An Elective Course Decision Support System Using Decision Tree and Fuzzy Logic
In many universities, a student has to take several mandatory courses and some elective courses which the student can individually choose. Many a time, elective course contents are not clear and prerequisites are not seriously considered. In this paper, we show that it is possible to get insight into the student’s performance in the electives offered, by the knowledge of student’s past performance in related courses. Data of similar students, who have recently graduated, is used to build a decision support system (DSS) using decision tree and fuzzy logic. Rules are extracted that establish the relationship between prerequisites and elective courses and their performance. Current course performance is entered into this elective course DSS (E-DSS), which can predict suitable electives and how the student might perform in those electives. Satisfactory results are obtained from the tests, and it is found that the students who performed successfully well at the required prerequisite courses have also performed well in the related elective courses.
KeywordsElective course decision support Course performance prediction Fuzzy logic Decision tree
We would like to thank PESIT, Bangalore, for providing us the student data set to conduct this experiment. The authors would also like to thank Prof. Natarajan for his valuable suggestions and encouragement.
- 1.Bendakir, N., Aimeur, E.: Using association rules for course recommendation, American Association for Artificial Intelligence (2006)Google Scholar
- 4.Parameswaran, A., Venetis P., Molina H.G.: Recommendation systems with complex constraints: a course recommendation perspective. ACM Trans. Inf. Syst. (2011)Google Scholar
- 5.Farzan, R., Brusilovsky, P.: Social navigation support in a course recommendation system. In: Adaptive Hypermedia and Adaptive Web-Based Systems, pp. 91–100. Springer, Berlin Heidelberg (2006)Google Scholar
- 8.Sobecki, J., Tomczak, J.M.: Student courses recommendation using ant colony optimization. Intell. Inf. Database Syst. 5991, 124–133 (2010)Google Scholar
- 9.Harsiti, M.A., Sigit, H.T.: Implementation of fuzzy-C4.5 classification as a decision support for students choice of major specialization (IJERT). Int. J. Eng. Res. Technol. 2(110) (2013)Google Scholar
- 10.Adak, M.F., Yumusak, N., Taskin, H.: An elective course suggestion system developed in computer engineering department using fuzzy logic. In: Industrial Informatics and Computer Systems International Conference on (CIICS), pp. 1–5 (2016)Google Scholar
- 11.Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)Google Scholar
- 12.Quinlan, J.R.: C4.5: programs for machine learning. Elsevier, Armsterdam (2014)Google Scholar
- 13.Hssina, B., et al.: A comparative study of decision tree ID3 and C4.5. Int. J. Adv. Comput. Sci. Appl. 4(2) (2014)Google Scholar
- 14.Eibe, F., Hall, M.A., Witten, I.H.: The WEKA Workbench. Online Appendix for “Data Mining: Practical Machine Learning Tools and Techniques”, 4th edn. Morgan Kaufmann, Burlington (2016)Google Scholar