This study investigates the estimation of classification consistency and accuracy indices for composite summed and theta scores within the SS-MIRT framework, using five popular approaches, including the Lee, Rudner, Guo, Bayesian EAP, and Bayesian MCMC approaches. The procedures are illustrated through analysis of two real datasets and further…

This study proposes a Bayesian method for diagnostic classification models (DCMs) for a partially known Q-matrix setting between exploratory and confirmatory DCMs. This Q-matrix setting is practical and useful because test experts have pre-knowledge of the Q-matrix but cannot readily specify it completely. The proposed method employs priors for…

A critical aspect of spoken language development is learning to categorize the sounds of the child's language(s). This process was thought to develop early during infancy to set the stage for the later development of higher-level aspects of language (e.g., vocabulary, syntax). However, many recent studies have shown that speech categorization…

Item compromise has long posed challenges in educational measurement, jeopardizing both test validity and test security of continuous tests. Detecting compromised items is therefore crucial to address this concern. The present literature on compromised item detection reveals two notable gaps: First, the majority of existing methods are based upon…

Parental involvement is essential to children's learning engagement activities and academic performance. Much research revolves around the impact of parental involvement on students' academic performance or the relationship between student engagement and grades. However, few studies have used process data to examine the relationship between…

This paper introduces AMMORE, a new dataset of 53,000 math open-response question-answer pairs from Rori, a mathematics learning platform used by middle and high school students in several African countries. Using this dataset, we conducted two experiments to evaluate the use of large language models (LLM) for grading particularly challenging…