Abstract
Bayesian Knowledge Tracing (BKT) has been employed successfully in intelligent learning environments to individualize curriculum sequencing and help messages. Standard BKT employs four parameters, which are estimated separately for individual knowledge components, but not for individual students. Studies have shown that individualizing the parameter estimates for students based on existing data logs improves goodness of fit and leads to substantially different practice recommendations. This study investigates how well BKT parameters in a tutor lesson can be individualized ahead of time, based on learners’ prior activities, including reading text and completing prior tutor lessons. We find that directly applying best-fitting individualized parameter estimates from prior tutor lessons does not appreciably improve BKT goodness of fit for a later tutor lesson, but that individual differences in the later lesson can be effectively predicted from measures of learners’ behaviors in reading text and in completing the prior tutor lessons.
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Acknowledgements
This research was supported by the National Science Foundation via the grant “Knowing What Students Know: Using Education Data Mining to Predict Robust STEM Learning”, award number DRL1420609.
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Eagle, M. et al. (2017). Exploring Learner Model Differences Between Students. 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_48
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DOI: https://doi.org/10.1007/978-3-319-61425-0_48
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