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Dynamic Key-Value Memory Network for Knowledge Tracing | | Jiani Zhang
; Xingjian Shi
; Irwin King
; Dit-Yan Yeung
; | | Date: |
24 Nov 2016 | | Abstract: | The goal of knowledge tracing is to model students’ mastering levels of
underlying knowledge concepts, termed knowledge state, based on students’
exercise performance data. However, existing methods, such as Bayesian
Knowledge Tracing (BKT) or Deep Knowledge Tracing (DKT), either require costly
human-labeled concept annotations or fail to exactly pinpoint which concepts a
student is good at or unfamiliar with. To solve these problems, in this paper
we introduce a new model called Dynamic Key-Value Memory Network (DKVMN) that
can learn representations using nonlinear transformations and directly output a
student’s mastering level of each concept. Unlike standard Memory-Augmented
Neural Networks (MANNs) that facilitate a single memory matrix or two static
memory matrices, our model has one static matrix called key that stores the
knowledge concepts and the other dynamic matrix called value that stores and
updates corresponding concepts’ mastery levels. Experiments show that our DKVMN
model, which is trained end-to-end, consistently outperforms the
state-of-the-art model on a range of knowledge tracing data-sets. We also
illustrate that the learned DKVMN can automatically discover underlying
concepts of the exercises which are typically performed by human annotations,
and depict a student’s changing knowledge state. | | Source: | arXiv, 1611.8108 | | Services: | Forum | Review | PDF | Favorites | |
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