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About How Deep Is Knowledge Tracing
This document discusses a study that compares deep knowledge tracing (DKT), a deep learning approach to predicting student performance, to Bayesian knowledge tracing (BKT), the traditional approach used in educational data mining. The study finds that DKT outperforms BKT on several datasets. It hypothesizes that DKT is able to exploit four sources of statistical regularity in the data that BKT cannot: recency effects, contextualized trial sequences, inter-skill similarity, and individual variation in ability. When BKT is extended to allow for more flexibility in modeling these regularities, its performance becomes comparable to DKT. This suggests that knowledge tracing may not require the "depth" of DKT and that shallow models like BKT
Detailed Information
| Author: | ['Ho Manh Linh'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 8 |
| Language: | English |
| Format: | |
| Price: | FREE |
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