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基于深度學(xué)習的知識追蹤研究綜述
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青島科技大學(xué)

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國家自然科學(xué)基金項目(面上項目,重點(diǎn)項目,重大項目)


A review of the progress of knowledge tracking research based on deep learning
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    摘要:

    隨著(zhù)人工智能與教育的不斷發(fā)展,知識追蹤在智慧教學(xué)領(lǐng)域具有廣闊的應用前景。而深度學(xué)習以其強大特征提取能力廣泛應用于知識追蹤,以深度學(xué)習知識追蹤模型為起點(diǎn),其改進(jìn)模型為主線(xiàn),全面回顧了知識追蹤模型的研究進(jìn)展,簡(jiǎn)要介紹了知識追蹤領(lǐng)域傳統模型的特點(diǎn)及不足,闡述了基于深度學(xué)習知識追蹤模型的原理及局限性,同時(shí)全面整理并分析了針對可解釋性問(wèn)題、缺少學(xué)習特征、記憶增強網(wǎng)絡(luò )、圖神經(jīng)網(wǎng)絡(luò )、基于注意力機制五個(gè)方面的改進(jìn)模型,梳理了知識追蹤領(lǐng)域常用的公開(kāi)數據集、評價(jià)指標及模型性能對比分析,最后總結并探討了知識追蹤在智慧教學(xué)方面的應用以及當前該研究領(lǐng)域的研究現狀與未來(lái)的研究方向。

    Abstract:

    With the continuous development of artificial intelligence and education, knowledge tracking has broad application prospects in the field of smart teaching. Deep learning is widely used in the field of knowledge tracking with its powerful feature extraction ability, taking the deep learning knowledge tracking model as the starting point, and its improved model as the main line, comprehensively reviewing the research progress of the knowledge tracking model, briefly introducing the characteristics and shortcomings of the traditional model in the field of knowledge tracking, expounding the principles and limitations of the knowledge tracking model based on deep learning, and comprehensively sorting out and analyzing the problems of interpretability, lack of learning features, memory enhancement networks, graph neural networks, Based on the improvement model of five aspects of attention mechanism, the comparative analysis of public data sets, evaluation indicators and model performance commonly used in the field of knowledge tracking is sorted out, and finally the application of knowledge tracking in smart teaching and the current research status and future research direction of this research field are summarized and discussed.

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王丹萍,王忠,梁宏濤.基于深度學(xué)習的知識追蹤研究綜述計算機測量與控制[J].,2022,30(12):1-10.

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歷史
  • 收稿日期:2022-09-22
  • 最后修改日期:2022-09-27
  • 錄用日期:2022-09-28
  • 在線(xiàn)發(fā)布日期: 2022-12-22
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