Automated estimation of item difficulty for multiple-choice tests: An application of word embedding techniques

• Published in: Information processing & management Vol. 54; no. 6; pp. 969 - 984
• Main Authors: Hsu, Fu-Yuan, Lee, Hahn-Ming, Chang, Tao-Hsing, Sung, Yao-Ting
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.11.2018; Elsevier Science Ltd
• Subjects: Artificial intelligence; Automation; Classifiers; Cognitive processing model; Construction materials; Embedding; Item difficulty estimation; Language proficiency; Language tests; Machine learning; Materials selection; Mathematical analysis; Multiple choice; Multiple-choice item; Semantic features; Semantic relations; Semantic similarity; Semantics; Similarity; Tests; Vectors (mathematics); Word embedding; Multiple-choice item; Semantic similarity; Word embedding; Item difficulty estimation; Cognitive processing model; Machine learning

Pretesting is the most commonly used method for estimating test item difficulty because it provides highly accurate results that can be applied to assessment development activities. However, pretesting is inefficient, and it can lead to item exposure. Hence, an increasing number of studies have inve...