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MultiHop attention for knowledge diagnosis of mathematics examination

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Abstract

Intelligent educational diagnosis can effectively promote the development of artificial intelligence in education. The knowledge diagnosis of specific domains (e.g., mathematics, physics) plays an important role in intelligent educational diagnosis but typically relies on complex semantic information. Most existing methods only produce single sentence representations that have difficulty detecting multiple knowledge points from text. The resources of knowledge point diagnosis of specific domains are also relatively sparse. In this study, we build a dataset about mathematics that is collected from real mathematical examination and artificially annotated 18 knowledge points. We also propose the MultiHop Attention mechanism (MHA) model to focus on different important information in mathematical questions using a multiple attention mechanism. Each attention mechanism obtains different attention weights for different parts of mathematical questions. The MHA allows us to effectively obtain a comprehensive semantic representation of mathematical questions. Additionally, because the ALBERT model is advanced and efficient, we use it for word embedding in this study. The proposed method synthetically considers multiple keywords related to knowledge points in mathematical questions for knowledge diagnosis research. Experimental results with the proposed mathematical dataset show that MHA achieves marked improvements compared to existing methods.

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Authors and Affiliations

  1. College of Computer and Information Engineering, Tianjin Normal University, No.393, Extension of Bin Shui West Road, Xi Qing District, Tianjin, 300387, China

    Xinyu He, Tongxuan Zhang & Guiyun Zhang

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  1. Xinyu He
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  2. Tongxuan Zhang
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  3. Guiyun Zhang
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Correspondence to Tongxuan Zhang.

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Xinyu He and Guiyun Zhang are contributed equally to this work.

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He, X., Zhang, T. & Zhang, G. MultiHop attention for knowledge diagnosis of mathematics examination. Appl Intell 53, 10636–10646 (2023). https://doi.org/10.1007/s10489-022-04033-x

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