The comparison between the state-of-the-arts and the proposed models on the datasets.

<p>The comparison between the state-of-the-arts and the proposed models on the datasets.</p>

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Bibliographic Details
Main Author:	Ailian Gao (20629841) (author)
Other Authors:	Zenglei Liu (20629838) (author)
Published:	2025
Subjects:	Cancer Science Policy Biological Sciences not elsewhere classified students &# 8217 integrate temporal information conducted comparison experiments bidirectional lstm model series forecasting pipeline machine learning algorithms proposed lstkt model proposed informer model publicly available dataset individual knowledge states informer </ p achieved promising outcomes short sequence prediction probability sparse self implement knowledge tracing long sequence time sparse self series prediction knowledge tracing sequence time tracing studies time stamps time stamp ednet dataset assistments2017 dataset assistments2009 dataset current knowledge target exercises previous approaches learning performance extensively utilized existing models exercising recordings decoder architecture canonical encoder attention module attention mechanism answering records 82 %. 81 %.
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