Cognitive diagnosis models have become popular in educational assessment and are used to provide more individualized feedback about a student's specific strengths and weaknesses than traditional total scores. However, if the testing data are contaminated by certain biases or aberrant response patterns, such predictions may not be accurate. The current research objective is to develop a new person-fit method that is based on machine learning and improves the functionality of existing person-fit methods. Various simulations were designed under three aberrant conditions: cheating, sleeping and random guessing. Simulation results showed that the new method was more powerful and effective than previous methods, especially for short-length tests.

中文翻译：

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基于机器学习的CDM教育中的一种新的person-fit方法。

认知诊断模型在教育评估中变得很流行，与传统的总分相比，认知诊断模型用于就学生的具体优势和劣势提供更个性化的反馈。但是，如果测试数据受到某些偏差或异常响应模式的污染，则此类预测可能不准确。当前的研究目标是开发一种基于机器学习的新的person-fit方法，并改进现有的person-fit方法的功能。在三种异常条件下设计了各种模拟：作弊、睡眠和随机猜测。仿真结果表明，新方法比以前的方法更加强大和有效，特别是对于短长度测试。