Abstract
We describe a study on the use of an online laboratory for self-directed learning through the construction and simulation of conceptual models of ecological systems. We analyzed the modeling behaviors of 315 learners and 822 instances of learner-generated models using a sequential pattern mining technique. We found three types of learner behaviors: observation, construction, and exploration. We found that while the observation behavior was most common, exploration led to models of higher quality.
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Acknowledgements
This research was supported by US NSF grant #1636848. We thank members of the VERA project, especially Luke Eglington and Stephen Buckley. This research was conducted in accordance with IRB protocol #H18258.
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An, S., Rugaber, S., Hammock, J., Goel, A.K. (2022). Understanding Self-Directed Learning with Sequential Pattern Mining. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. AIED 2022. Lecture Notes in Computer Science, vol 13356. Springer, Cham. https://doi.org/10.1007/978-3-031-11647-6_102
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