Abstract
Knowledge tracing aims to quantify how well students master the knowledge (tags) being tutored by analyzing their learning activities (e.g., coursework interaction data). It plays an important role in intelligent tutoring systems. In this paper, we cast knowledge tracing as a performance-prediction problem, which predicts the performances of students on exercises labeled by multiple knowledge tags, and propose to tackle this problem using Deep Learning techniques. We applied several Recurrent Neural Network architectures to model complex representations of student knowledge and predict future performances of students. Our experimental results demonstrate that the neural network architecture based on stacked Long Short Term Memory and residual connections give superior predictions on the future performances of learners. To model how a student answered a question that contains multiple knowledge tags, we explored three different variants to map knowledge states to prediction.
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Sha, L., Hong, P. (2017). Neural Knowledge Tracing. In: Frasson, C., Kostopoulos, G. (eds) Brain Function Assessment in Learning. BFAL 2017. Lecture Notes in Computer Science(), vol 10512. Springer, Cham. https://doi.org/10.1007/978-3-319-67615-9_10
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DOI: https://doi.org/10.1007/978-3-319-67615-9_10
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