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Jonas Flodén – British Educational Research Journal, 2025
This study compares how the generative AI (GenAI) large language model (LLM) ChatGPT performs in grading university exams compared to human teachers. Aspects investigated include consistency, large discrepancies and length of answer. Implications for higher education, including the role of teachers and ethics, are also discussed. Three…
Descriptors: College Faculty, Artificial Intelligence, Comparative Testing, Scoring
Joshua B. Gilbert; James G. Soland; Benjamin W. Domingue – Annenberg Institute for School Reform at Brown University, 2025
Value-Added Models (VAMs) are both common and controversial in education policy and accountability research. While the sensitivity of VAMs to model specification and covariate selection is well documented, the extent to which test scoring methods (e.g., mean scores vs. IRT-based scores) may affect VA estimates is less studied. We examine the…
Descriptors: Value Added Models, Tests, Testing, Scoring
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Louise Badham – Oxford Review of Education, 2025
Different sources of assessment evidence are reviewed during International Baccalaureate (IB) grade awarding to convert marks into grades and ensure fair results for students. Qualitative and quantitative evidence are analysed to determine grade boundaries, with statistical evidence weighed against examiner judgement and teachers' feedback on…
Descriptors: Advanced Placement Programs, Grading, Interrater Reliability, Evaluative Thinking
Jeff Allen; Ty Cruce – ACT Education Corp., 2025
This report summarizes some of the evidence supporting interpretations of scores from the enhanced ACT, focusing on reliability, concurrent validity, predictive validity, and score comparability. The authors argue that the evidence presented in this report supports the interpretation of scores from the enhanced ACT as measures of high school…
Descriptors: College Entrance Examinations, Testing, Change, Scores