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Hung Tan Ha; Duyen Thi Bich Nguyen; Tim Stoeckel – Language Assessment Quarterly, 2025
This article compares two methods for detecting local item dependence (LID): residual correlation examination and Rasch testlet modeling (RTM), in a commonly used 3:6 matching format and an extended matching test (EMT) format. The two formats are hypothesized to facilitate different levels of item dependency due to differences in the number of…
Descriptors: Comparative Analysis, Language Tests, Test Items, Item Analysis
Yunting Liu; Shreya Bhandari; Zachary A. Pardos – British Journal of Educational Technology, 2025
Effective educational measurement relies heavily on the curation of well-designed item pools. However, item calibration is time consuming and costly, requiring a sufficient number of respondents to estimate the psychometric properties of items. In this study, we explore the potential of six different large language models (LLMs; GPT-3.5, GPT-4,…
Descriptors: Artificial Intelligence, Test Items, Psychometrics, Educational Assessment
Peter A. Edelsbrunner; Bianca A. Simonsmeier; Michael Schneider – Educational Psychology Review, 2025
Knowledge is an important predictor and outcome of learning and development. Its measurement is challenged by the fact that knowledge can be integrated and homogeneous, or fragmented and heterogeneous, which can change through learning. These characteristics of knowledge are at odds with current standards for test development, demanding a high…
Descriptors: Meta Analysis, Predictor Variables, Learning Processes, Knowledge Level
Selcuk Acar; Yuyang Shen – Journal of Creative Behavior, 2025
Creativity tests, like creativity itself, vary widely in their structure and use. These differences include instructions, test duration, environments, prompt and response modalities, and the structure of test items. A key factor is task structure, referring to the specificity of the number of responses requested for a given prompt. Classic…
Descriptors: Creativity, Creative Thinking, Creativity Tests, Task Analysis
Joshua B. Gilbert; Luke W. Miratrix; Mridul Joshi; Benjamin W. Domingue – Journal of Educational and Behavioral Statistics, 2025
Analyzing heterogeneous treatment effects (HTEs) plays a crucial role in understanding the impacts of educational interventions. A standard practice for HTE analysis is to examine interactions between treatment status and preintervention participant characteristics, such as pretest scores, to identify how different groups respond to treatment.…
Descriptors: Causal Models, Item Response Theory, Statistical Inference, Psychometrics
Rümeysa Kaya; Bayram Çetin – International Journal of Assessment Tools in Education, 2025
In this study, the cut-off scores obtained from the Angoff, Angoff Y/N, Nedelsky and Ebel standard methods were compared with the 50 T score and the current cut-off score in various aspects. Data were collected from 448 students who took Module B1+ English Exit Exam IV and 14 experts. It was seen that while the Nedelsky method gave the lowest…
Descriptors: Standard Setting, Cutting Scores, Exit Examinations, Academic Achievement