Analysis and Prediction of Student Academic Performance Using Machine Learning

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Published Mar 30, 2020
DOI https://doi.org/10.25077/jitce.4.01.10-15.2020

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Ajibola Oluwafemi Oyedeji
Olabisi Onabanjo University
http://orcid.org/0000-0002-0180-492X Abdulrazaq M Salami Olaolu Folorunsho Olatilewa R. Abolade

Abstract

Analyzing the academic performance of students is of utmost importance for academic institutions and educationists, so as to know the ways of improving individual student’s performance. The project analyzed the past results of students including their individual attributes including age, demographic distribution, family background and attitude to study and tests this data using machine learning tools. Three models which are; Linear regression for supervised learning, linear regression with deep learning and neural network were tested using the test and train data with the Linear regression for supervised learning having the best mean average error (MAE).

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How to Cite
Oyedeji, A., Salami, A., Folorunsho, O., & Abolade, O. (2020, March 30). Analysis and Prediction of Student Academic Performance Using Machine Learning. JITCE (Journal of Information Technology and Computer Engineering), 4(01), 10-15. https://doi.org/https://doi.org/10.25077/jitce.4.01.10-15.2020
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Vol 4 No 01 (2020): Journal of Information Technology and Computer Engineering
Section
Articles

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References

[1] S. Huang and N. Fang, "Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models," Computers & Education, vol. 61, pp. 133-145, 2013.
[2] L. Cohen, L. Manion and K. Morrison, Research Methods in Education, 6th ed., Routledge: Oxon, UK, 2007.
[3] W. B. Ware and J. P. Galassi, "Using Correlational and Prediction Data to Enhance Student Achievement in K-12 Schools: A Practical Application for School Counselors," Professional School Counselling, vol. 9, no. 5, pp. 344-356, 2006.
[4] S. K. Yadav and S. Pal, "Data Mining: A Prediction for Performance Improvement of Engineering Students using Classification," World of Computer Science and Information Technology Journal, vol. 2, no. 2, pp. 51-56, 2012.
[5] P. Cortez and A. Silva, "Using Data Mining to Predict Secondary School Student Performance".
[6] M. Pandey and V. K. Sharma, "A Decision Tree Algorithm Pertaining to the Student Performance Analysis and Prediction," International Journal of Computer Applications, vol. 61, no. 13, pp. 1-5, 2013.
[7] D. Kabakchieva, "Predicting Student Performance by Using Data Mining Methods for Classification," Cybernetics and Information Technologies, vol. 13, no. 1, pp. 61-72, 2013.
[8] B. A. Kalejaye, O. Folorunsho and O. L. Usman, "Predicting Students’ Grade Scores using Training Functions of Artificial Neural Network," Journal of Natural Science, Engineering and Technology, vol. 14, no. 1, pp. 25-42, 2015.