The ability to predict a student's performance could be useful in a great number of different ways associated with university-level learning. In this paper, a grammar guided genetic programming algorithm, G3P-MI, has been applied to predict if the student will fail or pass a certain course and identifies activities to promote learning in a positive or negative way from the perspective of Multiple Instance Learning (MIL). Computational experiments compare our proposal with the most popular techniques of MIL. Results show that G3P-MI achieves better performance with more accurate models and a better trade-off between such contradictory metrics as sensitivity and specificity. Moreover, it adds comprehensibility to the knowledge discovered and finds interesting relationships that correlate certain tasks and the time devoted to solving exercises with the final marks obtained in the course. (Contains 4 tables.) [For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, July 1-3, 2009)," see ED539041.]
Authors
- Authorizing Institution
- International Working Group on Educational Data Mining
- Education Level
- ['Higher Education', 'Postsecondary Education']
- Location
- Spain
- Peer Reviewed
- F
- Publication Type
- ['Reports - Research', 'Speeches/Meeting Papers']
- Published in
- United States of America
Table of Contents
- 1 Introduction 1
- 2 Multiple Instance Learning 2
- 3 Predicting Students performance based on the e-learning Platform 2
- 3.1 MIL representation of the problem 3
- ASSIGNMENTS ASSIGNMENT QUIZP 3
- QUIZF QUIZ FORUMPOST FORUMREAD FORUM . 4
- 4 Experimentation and Results 4
- 4.1 Problem domain used in Experimentation 4
- 4.2 Multi-Instance Grammar Guided Genetic Programming 5
- 4.3 Comparison with Multiple Instance Learning techniques 5
- Methods based on Diverse Density Methods based on Logistic Regression Methods based on Support Vector Machines Distance-based Approaches Methods based on Supervised Learning Algorithms 5
- 4.4 Comprehensibility in the knowledge discovery process 6
- 5 Conclusions and Future Works 7
- References 7
- . 2005. 7
