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Direct linkAndreea Dutulescu; Stefan Ruseti; Denis Iorga; Mihai Dascalu; Danielle S. McNamara – Grantee Submission, 2025
Automated multiple-choice question (MCQ) generation is valuable for scalable assessment and enhanced learning experiences. How-ever, existing MCQ generation methods face challenges in ensuring plausible distractors and maintaining answer consistency. This paper intro-duces a method for MCQ generation that integrates reasoning-based explanations…
Descriptors: Automation, Computer Assisted Testing, Multiple Choice Tests, Natural Language Processing
Sun-Joo Cho; Goodwin Amanda; Jorge Salas; Sophia Mueller – Grantee Submission, 2025
This study incorporates a random forest (RF) approach to probe complex interactions and nonlinearity among predictors into an item response model with the goal of using a hybrid approach to outperform either an RF or explanatory item response model (EIRM) only in explaining item responses. In the specified model, called EIRM-RF, predicted values…
Descriptors: Item Response Theory, Artificial Intelligence, Statistical Analysis, Predictor Variables
Xin Wei – Grantee Submission, 2025
This study investigates the time-use patterns of students with learning disabilities during digital mathematics assessments and explores the role of extended time accommodations (ETA) in shaping these patterns. Using latent profile analysis, four distinct time-use profiles were identified separately for students with and without ETA. "Initial…
Descriptors: Computer Assisted Testing, Mathematics Tests, Students with Disabilities, Testing Accommodations
