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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The unidimensional approach in LA research has been criticised for using easy-to-count engagement indicators rather than considering differences in indicator value and distinguishing meaningful from less meaningful ones.
“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
Section 5.1 says that studying multiple types of engagement can yield more comprehensive measures, and Section 6 highlights the value of multifaceted engagement. The paper also describes measuring behavioural engagement through the frequency of LMS activity (Section 2.1). It does not, however, criticise unidimensional research for choosing indicators because they are easy to count or for failing to distinguish more meaningful indicators from less meaningful ones; no page numbers are available in our copy. (LLM, conf=0.83)
Experimental — accuracy not yet measuredCalls have been made for future LA research to explore all dimensions of engagement beyond purely behavioural measures.
“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
Reviews focused on LA's impact on student engagement have emphasised the need for studies that consider all dimensions of student engagement more robustly rather than focusing on one in isolation.
“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)
Johar et al.'s 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.
“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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Unpacking student engagement in higher education learning analytics: a systematic review
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“Further, Johar et al. (2023) systematically analyzed studies regarding student engagement and suggested that research should incorporate multifaceted types of LE such as social, cognitive, collaborative, behavioral, and emotional engagement.”
BackgroundDesign of Moodle-based collaborative learning activities to enhance student interactions
Peramunugamage, A., Ratnayake, U. W., Karunanayaka, S. P., & Jayawardena, C. L. (2024) · AAOU Journal/AAOU journal · doi:10.1108/aaouj-06-2023-0079 · Cited by 18 · see citing works
Pei, B., Cheng, Y., Ambrose, A., Dziadula, E., Xing, W., & Lu, J. (2024) · Smart Learning Environments · doi:10.1186/s40561-024-00346-1 · Cited by 14 · see citing works
“Recently, researchers have increasingly suggested that examining the performance gaps across student groups and identifying the potential causes offer another approach to improve students’ overall learning performance in classes (Al-Tameemi et al., 2023; Johar et al., 2023; Meyer et al., 2024).”
Ning, J., Ma, Z., Yao, J., Wang, Q., & Zhang, B. (2025) · Interactive Learning Environments · doi:10.1080/10494820.2025.2478437 · Cited by 14 · see citing works
“The impact of learning analytics on personalized learning hinges on its ability to address diverse learner needs, offering data-driven insights to adjust teaching content, feedback, and pace (Johar et al., 2023).”
Kurent, B., & Avsec, S. (2023) · Sustainability · doi:10.3390/su152015131 · Cited by 13 · see citing works
Kong, L., Sun, H., He, W., & Hu, W. (2024) · BMC Psychology · doi:10.1186/s40359-024-02164-z · Cited by 12 · see citing works
“Previous studies have primarily focused on exploring how personal factors, such as personality, emotions, or motivations, influence academic engagement [10–12].”
Avsec, S., Jagiełło-Kowalczyk, M., Żabicka, A., Gawlak, A., & Gil-Mastalerczyk, J. (2023) · Sustainability · doi:10.3390/su152015115 · Cited by 15 · see citing works
“Thus, it is expected that systems thinking may affect engagement in learning using ICT [73] and ICT self-concept might moderate the relationship between systems thinking and design course achievement [74].”
BackgroundEmotional engagement in a humor-understanding reading task: an AI study perspective
Martucci, A., Graziani, D., Bei, E., Bischetti, L., Bambini, V., Gursesli, M. C., Guazzini, A., & Pecini, C. (2025) · Current Psychology · doi:10.1007/s12144-024-07228-2 · Cited by 10 · see citing works
“LA methodology has demonstrated its value by offer - ing a detailed understanding of how students engage with the learning environment, either through collaborative or cognitive behavior type of engagement to learning (Johar et al., 2023 ).”
Georgieva, M., Henshall, C., Scott, I., & Merriman, C. (2024) · Nurse Education Today · doi:10.1016/j.nedt.2024.106531 · Cited by 5 · see citing works
Sheikh, R. A., Masmali, F. H., Alhazemi, A. A., Onn, C. W., & Khan, S. M. F. A. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13647-w · Cited by 8 · see citing works
“…additional systematic review investigated the efficacy of cognitive, behavioral, and affective engagement in the learning process in enhancing student performance (Johar et al., 2023: Alzahrani et al., 2023) investigated in a distinct study the impact of trust on LA integration in higher education.”
InfluentialZhou, T., Zhou, X.-J., & Wang, B. (2025) · BMC Psychology · doi:10.1186/s40359-025-03114-z · Cited by 12 · see citing works
“It can significantly and positively predict academic achievement [8], learning performance [9], and mental health levels [10].”
A Learning Resource Management Model for high-enrollment Programming courses in Engineering
López-Chila, R., Saltos, N. S. M., Cedeño-Tello, A., & Llerena-Izquierdo, J. (2023) · International Conference on Electrical, Communication, and Computer Engineering (ICECCE) · doi:10.1109/icecce61019.2023.10442311 · Cited by 7 · see citing works
“These elements, content instructional design [27][28], pedagogical strategies [29][30] and the exchange of experiences among professors and peers [31][32] allow the development of effective learning spaces that minimize course dropout [33] and strengthen student retention [34].”
BackgroundMinaye, A., Admas, F., Kassa, T., Teklu, F., Habtamu, K., Zeleke, S., Andualem, T., & Desie, Y. (2024) · Cogent Education · doi:10.1080/2331186x.2024.2403275 · Cited by 7 · see citing works
“However, studies in other parts of the world, such as Johar et al. (2023), have considered student engagement in online learning.”
Paik, J. H., Himelfarb, I., Yoo, S. H., Yoo, K., & Ha, H. (2024) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3636555.3636871 · Cited by 7 · see citing works
Mohammed, A. B., Sabahath, A., & Naim, A. (2024) · Advances in higher education and professional development book series · doi:10.4018/979-8-3693-1698-6.ch003 · Cited by 6 · see citing works
Solissa, E. M., Rakhmawati, E., Maulinda, R., Syamsuri, S., & Putri, I. D. A. (2024) · Al-Madrasah Jurnal Pendidikan Madrasah Ibtidaiyah · doi:10.35931/am.v8i2.3284 · Cited by 6 · see citing works
A Systematic Review on Student-Facing Learning Analytics Dashboards: Reference Frames and Indicators
Kew, S. N., Koh, E., Choo, Z. L., & Jonathan, C. R. (2024) · International Conference on Computer Science and Technologies in Education (CSTE) · doi:10.1109/cste62025.2024.00015 · Cited by 4 · see citing works
Self-citationUnderstanding Data Privacy and Ethical Considerations in Learning Analytics
Balaji, C. G., Rajeswari, G., Jain, H., Menaka, S. R., & Shukla, S. A. (2025) · IGI Global eBooks · doi:10.4018/979-8-3693-8593-7.ch034 · Cited by 4 · see citing works
Byers, R. E., Carter, C. R., & Wang, Y. (2024) · Decision Sciences Journal of Innovative Education · doi:10.1111/dsji.12309 · Cited by 4 · see citing works
Evaluasi Keterlibatan Siswa dalam Lingkungan Pembelajaran Daring: Tinjauan Sistematis Literatur
Ibrahim, M., Riana, R., & Soraya, S. (2024) · Jurnal Ulul Albab · doi:10.31764/jua.v28i2.26058 · Cited by 4 · see citing works
Kushwaha, P. S., Badhera, U., & Kamila, M. K. (2024) · Journal of Science and Technology Policy Management · doi:10.1108/jstpm-12-2023-0242 · Cited by 4 · see citing works
Optimizing Education with Data Analytics: A Feature Comparison of LMS and SISPreprint
Rodríguez, J. A. L., Díaz–Díaz, N., & González, C. D. B. (2024) · Preprints.org · doi:10.20944/preprints202405.0459.v1 · Cited by 4 · see citing works
Chain Mediation Effect of Learning Satisfaction on Achievement Perception in Adult Online Education
Pan, G., Xu, K., Nie, H., & Nie, Z. (2025) · Adult Education Quarterly · doi:10.1177/07417136251315141 · Cited by 3 · see citing works
“A signi fi cant body of research conducted in recent years has corroborated the relationship between the quality of online instructional services, engagement in online learning, and learning accomplishment (Alemayehu & Chen, 2023; Johar et al., 2023).”
Data Analytics in Education: Enhancing Student Learning Outcomes
Kaushik, P., Kakkar, M., Yadav, M., Jegadheesan, C., Sripada, P. N., & Chahal, K. (2024) · International Conference on Advances in Computing, Communication and Networking (ICAC2N) · doi:10.1109/icac2n63387.2024.10895243 · Cited by 3 · see citing works
“Rathore (2021) provides a study on the application of queuing models to optimize ATM systems, offering a comparative analysis of different models, which, although not directly related to education, demonstrates data-driven decision-making techniques [12].”
Multimodality in online education: a comparative study
Immadisetty, P., Rajesh, P., Gupta, A., Anala, M. R., Dr. Soumya, A., & Subramanya, K. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20540-0 · Cited by 3 · see citing works
Techniques and Tools in Learning Analytics and Educational Data Mining: A Review
Noviandy, T. R., Idroes, G. M., Paristiowati, M., & Idroes, R. (2025) · Journal of Educational Management and Learning · doi:10.60084/jeml.v3i1.308 · Cited by 2 · see citing works
Abdi, M. K., Cumar, M. A., Muse, A. H., Ali, T. A., & Ali, J. A. (2025) · Equity in Education & Society · doi:10.1177/27526461251393410 · Cited by 2 · see citing works
Alkhuzaim, K. M., Alzuhair, N. F., & Al-Qutaim, A. M. (2024) · Journal of Asian Scientific Research · doi:10.55493/5003.v14i2.5039 · Cited by 2 · see citing works
“Johar, et al. [13] highlights the pervasive influence of the technological boom in education, necessitating a thoughtful approach.”
BackgroundSAW, R. Y. J., & Mohamad, M. B. (2024) · Social Science and Humanities Journal · doi:10.18535/sshj.v8i04.1008 · Cited by 2 · see citing works
“In parallel, the study unveils challenges minority students face, including limited access to resources and cultural barriers (Johar et al., 2023).”
BackgroundVirtual Learning Experiences Among Postgraduate Students in Namibia
Romeo, A., Plessis, D. J. D., & Atiku, S. O. (2023) · International Journal of Innovation and Economic Development · doi:10.18775/ijied.1849-7551-7020.2015.101.2001 · Cited by 2 · see citing works
Carniel, L., Espinosa, T., & Heidemann, L. A. (2025) · Educação em Revista · doi:10.1590/0102-469853973 · Cited by 6 · see citing works
Carniel, L., Espinosa, T., & Heidemann, L. (2025) · Educação em revista · doi:10.1590/0102-469853973t
“…published after the start of the COVID-19 pandemic (n=6; Ortega & Irala, 2022; Salas-Pilco et al., 2022; Martin & Borup, 2022; Osama et al., 2022; Johar et al., 2023; Yang et al., 2023) had low engagement compared to face-to-face teaching and its potential to address early dropout and low…”
InfluentialEmotional Engagement Prediction and LLM Feedback in Smart Learning Environments
Almasre, M., Alsaggaf, W., Al-Malki, N. A., & Brdesee, H. S. (2025) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-025-00504-7 · Cited by 1 · see citing works
“…researched using conventional sensor-free methods that utilize single data sources (e.g. observation, and self-reported engagement) (Havik & Westergård, 2020) or, more advanced technology-based methods such as Learning Analytics (LA) through Learning Management Systems (LMSs) (Johar et al., 2023).”
Stančin, K., Jakšić, D., & Petrović, A. V. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-00056-9_6 · Cited by 2 · see citing works
Predicting students engagement in asynchronous online learning: a mixed-method approach
Kapuza, A., Витальевна, Г. А., Kotlikova, A., Gerasimova, I., Уртенова, М. А., & Kharakhordina, M. (2025) · Educational Technology Research and Development · doi:10.1007/s11423-025-10558-y · Cited by 1 · see citing works
Evaluating online class participation in higher education: a scoping review and a proposed framework
Hoo, H.-T., Ho, R., Chen, G., Tan, M., Goh, S. L., & Teo, N. (2025) · Learning Research and Practice · doi:10.1080/23735082.2025.2567279 · Cited by 1 · see citing works
Reviewing Evaluation Methods for Measuring Engagement Attributes in Online Learning
Mansor, N. A., & Abdullah, N. (2025) · IEEE International Conference on System Engineering and Technology (ICSET) · doi:10.1109/icset65917.2025.11284224 · Cited by 1 · see citing works
Automated Student Engagement Detection Using Hybrid Deep Learning and Machine Learning Techniques
Abdelhamid, S. E., & Aly, M. (2025) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie63693.2025.11328252 · Cited by 1 · see citing works
“Research indicates that students who actively engage with learning materials and participate in collaborative activities achieve better academic outcomes [1]– [3].”
Temporal Modeling of LMS Logs and Zero-Shot LLM Prediction: A Multi-Course Study in Moodle
Shehada, W., Ashqar, H. I., Ewais, A., & Hatzilygeroudis, I. (2026) · Applied Sciences · doi:10.3390/app16062707 · Cited by 1 · see citing works
Teng, M. F., & Yin, F. (. (2026) · Computers & Education X Reality · doi:10.1016/j.cexr.2026.100168 · Cited by 1 · see citing works
García-García, F. J., Gómez‐Núñez, M. I., & Molla‐Esparza, C. (2026) · Higher Education · doi:10.1007/s10734-026-01709-y · Cited by 3 · see citing works
Supentri, S., Isjoni, I., Caska, C., & Erlinda, S. (2026) · F1000Research · doi:10.12688/f1000research.180069.2 · Cited by 1 · see citing works
CARE-AI: a responsible framework for integrating artificial intelligence in primary education
Zulfiqar, M., & Iqbal, U. (2026) · Education Innovations Systems and Future Learning · doi:10.1108/eisfl-03-2026-0040 · Cited by 1 · see citing works
Kesylė, K., & Melnikova, J. (2024) · Regional Formation and Development Studies · doi:10.15181/rfds.v42i1.2612 · Cited by 1 · see citing works
Predicting Academic Success in Large Online Courses at a Mega ODL University
Saykılı, A., Aydın, S., UĞURHAN, Y. Z. C., Öztürk, A., & Birgin, M. K. (2024) · Technology Knowledge and Learning · doi:10.1007/s10758-024-09757-y
“This study also adds to the LA literature by reporting positive contributors of academic success including LA data regarding multiple learning resources as well as learner demographic characteristics (Eltayar et al., 2023; Johar et al., 2023; Ren et al., 2016; Yang et al., 2017).”
Boz, H. (2025) · Akademisyen Kitabevi eBooks · doi:10.37609/akya.3512
Riza, L. S., & Abazi-Bexheti, L. (2025) · Springer proceedings in business and economics · doi:10.1007/978-3-031-73510-3_16
Monitoring of Student Classroom Learning Status based on Hybrid ViT-YOLOv5s
Yang, L. (2025) · International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) · doi:10.1109/icdcece65353.2025.11035485
A Learning Analytics Framework for Higher Education in South Africa
Matsebula, F., Mnkandla, E., & Masombuka, T. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-98185-2_27
Enhancing Hiring Processes With E-Learning and Recommendation Systems
Boulmane, E.-S., Badouch, M., Mahmoud, H., & Boutaounte, M. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-6516-9.ch001
A systematic review of data analytics adoption in faith-based universities in Kenya
Kibe, M., Akoth, S. O., & MWALW’A, D. S. M. (2025) · African Journal of Empirical Research · doi:10.51867/ajernet.6.4.25
“…embedded in learning management systems (LMS) and student information systems (SIS) generate fine-grained data on student engagement and performance, which can be used to personalize learning pathways, tailor feedback, and guide pedagogical reforms (Johar et al., 2023; Philip and Nyakerario, 2023).”
Learning analytics for communication skills: A systematic review
Kew, S. N., Hussin, S. C., Tee, L.-T., Wei, L. T., & Lee, S. C. (2025) · Multidisciplinary Reviews · doi:10.31893/multirev.2026217
Self-citationОСОБЕННОСТИ ТЕХНОЛОГИИ АНАЛИТИКИ ОБУЧЕНИЯ СТУДЕНТОВ
А.Е., В., Mussina, A. A., & К.А., В. К. А. В. (2026) · Научный журнал "Вестник Актюбинского регионального университета имени К Жубанова" · doi:10.70239/arsu.2025.t82.n4.07
Developing a Mobile Application for Student Learning and Time Management
Selani, K., Mwansa, G., & Ngandu, M. R. (2026) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-032-10310-9_17
Data-Driven LMS Analytics for Enhancing Student Performance in EdTech Platforms
R., P., C, H., R, H., M, N., & Nivetha, I. (2025) · 2025 IEEE International Conference on Blockchain and Distributed Systems Security (ICBDS) · doi:10.1109/icbds67396.2025.11378105
“Moreover, the analysis for engagement allowed a prediction in the performance of approximately 24% of the highest participation levels compared with the level of minimal participation of the subjects, which underlines the relevance of the behavioral cues present in the academic prediction [11].”
Bhatt, P., Kumar, A., Choudhary, R., Dahiya, A., & Raj, S. (2026) · SAGE Open · doi:10.1177/21582440261416745
“Existing research also supports the results of the study such as (Johar et al., 2023) and (Du et al., 2023) study disclosed the application of adequate and appropriate use of ICT tools enhances self-directed learning and social interaction resulted in better teaching and learning outcomes and…”
2079 Predicting Mathematics Achievement in Secondary Education Using Regression Analysis
Sowmyayani, S., Therese, S. K., Immanuel, R. R., Maheswari, S., Roy, P. M. J., & Kavitha, J. (2026) · De Gruyter eBooks · doi:10.1515/9783112214374-009
Predicting Teaching Quality Using Student Performance Data and Behavioral Metrics
Jackson, V., Tjandraatmadja, E., & Bernando, C. (2026) · doi:10.1109/it67293.2026.11435788
“Systematic reviews further confirm that LMS log data such as access frequency, submission behaviors, and participation patterns serve as reliable measures of student engagement in digital learning environments [10] ,[8].”
Online Classroom Engagement and Group Collaboration
Zulfqar, A., & Niazi, A. (2026) · IGI Global eBooks · doi:10.4018/979-8-2600-0934-5.ch005
Xu, G. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1800462
“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 · see citing works
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 · see citing works
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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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