International Journal of Modern Education and Computer Science (IJMECS)
ISSN: 2075-0161 (Print), ISSN: 2075-017X (Online)
Published By: MECS Press
IJMECS Vol.10, No.6, Jun. 2018
Predicting Student Academic Performance in Computer Science Courses: A Comparison of Neural Network Models
Full Text (PDF, 1203KB), PP.1-9
This study compared two neural network models (Multilayer Perceptron and Generalized Regression Neural Network) with a view to identifying the best model for predicting students’ academic performance based on single performance factor. Only academic factor (students’ results) was considered as the single performance factor of the study. One cohort of graduated students’ academic data was collected from the Computer Science and Engineering Department of Obafemi Awolowo University, Nigeria using documents and records technique. The models were simulated using MATLAB version 2015a and evaluated using mean square error, receiver operating characteristics and accuracy as the performance metrics. The results obtained show that although Multilayer Perceptron had prediction accuracy of 75%, Generalized Regression Neural Network had a better accuracy. The response time of Generalized Regression Neural Network (0.016sec) was faster than Multilayer Perceptron (0.03sec) and its memory consumption size (5kb) lower than that of Multilayer Perceptron (8kb). The simulated models were further compared with t-test method using a confidence interval of 95%. The attained t-test result from p-value (0.6854) suggests acceptance of null hypothesis, which shows that there is no significant difference between the predicted Grade Point Average and the actual Grade Point Average. The findings therefore reveal that the overall performance of Generalized Regression Neural Network outperforms the Multilayer Perceptron model with an accuracy of 95%. The study concluded that Generalized Regression Neural Network model which was simulated and with 95 % accuracy could be deployed by educationists to predict students’ academic performance using single performance factor.
Cite This Paper
Abimbola R. Iyanda, Olufemi D. Ninan, Anuoluwapo O. Ajayi, Ogochukwu G. Anyabolu, " Predicting Student Academic Performance in Computer Science Courses: A Comparison of Neural Network Models", International Journal of Modern Education and Computer Science(IJMECS), Vol.10, No.6, pp. 1-9, 2018.DOI: 10.5815/ijmecs.2018.06.01
D. D. Felisoni and A. S. Godoi, “Cell phone usage and academic performance: An experiment”. Computers and Education, 117, 175-187. 2018.
M. J. Akomolafe, “Personality Characteristics as Predictors of Academic Performance of Secondary School Students” Mediterranean Journal of Social Sciences, 4(2):657-664. 2013.
T. Farsides, and R. Woodfield, “Individual Differences and Undergraduate Academic Success: The Roles of Personality, Intelligence, and Application”. Personality and Individual Differences, 34:1225–1243. 2003.
K. McKenzie and R. Schweitzer, “Who succeeds at university? Factors predicting academic performance in first year Australian university students”. Higher Education Research and Development, 20(1):21-33. 2001.
E. Osmanbegovic, H. Agić and M. Suljic, “Prediction of Students' Success by Applying Data Mining Algorithams”. Journal of Theoretical and Applied Information Technology, 61(2):378 - 388. 2014.
M. Ramaswami and R. Bhaskaran, “A CHAID based performance prediction model in educational data mining”. IJCSI International Journal of Computer Science Issues, 7(1):10-18. 2010.
F. Sarker, T. Tiropanis and H. C. Davis, (). Students’ performance prediction by using institutional internal and external open data sources. Available at: http://eprints.soton.ac.uk/353532/ Date Accessed: 21st January, 2016. 2013.
S. Venkatramaphanikumar, K. Prudhvi Raj, D. S. Bhupal Naik and K. V. Krishna Kishore, “A Novel Prediction Model for Academic Emotional Progression of Graduates”. ARPN Journal of Engineering and Applied Sciences, 10(6):2561-2569. 2015.
K. J. Sathick and A. Jaya, “Extraction of Actionable Knowledge to Predict Students’ Academic Performance using Data Mining Technique- An Experimental Study”. International Journal of Knowledge Based Computer System, 1(1). 2013.
R. Asif, A. Merceron and M. K. Pathan, “Predicting Student Academic Performance at Degree Level: A Case Study”. International Journal of Intelligent Systems and Applications, 7(1):49. 2014.
P. Cortez and A. Silva, “Using Data Mining to Predict Secondary School Student Performance” 15th Portuguese Conference on Artificial Intelligence, EPIA 2011, Lisbon Portugal, 491-505. 2011.
A. Mueen, B. Zafar and U. Manzoor, “Modeling and Predicting Students' Academic Performance Using Data Mining Techniques”. International Journal of Modern Education and Computer Science, 8(11), p.36. 2016.
M. Singh and J. Singh, “Machine Learning Techniques for Prediction of Subject Scores: A Comparative Study”. International Journal of Computer Science and Network, 2 (4):77-79. 2013.
C. T. Lye, L. N. Ng, M. D. Hassan, W. W. Goh, C. Y. Law, and N. Ismail, “Predicting Pre-university. student's Mathematics achievement. Procedia-Social and Behavioral Sciences, 8:299-306.
G. A. El-Refae and Q. K. Al-Shayea, “Predicting Students’ Academic Performance Using Artificial Neural Networks: A Case Study”. International Journal of Computer Science and Information Security (IJCSIS), 8 (5):97-100. 2010.
S. Huang and N. Fang, “Prediction Of Student Academic Performance In An Engineering Dynamics Course: Development and validation of multivariate regression models”. International Journal of Engineering Education, 26(4):1008-1017. 2010.
N. Arora and J. R Saini, “A Fuzzy Probabilistic Neural Network for Student’s Academic Performance Prediction”. International Journal of Innovative Research in Science, Engineering and Technology, 2(9):4425-4432. 2013.
J. N. Undavia, P.M. Dolia and A. Patel, “Comparison of Decision Tree Classification Algorithm to Predict Students Post Graduate Degree in Weka Environment”. International Journal of Innovative and Emerging Research in Engineering, 1(2):17-21. 2014.
A. M. Shahiri and W. Husain, “A Review on Predicting Student's Performance Using Data Mining Techniques”. Procedia Computer Science, 72:414-422. 2015.
R. Asif, A. Merceron, S. A. Ali and N. G. Haider, “Analyzing undergraduate students' performance using educational data mining”. Computers & Education, 113, 177-194. 2017.
K. D. Kolo, S. A. Adepoju and J. K. Alhassan,"A Decision Tree Approach for Predicting Students Academic Performance", International Journal of Education and Management Engineering(IJEME), Vol.5, No.5, pp.12-19, 2015.DOI: 10.5815/ijeme.2015
A. Kangaiammal, R. Silambannan, C. Senthamarai, and M. V. Srinath. “Student Learning Ability Assessment using Rough Set and Data Mining Approaches”. International Journal of Modern Education and Computer Science, 5(5), 1. 2013.
Ö. Kisi, “Multi-layer perceptrons with Levenberg-Marquardt training algorithm for suspended sediment concentration prediction and estimation”. Hydrological Sciences Journal, 49(6). 2004.
H. Yu and B. M. Wilamowski, “Levenberg–Marquardt Training”. Industrial Electronics Handbook, 5(12):1. 2011.