Abstract
We attempted to model the Incubation Effect, a phenomenon in which a momentary break helps the generation of a solution to a problem, among students playing Physics Playground. We performed a logistic regression analysis to predict the outcome of the incubation using a genetic algorithm for feature selection. Out of 14 candidate features, those that significantly predicted the outcome were total badges earned prior to post-incubation, the problem’s level of difficulty, total attempts made prior to post-incubation, and time interval of post-incubation. We found evidence that incubation in the earlier part of the game is more beneficial than breaks at the later part where students may already be mentally exhausted.
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Acknowledgement
The authors would like to thank Mr. Joshua Martinez and Mr. Jun Rangie Obispo for their valuable contribution in the initial investigation of incubation effect in Physics Playground which paved way for this study. We also would like to thank the Ateneo Laboratory for the Learning Sciences and the Ateneo de Manila University Department of Information Systems and Computer Science. This study was made possible through a grant from the Philippines’ Department of Science and Technology Philippine Council for Industry, Energy and Emerging Technology Research and Development entitled “Stealth assessment of student conscientiousness, cognitive-affective states, and learning using an educational game for Physics.”
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Talandron, M.M.P., Rodrigo, M.M.T., Beck, J.E. (2017). Modeling the Incubation Effect Among Students Playing an Educational Game for Physics. In: André, E., Baker, R., Hu, X., Rodrigo, M., du Boulay, B. (eds) Artificial Intelligence in Education. AIED 2017. Lecture Notes in Computer Science(), vol 10331. Springer, Cham. https://doi.org/10.1007/978-3-319-61425-0_31
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DOI: https://doi.org/10.1007/978-3-319-61425-0_31
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