Summary of Dygkt: Dynamic Graph Learning For Knowledge Tracing, by Ke Cheng et al.
DyGKT: Dynamic Graph Learning for Knowledge Tracing
by Ke Cheng, Linzhi Peng, Pengyang Wang, Junchen Ye, Leilei Sun, Bowen Du
First submitted to arxiv on: 30 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Dynamic Graph-based Knowledge Tracing model, DyGKT, revolutionizes the existing knowledge tracing methods by accounting for three dynamical characteristics: constantly growing student answering records, varying time intervals between records, and evolving relationships between students, questions, and concepts. The model constructs a continuous-time dynamic question-answering graph to capture long-term and short-term semantics among different time intervals using a dual time encoder. Additionally, the multiset indicator is utilized to model the evolving relationships via graph structural features. Experimental results on five real-world datasets demonstrate the superiority of DyGKT. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DyGKT is a new way to understand how students learn by looking at their answers over time. Right now, other methods only look at short periods of time and don’t account for changes in what students know or how they relate to each other. This model fixes that by using graphs to show the relationships between students, questions, and concepts. It also uses special tools called dual time encoders and multiset indicators to capture different types of information. The results from testing this model on real data show that it does a better job than other methods at understanding student learning. |
Keywords
» Artificial intelligence » Encoder » Question answering » Semantics
