NurulAtiqahJohar2023_LearningAnalyticsonStudentEngagementtoEnhance.pdf
http://eprints.utm.my/107320/
http://dx.doi.org/10.3390/su15107849
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Institution: Universiti Teknologi Malaysia
Language: English
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spelling my.utm.1073202024-09-01T07:12:08Z http://eprints.utm.my/107320/ Learning analytics on student engagement to enhance students' learning performance: a systematic review Johar, Nurul Atiqah Kew, Si Na Tasir, Zaidatun Koh, Elizabeth L Education (General) 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. MDPI 2023-05 Article PeerReviewed application/pdf en http://eprints.utm.my/107320/1/NurulAtiqahJohar2023_LearningAnalyticsonStudentEngagementtoEnhance.pdf Johar, Nurul Atiqah and Kew, Si Na and Tasir, Zaidatun and Koh, Elizabeth (2023) Learning analytics on student engagement to enhance students' learning performance: a systematic review. Sustainability (Switzerland), 15 (10). pp. 1-25. ISSN 2071-1050 http://dx.doi.org/10.3390/su15107849 DOI:10.3390/su15107849
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic L Education (General)
spellingShingle L Education (General)
Johar, Nurul Atiqah
Kew, Si Na
Tasir, Zaidatun
Koh, Elizabeth
Learning analytics on student engagement to enhance students' learning performance: a systematic review
description 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.
format Article
author Johar, Nurul Atiqah
Kew, Si Na
Tasir, Zaidatun
Koh, Elizabeth
author_facet Johar, Nurul Atiqah
Kew, Si Na
Tasir, Zaidatun
Koh, Elizabeth
author_sort Johar, Nurul Atiqah
title Learning analytics on student engagement to enhance students' learning performance: a systematic review
title_short Learning analytics on student engagement to enhance students' learning performance: a systematic review
title_full Learning analytics on student engagement to enhance students' learning performance: a systematic review
title_fullStr Learning analytics on student engagement to enhance students' learning performance: a systematic review
title_full_unstemmed Learning analytics on student engagement to enhance students' learning performance: a systematic review
title_sort learning analytics on student engagement to enhance students' learning performance: a systematic review
publisher MDPI
publishDate 2023
url http://eprints.utm.my/107320/1/NurulAtiqahJohar2023_LearningAnalyticsonStudentEngagementtoEnhance.pdf
http://eprints.utm.my/107320/
http://dx.doi.org/10.3390/su15107849
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