In search for the most informative data for feedback generation: Learning analytics in a data-rich context

Visible learning: a synthesis of over 800 meta‐analyses relating to achievement

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Measuring emotions in students’ learning and performance: the Achievement Emotions Questionnaire (AEQ)

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Handbook of Research on Educational Communications and Technology

Jonassen, D. H., Spector, M. J., Driscoll, M., Merrill, M. D., van Merriënboer, J. J. G., & Driscoll, M. P. (2008) · Routledge eBooks · doi:10.4324/9780203880869 · Cited by 1,692

Aligning assessment with long‐term learning

Boud, D. J., & Falchikov, N. (2006) · Assessment & Evaluation in Higher Education · doi:10.1080/02602930600679050 · Cited by 1,516

Learning Analytics: The Emergence of a Discipline

Siemens, G. (2013) · American Behavioral Scientist · doi:10.1177/0002764213498851 · Cited by 1,284

Mining LMS data to develop an “early warning system” for educators: A proof of concept

Macfadyen, L. P., & Dawson, S. (2010) · Computers & Education · doi:10.1016/j.compedu.2009.09.008 · Cited by 1,045

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Micheletta, S. (2013) · Form re - Open Journal per la formazione in rete · doi:10.13128/formare-13251 · Cited by 1,016

تطوير نظام قائم على تحليلات التعلم لتقييم العمل الجماعي في بيئة تعلم الكتروني وأثره على إنجاز المشروعات التعليمية والاتجاه لدى طالبات تكنولوجيا التعليم والمعلومات ...

موسي, ع. ح. ف. (2021) · تکنولوجيا التعليم سلسلة دراسات وبحوث · doi:10.21608/tesr.2021.318447

Modelling the Contribution of Learning Analytics to Sustainable Education

Atta, E., Morphitis, A., & Efthymiou, L. (2023) · Advances in educational technologies and instructional design book series · doi:10.4018/978-1-6684-9527-8.ch013

Learning Analytics to Validate Academic Performance Analysis

Oliveira, P. F., & Matos, P. J. (2023) · International Conference on Engineering and Emerging Technologies (ICEET) · doi:10.1109/iceet60227.2023.10525855

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

Knowledge Map of Learner characteristics in Blended Learning: A Bibliometric Analysis

Abdullah, N. S., & Yaru, X. (2024) · International Journal of Advanced Research in Future Ready Learning and Education · doi:10.37934/frle.37.1.6588

Unsupervised online quiz-taking behaviors of undergraduate microbiology students: Canvas quiz-log analytics and clustering

Harindranathan, P., James, F., & Jemshid, K. (2025) · i-manager’s Journal on Educational Psychology · doi:10.26634/jet.21.4.21579

Contextualisation of Predictor Variables for Students’ At-Risk of Dropping Out of University

Jongile, S. (2022) · International journal on e-learning · doi:10.70725/451140hqaafd

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van Dijck, J., Poell, T., & de Waal, M. (2018) · Oxford University Press eBooks · doi:10.1093/oso/9780190889760.002.0005

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van Dijck, J., Poell, T., & de Waal, M. (2018) · Oxford University Press eBooks · doi:10.1093/oso/9780190889760.002.0007

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van Dijck, J., Poell, T., & de Waal, M. (2018) · Oxford University Press eBooks · doi:10.1093/oso/9780190889760.002.0003

Predictive Analytics for Personalized Education Pathways

author, & Harshitha, M. (2025) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem47531

Graduate students' understanding, perception, and preference of time management in online learning

Ali, M. (2021) · Scholars Archive - University at Albany (University at Albany, State University of New York) · doi:10.54014/32wm-1tcn

Predicting Achievers in an Online Theatre Course Designed upon the Principles of Sustainable Education

Ntanos, S., Georgakopoulos, I., & Zakopoulos, V. (2025) · Information · doi:10.3390/info16090780

Effects of perceived instructional support and self-regulated learning support on students’ learning behavior in an online learning environment

Juhaňák, L., Brücknerová, K., Jarina, J., Zounek, J., & Juřík, V. (2025) · PLoS ONE · doi:10.1371/journal.pone.0332296

The Influence of Gender and Academic Achievement on University Students' Perceptions of LMS-Based Self-Regulated Learning in Korea

Kim, Y., Kwak, S., & Koh, T. (2025) · OBM Neurobiology · doi:10.21926/obm.neurobiol.2504304

Finding Evidence of Critical Thinking in E-Learning: An Application from Sentiment and Discourse Analysis PerspectivePreprint

Torres-Carrión, P., Gómez, H. F., Chalacán, L. J. M., & Tapia, S. A. A. (2025) · Research Square · doi:10.21203/rs.3.rs-7110858/v1

Digital Tech Use and Employment Satisfaction Among Vocational College Students: Chain Mediation via Experience and Engagement

Chen, L., Huang, Y., Li, L., & Liu, C. (2025) · Communications in computer and information science · doi:10.1007/978-981-95-4499-8_26

Predicting Student Academic Performance Via Deep Learning: PyTorch-Based Study on UCI Dataset

Eli, A. A., Rahman, A., & Kshetri, N. (2025) · International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings) · doi:10.1109/aibthings66987.2025.11296171 · Cited by 1

Improving CSCL Performance: A Quasiexperimental Study with Control and Intervention Groups Comparing Informative and Suggestive Feedback

Zheng, L., Bhagat, K. K., Long, M., & Jha, N. K. (2025) · SAGE Open · doi:10.1177/21582440251363298

Sentiment-aware NLP in reflective writing: toward fair and adaptive dashboards for Indonesian project-based learning

Setiawan, I. (2026) · Interactive Learning Environments · doi:10.1080/10494820.2026.2624678 · Cited by 1

AI and ML for Continuous Monitoring of Cognitive Learning

Kanthavel, R., Freeda, R. A., & Dhaya, R. (2026) · Springer eBooks · doi:10.1007/978-981-95-4925-2_5

Formulating an Innovative Gamified Personalized Learning Ecosystem Integrating 3D/VR Environments, Machine Learning, and Business Intelligence

Raftopoulou, N.-M., & Petros, L. (2026) · Sociology Study · doi:10.17265/2159-5526/2026.01.002

Learning Analytics for Predicting Student Performance in Online Learning EnvironmentsPreprint

Amiri, S. M. H. (2026) · doi:10.24108/preprints-3114750

Closing equity gaps in first-year mathematics through structured intervention

Xufre, P., & Quintino, Á. C. (2026) · International Journal of Mathematical Education in Science and Technology · doi:10.1080/0020739x.2026.2653990

Exploring new dimensions of learning growth orientation: an empirical investigation

Tempelaar, D. T. (2026) · European Journal of Psychology of Education · doi:10.1007/s10212-026-01131-z

Educator Readiness in Leveraging Learning Analytics for Adaptive Learning Pathways and Student Success

Maluleke, A. F. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-6182-6.ch005

A Predictive-Cognitive Framework for Using Early Alert Analytics to Reduce Student Dropout in Higher Education

Mitra, A., Mukherjee, S., Jain, V., & Gupta, S. K. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-7946-3.ch005

Reward Processing and Disengagement: Clickstream Trajectories Reflecting Motivational Neural Substrates in Distance EducationPreprint

Bala, K. (2026) · Research Square · doi:10.21203/rs.3.rs-10095618/v1

Assessment and Evaluation in STEM Education in the Age of AI

Cuhadar, I., Yalvaç, B., & Akkır, A. (2026) · Maarif global education series · doi:10.1007/978-981-95-9023-0_7 · Cited by 1

Beyond Assessment: The Pedagogical Role of Digital Quizzes in Engineering Education

Karunaratne, T., Enoksson, F., Davis, R. L., Land, A., Bern, C., & Blomstervall, J. Y. (2026) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon67543.2026.11574346

Aligning Motivation, Expectations and Pedagogy: A Behavioural Science Framework for Widening Access Mature Students (WAMSs) in Higher Education

Uddin, N. (2026) · Behavioral Sciences · doi:10.3390/bs16071105

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

Protocol for evaluating the efficacy of GoaleTest, an AI-based vocational guidance system using RIASEC and Nonlinear Vector Alignment

Martí-Escolano, M. Á., & Rodríguez‐Lara, A. (2026) · F1000Research · doi:10.12688/f1000research.186798.1

Students Use of Self-testing Learning Strategies: What Dispositional Learning Analytics Can Tell

Tempelaar, D. T., Bátori, A., & Giesbers, B. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-21997-8_23

Lifestyle-based prediction of educational outcomes and factor impact diagnosis using K-fold validated FAST-ensemble models

Yang, M., & Wang, Z. (2026) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-026-03397-4

When Machines Can Pass Exams A Conceptual Framework for Ethical, Evidence-Based, and Experiential Assessment in Accounting EducationPreprint

Modugu, K. P. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7287958

Utilizing Big Data to Understand and Improve Student Collaboration in Group Projects

Tashlanova, N., Tishabayeva, I., Rustamov, I., Kholmatov, S., Inamov, O., & Khaliljonovich, S. S. (2026) · International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI) · doi:10.1109/icscai68849.2026.11649164

Assessment Alignment and Conceptual-Procedural Learning in Precalculus: A Learning Analytics Study of Performance

Eltoum, R. A. (2026) · International Conference on Education and Training Technologies (ICETT) · doi:10.1109/icett70124.2026.11666303

Analysis of learning in Moroccan secondary education: computer model and data-driven pedagogy

Lamalif, L., Machkour, M., Faris, S., & Mansouri, K. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1890197

基于深度学习的新工科学生学业质量评价模型研究

唐, 翔., 涂, 林., & 郑, 卫. (2026) · 教育发展论坛 · doi:10.65976/3080-0374.2026.10.022

Detecting population-level concept drift in educational assessment data: A vulnerability gradient framework for LLM-era predictive validity monitoring

Belbekri, A., Bouarroudj, W., Benmounah, K., & Medjroubi, M. L. (2026) · Intelligent Data Analysis · doi:10.1177/1088467x261488155

Who Gets Back on Track? Engagement-Level Differences in Metacognitive Attention Refocusing in Open and Distance Learning

Xue, Y., & Khalid, F. B. (2026) · SAGE Open · doi:10.1177/21582440261490990

AI-Powered Dashboards for Tracking Learning Behavior in Medical Students

AlFaress, M. Y., Janarthanan, M., Dixit, C. K., Jadaan, O. A., Natarajan, S. B., & Gupta, S. K. (2026) · Studies in big data · doi:10.1007/978-3-032-20412-7_30

Behavioural telemetry is a weak substitute for in-course assessment: predicting engineering students’ examination performance from digital laboratory process data

Wu, M., Jia, J., & Pham, T. T. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1916958

Embedding Self-regulation in a Quiz-Based Online Learning Environment: A Design-Based Research Study

Ansari, A., Shrestha, S., Atamanesh, A., Coşkun, R., Niri, G., Krebs, A., Dennerlein, S., Rohlfs, W., & Steinmaurer, A. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37982-5_6

Early warning of academic risk from the first four weeks of online learning activity and assessment performance: a leakage-controlled, demographic-data-minimizing study on the OULAD benchmark

Wu, M., Jia, J., & Pham, T. T. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1917092

Following freshman footprints: early student activity & academic performance

Gößl, T., Rumert, L., & Schneider, K. (2026) · European Journal of Higher Education · doi:10.1080/21568235.2026.2746668

Clustering Learner Trajectories to Identify Cognitive Fatigue: A Big Data Analysis of EdNet

Amazal, H. (2026) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-032-18927-1_1

Artificial intelligence in advancing medical learning, training and simulation

Meo, S. A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45973-3.00006-3

Stability and Sensitivity of Learning Analytics based Prediction Models

Tempelaar, D. T., Rienties, B., & Giesbers, B. (2015) · International Conference on Computer Supported Education · doi:10.5220/0005497001560166 · Cited by 14

Computer Assisted, Formative Assessment and Dispositional Learning Analytics in Learning Mathematics and Statistics

Tempelaar, D. T., Rienties, B., & Giesbers, B. (2014) · Communications in computer and information science · doi:10.1007/978-3-319-08657-6_7 · Cited by 16

Dispositional Learning Analytics for Supporting Individualized Learning Feedback

Tempelaar, D. T., Rienties, B., & Nguyen, Q. (2021) · Frontiers in Education · doi:10.3389/feduc.2021.703773 · Cited by 23

Learning Feedback Based on Dispositional Learning Analytics

Tempelaar, D. T., Nguyen, Q., & Rienties, B. (2019) · Intelligent systems reference library · doi:10.1007/978-3-030-13743-4_5 · Cited by 7

Formative assessment and learning analytics

Tempelaar, D. T., Heck, A., Cuypers, H., van der Kooij, H., & van de Vrie, E. (2013) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/2460296.2460337 · Cited by 75