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Markus T. Jansen; Ralf Schulze – Educational and Psychological Measurement, 2024
Thurstonian forced-choice modeling is considered to be a powerful new tool to estimate item and person parameters while simultaneously testing the model fit. This assessment approach is associated with the aim of reducing faking and other response tendencies that plague traditional self-report trait assessments. As a result of major recent…
Descriptors: Factor Analysis, Models, Item Analysis, Evaluation Methods
Falk, Carl F.; Feuerstahler, Leah M. – Educational and Psychological Measurement, 2022
Large-scale assessments often use a computer adaptive test (CAT) for selection of items and for scoring respondents. Such tests often assume a parametric form for the relationship between item responses and the underlying construct. Although semi- and nonparametric response functions could be used, there is scant research on their performance in a…
Descriptors: Item Response Theory, Adaptive Testing, Computer Assisted Testing, Nonparametric Statistics
Feuerstahler, Leah M.; Waller, Niels; MacDonald, Angus, III – Educational and Psychological Measurement, 2020
Although item response models have grown in popularity in many areas of educational and psychological assessment, there are relatively few applications of these models in experimental psychopathology. In this article, we explore the use of item response models in the context of a computerized cognitive task designed to assess visual working memory…
Descriptors: Item Response Theory, Psychopathology, Intelligence Tests, Psychological Evaluation
Fuchimoto, Kazuma; Ishii, Takatoshi; Ueno, Maomi – IEEE Transactions on Learning Technologies, 2022
Educational assessments often require uniform test forms, for which each test form has equivalent measurement accuracy but with a different set of items. For uniform test assembly, an important issue is the increase of the number of assembled uniform tests. Although many automatic uniform test assembly methods exist, the maximum clique algorithm…
Descriptors: Simulation, Efficiency, Test Items, Educational Assessment
Maya Usher – Assessment & Evaluation in Higher Education, 2025
The integration of Generative Artificial Intelligence (GenAI) in education has introduced innovative approaches to assessment. One such approach is AI chatbot-based assessment, which utilizes large language models to provide students with timely and consistent feedback. However, the effectiveness of AI chatbots in generating assessments comparable…
Descriptors: Artificial Intelligence, Computer Assisted Testing, Student Evaluation, Peer Evaluation
Christina Hubertina Helena Maria Heemskerk; Claudia M. Roebers – Journal of Cognition and Development, 2024
Young children tend to rely on reactive cognitive control (e.g. strongly slow down after an error), even when task accuracy would benefit from proactive cognitive control (taking a slower task approach up front). We investigated if giving young primary school children opportunities to repeatedly experience tasks where success rates depend on…
Descriptors: Cognitive Ability, Reaction Time, Accuracy, Feedback (Response)
Benton, Tom – Research Matters, 2021
Computer adaptive testing is intended to make assessment more reliable by tailoring the difficulty of the questions a student has to answer to their level of ability. Most commonly, this benefit is used to justify the length of tests being shortened whilst retaining the reliability of a longer, non-adaptive test. Improvements due to adaptive…
Descriptors: Risk, Item Response Theory, Computer Assisted Testing, Difficulty Level
Gorney, Kylie; Wollack, James A. – Practical Assessment, Research & Evaluation, 2022
Unlike the traditional multiple-choice (MC) format, the discrete-option multiple-choice (DOMC) format does not necessarily reveal all answer options to an examinee. The purpose of this study was to determine whether the reduced exposure of item content affects test security. We conducted an experiment in which participants were allowed to view…
Descriptors: Test Items, Test Format, Multiple Choice Tests, Item Analysis
Soland, James; Kuhfeld, Megan; Rios, Joseph – Large-scale Assessments in Education, 2021
Low examinee effort is a major threat to valid uses of many test scores. Fortunately, several methods have been developed to detect noneffortful item responses, most of which use response times. To accurately identify noneffortful responses, one must set response time thresholds separating those responses from effortful ones. While other studies…
Descriptors: Reaction Time, Measurement, Response Style (Tests), Reading Tests
Wang, Zhen; Cao, Yang; Gong, Shaoying – Journal of Educational Computing Research, 2023
Although learner characteristics have been identified as important moderator variables for feedback effectiveness, the question of why learners benefit differently from feedback has only received limited attention. In this study, we investigated: (1) whether learners' dominant goal orientation moderated the effects of computer-based elaborated…
Descriptors: Goal Orientation, Feedback (Response), Cues, Student Characteristics
von Davier, Matthias; Khorramdel, Lale; He, Qiwei; Shin, Hyo Jeong; Chen, Haiwen – Journal of Educational and Behavioral Statistics, 2019
International large-scale assessments (ILSAs) transitioned from paper-based assessments to computer-based assessments (CBAs) facilitating the use of new item types and more effective data collection tools. This allows implementation of more complex test designs and to collect process and response time (RT) data. These new data types can be used to…
Descriptors: International Assessment, Computer Assisted Testing, Psychometrics, Item Response Theory
Cikrikci, Nukhet; Yalcin, Seher; Kalender, Ilker; Gul, Emrah; Ayan, Cansu; Uyumaz, Gizem; Sahin-Kursad, Merve; Kamis, Omer – International Journal of Assessment Tools in Education, 2020
This study tested the applicability of the theoretical Examination for Candidates of Driving License (ECODL) in Turkey as a computerized adaptive test (CAT). Firstly, various simulation conditions were tested for the live CAT through an item response theory-based calibrated item bank. The application of the simulated CAT was based on data from…
Descriptors: Motor Vehicles, Traffic Safety, Computer Assisted Testing, Item Response Theory
Ferman, Sara; Shmuel, Sapir Amira; Zaltz, Yael – Language Learning and Development, 2022
The acquisition of a new morphological rule can be influenced by numerous factors, including the type of feedback provided during learning. The present study aimed to test the effect of different feedback types on children's ability to learn and generalize an artificial morphological rule (AMR). Two groups of eight-year-olds learned to judge and…
Descriptors: Morphology (Languages), Feedback (Response), Error Correction, Learning Processes
Konopka, Agnieszka E. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2019
Two experiments tracked the encoding of relational information (actions at the level of the prelinguistic message and verbs at the level of the sentence) during formulation of transitive event descriptions (e.g., The tiger is scratching the photographer). At what point during message and sentence formulation do speakers encode actions and verbs?…
Descriptors: Verbs, Language Processing, Psycholinguistics, Sentences
Limei Shan – International Journal of Technology in Teaching and Learning, 2023
This paper explores the impact of AI applications in adaptive learning on learners with varying levels of Chinese proficiency and diverse learning needs. The study investigates the robustness of VR technology in enhancing Chinese oral proficiency and pragmatic skills among 1st year and 2nd year learners. It compares the established VR app Mondly…
Descriptors: Artificial Intelligence, Chinese, Second Language Learning, Second Language Instruction

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