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Social Recommender Systems: Recommendations in Support of E-Learning
Abstract
Recommendation systems can play an extensive role in online learning. In such systems, learners can receive guidance in locating and ranking references, knowledge bits, test items, and so forth. In recommender systems, users’ ratings can be applied toward items, users, other users’ ratings, and, if allowed, raters of raters of items recursively. In this chapter, we describe an online learning system — QSIA — an active recommender system for Questions Sharing and Interactive Assignments, designed to enhance knowledge sharing among learners. First, we lay out some of the theoretical background for social, open-rating mechanisms in online learning systems. We discuss concepts such as social versus black-box recommendations and the advice of neighbors as opposed to that of friends. We argue that enabling subjective views and ratings of other users is an inevitable phase of social collaboration systems. We also argue that social recommendations are critical for the exploitation of the value associated with recommendation.
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