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Xin Wei; Susu Zhang; Jihong Zhang; Jennifer Yu – Autism: The International Journal of Research and Practice, 2023
For autistic students receiving special education services, little is known about their relative strengths, weaknesses, and enjoyment across different math content areas; their overall math interest and persistence are also not well-studied. Using the 2017 eighth-grade National Assessment of Education Progress data, this study finds, relative to…
Descriptors: Mathematics Achievement, Reaction Time, Autism Spectrum Disorders, Grade 8
Shin, Hyo Jeong; Jewsbury, Paul A.; van Rijn, Peter W. – Large-scale Assessments in Education, 2022
The present paper investigates and examines the conditional dependencies between cognitive responses (RA; Response Accuracy) and process data, in particular, response times (RT) in large-scale educational assessments. Using two prominent large-scale assessments, NAEP and PISA, we examined the RA-RT conditional dependencies within each item in the…
Descriptors: Cognitive Processes, Reaction Time, Educational Assessment, Achievement Tests
Congning Ni; Bhashithe Abeysinghe; Juanita Hicks – International Electronic Journal of Elementary Education, 2025
The National Assessment of Educational Progress (NAEP), often referred to as The Nation's Report Card, offers a window into the state of U.S. K-12 education system. Since 2017, NAEP has transitioned to digital assessments, opening new research opportunities that were previously impossible. Process data tracks students' interactions with the…
Descriptors: Reaction Time, Multiple Choice Tests, Behavior Change, National Competency Tests
Zehner, Fabian; Harrison, Scott; Eichmann, Beate; Deribo, Tobias; Bengs, Daniel; Andersen, Nico; Hahnel, Carolin – International Educational Data Mining Society, 2020
The "2nd Annual WPI-UMASS-UPENN EDM Data Mining Challenge" required contestants to predict efficient testtaking based on log data. In this paper, we describe our theory-driven and psychometric modeling approach. For feature engineering, we employed the Log-Normal Response Time Model for estimating latent person speed, and the Generalized…
Descriptors: Data Analysis, Competition, Classification, Prediction
Jing Lu; Chun Wang; Ningzhong Shi – Grantee Submission, 2023
In high-stakes, large-scale, standardized tests with certain time limits, examinees are likely to engage in either one of the three types of behavior (e.g., van der Linden & Guo, 2008; Wang & Xu, 2015): solution behavior, rapid guessing behavior, and cheating behavior. Oftentimes examinees do not always solve all items due to various…
Descriptors: High Stakes Tests, Standardized Tests, Guessing (Tests), Cheating
Lee, Yi-Hsuan; Jia, Yue – Large-scale Assessments in Education, 2014
Background: Large-scale survey assessments have been used for decades to monitor what students know and can do. Such assessments aim at providing group-level scores for various populations, with little or no consequence to individual students for their test performance. Students' test-taking behaviors in survey assessments, particularly the level…
Descriptors: Measurement, Test Wiseness, Student Surveys, Response Style (Tests)
Ling, Guangming – International Journal of Testing, 2016
To investigate possible iPad related mode effect, we tested 403 8th graders in Indiana, Maryland, and New Jersey under three mode conditions through random assignment: a desktop computer, an iPad alone, and an iPad with an external keyboard. All students had used an iPad or computer for six months or longer. The 2-hour test included reading, math,…
Descriptors: Educational Testing, Computer Assisted Testing, Handheld Devices, Computers