Predicting academic performance: a systematic literature review

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Educational Data Mining: A review of the state of the art

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A meta systematic review of artificial intelligence in higher education: a call for increased ethics, collaboration, and rigour

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Educational data mining: prediction of students' academic performance using machine learning algorithms

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Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review

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A Systematic Literature Review of Student’ Performance Prediction Using Machine Learning Techniques

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The Robots Are Here: Navigating the Generative AI Revolution in Computing Education

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Multiclass Prediction Model for Student Grade Prediction Using Machine Learning

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Student Retention Using Educational Data Mining and Predictive Analytics: A Systematic Literature Review

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Student Dropout Prediction

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Predicting Student Performance and Its Influential Factors Using Hybrid Regression and Multi-Label Classification

Alshanqiti, A. M., & Namoun, A. (2020) · IEEE Access · doi:10.1109/access.2020.3036572 · Cited by 93

Machine learning for the educational sciences

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Mining Educational Data to Predict Students’ Performance through Procrastination Behavior

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Explainable Student Performance Prediction Models: A Systematic Review

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Proactive and reactive engagement of artificial intelligence methods for education: a review

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Learning analytics dashboards are increasingly becoming about learning and not just analytics - A systematic review

Paulsen, L., & Lindsay, E. (2024) · Education and Information Technologies · doi:10.1007/s10639-023-12401-4 · Cited by 111

Factores personales, familiares y académicos que afectan al fracaso escolar en la Educación Secundaria

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Should College Dropout Prediction Models Include Protected Attributes?

Yu, R., Lee, H., & Kizilcec, R. F. (2021) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3430895.3460139 · Cited by 101

Beyond the Hype: A Comprehensive Review of Current Trends in Generative AI Research, Teaching Practices, and Tools

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Students’ interaction patterns in different online learning activities and their relationship with motivation, self-regulated learning strategy and learning performance

Çebi, A., & Güyer, T. (2020) · Education and Information Technologies · doi:10.1007/s10639-020-10151-1 · Cited by 112

Toward Predicting Student’s Academic Performance Using Artificial Neural Networks (ANNs)

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Notional Machines in Computing Education

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Educational data mining: a systematic review of research and emerging trends

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Predicting Student Performance from Online Engagement Activities Using Novel Statistical Features

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Predicting Student Dropout in Self-Paced MOOC Course Using Random Forest Model

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AI-enhanced education: exploring the impact of AI literacy on generation Z’s academic performance in Northern India

Singh, E., Vasishta, P., & Singla, A. (2025) · Quality Assurance in Education · doi:10.1108/qae-02-2024-0037 · Cited by 67

Predicting Students’ Academic Performance with Conditional Generative Adversarial Network and Deep SVM

Sarwat, S., Ullah, N., Sadiq, S., Saleem, R., Umer, M., Eshmawi, A. A., Mohamed, A., & Ashraf, I. (2022) · Sensors · doi:10.3390/s22134834 · Cited by 74

Deep learning with data transformation and factor analysis for student performance prediction

Dien, T. T., Hoai, S., Thanh-Hai, N., & Thai-Nghe, N. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.0110886 · Cited by 58

Using Educational Data Mining to Predict Students’ Academic Performance for Applying Early Interventions

Alturki, S., & Alturki, N. M. (2021) · Journal of Information Technology Education Innovations in Practice · doi:10.28945/4835 · Cited by 54

Predicting course achievement of university students based on their procrastination behaviour on Moodle

Yang, Y., Hooshyar, D., Pedaste, M., Wang, M., Huang, Y., & Lim, H. (2020) · Soft Computing · doi:10.1007/s00500-020-05110-4 · Cited by 53

Contextualizing the current state of research on the use of machine learning for student performance prediction: A systematic literature review

Alalawi, K., Athauda, R., & Chiong, R. (2023) · Engineering Reports · doi:10.1002/eng2.12699 · Cited by 57

Machine learning approach to student performance prediction of online learning

Wang, J., & Yu, Y. S. (2025) · PLoS ONE · doi:10.1371/journal.pone.0299018 · Cited by 47

Automated Assessment and Microlearning Units as Predictors of At-Risk Students and Students’ Outcomes in the Introductory Programming Courses

Skalka, J., & Drlík, M. (2020) · Applied Sciences · doi:10.3390/app10134566 · Cited by 46

Academic achievement prediction in higher education through interpretable modeling

Wang, S., & Luo, B. (2024) · PLoS ONE · doi:10.1371/journal.pone.0309838 · Cited by 64

Predicting students’ academic performance by mining the educational data through machine learning-based classification model

Nayak, P., Vaheed, S., Gupta, S., & Mohan, N. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-11706-8 · Cited by 41

Integrating Learning Analytics and Collaborative Learning for Improving Student’s Academic Performance

Rafique, A., Khan, M. S., Jamal, M. H., Tasadduq, M., Rustam, F., Lee, E., Washington, P. B., & Ashraf, I. (2021) · IEEE Access · doi:10.1109/access.2021.3135309 · Cited by 41

The role of indoor positioning analytics in assessment of simulation‐based learning

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Determining factors that affect student performance using various machine learning methods

Beckham, N. R., Akeh, L. J., Mitaart, G. N. P., & Moniaga, J. V. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2022.12.174 · Cited by 40

Predicting Dropout Using High School and First-semester Academic Achievement Measures

Kiss, B., Nagy, M., Molontay, R., & Csabay, B. (2019) · International Conference on Emerging eLearning Technologies and Applications (ICETA) · doi:10.1109/iceta48886.2019.9040158 · Cited by 40

Predicting Student Academic Performance by Means of Associative Classification

Cagliero, L., Canale, L., Farinetti, L., Baralis, E., & Venuto, E. (2021) · Applied Sciences · doi:10.3390/app11041420 · Cited by 38

Deep learning for early performance prediction of introductory programming students: a comparative and explanatory study

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Prediction of student academic performance using Moodle data from a Further Education setting.

Quinn, R., & Gray, G. (2019) · Irish Journal of Technology Enhanced Learning · doi:10.22554/ijtel.v5i1.57 · Cited by 35

Predicting students’ future final exam results using machine learning algorithms: A comparative analysis

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Predicting academic success of autistic students in higher education

Bakker, T., Krabbendam, L., Bhulai, S., Meeter, M., & Begeer, S. (2023) · Autism · doi:10.1177/13623613221146439 · Cited by 38

Prediction of Student Academic Performance using Neural Network, Linear Regression and Support Vector Regression: A Case Study

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Identification of the Factors That Influence University Learning with Low-Code/No-Code Artificial Intelligence Techniques

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Role of convolutional features and machine learning for predicting student academic performance from MOODLE data

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Exploring the relationship between LMS interactions and academic performance: A Learning Cycle approach

Hernández-García, Á., Cuenca-Enrique, C., Del-Río-Carazo, L., & Iglesías-Pradas, S. (2024) · Computers in Human Behavior · doi:10.1016/j.chb.2024.108183 · Cited by 55

A Human-Centered Review of Algorithms in Decision-Making in Higher Education

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Developing Web-based Support Systems for Predicting Poor-performing Students using Educational Data Mining Techniques

Sokkhey, P., & Okazaki, T. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.0110704 · Cited by 31

Prediction of Student’s Performance With Learning Coefficients Using Regression Based Machine Learning Models

Asthana, P., Mishra, S., Gupta, N., Derawi, M. O., & Kumar, A. (2023) · IEEE Access · doi:10.1109/access.2023.3294700 · Cited by 30

Educational Data Mining: Employing Machine Learning Techniques and Hyperparameter Optimization to Improve Students’ Academic Performance

Bellaj, M., Bendahmane, A., Boudra, S., & Sefian, M. L. (2024) · International Journal of Online and Biomedical Engineering (iJOE) · doi:10.3991/ijoe.v20i03.46287 · Cited by 30

Accurate, timely, and portable: Course-agnostic early prediction of student performance from LMS logs

Santos, R., & Henriques, R. (2023) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2023.100175 · Cited by 39

Complementary CatBoost based on residual error for student performance prediction

Fan, Z., Gou, J., & Weng, S. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.111265 · Cited by 29

Integrating machine learning into business and management in the age of artificial intelligence

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A neuro-fuzzy model for predicting and analyzing student graduation performance in computing programs

Mehdi, R. A. K., & Nachouki, M. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11205-2 · Cited by 34

Fuzzy Artificial Intelligence—Based Model Proposal to Forecast Student Performance and Retention Risk in Engineering Education: An Alternative for Handling with Small Data

Bressane, A., Spalding, M., Zwirn, D., Loureiro, A. I. S., Bankole, A. O., Negri, R. G., de Brito, I., Formiga, J. K. S., Medeiros, L. C. D. C., Pampuch, L. A., et al. (2022) · Sustainability · doi:10.3390/su142114071 · Cited by 29

The Role of Stakeholders Participation, Goal Directness and Learning Context in Determining Student Academic Performance: Student Engagement as a Mediator

Sattar, T., Ullah, M. I., & Ahmad, B. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.875174 · Cited by 41

Predicting secondary school student performance using a double particle swarm optimization-based categorical boosting model

Fan, Z., Gou, J., & Wang, C. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106649 · Cited by 28

Student Performance Prediction on Primary and Secondary Schools-A Systematic Literature Review

Rodrigues, L. S., dos Santos, M., Costa, I. P. D. A., & Moreira, M. Â. L. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.11.229 · Cited by 27

Evaluating Online Learning Adaptability in Students Using Machine Learning-Based Techniques: A Novel Analytical Approach

Khan, A. B. F., & Samad, S. R. A. (2024) · Education Science and Management · doi:10.56578/esm020103 · Cited by 27

Comparative analysis of feature selection and extraction methods for student performance prediction across different machine learning models

Hemdanou, A. L., Sefian, M. L., Achtoun, Y., & Tahırı, I. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100301 · Cited by 37

Customized Rule-Based Model to Identify At-Risk Students and Propose Rational Remedial Actions

Albreiki, B., Habuza, T., Shuqfa, Z., Serhani, M. A., Zaki, N., & Harous, S. (2021) · Big Data and Cognitive Computing · doi:10.3390/bdcc5040071 · Cited by 27

ML-based intelligent real-time feedback system for blended classroom

Biswas, U., & Bhattacharya, S. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-11949-5 · Cited by 31

Early prediction of student performance in CS1 programming courses

Llanos, J., Bucheli, V., & Restrepo‐Calle, F. (2023) · PeerJ Computer Science · doi:10.7717/peerj-cs.1655 · Cited by 26

Can Synthetic Data be Fair and Private? A Comparative Study of Synthetic Data Generation and Fairness Algorithms

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Academic Performance Prediction Using Machine Learning Approaches: A Survey

Pan, J., Zhao, Z., & Han, D. (2025) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2025.3554174 · Cited by 26

A Generic AI-Based Technique for Assessing Student Performance in Conducting Online Virtual and Remote Controlled Laboratories

El‐Haleem, A. M. A., Eid, M. M., Elmesalawy, M. M., & Hassan, H. A. (2022) · IEEE Access · doi:10.1109/access.2022.3227505 · Cited by 25

Prediction of students’ procrastination behaviour through their submission behavioural pattern in online learning

Yang, Y., Hooshyar, D., Pedaste, M., Wang, M., Huang, Y., & Lim, H. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02041-8 · Cited by 25

A Study of Keystroke Data in Two Contexts

Edwards, J., Leinonen, J., & Hellas, A. (2020) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3328778.3366863 · Cited by 24

Educational Anomaly Analytics: Features, Methods, and Challenges

Guo, T., Bai, X., Tian, X., Firmin, S., & Xia, F. (2022) · Frontiers in Big Data · doi:10.3389/fdata.2021.811840 · Cited by 31

Does the Early Bird Catch the Worm? Earliness of Students' Work and its Relationship with Course Outcomes

Leinonen, J., Castro, F. E. V., & Hellas, A. (2021) · Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education V. 1 · doi:10.1145/3430665.3456383 · Cited by 23

Contrasting Classical and Machine Learning Approaches in the Estimation of Value-Added Scores in Large-Scale Educational Data

Levy, J., Mussack, D., Brunner, M., Keller, U., Cardoso-Leite, P., & Fischbach, A. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.02190 · Cited by 24

Predictive learning analytics and the creation of emotionally adaptive learning environments in higher education institutions: a study of students' affect responses

Joseph‐Richard, P., Uhomoibhi, J. O., & Jaffrey, A. (2021) · International Journal of Information and Learning Technology · doi:10.1108/ijilt-05-2020-0077 · Cited by 23

Spatial Skills and Demographic Factors in CS1

Ly, A., Parkinson, J., Cutts, Q. I., Liut, M., & Petersen, A. (2021) · Koli Calling International Conference on Computing Education Research · doi:10.1145/3488042.3488049 · Cited by 23

Ensemble-Learning Techniques for Predicting Student Performance on Video-Based Learning

Teoh, C.-W., Ho, S.-B., Dollmat, K. S., & Tan, C.-H. (2022) · International Journal of Information and Education Technology · doi:10.18178/ijiet.2022.12.8.1679 · Cited by 22

Predicting Computer Science Student's Performance using Logistic Regression

Sagala, T. M. N., Permai, S. D., Gunawan, A. A. S., Barus, R. O., & Meriko, C. (2022) · International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) · doi:10.1109/isriti56927.2022.10052968 · Cited by 22

Visual-Predictive Data Analysis Approach for the Academic Performance of Students from a Peruvian University

Granados, D. O., Ugalde, J., Rodrigo Salas, F., Torres, R., & López‐Gonzales, J. L. (2022) · Applied Sciences · doi:10.3390/app122111251 · Cited by 21

Clustering-based knowledge graphs and entity-relation representation improves the detection of at risk students

Albreiki, B., Habuza, T., Palakkal, N., & Zaki, N. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-11938-8 · Cited by 29

How the predictors of math achievement change over time: A longitudinal machine learning approach.

Lavelle-Hill, R. E., Frenzel, A. C., Goetz, T., Lichtenfeld, S., Marsh, H. W., Pekrun, R. H., Sakaki, M., Smith, G. J. D., & Murayama, K. (2024) · Journal of Educational Psychology · doi:10.1037/edu0000863 · Cited by 29

Motivation and Academic Performance of Secondary Students in Science: A Correlational Study

Sabanal, G. J. A., Reputana, K. G. D., Palwa, S. S., Labandero, C. L. H., & Alimbon, J. A. (2023) · Asian Journal of Science Education · doi:10.24815/ajse.v5i2.31668 · Cited by 26

Temporal and Between-Group Variability in College Dropout Prediction

Glandorf, D., Lee, H. R., Orona, G. A., Pumptow, M., Yu, R., & Fischer, C. (2024) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3636555.3636906 · Cited by 19

The Role of Predictive Analytics in Personalizing Education

Dwivedi, D. N., Mahanty, G., & Dwivedi, V. N. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-2169-0.ch003 · Cited by 19

Time-on-Task Metrics for Predicting Performance

Leinonen, J., Castro, F. E. V., & Hellas, A. (2022) · Proceedings of the 53rd ACM Technical Symposium on Computer Science Education · doi:10.1145/3478431.3499359 · Cited by 18

Study Behavior in Computing Education—A Systematic Literature Review

Lorås, M., Sindre, G., Trætteberg, H., & Aalberg, T. (2021) · ACM Transactions on Computing Education · doi:10.1145/3469129 · Cited by 23

Exploring statistical approaches for predicting student dropout in education: a systematic review and meta-analysis

Venkatesan, R. G., Karmegam, D., & Mappillairaju, B. (2023) · Journal of Computational Social Science · doi:10.1007/s42001-023-00231-w · Cited by 18

Early Prediction of Students at Risk of Failing a Face-to-Face Course in Power Electronic Systems

Alcaraz, R., Martínez‐Rodrigo, A., Zangróniz, R., & Rieta, J. J. (2021) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2021.3118279 · Cited by 19

Machine Learning Approaches for Student Performance Prediction

Gupta, S. R., & Agarwal, J. (2022) · International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) · doi:10.1109/icrito56286.2022.9964821 · Cited by 18

A model for predicting academic performance on standardised tests for lagging regions based on machine learning and Shapley additive explanations

Suaza-Medina, M. E., Peñabaena‐Niebles, R., & Jubiz‐Diaz, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-76596-3 · Cited by 17

Motion-Based Educational Games: Using Multi-Modal Data to Predict Player’s Performance

Lee‐Cultura, S., Sharma, K., Papavlasopoulou, S., & Giannakos, M. N. (2020) · IEEE Conference on Games (CoG) · doi:10.1109/cog47356.2020.9231892 · Cited by 17

Student performance prediction in higher education: A comprehensive review

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Study Major, Gender, and Confidence Gap: Effects on Experience, Performance, and Self-Efficacy in Introductory Programming

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Computational Intelligence Enabled Student Performance Estimation in the Age of COVID-19

Bansal, V., Buckchash, H., & Raman, B. (2021) · SN Computer Science · doi:10.1007/s42979-021-00944-7 · Cited by 16

Mapping from proximity traces to socio‐spatial behaviours and student progression at the school

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Curricular Concept Maps as Structured Learning Diaries: Collecting Data on Self-Regulated Learning and Conceptual Thinking for Learning Analytics Applications

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