Artificial Neural Network (ANN) to Predict Mathematics Students’ Performance

Authors

  • Norpah Mahat Universiti Teknologi MARA, Perlis Branch, Arau Campus
  • Nor Idayunie Nording Universiti Teknologi MARA, Perlis Branch, Arau Campus
  • Jasmani Bidin Universiti Teknologi MARA, Perlis Branch, Arau Campus
  • Suzanawati Abu Hasan Universiti Teknologi MARA, Perlis Branch, Arau Campus
  • Teoh Yeong Kin Universiti Teknologi MARA, Perlis Branch, Arau Campus

DOI:

https://doi.org/10.24191/jcrinn.v7i1.264

Keywords:

Artificial neural network, ANN, Marquardt, Levenberg , prediction model

Abstract

Predicting students’ academic performance is very essential to produce high-quality students. The main goal is to continuously help students to increase their ability in the learning process and to help educators as well in improving their teaching skills. Therefore, this study was conducted to predict mathematics students’ performance using Artificial Neural Network (ANN). The secondary data from 382 mathematics students from UCI Machine Learning Repository Data Sets used to train the neural networks. The neural network model built using nntool. Two inputs are used which are the first and the second period grade while one target output is used which is the final grade. This study also aims to identify which training function is the best among three Feed-Forward Neural Networks known as Network1, Network2 and Network3. Three types of training functions have been selected in this study, which are Levenberg-Marquardt (TRAINLM), Gradient descent with momentum (TRAINGDM) and Gradient descent with adaptive learning rate (TRAINGDA). Each training function will be compared based on Performance value, correlation coefficient, gradient and epoch. MATLAB R2020a was used for data processing. The results show that the TRAINLM  function is the most suitable function in predicting mathematics students’ performance because it has a higher correlation coefficient and a lower Performance value.

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References

Aydoğdu, S. (2020). Predicting student final performance using artificial neural networks in online learning environments. Education and Information Technologies, 25(3), 1913-1927.

Amirah, M. S., & Nur'aini, H. R. (2015). A Review on Predicting Student’s Performance Using Data Mining Techniques. Procedia Computer Science, 72, 414-422.

Cheewaprakobkit, P. (2015). Predicting Student Academic Achievement by Using the Decision Tree and Neural Network Techniques. Catalyst Journal of the Institute for Interdisciplinary Studies, 12(2), 34-43.

Chen, J.F., Hsieh, H.N. & Quang, H.D. (2014). Predicting Student Academic Performance: A Comparison of Two Meta-Heuristic Algorithms Inspired by Cuckoo Birds for Training Neural Networks. Algorithms, 7(4), 538-553.

Cortez, P. & Silva, A. (2008). Using Data Mining to Predict Secondary School Student Performances. 15th European Concurrent Engineering Conference 2008, ECEC 2008 - 5th Future Business Technology Conference, FUBUTEC 2008, 5-12.

Feng, J. (2019). Predicting Students' Academic Performance with Decision Tree and Neural Network. Electronic Theses and Dissertations, 2004-2019, 1-35.

Isong, E., Udonyah, K. & Ansa, G. (2018). Cognitive Factors in Students' Academic Performance Evaluation using Artificial Neural Networks. Information and Knowledge Management, 8(5), 57-71.

Kalejaye, B.A, Folorunso, O. & Usman, O.L. (2016). Predicting Students' Grade Scores Using Taining Functions of Artificial Neural Network. Journal of Natural Sciences, Engineering and Technology, 14, 1-23.

Livieris, I.E., Drakopoulou, K. & Pintelas, P. (2012). Predicting students' performance using artificial neural networks. In Proceedings of the 8th Pan-Hellenic Conference “Information and Communication Technology in Education., 28-30.

Pal, V.K. & Bhatt, K.K. (May, 2019). Performance Prediction for Post Graduate Students using Artificial Neural Network. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(7), 446-454.

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Published

2022-03-30

How to Cite

Mahat, N., Nording, N. I., Bidin, J., Abu Hasan, S., & Teoh Yeong Kin. (2022). Artificial Neural Network (ANN) to Predict Mathematics Students’ Performance. Journal of Computing Research and Innovation, 7(1), 29–40. https://doi.org/10.24191/jcrinn.v7i1.264

Issue

Section

General Computing

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