Towards Adaptive Social Comparison for Education
Publication date
2020-08-21
Editors
Alario-Hoyos, Carlos
Rodríguez-Triana, María Jesús
Scheffel, Maren
Arnedillo-Sánchez, Inmaculada
Dennerlein, Sebastian Maximilian
Advisors
Supervisors
Document Type
Part of book
Metadata
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License
taverne
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.
Keywords
Social comparison, Learning analytics, Taverne
Citation
Sosnovsky, S A, Fang, Q, de Vries, B, Luehof, S & Wiegant, F A C 2020, Towards Adaptive Social Comparison for Education. in C Alario-Hoyos, M J Rodríguez-Triana, M Scheffel, I Arnedillo-Sánchez & S M Dennerlein (eds), Addressing Global Challenges and Quality Education : 15th European Conference on Technology Enhanced Learning, EC-TEL 2020, Heidelberg, Germany, September 14–18, 2020, Proceedings. 1 edn, Lecture Notes in Computer Science, vol. 12315, Springer, Cham, pp. 421-426. https://doi.org/10.1007/978-3-030-57717-9_38