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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
Running out of Time: Leveraging Process Data to Identify Students Who May Benefit from Extended Time
Burhan Ogut; Ruhan Circi; Huade Huo; Juanita Hicks; Michelle Yin – International Electronic Journal of Elementary Education, 2025
This study explored the effectiveness of extended time (ET) accommodations in the 2017 NAEP Grade 8 Mathematics assessment to enhance educational equity. Analyzing NAEP process data through an XGBoost model, we examined if early interactions with assessment items could predict students' likelihood of requiring ET by identifying those who received…
Descriptors: Identification, Testing Accommodations, National Competency Tests, Equal Education
Emily B. Goldberg; Sheila R. Pratt; Malcolm R. McNeil; Neil Szuminsky; Kenneth DeHaan; Leslie Q. Zhen – Journal of Speech, Language, and Hearing Research, 2025
Purpose: The present study assessed the test-retest reliability of the American Sign Language (ASL) version of the Computerized Revised Token Test (CRTT-ASL) and compared the differences and similarities between ASL and English reading by Deaf and hearing users of ASL. Method: Creation of the CRTT-ASL involved filming, editing, and validating CRTT…
Descriptors: American Sign Language, Reliability, Validity, Test Construction
Bernis Sütçübasi; Tugçe Balli; Herbert Roeyers; Jan R. Wiersema; Sami Çamkerten; Ozan Cem Öztürk; Baris Metin; Edmund Sonuga-Barke – Journal of Attention Disorders, 2025
Objective: ADHD and autism are complex and frequently co-occurring neurodevelopmental conditions with shared etiological and pathophysiological elements. In this paper, we attempt to differentiate these conditions among the young people in terms of intrinsic patterns of brain connectivity revealed during resting state using machine learning…
Descriptors: Elementary School Students, Secondary School Students, Attention Deficit Hyperactivity Disorder, Autism Spectrum Disorders

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