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Challenges to Use Recommender Systems to Enhance Meta-Cognitive Functioning in Online Learners
Abstract
A wealth of research has shown that meta-cognition plays a crucial role in the promotion of effective school learning. In most of the e-learning environment designs, however, meta-cognitive strategies have generally been neglected, and therefore, satisfactory uses of these strategies have rarely been realized. Most learners are not even aware of what they have been studying. If the learning system could automatically guide and intelligently recommend learning activities or strategies to facilitate student monitoring and control of their learning, it would favor and improve their learning process and performance. Unfortunately, nearly no e-learning systems to date have attempted to do so. In this chapter, we first described the need for enhancing meta-cognitive skills in e-learning environment, followed by an outline of major challenges for meta-cognitive activity recommendations. We then proposed to adopt data mining algorithms (i.e., content-based and sequence-based recommendation techniques) to meet the identified issues with a toy example.
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