An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering Export

@article{citeulike:3634540, abstract = {Nowadays we find more and more applications for data mining techniques in e-learning and web-based adaptive educational systems. The useful information discovered can be used directly by the teacher or author of the course in order to improve instructional/learning performance. This can, however, imply a lot of work for the teacher who can greatly benefit from the help of educational recommender systems for doing this task. In this paper we propose a system oriented to find, share and suggest the most appropriate modifications to improve the effectiveness of the course. We describe 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 using a web-based adaptive course. The results obtained demonstrate that the proposed architecture constitutes a good starting point to future investigations in order to generalize the results over many course contents.}, address = {Hingham, MA, USA}, author = {Garc'{i}a, Enrique and Romero, Crist\'{o}bal and Ventura, Sebasti\'{a}n and De Castro, Carlos}, citeulike-article-id = {3634540}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1499122}, citeulike-linkout-1 = {http://dx.doi.org/10.1007/s11257-008-9047-z}, citeulike-linkout-2 = {http://www.springerlink.com/content/373u703534x441tj}, doi = {10.1007/s11257-008-9047-z}, issn = {0924-1868}, journal = {User Modeling and User-Adapted Interaction}, keywords = {authoring, collaborative-filtering, e-learning, en, recommender}, number = {1-2}, pages = {99--132}, posted-at = {2009-07-14 14:30:59}, priority = {2}, publisher = {Kluwer Academic Publishers}, title = {An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering}, url = {http://dx.doi.org/10.1007/s11257-008-9047-z}, volume = {19}, year = {2009} }

TY - JOUR ID - citeulike:3634540 L3 - citeulike-article-id:3634540 N2 - Nowadays we find more and more applications for data mining techniques in e-learning and web-based adaptive educational systems. The useful information discovered can be used directly by the teacher or author of the course in order to improve instructional/learning performance. This can, however, imply a lot of work for the teacher who can greatly benefit from the help of educational recommender systems for doing this task. In this paper we propose a system oriented to find, share and suggest the most appropriate modifications to improve the effectiveness of the course. We describe 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 using a web-based adaptive course. The results obtained demonstrate that the proposed architecture constitutes a good starting point to future investigations in order to generalize the results over many course contents. IS - 1-2 JF - User Modeling and User-Adapted Interaction SN - 0924-1868 AD - Hingham, MA, USA EP - 132 TI - An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering PB - Kluwer Academic Publishers VL - 19 SP - 99 KW - authoring KW - collaborative-filtering KW - e-learning KW - en KW - recommender AU - Garc'{i}a, Enrique AU - Romero, Cristóbal AU - Ventura, Sebastián AU - De Castro, Carlos PY - 2009/// UR - http://dx.doi.org/10.1007/s11257-008-9047-z ER -