FDKT:通过模糊推理实现可解释的深度知识追踪

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Fei Liu, Chenyang Bu, Haotian Zhang, Le Wu, Kui Yu, Xuegang Hu
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引用次数: 0

摘要

在教育数据挖掘中,知识追踪(Knowledge Tracing,KT)旨在根据学生对知识的掌握情况为学习成绩建模。基于深度学习的知识追踪模型的表现明显优于传统的知识追踪模型,引起了广泛关注。然而,这些模型大多缺乏可解释性,这使得解释模型在预测中表现良好的原因具有挑战性。在本文中,我们通过模糊推理提出了一种可解释的深度知识追踪模型,即模糊深度知识追踪(FDKT)。具体来说,我们使用模糊化模块将连续分数形式化为多个模糊分数。然后,我们将模糊分数输入模糊推理模块(FRM)。模糊推理模块旨在推断当前的认知能力,并据此预测未来的表现。通过对学生认知推理的解释,FDKT 极大地增强了基于深度学习的 KT 的内在可解释性。此外,它还拓宽了连续分数 KT 的应用范围。通过与最先进的模型进行比较,FDKT 在这两个优势方面的性能都得到了提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FDKT: Towards an interpretable deep knowledge tracing via fuzzy reasoning

In educational data mining, knowledge tracing (KT) aims to model learning performance based on student knowledge mastery. Deep-learning-based KT models perform remarkably better than traditional KT and have attracted considerable attention. However, most of them lack interpretability, making it challenging to explain why the model performed well in the prediction. In this paper, we propose an interpretable deep KT model, referred to as fuzzy deep knowledge tracing (FDKT) via fuzzy reasoning. Specifically, we formalize continuous scores into several fuzzy scores using the fuzzification module. Then, we input the fuzzy scores into the fuzzy reasoning module (FRM). FRM is designed to deduce the current cognitive ability, based on which the future performance was predicted. FDKT greatly enhanced the intrinsic interpretability of deep-learning-based KT through the interpretation of the deduction of student cognition. Furthermore, it broadened the application of KT to continuous scores. Improved performance with regard to both the advantages of FDKT was demonstrated through comparisons with the state-of-the-art models.

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来源期刊
ACM Transactions on Information Systems
ACM Transactions on Information Systems 工程技术-计算机:信息系统
CiteScore
9.40
自引率
14.30%
发文量
165
审稿时长
>12 weeks
期刊介绍: The ACM Transactions on Information Systems (TOIS) publishes papers on information retrieval (such as search engines, recommender systems) that contain: new principled information retrieval models or algorithms with sound empirical validation; observational, experimental and/or theoretical studies yielding new insights into information retrieval or information seeking; accounts of applications of existing information retrieval techniques that shed light on the strengths and weaknesses of the techniques; formalization of new information retrieval or information seeking tasks and of methods for evaluating the performance on those tasks; development of content (text, image, speech, video, etc) analysis methods to support information retrieval and information seeking; development of computational models of user information preferences and interaction behaviors; creation and analysis of evaluation methodologies for information retrieval and information seeking; or surveys of existing work that propose a significant synthesis. The information retrieval scope of ACM Transactions on Information Systems (TOIS) appeals to industry practitioners for its wealth of creative ideas, and to academic researchers for its descriptions of their colleagues'' work.
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