Title :
LRMDCR: A Learner´s Role-Based Multi Dimensional Collaborative Recommendation for Group Learning Support
Author :
Wan, Xin ; Ninomiya, Toshie ; Okamoto, Toshio
Author_Institution :
Grad. Sch. of Inf. Syst., Univ. of Electro-Commun., Chofu
Abstract :
In order to improve the ldquoeducational provisionrdquo to implement the e-learning recommender system, we propose a new recommendation approach which has been proven to be more suitable to realize personalized recommendation based on not only learning histories but also learning activities and learning processes which is defined as LRMDCR (a learnerpsilas role-based multidimensional collaborative recommendation). In the approach, firstly we use the Markov chain model to divide the group learners into advanced learners and beginner learners by using the learnerspsila learning activities and learning processes. Secondly we use the multidimensional collaborative filtering to decide the recommendation learning objects to every learner of the group.
Keywords :
Markov processes; computer aided instruction; information filters; LRMDCR; Markov chain model; e-learning recommender system; group learning support; learner role-based multi dimensional collaborative recommendation; multidimensional collaborative filtering; Collaborative work; Educational technology; Electronic learning; Filtering; History; Information systems; International collaboration; Multidimensional systems; Recommender systems; Wide area networks; Markov Chain Model; multidimensional collaborative filtering; role-based;
Conference_Titel :
Advanced Learning Technologies, 2008. ICALT '08. Eighth IEEE International Conference on
Conference_Location :
Santander, Cantabria
Print_ISBN :
978-0-7695-3167-0
DOI :
10.1109/ICALT.2008.63