Dynamic domain learning ability enhanced knowledge tracing with stability
Xiuli Diao, Ruiqing Hu, Qingtian Zeng, Zhengguo Song, Hua Duan · 2026
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Description
Xiuli Diao, Ruiqing Hu, Qingtian Zeng, Zhengguo Song, and Hua Duan present a steadier way to track what a student knows, in a research paper from 2026. Knowledge tracing models a student's grasp of a topic over time to predict future answers. Many models swing up and down too much, and they treat a student's ability as one fixed level.
Their model adds a separate, growing measure of learning ability for each domain. It tracks forgetting and gains through a memory network, and a smoothing step calms the swings. Tests on three real education data sets show it beats leading models on accuracy. The authors say it gives a clearer picture of each learner.
Catalog record
| CREATOR | Xiuli Diao, Ruiqing Hu, Qingtian Zeng, Zhengguo Song, Hua Duan |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 72 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |