Recommendation System of Mobile Language Learning Applications: Similarity versus Diversity in Learner Preference

Recognizing the potential of the preference-inconsistent recommendation for learning, this paper aims to examine two recommendation algorithms for mobile language learning applications: RS with similarity and RS with diversity. Diversity was measured through learning styles (how learners learn) and achievement goals (why learners learn). A total of 160 learners participated in two experiments for building learner profiles and the recommendation algorithm. Overall, our results with RSME indicate that RS with similarity and RS with diversity performed better than the random recommendation.