Abstract
Virtual labs are exploratory learning environments in which students learn by conducting inquiry to uncover the underlying scientific model. Although students often fail to learn efficiently in these environments, providing effective support is challenging since it is unclear what productive engagement looks like. This paper focuses on the mining and identification of student inquiry strategies during an unstructured activity with the DC Circuit Construction Kit (https://phet.colorado.edu/). We use an information theoretic sequence mining method to identify productive and unproductive strategies of a hundred students. Low domain knowledge students who successfully learned during the activity paused more after testing their circuits, particularly on simply structured circuits that target the activity’s learning goals, and mainly earlier in the activity. Moreover, our results show that a strategic use of pauses so that they become opportunities for reflection and planning is highly associated with productive learning. Implication to theory, support, and assessment are discussed.
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Perez, S. et al. (2017). Identifying Productive Inquiry in Virtual Labs Using Sequence Mining. 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_24
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DOI: https://doi.org/10.1007/978-3-319-61425-0_24
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