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Modeling how incoming knowledge, persistence, affective states, and in-game progress influence student learning from an educational game
ARTICLE

, Florida State University, United States ; , University of Notre Dame, United States ; , Teachers College, United States ; , Florida State University, United States ; , University of Notre Dame, United States ; , Teachers College, United States ; , Florida State University, United States ; , Teachers College, United States

Computers & Education Volume 86, Number 1, ISSN 0360-1315 Publisher: Elsevier Ltd

Abstract

This study investigated the relationships among incoming knowledge, persistence, affective states, in-game progress, and consequently learning outcomes for students using the game Physics Playground. We used structural equation modeling to examine these relations. We tested three models, obtaining a model with good fit to the data. We found evidence that both the pretest and the in-game measure of student performance significantly predicted learning outcome, while the in-game measure of performance was predicted by pretest data, frustration, and engaged concentration. Moreover, we found evidence for two indirect paths from engaged concentration and frustration to learning, via the in-game progress measure. We discuss the importance of these findings, and consider viable next steps concerning the design of effective learning supports within game environments.

Citation

Shute, V.J., D'Mello, S., Baker, R., Cho, K., Bosch, N., Ocumpaugh, J., Ventura, M. & Almeda, V. (2015). Modeling how incoming knowledge, persistence, affective states, and in-game progress influence student learning from an educational game. Computers & Education, 86(1), 224-235. Elsevier Ltd. Retrieved July 10, 2020 from .

This record was imported from Computers & Education on January 29, 2019. Computers & Education is a publication of Elsevier.

Full text is availabe on Science Direct: http://dx.doi.org/10.1016/j.compedu.2015.08.001

Keywords

Cited By

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    Computers & Education Vol. 139, No. 1 (October 2019) pp. 173–190

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