Predicting Student Academic Performance Using Neural Networks: Analyzing the Impact of Transfer Functions, Momentum and Learning Rate

Mini Agarwal; Bharat Bhushan Agarwal

doi:10.52756/ijerr.2024.v40spl.005

Authors

Mini Agarwal CSE Department, IFTM University, Moradabad, India https://orcid.org/0000-0002-7233-1787
Bharat Bhushan Agarwal CSE Department, IFTM University, Moradabad, India https://orcid.org/0000-0001-5519-3434

DOI:

https://doi.org/10.52756/ijerr.2024.v40spl.005

Keywords:

Artificial Neural Network (ANN), Back Propagation (BP), Prediction, Multilayer Perceptron (MLP), COVID

Abstract

Artificial Neural Networks (ANN) demonstrate a compelling application of AI in predicting student performance, a critical aspect for both students and educators. Accurate forecasting of student achievements enables educators to monitor progress effectively, allowing educational institutions to optimize outcomes and improve student results. This study focuses on leveraging ANN for predictive analytics in student performance. Through a detailed evaluation of transfer functions, optimizers, learning rates, and momentum values, the model achieves an impressive 98% accuracy with specific configurations: a learning rate of 0.005, momentum of 0.7, Sigmoid transfer function, and SGD optimizer. Additionally, the study performs a comparative analysis of various Machine Learning Algorithms, including ANN, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Decision Trees (DT), Naive Bayes (NB), and Logistic Regression (LR). Using data from 689 B.Tech students at IP University, the analysis reveals that ANN outperforms other algorithms with an accuracy of 97%. This high accuracy demonstrates the potential of ANN in educational settings, providing a valuable tool for educators to enhance student performance and outcomes.

References

Abulhaija, S., Hattab, S., & Etaiwi, W. (2023). Predicting students' performance using machine learning. In 2023 International Conference on Information Technology (ICIT), Amman, Jordan. pp. 1-6. https://doi.org/10.1109/ICIT58056.2023.10225950

Abu-Zohair, L. M. (2019). Prediction of student’s performance by modeling small dataset size. International Journal of Educational Technology in Higher Education, 16, Article number: 27. https://doi.org/10.1186/s41239-019-0160-3.

Adekitan, A. I., & Salau, O. (2019). The impact of engineering students’ performance in the first three years on their graduation result using educational data mining. Heliyon, 5, e01250. https://doi.org/10.1016/j.heliyon.2019.e01250.

Agarwal, M., & Agarwal, B. B. (2021). Towards Prediction of Students Educational Accomplishments Using Data Mining. Micro-Electronics and Telecommunication Engineering, Lecture Notes in Networks and Systems, 617. https://doi.org/10.1007/978-981-19-9512-5_2.

Ahmad, N., Hassan, N., Jaafar, H., & Enzai, N. I. M. (2021). Students’ performance prediction using artificial neural network. IOP Conf. Series: Materials Science and Engineering, 1176, 012020. https://doi.org/10.1088/1757-899X/1176/1/012020.

Abulhaija, S., Hattab, S., & Etaiwi, W. (2023). Predicting students' performance using machine learning. In 2023 International Conference on Information Technology (ICIT), Amman, Jordan. pp. 1-6. https://doi.org/10.1109/ICIT58056.2023.10225950

Abu-Zohair, L. M. (2019). Prediction of student’s performance by modeling small dataset size. International Journal of Educational Technology in Higher Education, 16, Article number: 27. https://doi.org/10.1186/s41239-019-0160-3.

Adekitan, A. I., & Salau, O. (2019). The impact of engineering students’ performance in the first three years on their graduation result using educational data mining. Heliyon, 5, e01250. https://doi.org/10.1016/j.heliyon.2019.e01250.

Agarwal, M., & Agarwal, B. B. (2021). Towards Prediction of Students Educational Accomplishments Using Data Mining. Micro-Electronics and Telecommunication Engineering, Lecture Notes in Networks and Systems, 617. https://doi.org/10.1007/978-981-19-9512-5_2.

Ahmad, N., Hassan, N., Jaafar, H., & Enzai, N. I. M. (2021). Students’ performance prediction using artificial neural network. IOP Conf. Series: Materials Science and Engineering, 1176, 012020. https://doi.org/10.1088/1757-899X/1176/1/012020.

Ahmad, Z., & Shahzadi, E. (2018). Prediction of students' academic performance using artificial neural network. Bulletin of Education and Research, 40(3), 157–164.

Alves, A. F., Gomes, C. M. A., Martins, A., & Almeida, L. d. S. (2017). Cognitive performance and academic achievement: How do family and school converge? European Journal of Education and Psychology, 10(2), 49–56. https://doi.org/10.1016/j.ejeps.2017.07.001

Chavez, H., Chavez-Arias, B., Contreras-Rosas, S., Alvarez-Rodríguez, J. M., & Raymundo, C. (2023). Artificial neural network model to predict student performance using nonpersonal information. Front. Educ., Sec. Assessment, Testing and Applied Measurement, 8(202). https://doi.org/10.3389/feduc.2023.1106679

Ghosh, S. K., & Janan, F. (2021). Prediction of student’s performance using random forest classifier. In Proceedings of the 11th Annual International Conference on Industrial Engineering and Operations Management Singapore, March 7-11, 2021.

Haloi, R., Chanda, D., Hazarika, J., & Barman, A. (2023). Statistical feature-based EEG signals classification using ANN and SVM classifiers for Parkinson’s disease detection. Int. J. Exp. Res. Rev., 31(Spl Volume), 141-149.

https://doi.org/10.52756/10.52756/ijerr.2023.v31spl.014

Hamadneh, N. N., Atawneh, S., Khan, W. A., Almejalli, K. A., & Alhomoud, A. (2022). Using artificial intelligence to predict students’ academic performance in blended learning. Sustainability, 14, 11642. https://doi.org/10.3390/su141811642

Jiao, P., Ouyang, F., Zhang, Q., & Alavi, A. H. (2022). Artificial intelligence-enabled prediction model of student academic performance in online engineering education. Artificial Intelligence Review, 55, 6321–6344. https://doi.org/10.1007/s10462-022-10155-y

Jin, H. (2022). Hyperparameter Importance for Machine Learning Algorithms. arXiv:2201.05132. https://doi.org/10.48550/arXiv.2201.05132

Kuppusamy, P., & Joseph, S. K. (2021). A deep learning model to smart education system. In e-Conference on Artificial Intelligence and Machine Learning. Mumbai.

Kyndt, E., Musso, M., Cascallar, E., & Dochy, F. (2015). Predicting academic performance: The role of cognition, motivation, and learning approaches. A neural network analysis. In Methodological Challenges in Research on Student Learning, 1, 55–76.

Liu, X., Wu, J., & Chen, S. (2023). Efficient hyperparameters optimization through model-based reinforcement learning with experience exploiting and meta-learning. Data Analytics and Machine Learning, 27, 8661–8678. https://doi.org/10.1007/s00500-023-08050-x

Mesaric, J. (2016). Decision trees for predicting the academic success of students. Croatian Operational Research Review, 7(2). https://doi.org/10.17535/crorr.2016.0025.

Musso, M. F., Kyndt, E., Cascallar, E. C., & Dochy, F. (2013). Predicting general academic performance and identifying the differential contribution of participating variables using artificial neural networks. Frontline Learning Research, 1(1), 42–71. https://doi.org/10.14786/flr.v1i1.13

Orji, F. A., & Vassileva, J. (2021). Machine learning approach for predicting students academic performance and study strategies based on their motivation. Machine Learning (cs.LG), arXiv:2210.08186 [cs.LG]. https://doi.org/10.48550/arXiv.2210.0818

Preetha, S., & Anitha, D. (2022). Prediction of academic performance of students using machine learning. International Journal of Health Science. https://doi.org/10.53730/ijhs.v6nS1.7868

Rodríguez-Hernandez, C. F., Musso, M., Kyndt, E., & Cascallar, E. (2021). Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation. Computers and Education: Artificial Intelligence, 2, 100018. https://doi.org/10.1016/j.caeai.2021.100018.

Triventi, M. (2014). Does working during higher education affect students’ academic progression? Economics of Education Review, 41, 1–13. https://doi.org/10.1016/j.econedurev.2014.03.006

Vairachilai, S., & Vamshidharreddy. (2020). Student’s academic performance prediction using machine learning approach. International Journal of Advanced Science and Technology, 29(9s), 6731–6737.

Venkata, G., & Damodar, M. (2023). TLBO-Trained ANN-Based Shunt Active Power Filter for Mitigation of Current Harmonics. International Journal of Experimental Research and Review, 34, (Special Vol.), 11-21. https://doi.org/10.52756/ijerr.2023.v34spl.002.

Wojciuk, M., Swiderska-Chadaj, Z., Siwek, K., & Gertych, A. (2022). The role of hyperparameter optimization in fine-tuning of CNN models. ArXiv. http://dx.doi.org/10.2139/ssrn.4087642

Yadav, N. R., & Deshmukh, S. S. (2022). Prediction of student performance using machine learning techniques: A review. ICAMIDA 2022, ACSR105, pp. 735–741. https://doi.org/10.2991/978-94-6463-136-4_63

Zaffar, M., Hashmani, M. A., Savita, K. S., Rizvi, S. S. H., & Rehman, M. (2020). Role of FCBF feature selection in educational data mining. Mehran University Research Journal of Engineering and Technology, 39(4), 772-778. https://doi.org/10.22581/muet1982.2004.09.

Zhang, Y., Yun, Y., An, R., Cui, J., Dai, H., & Shang, X. (2021). Educational data mining techniques for student performance prediction: Method review and comparison analysis. Front. Psychol., Sec. Educational Psychology, 12. https://doi.org/10.3389/fpsyg.2021.698490.

Zhu, Z.T., Yu, M.H., & Riezebos, P. (2016). A research framework of smart education. Smart Learning Environments, 3(4). https://doi.org/10.1186/s40561-016-0026-2