Ph.D. Student: Enrique García
Advisors: Cristóbal Romero, Carlos de Castro
Defended on: December 2009
Keywords: data mining, recommender systems, e-learning, web-based adaptive education
This thesis proposes a system oriented to find, share and suggest the most appropriate modifications to improve the effectiveness of the course. We use an iterative methodology to develop and carry out the maintenance of web-based courses to which we have added a specific data mining step. We apply association rule mining to discover interesting information through students? usage data in the form of IF-THEN recommendation rules. We have also used a collaborative recommender system to share and score the recommendation rules obtained by teachers with similar profiles along with other experts in education. Finally, we have carried out experiments with several real groups of students. The results obtained show the effectiveness of the system implemented and the usefulness of the rules recommended.
PUBLICATIONS ASSOCIATED WITH THIS THESIS
- E. García, C. Romero, S. Ventura and C. de Castro. An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering. User Model. User-Adapt. Interact., vol. 19(1-2), pp. 99-132. 2009.
- E. García, C. Romero, S. Ventura and C. de Castro. Using Rules Discovery for the Continuous Improvement of e-Learning Courses. IDEAL 2006, pp. 887-895. 2006.