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J Buder, C Schwind/Computers in Human Behavior 28(2012)207-216 in educational contexts. Though some applications are aroun Buunk, B P, Collins, R L, Taylor, S E, van Yperen, N w,& Dakof, G.A.(1990). Th many of them do not move beyond a prototype develo pment. ffective consequences of social comparison: Either direction has its ups and Implementation studies would typically be case studies or experi- downs. Journal of personality and Social Psychology, 59, 1238-1249. doi: 10. 1037/ ments with atool vS no tool "condition. Second, we need more Cai, x, Bain, M, Krzywicki, A, Wobcke, w Kim, Y S, Compton, P et al.(2011). pplied experimental research that varies boundary condition twor but keeps technology constant. For instance, it would be interes ing to see how one and the same recommender system works with Cialdini, R B (2001). Influence: Science and practice(4th ed. )Boston, MA: Allyn different tasks, or different learners with different learning styles and different proficiency levels. This would help illuminating the Cohen, E. G.( 1994) Restructuring the classroom: Conditions for productive small doi:10.3102 potentials and the limitations of educational recommender sys- tems And third, we need more research on the basic psychological Cosley. D. Lam,S K Albert, L, Konstan, J.A.& Riedl (2003) Is seeing believing? mechanisms that are addressed when learners use a recommender Korhonen (Eds h. Proceedings of the ACM CH/ Cons opinions. In G. Cockton system. For instance, our own empirical work on the effectiveness Computing Systems (pp 585-592) New York, NY: ACM Press. doi: 10.1145/ of preference-inconsistent recommendations, while not employing 611.642713 a full-blown recommender system, can be regarded as a step to- Cronbach, LJ.& Snow, R.E. (1977). Aptitudes and instructional methods: A handbook wards uncovering the psychological dynamics that specific types Dawes, R M (1980). Social dilemmas. Annual Review of Psychology, 31.169-193 Technologies need to have an added value in order to become De wit. FRC, Greer, LL. 2008. The black-box deciphered: meta-analysis of tea incorporated in everyday learning settings. Technologies must make us capable of accomplishing things that are impossible to Dillard, en, L(2005) On the nature of reactance and its role in persuasive yield by any other means Recommender systems have many fasci- health communication. Communication Monographs. 72, 144-168. doi: 10.1080/ ating features, among them providing learners with access to Drachsler, H. Hummel, H G K. Koper R (2009). Identifying the goal, user model information that no other method can accomplish. In light of these ns of recommender systems for formal and informal learning potentials it is more than likely that recommenders for learning are here to stay. However, their exploration by social scientists has just Drachsler H Hummel.H GK, an den Berg. B.Eshuis. 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M, Li, S.X Chen, Y, Konstan,J. A(2007). So Midd 148-159) Berlin. Germany: Springer.doi:10.1007/9783-540-77006020 Herlocker, J. L, Konstan, I A, Borchers, A&RiedL, J.(1999). An algorithmic orative Learning (vol. 2, pp. 796-800) Hong Kong, China: Internationa rmation Retrieval (pp. 203-237). Berkeley, CA: ACM Press. doi: 10. 1145/ 312624312682 and User-Adapted Interaction, 12. 331-370. doi: 10.1023 Herlocker, J L, Konstan, J. A, Riedl, J(2000), Explaining collaborative filterin A:1021240730564.in educational contexts. Though some applications are around, many of them do not move beyond a prototype development. Implementation studies would typically be case studies or experi￾ments with a ‘‘tool vs. no tool’’ condition. Second, we need more applied experimental research that varies boundary conditions, but keeps technology constant. For instance, it would be interest￾ing to see how one and the same recommender system works with different tasks, or different learners with different learning styles and different proficiency levels. This would help illuminating the potentials and the limitations of educational recommender sys￾tems. And third, we need more research on the basic psychological mechanisms that are addressed when learners use a recommender system. For instance, our own empirical work on the effectiveness of preference-inconsistent recommendations, while not employing a full-blown recommender system, can be regarded as a step to￾wards uncovering the psychological dynamics that specific types of recommendations create. Technologies need to have an added value in order to become incorporated in everyday learning settings. Technologies must make us capable of accomplishing things that are impossible to yield by any other means. 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