A Recommendation Cascade for E-learning
J. Buncle, Rachid Anane, Minoru Nakayama · 2013
This paper is concerned with the presentation of a collaborative recommendation system that implements a cascade of strategies in order to support the learning process. Similarities between learners are determined by taking advantage of the underlying implicit or explicit personalization and of the non-personalised modes of interaction. In the personalised approach implicit profiles are based on the patterns of behaviour of learners, while explicit profiles are generated from the results of a questionnaire on learning style. The non-personalisation approach relies on the cumulative intervention of a community of learners implied by the recorded frequency of the usage of objects by learners, and by the expert rating of objects by teachers. Content-based and collaborative approaches are combined into a hybrid model that widens the range of objects to which a learner may be exposed. The quality of service of the recommendation system is evaluated by considering the accuracy of its predictive capability on a publicly available data set.