A Review of Classification Approaches in Educational Data Mining for Predicting Student Performance
DOI:
https://doi.org/10.21015/vtcs.v12i2.1944Abstract
With the rapid increase in student data, and the growing interest in finding insights into student learning patterns,
Educational Data Mining (EDM) methods are increasingly being used by educational institutes. Classification, a popular EDM method, enables the in-depth, efficient, and thorough analysis of student data while providing insights that directly assist in understanding student learning patterns and identifying elements that influence academic success. This review seeks to identify common trends and assess the effectiveness of four popularly explored classification approaches for predicting student performance. To assure the selection of research that specifically addresses the use of classification approaches for predicting student academic achievement, this review follows a systematic approach. A quality evaluation step was also included to help ensure that only reliable and credible studies were included in the review. According to the review findings of thirty two studies, most researchers used assessment results, academic performance index, and demographics to predict student performance. Decision Trees and Probabilistic classifiers were found to be the most popular and commonly used classification approaches for predicting student performance. The review also focuses on the challenges often faced while undertaking classification tasks in EDM and outlines future research directions in the context of analyzing student data.
References
M. N. Velasco and J. M. Victoriano, "Predicting Board Performance Using Classification Algorithms and Time Series Analysis," in 2024 7th International Conference on Informatics and Computational Sciences (ICICoS), pp. 121-125, IEEE, 2024.
A. Abu Saa, M. Al-Emran, and K. Shaalan, "Factors affecting students’ performance in higher education: a systematic review of predictive data mining techniques," Technology, Knowledge and Learning, vol. 24, pp. 567-598, 2019.
K. Karthikeyan and P. Kavipriya, "On improving student performance prediction in education systems using enhanced data mining techniques," International Journal of Advanced Research in Computer Science and Software Engineering, vol. 7, no. 5, 2017.
A. F. Meghji, F. B. Shaikh, S. A. Wadho, S. Bhatti, and R. K. Ayyasamy, "Using Educational Data Mining to Predict Student Academic Performance," VFAST Transactions on Software Engineering, vol. 11, no. 2, pp. 43–49, 2023, doi: 10.21015/vtse.v11i2.1475.
E. Alhazmi and A. Sheneamer, "Early predicting of students' performance in higher education," IEEE Access, vol. 11, pp. 27579-27589, 2023.
A. Çetinkaya, Ö. K. Baykan, and H. Kırgız, "Analysis of Machine Learning Classification Approaches for Predicting Students’ Programming Aptitude," Sustainability, vol. 15, no. 17, 12917, 2023.
A. G. Daligcon, J. Priyadarshini, and L. R. Decena, "Unveiling the Best-fit Model: A Comparative Analysis of Classification Methods in Predicting Student Success," International Journal of Information Technology, Research and Applications, vol. 3, no. 1, pp. 12-19, 2024.
R. Asif, A. Merceron, S. A. Ali, and N. G. Haider, "Analyzing undergraduate students’ performance using educational data mining," Computers & Education, vol. 113, pp. 177-194, 2017.
S. D. A. Bujang, A. Selamat, R. Ibrahim, O. Krejcar, E. Herrera-Viedma, H. Fujita, and N. A. M. Ghani, "Multiclass prediction model for student grade prediction using machine learning," IEEE Access, vol. 9, pp. 95608-95621, 2021.
B. Kitchenham, O. P. Brereton, D. Budgen, M. Turner, J. Bailey, and S. Linkman, "Systematic literature reviews in software engineering–a systematic literature review," Information and Software Technology, vol. 51, no. 1, pp. 7-15, 2009.
H. Sahlaoui, A. Nayyar, S. Agoujil, and M. M. Jaber, "Predicting and interpreting student performance using ensemble models and shapley additive explanations," IEEE Access, vol. 9, pp. 152688-152703, 2021.
H. A. Mengash, "Using data mining techniques to predict student performance to support decision making in university admission systems," IEEE Access, vol. 8, pp. 55462-55470, 2020.
B. Mehboob, R. M. Liaqat, and N. A. Saqib, "Predicting student performance and risk analysis by using data mining approach," International Journal of Computer Science and Information Security (IJCSIS), vol. 14, no. 7, pp. 69-76, 2016.
A. Rafique, M. S. Khan, M. H. Jamal, M. Tasadduq, F. Rustam, E. Lee, and I. Ashraf, "Integrating learning analytics and collaborative learning for improving student’s academic performance," IEEE Access, vol. 9, pp. 167812-167826, 2021.
P. Shruthi and B. P. Chaitra, "Student performance prediction in education sector using data mining," International Journal of Advanced Research in Computer Science and Software Engineering, vol. 6, no. 3, pp. 212–218, 2016.
S. T. Jishan, R. I. Rashu, N. Haque, and R. M. Rahman, "Improving accuracy of students’ final grade prediction model using optimal equal width binning and synthetic minority over-sampling technique," Decision Analytics, vol. 2, pp. 1-25, 2015.
M. Yağcı, "Educational data mining: prediction of students’ academic performance using machine learning algorithms," Smart Learning Environments, vol. 9, no. 1, pp. 11, 2022.
A. Ashraf, S. Anwer, and M. G. Khan, "A Comparative study of predicting student’s performance by use of data mining techniques," American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS), vol. 44, no. 1, pp. 122-136, 2018.
M. Durairaj and C. Vijitha, "Educational data mining for prediction of student performance using clustering algorithms," International Journal of Computer Science and Information Technologies, vol. 5, no. 4, pp. 5987-5991, 2014.
M. E. Khudhur, M. S. Ahmed, and S. M. Maher, "Prediction of the Academic Achievement of Pupils Using Data Mining Techniques," Webology, vol. 18, no. 2, pp. 1355-1364, 2021.
A. Mueen, B. Zafar, and U. Manzoor, "Modeling and predicting students’ academic performance using data mining techniques," *Int. J. Mod. Educ. Comput. Sci.*, vol. 8, no. 11, pp. 36, 2016.
H. Mousa and A. Maghari, "School student’s performance prediction using data mining classification," *Int. J. Adv. Res. Comput. Commun. Eng.*, vol. 6, no. 8, pp. 136-141, 2017.
F. Ahmad, N. H. Ismail, and A. A. Aziz, "The prediction of students’ academic performance using classification data mining techniques," *Appl. Math. Sci.*, vol. 9, no. 129, pp. 6415-6426, 2015.
J. Wong, M. Khalil, M. Baars, B. B. de Koning, and F. Paas, "Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course," *Comput. Educ.*, vol. 140, p. 103595, 2019.
A. Pavithra and S. Dhanaraj, "Prediction accuracy on academic performance of students using different data mining algorithms with influencing factors," *Int. J. Sci. Res. Manage. Stud.*, vol. 7, no. 5, 2018.
A. F. Meghji, N. A. Mahoto, Y. Asiri, H. Alshahrani, A. Sulaiman, and A. Shaikh, "Early detection of student degree-level academic performance using educational data mining," *PeerJ Comput. Sci.*, vol. 9, p. e1294, 2023.
E. Osmanbegović, M. Suljić, and H. Agić, "Determining dominant factor for students performance prediction by using data mining classification algorithms," *Tranzicija*, vol. 16, no. 34, pp. 147-158, 2014.
Y. Altujjar, W. Altamimi, I. Al-Turaiki, and M. Al-Razgan, "Predicting critical courses affecting students performance: a case study," *Procedia Comput. Sci.*, vol. 82, pp. 65-71, 2016.
A. Zohair and L. Mahmoud, "Prediction of student’s performance by modelling small dataset size," *Int. J. Educ. Technol. High. Educ.*, vol. 16, no. 1, pp. 1-18, 2019.
W. Singh and P. Kaur, "Comparative analysis of classification techniques for predicting computer engineering students’ academic performance," *Int. J. Adv. Res. Comput. Sci.*, vol. 7, no. 6, 2016.
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.*, vol. 80, pp. 3782-3785, 2023.
S. Sarker, M. K. Paul, S. T. H. Thasin, and M. A. M. Hasan, "Analyzing the impact of various factors on student performance using machine learning algorithms," *Inf. Technol. Manage.*, vol. 25, pp. 1-15, 2024.
G. Feng and M. Fan, "Research on learning behavior patterns from the perspective of educational data mining: Evaluation, prediction and visualization," *Expert Syst. Appl.*, vol. 237, p. 121555, 2024.
R. Hasan, S. Palaniappan, S. Mahmood, A. Abbas, K. U. Sarker, and M. U. Sattar, "Predicting student performance in higher educational institutions using video learning analytics and data mining techniques," *Appl. Sci.*, vol. 10, no. 11, p. 3894, 2020.
K. Nahar, B. I. Shova, T. Ria, H. B. Rashid, and A. S. Islam, "Mining educational data to predict students performance: A comparative study of data mining techniques," *Educ. Inf. Technol.*, vol. 26, no. 5, pp. 6051-6067, 2021.
Downloads
Published
How to Cite
Issue
Section
License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC-By) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
This work is licensed under a Creative Commons Attribution License CC BY
