Analysis of AI- Empower Predictive Models for Predicting Student Performance in Higher Education
DOI:
https://doi.org/10.29304/jqcsm.2025.17.11967Keywords:
Predictive analytics, Student performance, Ensemble models, Classification & Regression, Deep learningAbstract
This research presents a study and review of previous research. It demonstrates the use of the most important techniques in predictive analytics and machine learning algorithms to analyze historical data and accurately predict future outcomes of student performance. This research focuses on specific objectives, including techniques used to identify students at risk of poor academic performance or dropout and enable timely interventions to improve outcomes. Moreover, in this paper, the answers to questions, such as their benefits and limitations. By using data from sources such as academic records, attendance, and engagement metrics, educational institutions can uncover patterns in student behavior and performance. The research also presents the most important findings that were reached. The results show that predictive analytics not only improves individual student performance but also enhances the effectiveness of the institution by promoting a supportive and proactive learning environment. This approach provides educators and educational institutions with actionable insights to effectively enhance student retention and enhance academic achievement.
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References
J. Douglass, "The global higher education race," International Higher Education, no. 49, 2007.
A. H. Alsaeedi, D. Al-Shammary, S. M. Hadi, K. Ahmed, A. Ibaida, and N. AlKhazraji, "A proactive grey wolf optimization for improving bioinformatic systems with high dimensional data," International Journal of Information Technology, vol. 16, no. 8, pp. 4797-4814, 2024.
W. W. Eckerson, "Predictive analytics," Extending the Value of Your Data Warehousing Investment. TDWI Best Practices Report, vol. 1, pp. 1-36, 2007.
R. V. McCarthy, M. M. McCarthy, W. Ceccucci, L. Halawi, R. McCarthy, M. McCarthy, W. Ceccucci, and L. Halawi, Applying predictive analytics. Springer, 2022.
A. H. Alsaeedi, S. M. Hadi, and Y. Alazzawi, "Adaptive Gamma and Color Correction for Enhancing Low-Light Images," International Journal of Intelligent Engineering and Systems, vol. 17, no. 4, pp. 188-201, 2024.
A. H. Alsaeedi, H. H. R. Al-Mahmood, Z. F. Alnaseri, M. R. Aziz, D. Al-Shammary, A. Ibaida, and K. Ahmed, "Fractal feature selection model for enhancing high-dimensional biological problems," BMC bioinformatics, vol. 25, no. 1, p. 12, 2024.
A. L. Albukhnefis, T. T. Al-Fatlawi, and A. H. Alsaeedi, "Image Segmentation Techniques: An In-Depth Review and Analysis," Journal of Al-Qadisiyah for Computer Science and Mathematics, vol. 16, no. 2, pp. Page 195-215, 2024.
D. Al-Shammary, M. Radhi, A. H. AlSaeedi, A. M. Mahdi, A. Ibaida, and K. Ahmed, "Efficient ECG classification based on the probabilistic Kullback-Leibler divergence," Informatics in Medicine Unlocked, vol. 47, p. 101510, 2024.
A. Y. Huang, O. H. Lu, J. C. Huang, C. Yin, and S. J. Yang, "Predicting students’ academic performance by using educational big data and learning analytics: evaluation of classification methods and learning logs," Interactive Learning Environments, vol. 28, no. 2, pp. 206-230, 2020.
A. H. Alsaeedi, M. A. Al-Sharqi, S. S. Alkafagi, R. R. Nuiaa, A. S. D. Alfoudi, S. Manickam, A. M. Mahdi, and A. M. Otebolaku, "Hybrid extend particle swarm optimization (EPSO) model for enhancing the performance of MANET routing protocols," Journal of Al-Qadisiyah for computer science and mathematics, vol. 15, no. 1, pp. Page 127-136, 2023.
T. M. Renick, "Predictive analytics, academic advising, early alerts, and student success," Big data on campus: Data analytics and decision making in higher education, pp. 177-197, 2020.
R. R. Nuiaa, S. A. A. A. Alsaidi, B. K. Mohammed, A. H. Alsaeedi, Z. A. A. Alyasseri, S. Manickam, and M. A. Hussain, "Enhanced PSO Algorithm for Detecting DRDoS Attacks on LDAP Servers," International Journal of Intelligent Engineering & Systems, vol. 16, no. 5, 2023.
R. Al Ogaili, A. H. Alsaeedi, S. S. Alkafagi, and A. S. D. Alfoudi, "A critical review of Optimization MANET routing protocols," Wasit Journal of Computer and Mathematics Science, vol. 1, no. 4, pp. 44-54, 2022.
E. Foster and R. Siddle, "The effectiveness of learning analytics for identifying at-risk students in higher education," Assessment & Evaluation in Higher Education, vol. 45, no. 6, pp. 842-854, 2020.
R. R. Nuiaa, S. Manickam, A. H. Alsaeedi, and D. E. J. Al-Shammary, "Evolving Dynamic Fuzzy Clustering (EDFC) to Enhance DRDoS_DNS Attacks Detection Mechnism," International Journal of Intelligent Engineering and Systems, vol. 15, no. 1, pp. 509-519, 2022.
R. R. Nuiaa, S. Manickam, and A. H. Alsaeedi, "A Comprehensive Review of DNS-based Distributed Reflection Denial of Service (DRDoS) Attacks: State-of-the-Art," International Journal on Advanced Science, Engineering and Information, vol. 6, no. 12, pp. 2452-2461, 2022.
R. R. Nuiaa, S. Manickam, and A. S. D. Alfoudi, "Dynamic Evolving Cauchy Possibilistic Clustering Based on the Self-Similarity Principle (DECS) for Enhancing Intrusion Detection System," 2022.
X. Bai, F. Zhang, J. Li, T. Guo, A. Aziz, A. Jin, and F. Xia, "Educational big data: Predictions, applications and challenges," Big Data Research, vol. 26, p. 100270, 2021.
M. A. Ashaari, K. S. D. Singh, G. A. Abbasi, A. Amran, and F. J. Liebana-Cabanillas, "Big data analytics capability for improved performance of higher education institutions in the Era of IR 4.0: A multi-analytical SEM & ANN perspective," Technological Forecasting and Social Change, vol. 173, p. 121119, 2021.
S. M. Ali, A. H. Alsaeedi, D. Al-Shammary, H. H. Alsaeedi, and H. W. Abid, "Efficient intelligent system for diagnosis pneumonia (SARSCOVID19) in X-ray images empowered with initial clustering," Indones. J. Electr. Eng. Comput. Sci, vol. 22, no. 1, pp. 241-251, 2021.
A. S. Alfoudi, A. H. Alsaeedi, M. H. Abed, A. M. Otebolaku, and Y. Sajid Razooqi, "Palm Vein Identification Based on Hybrid Feature Selection Model," International Journal of Intelligent Engineering and Systems, vol. 14, no. 5, pp. 469-478, 2021.
P. Alok, "Evaluation of Machine Learning Methods for Prediction Student Performance."
D. S. Singh, P. Jain, A. Bhat, A. Vishwakarma, Hod, A. Proffessor, and P. Student, "Predictive Analytics for Student Performance and Academic Success: A Multi-Model Approach."
R. Parkavi, P. Karthikeyan, S. Sujitha, and A. S. Abdullah, "Enhancing Educational Assessment: Predicting and Visualizing Student Performance using EDA and Machine Learning Techniques," Journal of Engineering Education Transformations, vol. 37, no. Special Issue 2, 2024.
W. Sabbir, M. Abdullah-Al-Kafi, A. S. Afridi, M. S. Rahman, and M. Karmakar, "Improving Predictive Analytics for Student Dropout: A Comprehensive Analysis and Model Evaluation," in 2024 11th International Conference on Computing for Sustainable Global Development (INDIACom), 2024: IEEE, pp. 951-956.
Z. Alamgir, H. Akram, S. Karim, and A. Wali, "Enhancing Student Performance Prediction via Educational Data Mining on Academic data," Informatics in Education, 2023, doi: 10.15388/infedu.2024.04.
J. G. C. Ramírez, "Predictive Analytics in Education: Utilizing Machine Learning to Forecast Student Performance and Dropout Rates," Asian American Research Letters Journal, vol. 1, no. 5, 2024.
J. Chukwunike and C. Cassia Anwar, "An Enhanced Student Engagement and Academic Performance Predictive System."
M. Tastanova, R. Jilkibayeva, Z. Arkhabayeva, and S. Tripathi, "Enhancing Predictive Analytics in Education through Collaborative Filtering Algorithms in Knowledge Management," presented at the 2023 4th International Conference on Computation, Automation and Knowledge Management (ICCAKM), 2023.
A. C. Doctor, "A predictive model using machine learning algorithm in identifying students probability on passing semestral course," arXiv preprint arXiv:2304.05565, 2023.
S. Albahli, "Efficient hyperparameter tuning for predicting student performance with Bayesian optimization," Multimedia Tools and Applications, vol. 83, no. 17, pp. 52711-52735, 2024.
L. Ni, S. Wang, Z. Zhang, X. Li, X. Zheng, P. Denny, and J. Liu, "Enhancing student performance prediction on learnersourced questions with sgnn-llm synergy," in Proceedings of the AAAI Conference on Artificial Intelligence, 2024, vol. 38, no. 21, pp. 23232-23240.
G. Ramaswami, T. Susnjak, and A. Mathrani, "Supporting students’ academic performance using explainable machine learning with automated prescriptive analytics," Big Data and Cognitive Computing, vol. 6, no. 4, p. 105, 2022.
S. Gaftandzhieva, A. Talukder, N. Gohain, S. Hussain, P. Theodorou, Y. K. Salal, and R. Doneva, "Exploring Online Activities to Predict the Final Grade of Student," Mathematics, vol. 10, no. 20, 2022, doi: 10.3390/math10203758.
M. Yağcı, "Educational data mining: prediction of students' academic performance using machine learning algorithms," Smart Learning Environments, vol. 9, no. 1, 2022, doi: 10.1186/s40561-022-00192-z.
M. H. B. Roslan and C. J. Chen, "Predicting students' performance in English and Mathematics using data mining techniques," Educ Inf Technol (Dordr), vol. 28, no. 2, pp. 1427-1453, 2023, doi: 10.1007/s10639-022-11259-2.
E. T. Khor, "A data mining approach using machine learning algorithms for early detection of low-performing students," The International Journal of Information and Learning Technology, vol. 39, no. 2, pp. 122-132, 2022, doi: 10.1108/ijilt-09-2021-0144.
A. Bressane, M. Spalding, D. Zwirn, A. I. S. Loureiro, A. O. Bankole, R. G. Negri, I. de Brito Junior, J. K. S. Formiga, L. C. d. C. Medeiros, L. A. Pampuch Bortolozo, and R. Moruzzi, "Fuzzy Artificial Intelligence—Based Model Proposal to Forecast Student Performance and Retention Risk in Engineering Education: An Alternative for Handling with Small Data," Sustainability, vol. 14, no. 21, 2022, doi: 10.3390/su142114071.
M. Adnan, A. Habib, J. Ashraf, S. Mussadiq, A. A. Raza, M. Abid, M. Bashir, and S. U. Khan, "Predicting at-Risk Students at Different Percentages of Course Length for Early Intervention Using Machine Learning Models," IEEE Access, vol. 9, pp. 7519-7539, 2021, doi: 10.1109/access.2021.3049446.
S. D. A. Bujang, A. Selamat, R. Ibrahim, O. Krejcar, E. Herrera-Viedma, H. Fujita, and N. A. M. Ghani, "Multiclass Prediction Model for Student Grade Prediction Using Machine Learning," IEEE Access, vol. 9, pp. 95608-95621, 2021, doi: 10.1109/access.2021.3093563.
J. Kabathova and M. Drlik, "Towards Predicting Student’s Dropout in University Courses Using Different Machine Learning Techniques," Applied Sciences, vol. 11, no. 7, 2021, doi: 10.3390/app11073130.
M. N. Yakubu and A. M. Abubakar, "Applying machine learning approach to predict students’ performance in higher educational institutions," Kybernetes, vol. 51, no. 2, pp. 916-934, 2021, doi: 10.1108/k-12-2020-0865.
J. Malini and Y. Kalpana, "Investigation of factors affecting student performance evaluation using education materials data mining technique," Materials Today: Proceedings, vol. 47, pp. 6105-6110, 2021, doi: 10.1016/j.matpr.2021.05.026.
R. Bertolini, S. J. Finch, and R. H. Nehm, "Enhancing data pipelines for forecasting student performance: integrating feature selection with cross-validation," International Journal of Educational Technology in Higher Education, vol. 18, pp. 1-23, 2021.
I. El Guabassi, Z. Bousalem, R. Marah, and A. Qazdar, "Comparative Analysis of Supervised Machine Learning Algorithms to Build a Predictive Model for Evaluating Students’ Performance," International Journal of Online and Biomedical Engineering (iJOE), vol. 17, no. 02, pp. 90-105, 2021, doi: 10.3991/ijoe.v17i02.20025.
R. Hasan, S. Palaniappan, S. Mahmood, A. Abbas, K. U. Sarker, and M. U. Sattar, "Predicting Student Performance in Higher Educational Institutions Using Video Learning Analytics and Data Mining Techniques," Applied Sciences, vol. 10, no. 11, 2020, doi: 10.3390/app10113894.
A. S. Rayasam, "Predicting at-risk students from disparate sources of institutional data," Massachusetts Institute of Technology, 2020.
N. Yılmaz and B. Sekeroglu, "Student Performance Classification Using Artificial Intelligence Techniques," in 10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions - ICSCCW-2019, (Advances in Intelligent Systems and Computing, 2020, ch. Chapter 76, pp. 596-603.
M. R. Alzahrani, "Predicting Student Performance Using Ensemble Models and Learning Analytics Techniques," 2024.
A. Al-Ameri, W. Al-Shammari, A. Castiglione, M. Nappi, C. Pero, and M. Umer, "Student Academic Success Prediction Using Learning Management Multimedia Data With Convoluted Features and Ensemble Model," Journal of Data and Information Quality, 2024, doi: 10.1145/3687268.
A. A. Nafea, M. Mishlish, A. M. S. Shaban, M. M. Al-Ani, K. M. A. Alheeti, and H. J. Mohammed, "Enhancing Student's Performance Classification Using Ensemble Modeling," Iraqi Journal For Computer Science and Mathematics, vol. 4, no. 4, pp. 204-214, 2023, doi: 10.52866/ijcsm.2023.04.04.016.
Y. S. Balcioğlu and M. Artar, "Predicting academic performance of students with machine learning," Information Development, 2023, doi: 10.1177/02666669231213023.
M. M. Hussain, S. Akbar, S. A. Hassan, M. W. Aziz, and F. Urooj, "Prediction of Student’s Academic Performance through Data Mining Approach," Journal of Informatics and Web Engineering, vol. 3, no. 1, pp. 241-251, 2024, doi: 10.33093/jiwe.2024.3.1.16.
K. Sharada, "Student Psychology Teaching Learning Optimisation-Based Deep Long Short-Term Memory for Predicting Student Performance," Journal of Information & Knowledge Management, vol. 23, no. 03, 2024, doi: 10.1142/s0219649224500400.
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, p. e15382, Apr 2023, doi: 10.1016/j.heliyon.2023.e15382.
R. Y.-Y. Chan, C. M. V. Wong, and Y. N. Yum, "Predicting Behavior Change in Students With Special Education Needs Using Multimodal Learning Analytics," IEEE Access, vol. 11, pp. 63238-63251, 2023, doi: 10.1109/access.2023.3288695.
D. A. Shafiq, M. Marjani, R. A. A. Habeeb, and D. Asirvatham, "A Conceptual Predictive Analytics Model for the Identification of at-risk students in VLE using Machine Learning Techniques," presented at the 2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS), 2022.
T. Tao, C. Sun, Z. Wu, J. Yang, and J. Wang, "Deep Neural Network-Based Prediction and Early Warning of Student Grades and Recommendations for Similar Learning Approaches," Applied Sciences, vol. 12, no. 15, 2022, doi: 10.3390/app12157733.
C. Liu, H. Wang, Y. Du, and Z. Yuan, "A Predictive Model for Student Achievement Using Spiking Neural Networks Based on Educational Data," Applied Sciences, vol. 12, no. 8, 2022, doi: 10.3390/app12083841.
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, 2021, doi: 10.1016/j.caeai.2021.100018.
A. Nabil, M. Seyam, and A. Abou-Elfetouh, "Prediction of Students’ Academic Performance Based on Courses’ Grades Using Deep Neural Networks," IEEE Access, vol. 9, pp. 140731-140746, 2021, doi: 10.1109/access.2021.3119596.
H. Waheed, S.-U. Hassan, R. Nawaz, N. R. Aljohani, G. Chen, and D. Gasevic, "Early prediction of learners at risk in self-paced education: A neural network approach," Expert Systems with Applications, vol. 213, p. 118868, 2023.
R. C. Raga and J. D. Raga, "Early prediction of student performance in blended learning courses using deep neural networks," in 2019 International Symposium on Educational Technology (ISET), 2019: IEEE, pp. 39-43.
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, 2020, doi: 10.3390/app10062145.
M. Karlık and B. Karlık, "Prediction of student’s performance with deep neural networks," Int. J. of Artificial Intel. and Expert Sys.(IJAE), vol. 9, no. 2, pp. 39-47, 2020.
S. Hussain, S. Gaftandzhieva, M. Maniruzzaman, R. Doneva, and Z. F. Muhsin, "Regression analysis of student academic performance using deep learning," Education and Information Technologies, vol. 26, no. 1, pp. 783-798, 2020, doi: 10.1007/s10639-020-10241-0.
A. Almasri, R. S. Alkhawaldeh, and E. Çelebi, "Clustering-Based EMT Model for Predicting Student Performance," Arabian Journal for Science and Engineering, vol. 45, no. 12, pp. 10067-10078, 2020, doi: 10.1007/s13369-020-04578-4.
T. Omar, A. Alzahrani, and M. Zohdy, "Clustering Approach for Analyzing the Student’s Efficiency and Performance Based on Data," Journal of Data Analysis and Information Processing, vol. 08, no. 03, pp. 171-182, 2020, doi: 10.4236/jdaip.2020.83010.
Y. L. Goh, Y. H. Goh, C.-C. Yip, C. H. Ting, R. L. Leh Bin, and K. P. Chen, "Students' Academic Performance Analysis by K-Means Clustering for Investigating Students' Health Conditions within Clusters," Annals of Tropical Medicine and Public Health, vol. 23, no. 13, 2020, doi: 10.36295/asro.2020.231332.
E. Ahmed, "Student Performance Prediction Using Machine Learning Algorithms," Applied Computational Intelligence and Soft Computing, vol. 2024, no. 1, p. 4067721, 2024.
K. Vimarsha, S. S. Prakash, K. Krinkin, and Y. A. Shichkina, "Student Performance Prediction: A Co-Evolutionary Hybrid Intelligence model," Procedia Computer Science, vol. 235, pp. 436-446, 2024.
G. Al-Tameemi, J. Xue, I. H. Ali, and S. Ajit, "A Hybrid Machine Learning Approach for Predicting Student Performance Using Multi-class Educational Datasets," Procedia Computer Science, vol. 238, pp. 888-895, 2024.
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: 10.1109/access.2020.3036572.
E. Evangelista, "A Hybrid Machine Learning Framework for Predicting Students’ Performance in Virtual Learning Environment," International Journal of Emerging Technologies in Learning (iJET), vol. 16, no. 24, pp. 255-272, 2021, doi: 10.3991/ijet.v16i24.26151.
A. A. Alharbi and J. Allohibi, "A new hybrid classification algorithm for predicting student performance," AIMS Mathematics, vol. 9, no. 7, pp. 18308-18323, 2024, doi: 10.3934/math.2024893.
J. Kuzilek, M. Hlosta, and Z. Zdrahal, "Open university learning analytics dataset," Scientific data, vol. 4, no. 1, pp. 1-8, 2017.
K.-E. l. system. Students' Academic Performance Dataset. [Online]. Available: https://www.kaggle.com/datasets/aljarah/xAPI-Edu-Data
P. Cortez, "Student Performance [Dataset]," UCI Machine Learning Repository, 2008, doi: https://doi.org/10.24432/C5TG7T.
P. Samarati and L. Sweeney, "Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression," 1998.
S. Barocas, M. Hardt, and A. Narayanan, "Fairness and Machine Learning Limitations and Opportunities," 2018.
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Copyright (c) 2025 Husam Kadhim Gharkan , Mustafa Jawad Radif, Ali Hakem Alsaeedi
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