Towards adaptive social comparison for education

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Abstract

Informing students about their progress in comparison to their peers has been widely used in educational research as a strong motivational factor, effective gamification technique and means for adaptive guidance to learning material. A typical social comparison interface helps students weight their individual levels against the average levels of other students. However, such uniform approach may not be effective for every category of students and every learning situation. Underachieving students might find the displayed social goal impossible, while overachieving students might decide that the learning goal has been attained and stop investing time and efforts. An alternative approach is an adaptive social comparison strategy that chooses different levels of the social goal for different categories of students. This paper presents one of the first steps towards developing such a strategy.

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Sosnovsky, S., Fang, Q., de Vries, B., Luehof, S., & Wiegant, F. (2020). Towards adaptive social comparison for education. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12315 LNCS, pp. 421–426). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-57717-9_38

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