Automatic tagging of knowledge points for K12 math problems
Xiaolu Wang, Ziqi Ding, Liangyu Chen

TL;DR
This paper introduces the LABS model, combining label-semantic attention and multi-label smoothing, to improve automatic knowledge point tagging for K12 math problems, addressing the complexity of math texts.
Contribution
It proposes a novel LABS model that integrates label semantics and multi-label smoothing to enhance accuracy in math problem tagging.
Findings
LABS model outperforms traditional BiLSTM in precision, recall, and F1-score.
Label-semantic attention guides neural networks to extract meaningful information.
Multi-label smoothing improves prediction for new data and overall classification accuracy.
Abstract
Automatic tagging of knowledge points for practice problems is the basis for managing question bases and improving the automation and intelligence of education. Therefore, it is of great practical significance to study the automatic tagging technology for practice problems. However, there are few studies on the automatic tagging of knowledge points for math problems. Math texts have more complex structures and semantics compared with general texts because they contain unique elements such as symbols and formulas. Therefore, it is difficult to meet the accuracy requirement of knowledge point prediction by directly applying the text classification techniques in general domains. In this paper, K12 math problems taken as the research object, the LABS model based on label-semantic attention and multi-label smoothing combining textual features is proposed to improve the automatic tagging of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsEducational Technology and Assessment · Online Learning and Analytics
MethodsTanh Activation · Sigmoid Activation · Long Short-Term Memory · Bidirectional LSTM
