Abstract
Both Knowledge Tracing and Performance Factors Analysis, are examples of student modeling frameworks commonly used in AIED systems (i.e., Intelligent Tutoring Systems). Both of them use student correctness as a binary input, but student performance on a question might better be represented with a continuous value representing a type of partial credit. Intuitively, a student who has to make more attempts, or has to ask for more hints, deserves a score closer to zero, while students who asks for no hints and just needs to make a second attempt on a question should get a score close to one. In this work, we present a simple change to the Knowledge Tracing model and a simple (non-optimized) method for assigning partial credit. We report our real data experiment result in which we compared the original Knowledge Tracing (OKT) model with this new Knowledge Tracing model that uses partial credit as input (KTPC). The new model outperforms the traditional model reliably. The practical implication of this work is that this new technique can be widely used easily, as it is a small change from the traditional way of fitting KT models.
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Wang, Y., Heffernan, N. (2013). Extending Knowledge Tracing to Allow Partial Credit: Using Continuous versus Binary Nodes. 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_19
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DOI: https://doi.org/10.1007/978-3-642-39112-5_19
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