Abstract
How students learn modeling skills and concepts of system dynamics through building models was investigated, focusing on how students’ behavior and understanding are influenced by the type of assistance and their prior knowledge. We implemented a function in a model-building learning environment that detects the difference between a model by students and the correct model and gives one of the two types of feedback: structural explanation which indicates structurally erroneous parts of a model by students to promote model completion, while behavioral explanation which suggests erroneous behavior of a model by students to promote reflection on the cause of error. Our experiment revealed: (1) Students assigned to structural explanation showed high model completion, but their understanding depended on whether they used feedback appropriately or not. (2) Students assigned to behavioral explanation showed less model completion, but once they completed models, they acquired a deeper understanding.
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Horiguchi, T., Masuda, T., Tomoto, T., Hirashima, T. (2018). Behavioral Explanation versus Structural Explanation in Learning by Model-Building. In: Penstein Rosé, C., et al. Artificial Intelligence in Education. AIED 2018. Lecture Notes in Computer Science(), vol 10948. Springer, Cham. https://doi.org/10.1007/978-3-319-93846-2_25
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DOI: https://doi.org/10.1007/978-3-319-93846-2_25
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