Classification and Predicting of Student’s Performance using Supervised Machine Learning
DOI:
https://doi.org/10.25098/8.2.39Keywords:
Student Performance, Academic Achievement, Random Forest Model, Performance Prediction, Supervised Machine Learning, Student demographicsAbstract
Predicting student performance is an issue in educational institutions that researchers frequently discuss as part of improving teaching and learning. If teachers make use of prediction techniques and features, appropriate educational content will be created. The research explores how machine learning can predict student performance using machine learning algorithms such as decision tree, random forest, gradient boosting, and others which are explored. two datasets including student demographic, academic, and behavioral variables have been combined with algorithms such as Decision Trees, Random forests, K-nearest neighbor, Support Vector Machine, Gradient Boosting and Ensemble Voting. The study focused on classification as Machine Learning task by implementing different classifier on two different datasets which are UCI student performance and E-Parwarda. The essential attributes that primarily affect evaluating the student’s performance are presented that increase the accuracy of the prediction of the student’s performance who wants to start study in university or institutes. The paper concludes that: Two different datasets are utilized to evaluate the models. In addition, various measures are computed such as train time, loss, precision, recall, f-score and accuracy. Consequently, Random Forest achieved highest accuracy (91.67) % based on E-parwarda dataset and GBoost achieved (87.69) % as highest accuracy-based UCI student performance dataset.
References
H. Altabrawee, O. Abdul, J. Ali, and Q. Ajmi, “Predicting Students’ Performance Using Machine Learning Techniques,” 2019.
V. A. Sungar, P. D. Shinde, and M. V Rupnar, “Predicting Student’s Performance using Machine Learning,” 2017. [Online]. Available: www.caeaccess.org
Y. Baashar, G. Alkawsi, N. Ali, H. Alhussian, and H. T. Bahbouh, “Predicting student’s performance using machine learning methods: A systematic literature review,” in Proceedings - International Conference on Computer and Information Sciences: Sustaining Tomorrow with Digital Innovation, ICCOINS 2021, Institute of Electrical and Electronics Engineers Inc., Jul. 2021, pp. 357–362. doi: 10.1109/ICCOINS49721.2021.9497185.
H. Pallathadka, A. Wenda, E. Ramirez-Asís, M. Asís-López, J. Flores-Albornoz, and K. Phasinam, “Classification and prediction of student performance data using various machine learning algorithms,” Mater Today Proc, Jul. 2021, doi: 10.1016/j.matpr.2021.07.382.
N. R. Beckham, L. J. Akeh, G. N. P. Mitaart, and J. V Moniaga, “Determining factors that affect student performance using various machine learning methods,” Procedia Comput Sci, vol. 216, pp. 597–603, 2023, doi: 10.1016/j.procs.2022.12.174.
K. Kazi, K. K. Solutions, and M. Solapur, “Implementation of Latest Machine Learning Approaches for Students Grade Prediction,” International Journal of Early Childhood Special Education (INT-JECS), vol. Vol 14, no. Issue 03 2022, 2022, doi: 10.9756/INT-JECSE/V14I3.1141.
F. Qiu et al., “Predicting students’ performance in e-learning using learning process and behaviour data,” Sci Rep, vol. 12, no. 1, Dec. 2022, doi: 10.1038/s41598-021-03867-8.
H. Nawang, M. Makhtar, and W. M. A. F. W. Hamzah, “Comparative analysis of classification algorithm evaluations to predict secondary school students’ achievement in core and elective subjects,” International Journal of Advanced Technology and Engineering Exploration, vol. 9, no. 89, pp. 430–445, 2022, doi: 10.19101/IJATEE.2021.875311.
R. Deshmukh, A. Kulkarni, A. Kumthekar, and P. Kottur, “Machine Learning Techniques for Predicting Student Performance,” Mathematical Statistician and Engineering ApplicationsISSN: 2094-0343Mathematical Statistician and Engineering Applications, vol. 71, no. 4, 2022, [Online]. Available: http://philstat.org.ph
N. Mohammad Suhaimi, S. Abdul-Rahman, S. Mutalib, N. H. Abdul Hamid, and A. Hamid, “Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms,” International Journal of Modern Education and Computer Science, vol. 11, no. 7, pp. 1–13, Jul. 2019, doi: 10.5815/ijmecs.2019.07.01.
J. L. Rastrollo-Guerrero, J. A. Gómez-Pulido, and A. Durán-Domínguez, “Analyzing and predicting students’ performance by means of machine learning: A review,” Applied Sciences (Switzerland), vol. 10, no. 3. MDPI AG, Feb. 01, 2020. doi: 10.3390/app10031042.
A. S. Hashim, W. A. Awadh, and A. K. Hamoud, “Student Performance Prediction Model based on Supervised Machine Learning Algorithms,” in IOP Conference Series: Materials Science and Engineering, IOP Publishing Ltd, Nov. 2020. doi: 10.1088/1757-899X/928/3/032019.
J. Dhilipan, N. Vijayalakshmi, S. Suriya, and A. Christopher, “Prediction of Students Performance using Machine learning,” IOP Conf Ser Mater Sci Eng, vol. 1055, no. 1, p. 012122, Feb. 2021, doi: 10.1088/1757-899x/1055/1/012122.
Engr. Sana Bhutto, Dr. Isma Farah Siddiqui, Dr. Qasim Ali Arain, and Maleeha Anwar, “Predicting Students’ Academic Performance Through Supervised Machine Learning,” 2020 International Conference on Information Science and Communication Technology, 2020.
E. T. Lau, L. Sun, and Q. Yang, “Modelling, prediction and classification of student academic performance using artificial neural networks,” SN Appl Sci, vol. 1, no. 9, Sep. 2019, doi: 10.1007/s42452-019-0884-7.
A. J. Khalil, A. M. Barhoom, B. S. Abu-Nasser, M. M. Musleh, and S. S. Abu-Naser, “Energy Efficiency Prediction using Artificial Neural Network,” 2019. [Online]. Available: www.ijeais.org/ijapr
A. U. Haq et al., “Feature Selection Based on L1-Norm Support Vector Machine and Effective Recognition System for Parkinson’s Disease Using Voice Recordings,” IEEE Access, vol. 7, pp. 37718–37734, 2019, doi: 10.1109/ACCESS.2019.2906350.
S. T. Ahmed, R. Al-Hamdani, and M. S. Croock, “Enhancement of student performance prediction using modified K-nearest neighbor,” Telkomnika (Telecommunication Computing Electronics and Control), vol. 18, no. 4, pp. 1777–1783, 2020, doi: 10.12928/TELKOMNIKA.V18I4.13849.
M. Bansal, A. Goyal, and A. Choudhary, “A comparative analysis of K-Nearest Neighbor, Genetic, Support Vector Machine, Decision Tree, and Long Short Term Memory algorithms in machine learning,” Decision Analytics Journal, vol. 3, p. 100071, Jun. 2022, doi: 10.1016/j.dajour.2022.100071.
Mastour, Haniye, et al. "Early prediction of medical students' performance in high-stakes examinations using machine learning approaches." Heliyon (2023).
Beckham, Nicholas Robert, et al. "Determining factors that affect student performance using various machine learning methods." Procedia Computer Science 216 (2023): 597-603.
Pallathadka, Harikumar, et al. "Classification and prediction of student performance data using various machine learning algorithms." Materials today: proceedings 80 (2023): 3782-3785.
Priyambada, Satrio Adi, Tsuyoshi Usagawa, and E. R. Mahendrawathi. "Two-layer ensemble prediction of students’ performance using learning behavior and domain knowledge." Computers and Education: Artificial Intelligence (2023): 100149.
Ajibade, Samuel-Soma M., et al. "Utilization of Ensemble Techniques for Prediction of the Academic Performance of Students." Journal of Optoelectronics Laser 41.6 (2022): 48-54.
Chen, Liyan, Lihua Wang, and Yuxin Zhou. "Research on data mining combination model analysis and performance prediction based on students’ behavior characteristics." Mathematical Problems in Engineering 2022 (2022): 1-10.
Sudais, Muhammad, et al. "Students’ academic performance prediction model using machine learning." (2022).
Zeineddine, Hassan, Udo Braendle, and Assaad Farah. "Enhancing prediction of student success: Automated machine learning approach." Computers & Electrical Engineering 89 (2021): 106903.
Varade, Rashmi V., and Blessy Thankanchan. "Academic performance prediction of undergraduate students using decision tree algorithm." SAMRIDDHI: A Journal of Physical Sciences, Engineering and Technology 13.SUP 1 (2021): 97-100.
Ma, Yuling, et al. "Multi-task MIML learning for pre-course student performance prediction." Frontiers of Computer Science 14 (2020): 1-10.
Downloads
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
SJCUS's open access articles are published under a Creative Commons Attribution CC-BY-NC-ND 4.0 license.