Abstract
Because feedback affects learning, it is central to many educational technologies. We analyze properties of hint feedback in an intelligent tutoring system for high school geometry. First, we examine whether feedback content or feedback sequence is a better predictor of student performance after feedback. Second, we investigate whether linguistic features of hints affect performance. We find that students respond to different hint types differently even after accounting for student proficiency, skill difficulty, and prior practice. We also find that hint content, but not linguistic features affects performance. The findings suggest that tutoring system developers should focus on individual learner differences and feedback content.
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Goldin, I.M., Carlson, R. (2013). Learner Differences and Hint Content. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds) Artificial Intelligence in Education. AIED 2013. Lecture Notes in Computer Science(), vol 7926. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39112-5_53
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DOI: https://doi.org/10.1007/978-3-642-39112-5_53
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