Opportunities for the Use of Recommendation and (original) (raw)
One major challenge of most current eLearning and mLearning environments is that course activities are generally 'one-size-fits-all' and hence not able to respond to the unique needs, motivation and interests of all learners. Learning environments will have to become more and more learner-centered and personalized in order to overcome this challenge. meLearning approaches have shown some promising results in this area. meLearning refers to personalized or individualized learning environments in mLearning and eLearning where learners' profiles, preferences, learning styles and optimal learning paths are continuously being monitored and evaluated with the aim of making the learning experience more effective and efficient to the learner. This paper describes a desk review which explored the potential and opportunities of recommendation and personalization algorithms for improving meLearning environments. An ever-increasing number of e-commerce websites and personalized search engines are using advanced algorithms to individualize their search results or online stores for each user or customer. A wide variety of algorithms and technologies is now availableincluding traditional collaborative filtering, cluster models, item-to-item collaborative filtering, people-to-people correlation, and aggregated rating. Though not always well documented or shared, these algorithms are already providing very effective forms of targeted marketing by creating personalized search and shopping experiences for each customer. On the other hand, the algorithms have not been fully exploited yet for improving meLearning environments. Against this background, this paper provides some innovative ideas for new applications of recommender and personalization systems in meLearning. This is expected to lead to useful suggestions and practical recommendations for the further advancement, implementation and up-scaling of effective meLearning systems.
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