No abstract

The personal learning environment (PLE) concept offers a learner-centric view of learning and suggests a shift from knowledge-push to knowledge-pull approach to learning. One concern with a PLE-driven knowledge-pull approach to learning, however, is information overload. Recommender systems can provide an effective mechanism to deal with the information overload problem in PLEs. In this paper, we study different tag-based collaborative filtering recommendation techniques on their applicability and effectiveness in PLE settings. We implement 16 different tag-based collaborative filtering recommendation algorithms, memory based as well as model based, and compare them in terms of accuracy and user satisfaction. The results of the conducted offline and user evaluations reveal that the quality of user experience does not correlate with high-recommendation accuracy.

The widespread use of mobile technologies has led to an increasing interest in mobile learning. Context is a central topic of research in that area. In fact, a major benefit of mobile devices is that they enable learning across contexts. In this paper, we explore how context can deliver significant benefits in mobile learning and provide an extensive review of the current literature and research on mobile learning in context. Furthermore, we identify various challenges and research opportunities in this area and propose the conceptual framework CAMeL for context-aware mobile learning.

No abstract

No abstract