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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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“Recent research supports this interpretation, suggesting that learners regulate both engagement intensity and engagement visibility under high-accountability environments (Johar et al., 2023).”
Analítica de aprendizaje para mejorar el rendimiento académico en estudiantes de secundaria
Morán-Castillo, G. G., Zamora-Moreno, Z. F., Jimenez-Villares, A. M., Zamora-Moreno, A. B., & Vera-Flores, S. C. (2026) · Revista Científica Ciencia y Método · doi:10.55813/gaea/rcym/v4/n2/182
“Abril – Junio 2026 estos factores no se reconocen a tiempo, la intervención docente tiende a ser reactiva, menos personalizada y con menor capacidad de seguimiento sostenido (Johar et al., 2023; de Sousa et al., 2021; van Leeuwen et al., 2022).”
Liu, Z., Li, L., Chao, J., Mondol, A., Xing, W., & Zhang, Y. (2026) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3785022.3785043
Kadarusman, R. (2026) · Journal of Chemical Learning Innovation. · doi:10.37251/jocli.v3i1.2990
Kew, S. N., Tasir, Z., Tran, T. P., & Tee, L.-T. (2026) · SAGE Open · doi:10.1177/21582440261416748
“Engagement is also an important indicator of teaching quality (Johar et al., 2023; Zhang et al., 2019).”
Self-citationResearch on the Construction of AI-Based Personalized Learning Paths and Curriculum Design
Sun, Y., Chen, B., & Li, C. (2026) · International Conference on Educational and Information Technology · doi:10.1109/iceit68991.2026.11521784
“AI tools can analyze the sentiment and quality of participation in online discussions through natural language processing and machine learning techniques, and can even identify students who may be disinterested or exhibit low engagement, enabling timely intervention [14].”
Maluleke, A. F. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-6182-6.ch005
Olórtegui-Alcalde, L. M., & Córdova-Buiza, F. (2026) · Contemporary Educational Technology · doi:10.30935/cedtech/18690 · Cited by 1
AI-Integrated Intelligent Evaluation Method for Mental Health Education Teaching Effectiveness
Li, M. (2026) · International Conference on Big Data and Informatization Education (ICBDIE) · doi:10.1145/3806980.3807051
An integrated model for digital student profiling based on multisource educational data
Soltan, G., Smaiyl, A., Zunimova, G., & Rakhimzhanova, M. (2026) · Frontiers in Computer Science · doi:10.3389/fcomp.2026.1815738
Ashari, D. P., Nugroho, I. A., Ekantini, A., Febriyani, U., Sembiring, Y. K., Fatimah, W. S., & Rahman, A. R. (2026) · Jurnal Penelitian Pendidikan IPA · doi:10.29303/jppipa.v12i5.13581
“Improving the quality of higher education requires systematic evaluation of various learning components, one of which is students’ active participation (Assefa et al., 2025; Bergdahl et al., 2024; Godsk & Møller, 2024; Heilporn et al., 2024; Johar et al., 2023; Waqar et al., 2025).”
Heilporn, G., Diallo, I. S., & Cano, C. (2026) · International Journal of Educational Research Open · doi:10.1016/j.ijedro.2026.100574
Multimodal AI for School Dropout Detection and Prevention: A Systematic Mapping Study
Ilal, R., Seghroucheni, Y. Z., Echchoayeby, A., & Ziti, S. (2026) · International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) · doi:10.1109/iraset68627.2026.11538518
Cumar, M. A., Muse, A. H., Abdi, M. K., Abdikarim, H., Abdi, M. A., & Ali, J. A. (2026) · International Journal of Educational Development · doi:10.1016/j.ijedudev.2026.103618 · Cited by 1
Pedagogical Design and Academic Equity: An Intersectional Learning Analytics Study
Isiwu, C. P., Nnadi, L. C., & Watanobe, Y. (2026) · International Conference on Information and Education Technology (ICIET) · doi:10.1109/iciet69664.2026.11561660
Love, time, and the algorithm: metaphysical humanism and ethical AI governance in education
Gardner-McTaggart, A. C. (2026) · AI and Ethics · doi:10.1007/s43681-026-01136-w
“Behavioural tracking platforms collect detailed data on students’ interactions with digital learning environments, including clicks, participation, and time spent on tasks [36].”
Rakhmetov, M., Kuanbayeva, B., Муханбетжанова, А., Bissenbayeva, R., Turebayeva, K., Uteshkaliyeva, A., & Zhusupkalieva, G. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1856574
Özgür, S. D., & Erdemir, A. Ü. (2026) · Research Papers in Education · doi:10.1080/02671522.2026.2697476
Machine Learning-Based Classification for Learning Outcome Evaluation in Online Learning
Sukhitatha, A. V., & Dewi, M. A. (2026) · International Conference on Enterprise and Industrial System (ICOEINS) · doi:10.1109/icoeins69996.2026.11605906
“…a result, although engagement-based analytics enhance the monitoring of student behavior, their alignment with Outcome-Based Education (OBE) principles remains limited, and the conceptual linkage between engagement indicators and multidimensional learning outcomes is insufficiently addressed [17].”
Wu, S., Phopli, W., & Zhang, T. (2026) · Acta Psychologica · doi:10.1016/j.actpsy.2026.107527
García, F. J. G., Francés, I. L., Moctezuma-Ramírez, E. E., & Muñoz, J. S. Á. (2026) · Revista Electrónica Interuniversitaria de Formación del Profesorado · doi:10.6018/reifop.685161
“…and educational data mining have been instrumental in analyzing university students’ engagement, offering insights into their academic outcomes (Johar et al., 2023; Nizam Ismail et al., 2021) and promoting more effective use of online educational resources (Karaoglan Yilmaz & Yilmaz, 2022).”
Neuroeducation and Attentional Engagement Analytics in Initial Teacher Education: A Mixed Study
Fernández, C. A. A., & Laguna, M. P. (2026) · NeuroData · doi:10.63688/neurodata2026172
Dick, C., Mai, V., Varney, V., & Richert, A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-20381-6_5
Pasturan, R. (2026) · Psychology and Education A Multidisciplinary Journal · doi:10.70838/pemj.600808
Rahman, A., Karim, A., & Destiarini (2026) · Journal of Nexural Intelligence · doi:10.71200/nexural.v1.i1.259
“Learning analytics and educational data mining have been used to transform raw educational data into actionable information for teachers, academic managers, and student-support units [3], [4].”
Robiños, J. R. O., & Sadiwa, M. R. (2026) · Journal of interdisciplinary perspectives · doi:10.69569/jip.2026.309
Artificial Intelligence in Higher Education
Roache, D. A. M. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-3673-0.ch003
Ebron, J. G. (2026) · International Conference on Education and Training Technologies (ICETT) · doi:10.1109/icett70124.2026.11666123
“Prior studies have operationalized engagement using indicators such as navigation frequency, time dwelling on learning resources, inactivity periods, and revisit behavior [2], [3], [4].”
Supentri, S., Isjoni, I., Caska, C., & Erlinda, S. (2026) · F1000Research · doi:10.12688/f1000research.180069.3
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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