Toward a New Hybrid System for the Prediction of Student Academic Performance
Received: 4 June 2025 | Revised: 5 July 2025, 27 July 2025, and 17 August 2025 | Accepted: 20 August 2025 | Online: 8 December 2025
Corresponding author: Younes Zidani
Abstract
Accurately predicting Student Academic Performance (SAP) is a complex task due to the interplay of cognitive, behavioral, and institutional factors. This paper presents a method for predicting SAP using a hybrid classification algorithm combining four complementary algorithms: Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Networks (ANNs). We employ a dataset of 32,000 students from the Open University of the United Kingdom for training and testing our hybrid system. The dataset contains information on student registration, courses, assessments, and interactions with resources. The algorithm is implemented using MATLAB software. We evaluate our system on several standard datasets and compare its performance with that of individual algorithms. The proposed method achieves an accuracy of 95.35%, a precision of 97.62%, a recall of 96.33%, and an F1-score of 96.97%. We used McNemar's test to assess the statistical significance of the model's performance, which reveals a very low p-value, indicating the robustness of the results. This system will help teachers predict students' exam results and take proactive measures to prevent academic failure.
Keywords:
Student Academic Performance (SAP), prediction, learning analytics, Machine Learning (ML), Random Forest (RF), Logistic Regression (LR), Artificial Neural Network (ANN), Support Vector Machine (SVM)Downloads
References
M. T. Batayneh, "Economic, Social, And Academic Factors Affecting Academic Achievement of Students at The University of Taibah," Res Militaris, vol. 13, no. 1, pp. 1660–1679, 2023.
A. Kukkar, R. Mohana, A. Sharma, and A. Nayyar, "Prediction of student academic performance based on their emotional wellbeing and interaction on various e-learning platforms," Education and Information Technologies, vol. 28, no. 8, pp. 9655–9684, Aug. 2023. DOI: https://doi.org/10.1007/s10639-022-11573-9
M. L. Bernacki, M. M. Chavez, and P. M. Uesbeck, "Predicting achievement and providing support before STEM majors begin to fail," Computers & Education, vol. 158, Dec. 2020, Art. no. 103999. DOI: https://doi.org/10.1016/j.compedu.2020.103999
B. K. Yousafzai et al., "Student-Performulator: Student Academic Performance Using Hybrid Deep Neural Network," Sustainability, vol. 13, no. 17, Sep. 2021, Art. no. 9775. DOI: https://doi.org/10.3390/su13179775
S. Hussain and M. Q. Khan, "Student-Performulator: Predicting Students' Academic Performance at Secondary and Intermediate Level Using Machine Learning," Annals of Data Science, vol. 10, no. 3, pp. 637–655, Jun. 2023. DOI: https://doi.org/10.1007/s40745-021-00341-0
C. F. Rodríguez-Hernández, E. Cascallar, and E. Kyndt, "Socio-economic status and academic performance in higher education: A systematic review," Educational Research Review, vol. 29, Feb. 2020, Art. no. 100305. DOI: https://doi.org/10.1016/j.edurev.2019.100305
J. C. Ayala and G. Manzano, "Academic performance of first-year university students: the influence of resilience and engagement," Higher Education Research & Development, vol. 37, no. 7, pp. 1321–1335, Nov. 2018. DOI: https://doi.org/10.1080/07294360.2018.1502258
C. MacCann, Y. Jiang, L. E. R. Brown, K. S. Double, M. Bucich, and A. Minbashian, "Emotional intelligence predicts academic performance: A meta-analysis," Psychological Bulletin, vol. 146, no. 2, pp. 150–186, Feb. 2020. DOI: https://doi.org/10.1037/bul0000219
B. A. Al-Sheeb, A. M. Hamouda, and G. M. Abdella, "Modeling of student academic achievement in engineering education using cognitive and non-cognitive factors," Journal of Applied Research in Higher Education, vol. 11, no. 2, pp. 178–198, Jan. 2019. DOI: https://doi.org/10.1108/JARHE-10-2017-0120
M. Goga, S. Kuyoro, and N. Goga, "A Recommender for Improving the Student Academic Performance," Procedia - Social and Behavioral Sciences, vol. 180, pp. 1481–1488, May 2015. DOI: https://doi.org/10.1016/j.sbspro.2015.02.296
S. Huang and N. Fang, "Work in progress — Prediction of students' academic performance in an introductory engineering course," in 2011 Frontiers in Education Conference, Rapid City, SD, USA, 2011, pp. S4D-1-S4D-3. DOI: https://doi.org/10.1109/FIE.2011.6142729
O. H. T. Lu, A. Y. Q. Huang, J. C. H. Huang, A. J. Q. Lin, H. Ogata, and S. J. H. Yang, "Applying Learning Analytics for the Early Prediction of Students' Academic Performance in Blended Learning," Journal of Educational Technology & Society, vol. 21, no. 2, pp. 220–232, Apr. 2018.
S. Mammadov, "Big Five personality traits and academic performance: A meta-analysis," Journal of Personality, vol. 90, no. 2, pp. 222–255, Apr. 2022. DOI: https://doi.org/10.1111/jopy.12663
C. Romero and S. Ventura, "Educational data mining: A survey from 1995 to 2005," Expert Systems with Applications, vol. 33, no. 1, pp. 135–146, Jul. 2007. DOI: https://doi.org/10.1016/j.eswa.2006.04.005
L. Juhaňák, J. Zounek, and L. Rohlíková, "Using process mining to analyze students' quiz-taking behavior patterns in a learning management system," Computers in Human Behavior, vol. 92, pp. 496–506, Mar. 2019. DOI: https://doi.org/10.1016/j.chb.2017.12.015
C. Romero and S. Ventura, "Educational Data Mining: A Review of the State of the Art," IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 40, no. 6, pp. 601–618, Nov. 2010. DOI: https://doi.org/10.1109/TSMCC.2010.2053532
R. Baker and G. Siemens, "Educational Data Mining and Learning Analytics," in The Cambridge Handbook of the Learning Sciences, 2nd ed., R. K. Sawyer, Ed. Cambridge, UK: Cambridge University Press, 2014, pp. 253–272. DOI: https://doi.org/10.1017/CBO9781139519526.016
E. T. Lau, L. Sun, and Q. Yang, "Modelling, prediction and classification of student academic performance using artificial neural networks," SN Applied Sciences, vol. 1, no. 9, Aug. 2019, Art. no. 982. DOI: https://doi.org/10.1007/s42452-019-0884-7
M. F. Musso, E. C. Cascallar, N. Bostani, and M. Crawford, "Identifying Reliable Predictors of Educational Outcomes Through Machine-Learning Predictive Modeling," Frontiers in Education, vol. 5, Jul. 2020, Art. no. 104. DOI: https://doi.org/10.3389/feduc.2020.00104
C. F. Rodríguez-Hernández, M. Musso, E. Kyndt, and E. Cascallar, "Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation," Computers and Education: Artificial Intelligence, vol. 2, Jan. 2021, Art. no. 100018. DOI: https://doi.org/10.1016/j.caeai.2021.100018
M. Yağcı, "Educational data mining: prediction of students' academic performance using machine learning algorithms," Smart Learning Environments, vol. 9, no. 1, Mar. 2022, Art. no. 11. DOI: https://doi.org/10.1186/s40561-022-00192-z
Z. Ahmad and E. Shahzadi, "Prediction of Students' Academic Performance Using Artificial Neural Network," Bulletin of Education and Research, vol. 40, no. 3, pp. 157–164, Dec. 2018.
A. Alshanqiti and A. Namoun, "Predicting Student Performance and Its Influential Factors Using Hybrid Regression and Multi-Label Classification," IEEE Access, vol. 8, pp. 203827–203844, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.3036572
I. Đurđević Babić, "Machine learning methods in predicting the student academic motivation," Croatian Operational Research Review, vol. 8, no. 2, pp. 443–461, Dec. 2017. DOI: https://doi.org/10.17535/crorr.2017.0028
M. Tsiakmaki, G. Kostopoulos, S. Kotsiantis, and O. Ragos, "Transfer Learning from Deep Neural Networks for Predicting Student Performance," Applied Sciences, vol. 10, no. 6, Mar. 2020, Art. no. 2145. DOI: https://doi.org/10.3390/app10062145
S. TK and Midhunchakkravarthy, "Academic Performance Prediction of At-Risk Students using Machine Learning Techniques," in 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering, Greater Noida, India, 2023, pp. 1222–1227. DOI: https://doi.org/10.1109/ICACITE57410.2023.10183199
E. A. Abbas, N. A. Hussein, and R. Alubady, "Machine Learning for Link Prediction between Nodes in Complex Networks," Fusion: Practice and Applications, vol. 18, no. 1, pp. 41–55, 2025. DOI: https://doi.org/10.54216/FPA.180104
I. D. Mienye and N. Jere, "A Survey of Decision Trees: Concepts, Algorithms, and Applications," IEEE Access, vol. 12, pp. 86716–86727, 2024. DOI: https://doi.org/10.1109/ACCESS.2024.3416838
Q. Hu and H. Rangwala, "Course-Specific Markovian Models for Grade Prediction," in 22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, Melbourne, Australia, 2018, pp. 29–41. DOI: https://doi.org/10.1007/978-3-319-93037-4_3
B. Xu and D. Yang, "Motivation Classification and Grade Prediction for MOOCs Learners," Computational Intelligence and Neuroscience, vol. 2016, no. 1, Jan. 2016, Art. no. 2174613. DOI: https://doi.org/10.1155/2016/2174613
Y. Zhang, Y. Yun, H. Dai, J. Cui, and X. Shang, "Graphs Regularized Robust Matrix Factorization and Its Application on Student Grade Prediction," Applied Sciences, vol. 10, no. 5, Mar. 2020, Art. no. 1755. DOI: https://doi.org/10.3390/app10051755
A. Cakmak, "Predicting Student Success in Courses via Collaborative Filtering," International Journal of Intelligent Systems and Applications in Engineering, vol. 5, no. 1, pp. 10–17, Mar. 2017. DOI: https://doi.org/10.18201/ijisae.2017526690
C. Kaope and Y. Pristyanto, "The Effect of Class Imbalance Handling on Datasets Toward Classification Algorithm Performance," MATRIK, vol. 22, no. 2, pp. 227–238, Mar. 2023. DOI: https://doi.org/10.30812/matrik.v22i2.2515
N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "SMOTE: Synthetic Minority Over-sampling Technique," Journal of Artificial Intelligence Research, vol. 16, pp. 321–357, Jun. 2002. DOI: https://doi.org/10.1613/jair.953
J. Kuzilek, M. Hlosta, and Z. Zdrahal, "Open University Learning Analytics dataset," Scientific Data, vol. 4, no. 1, Nov. 2017, Art. no. 170171. DOI: https://doi.org/10.1038/sdata.2017.171
C. Romero and S. Ventura, "Data mining in education," WIREs Data Mining and Knowledge Discovery, vol. 3, no. 1, pp. 12–27, Jan. 2013. DOI: https://doi.org/10.1002/widm.1075
S. T. Jishan, R. I. Rashu, N. Haque, and R. M. Rahman, "Improving accuracy of students' final grade prediction model using optimal equal width binning and synthetic minority over-sampling technique," Decision Analytics, vol. 2, no. 1, Mar. 2015, Art. no. 1. DOI: https://doi.org/10.1186/s40165-014-0010-2
S. Kumari, D. Kumar, and M. Mittal, "An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier," International Journal of Cognitive Computing in Engineering, vol. 2, pp. 40–46, Jun. 2021. DOI: https://doi.org/10.1016/j.ijcce.2021.01.001
P. Mahajan, S. Uddin, F. Hajati, and M. A. Moni, "Ensemble Learning for Disease Prediction: A Review," Healthcare, vol. 11, no. 12, Jun. 2023, Art. no. 1808. DOI: https://doi.org/10.3390/healthcare11121808
T. G. T. Tran and T. N. Tran, "Enhancing Regression Accuracy and Reliability with Ensemble Learning and Iterative Hyperparameter Optimization," Engineering, Technology & Applied Science Research, vol. 15, no. 4, pp. 24541–24548, Aug. 2025. DOI: https://doi.org/10.48084/etasr.11266
A. B. Altamimi, "Big Data in Education: Students at Risk as a Case Study," Engineering, Technology & Applied Science Research, vol. 13, no. 5, pp. 11705–11714, Oct. 2023. DOI: https://doi.org/10.48084/etasr.6190
E. C. Mutlu, T. Oghaz, A. Rajabi, and I. Garibay, "Review on Learning and Extracting Graph Features for Link Prediction," Machine Learning and Knowledge Extraction, vol. 2, no. 4, pp. 672–704, Dec. 2020. DOI: https://doi.org/10.3390/make2040036
S. Haghani and M. R. Keyvanpour, "A systemic analysis of link prediction in social network," Artificial Intelligence Review, vol. 52, no. 3, pp. 1961–1995, Oct. 2019. DOI: https://doi.org/10.1007/s10462-017-9590-2
S. Kim and W. Lee, "Does McNemar's test compare the sensitivities and specificities of two diagnostic tests?," Statistical Methods in Medical Research, vol. 26, no. 1, pp. 142–154, Feb. 2017. DOI: https://doi.org/10.1177/0962280214541852
R. Saha, "Comparing Classification Models on Kepler Data." arXiv, 2019.
Y. Jedidi et al., "Predicting students' academic performance and modeling using data mining techniques," Mathematical Modeling and Computing, vol. 11, no. 3, pp. 814–825, Sep. 2024. DOI: https://doi.org/10.23939/mmc2024.03.814
H. Chavez, B. Chavez-Arias, S. Contreras-Rosas, J. M. Alvarez-Rodríguez, and C. Raymundo, "Artificial neural network model to predict student performance using nonpersonal information," Frontiers in Education, vol. 8, Feb. 2023, Art. no. 1106679. DOI: https://doi.org/10.3389/feduc.2023.1106679
F. A. Al-azazi and M. Ghurab, "ANN-LSTM: A deep learning model for early student performance prediction in MOOC," Heliyon, vol. 9, no. 4, Apr. 2023, Art. no. e15382. DOI: https://doi.org/10.1016/j.heliyon.2023.e15382
Downloads
How to Cite
License
Copyright (c) 2025 Younes Zidani, Younes Zahrou, Salah Nissabouri, Moulay El Houssine Ech-Chhibat, Khalifa Mansouri
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
