Background: The study focuses on the need to optimise assessment scales for support needs in individuals with intellectual and developmental disabilities. Current scales are often lengthy and redundant, leading to exhaustion and response burden. The goal is to use machine learning techniques, specifically item-reduction methods and selection…

Item preknowledge refers to the phenomenon in which some examinees have access to live items before taking a test. It is one of the most common and significant concerns within the testing industry. Thus, various statistical methods have been proposed to detect item preknowledge in computerized linear or adaptive testing. However, the success of…

Multiple choice questions are traditionally expensive to produce. Recent advances in large language models (LLMs) have led to fine-tuned LLMs that generate questions competitive with human-authored questions. However, the relative capabilities of ChatGPT-family models have not yet been established for this task. We present a carefully-controlled…

This article explores whether or not it is possible for computers to be effectively used to analyze textual information. Computerization of human linguistic analysis would be enormously useful, because it would relieve many highly skilled linguistics professionals (researchers and teachers) from having to spend enormous amounts of time on the…