A Machine Learning Approach for the Simultaneous Detection of Preknowledge in Examinees and Items When Both Are Unknown

IF 2.7 4区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH
Yiqin Pan, James A. Wollack
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引用次数: 1

Abstract

Pan and Wollack (PW) proposed a machine learning method to detect compromised items. We extend the work of PW to an approach detecting compromised items and examinees with item preknowledge simultaneously and draw on ideas in ensemble learning to relax several limitations in the work of PW. The suggested approach also provides a confidence score, which is based on an autoencoder to represent our confidence that the detection result truly corresponds to item preknowledge. Simulation studies indicate that the proposed approach performs well in the detection of item preknowledge, and the confidence score can provide helpful information for users.

一种机器学习方法在未知的情况下同时检测考生和项目中的先验知识
Pan和Wollack (PW)提出了一种机器学习方法来检测受损物品。我们将PW的工作扩展到一种同时检测折衷项目和具有项目预知的考生的方法,并借鉴集成学习的思想来放宽PW工作中的几个限制。建议的方法还提供了一个置信度评分,该评分基于自动编码器来表示我们对检测结果真正对应于项目预知的置信度。仿真研究表明,该方法在项目预知检测中表现良好,置信度得分可以为用户提供有用的信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
3.90
自引率
15.00%
发文量
47
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