A Felder and Silverman Learning Styles Model Based Personalization Approach to Recommend Learning Objects

Abstract

In this paper, a new algorithmic personalization approach based on Felder and Silverman learning styles model is presented. The proposed approach uses learning objects modeled with the IEEE LOM metadata standard, which serves as the main standard for representation of learning objects’ metadata. Personalization is provided with two steps in the proposed approach. At the first step, each learning object is evaluated by taking into account how values of IEEE LOM metadata elements match each dimension of Felder and Silverman learning styles model. The second step involves recommending appropriate learning objects to learners. Four weight values are calculated for each learning object, describing how related the learner and the learning object in question is at each dimension of Felder and Silverman learning styles model. Then, weight values for each dimension is combined by using Manhattan distance metric to provide a single weight value as a fitness function representing the general relatedness of the learner and the learning object. Results of the personalization approach can be used to recommend learning objects ordered according to their weight values to the learners. An example scenario illustrating the proposed approach is provided, as well as a discussion of current limitations and future work directions.