Analyzing Student Academic Performance with a Decision Tree Predictive Workflow Model Using Classification Technique

Hasanien K Kuba, Oluwaseun A. Adelaja, Hussein Alkattan, Ali Subhi Alhumaima, Suhaf Ali Hussen and Maad M. Mijwil

References:

1. D. Alboaneen, M. Almelihi, R. Alsubaie, R. Alghamdi, L. Alshehri, and R. Alharthi, “Development of a Web-Based Prediction System for Students’ Academic Performance,” Data, vol. 7, p. 21, 2022.
2. E. B. Costa, B. Fonseca, M. A. Santana, F. F. de Araújo, and J. Rego, “Evaluating the effectiveness of educational data mining techniques for early prediction of students’ academic failure in introductory programming courses,” Computers in Human Behavior, vol. 73, pp. 247-256, 2017.
3. W. Ibrahim, S. Abdullaev, H. Alkattan, O. A. Adelaja, and A. A. Subhi, “Development of a Model Using Data Mining Technique to Test, Predict and Obtain Knowledge from the Academics Results of Information Technology Students,” Data, vol. 7, p. 67, 2022.
4. S. Sumanthi and S. N. Sivanandam, Introduction to Data Mining and its Applications, Springer, Studies in Computational Intelligence, vol. 29.
5. A. Daud, M. D. Lytras, N. R. Aljohani, F. Abbas, R. A. Abbasi, and J. S. Alowibdi, “Predicting Student Performance Using Advanced Learning Analytics,” in Proc. 26th International World Wide Web Conference Companion (WWW 2017), Perth, Australia, Apr. 2017, pp. 415-421.
6. R. Mikut and M. Reischl, “Data mining tools,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 1, pp. 431-443.
7. G. Alice, “Decision Trees,” University of Toronto Computer Science Department, 2021, [Online]. Available: https://www.cs.toronto.edu/
8. M. Vranić, D. Pintar, and Z. Skočir, “The use of data mining in education environment,” in Proc. 9th International Conference on IEEE, Winchester, UK, Jul. 2007, pp. 243-250.
9. K. Alsabti, S. Ranka, and V. Singh, “CLOUDS: A Decision Tree Classifier for Large Datasets,” in Proc. Knowledge Discovery and Data Mining Conference (KDD-98), Aug. 1998.
10. L. Rokach and O. Maimon, “Decision Trees,” Department of Industrial Engineering, Tel-Aviv University, Data Mining and Knowledge Discovery Handbook, [Online]. Available: https://www.researchgate.net/publication/225237661_Decision_Trees.pdf.
11. “Aktif Elektrotenik: Data Mining Decision Trees,” [Online]. Available: https://aktif.net/en/data-mining-decision-trees/.
12. H. Hamsa, S. Indiradevi, and J. J. Kizhakkethotteem, “Student Academic Performance Prediction Model Using Decision Tree and Fuzzy Genetic Algorithm,” Procedia Technology, pp. 327-332, 2016.
13. D. K. Kolo, A. A. Solomon, and K. A. John, “A Decision Tree Approach for Predicting Students Academic Performance,” [Online]. Available: http://www.mecs-press.net/ijeme.
14. M. A. Gotardo, “Using Decision Tree Algorithm to Predict Student Performance,” Indian Journal of Science and Technology, Feb. 2019.
15. B. K. Bharadwaj and S. Pal, “Mining Educational Data to Analyze Students Performance,” International Journal of Advanced Computer Science and Applications, pp. 63-69, Jan. 2012.
16. S. H. Mehta and A. Ashish, “Predicting Students’ Performance using J48 Decision Tree,” International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 4, pp. 122-129, 2019.
17. K. L. Khin and S. N. San, “Using ID3 Decision Tree Algorithm to Student Grade Analysis and Prediction,” pp. 1392-1395, Aug. 2019.
18. “What is Moodle?,” TechTerms, [Online]. Available: https://techterms.com/definition/moodle.
19. B. William and M. Dougiamas, Moodle for Teachers, Trainers and Administrators of Remote-Learner.net, Moodle.org, 2005.
20. J. Manuel, “Confusion Matrix,” [Online]. Available: https://www.scribd.com/presentation/447230331/CONFUSION-MATRIX-pptx.
21. R. Kundu, “Confusion Matrix: How to use it & Interpret Results,” V7labs Machine Learning Blog, Sep. 2022, [Online]. Available: https://www.v7labs.com/blog/confusion-matrix-guide.