Case Study

Author:

Leino Juha1

Affiliation:

1. University of Tampere, Finland

Abstract

As recommender systems are making inroads to e-learning, the new ecosystem is placing new challenges on them. This Chapter discusses the author’s experiences of adding recommender features to additional reading materials listing page in an undergraduate-level course. Discussion is based on use-log and student questionnaire data. Students could both add materials to lecture readings and peer-evaluate the pertinence of the materials by rating and commenting them. Students were required to add one material and rate five as part of the course requirements. Overall, students perceived the system as useful and did not resent compulsoriness. In addition, perceived social presence promoted social behavior in many students. However, many students rated materials without viewing them, thus undermining the reliability of aggregated ratings. Consequently, while recommenders can enhance the e-learner experience, they need to be robust against some students trying to get points without earning them.

Publisher

IGI Global

Reference36 articles.

1. Amatriain, X., Pujol, J. M., Tintarev, N., & Oliver, N. (2009). Rate it again: Increasing recommendation accuracy by user re-rating. In Proceedings of the Third ACM Conference on Recommender Systems (pp. 173-180). New York, NY: ACM.

2. Towards a customization of rating scales in adaptive systems;F.Cena;Lecture Notes in Computer Science 6075,2010

3. The Effect of Word of Mouth on Sales: Online Book Reviews

4. Cosley, D., Lam, S. K., Albert, I., Konstan, J. A., & Riedl, J. (2003). Is seeing believing? How recommender system interfaces affect users' opinions. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 585-592). New York, NY: ACM.

5. Identifying the goal, user model and conditions of recommender systems for formal and informal learning.;H.Drachsler;Journal of Digital Information,2009

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