Log Data Analysis with ANFIS: A Fuzzy Neural Network Approach

IF 1 Q2 SOCIAL SCIENCES, INTERDISCIPLINARY
Ying Cui, Qi Guo, Jacqueline P. Leighton, Man-Wai Chu
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引用次数: 4

Abstract

This study explores the use of the Adaptive Neuro-Fuzzy Inference System (ANFIS), a neuro-fuzzy approach, to analyze the log data of technology-based assessments to extract relevant features of student problem-solving processes, and develop and refine a set of fuzzy logic rules that could be used to interpret student performance. The log data that record student response processes while solving a science simulation task were analyzed with ANFIS. Results indicate the ANFIS analysis could generate and refine a set of fuzzy rules that shed lights on the process of how students solve the simulation task. We conclude the article by discussing the advantages of combining human judgments with the learning capacity of ANFIS for log data analysis and outlining the limitations of the current study and areas of future research.
基于ANFIS的测井数据分析:一种模糊神经网络方法
本研究探索使用神经模糊方法自适应神经模糊推理系统(ANFIS)分析基于技术的评估日志数据,以提取学生问题解决过程的相关特征,并开发和完善一套可用于解释学生表现的模糊逻辑规则。利用ANFIS分析了学生在解决科学模拟任务时的反应过程日志数据。结果表明,ANFIS分析可以生成并细化一组模糊规则,这些规则揭示了学生如何解决模拟任务的过程。最后,我们讨论了将人工判断与ANFIS的学习能力结合起来进行测井数据分析的优势,并概述了当前研究的局限性和未来研究的领域。
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来源期刊
International Journal of Testing
International Journal of Testing SOCIAL SCIENCES, INTERDISCIPLINARY-
CiteScore
3.60
自引率
11.80%
发文量
13
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