Abstract
PyKinetic is a mobile tutor for Python, which offers Parsons problems with incomplete lines of code (LOCs). This paper reports the results of a study in which we investigated the effect of menu-based self-explanation (SE) prompts. Students were asked to self-explain concepts related to incomplete LOCs they have solved. The goals of the study were (1) to investigate whether students are learning with PyKinetic and (2) to determine the effect of SE prompts. The scores of participants have significantly improved from the pre-test to the post-test. There was also a significant difference on the post-test scores of participants from the experimental group compared to the control group. In future work, we aim to add other activities to PyKinetic, and introduce a student model and a pedagogical model for an adaptive version of PyKinetic.
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Fabic, G.V.F., Mitrovic, A., Neshatian, K. (2017). Investigating the Effectiveness of Menu-Based Self-explanation Prompts in a Mobile Python Tutor. 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_49
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DOI: https://doi.org/10.1007/978-3-319-61425-0_49
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