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Ted M. Clark; Daniel A. Turner; Darian C. Rostam – Journal of Chemical Education, 2022
Administering exams in large enrollment courses is challenging and systems in place for accomplishing this task were upended in the spring of 2020 when a sudden transformation to online instruction and testing occurred due to the COVID-19 pandemic. In the following year, when courses remained online, approaches to improve exam security included…
Descriptors: Chemistry, Science Instruction, Supervision, Computer Assisted Testing
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Thomas, Michael L.; Brown, Gregory G.; Patt, Virginie M.; Duffy, John R. – Educational and Psychological Measurement, 2021
The adaptation of experimental cognitive tasks into measures that can be used to quantify neurocognitive outcomes in translational studies and clinical trials has become a key component of the strategy to address psychiatric and neurological disorders. Unfortunately, while most experimental cognitive tests have strong theoretical bases, they can…
Descriptors: Adaptive Testing, Computer Assisted Testing, Cognitive Tests, Psychopathology
Stephen G. Sireci; Javier Suárez-Álvarez; April L. Zenisky; Maria Elena Oliveri – Grantee Submission, 2024
The goal in personalized assessment is to best fit the needs of each individual test taker, given the assessment purposes. Design-In-Real-Time (DIRTy) assessment reflects the progressive evolution in testing from a single test, to an adaptive test, to an adaptive assessment "system." In this paper, we lay the foundation for DIRTy…
Descriptors: Educational Assessment, Student Needs, Test Format, Test Construction
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Stephen G. Sireci; Javier Suárez-Álvarez; April L. Zenisky; Maria Elena Oliveri – Educational Measurement: Issues and Practice, 2024
The goal in personalized assessment is to best fit the needs of each individual test taker, given the assessment purposes. Design-in-Real-Time (DIRTy) assessment reflects the progressive evolution in testing from a single test, to an adaptive test, to an adaptive assessment "system." In this article, we lay the foundation for DIRTy…
Descriptors: Educational Assessment, Student Needs, Test Format, Test Construction
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Zahra Banitalebi; Masoomeh Estaji; Gavin T. L. Brown – Educational Technology & Society, 2025
The significance of teacher's assessment literacy (AL) was originally captured by the 1990 standards for teacher's competence in educational assessment. Competence in assessment has changed with the widespread use of recent technology advancements in educational assessment. Consequently, new measures are needed to measure Teacher Assessment…
Descriptors: Assessment Literacy, Computer Assisted Testing, Measurement Techniques, Questionnaires
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Mo Zhang; Paul Deane; Andrew Hoang; Hongwen Guo; Chen Li – Educational Measurement: Issues and Practice, 2025
In this paper, we describe two empirical studies that demonstrate the application and modeling of keystroke logs in writing assessments. We illustrate two different approaches of modeling differences in writing processes: analysis of mean differences in handcrafted theory-driven features and use of large language models to identify stable personal…
Descriptors: Writing Tests, Computer Assisted Testing, Keyboarding (Data Entry), Writing Processes
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Sukru Murat Cebeci; Selcuk Acar – Journal of Creative Behavior, 2025
This study presents the Cebeci Test of Creativity (CTC), a novel computerized assessment tool designed to address the limitations of traditional open-ended paper-and-pencil creativity tests. The CTC is designed to overcome the challenges associated with the administration and manual scoring of traditional paper and pencil creativity tests. In this…
Descriptors: Creativity, Creativity Tests, Test Construction, Test Validity
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Salvatore G. Garofalo; Stephen J. Farenga – Science & Education, 2025
The purpose of this study was to gauge the attitudes towards artificial intelligence (AI) use in the science classroom by science teachers at the start of generative AI chatbot popularity (March 2023). The lens of distributed cognition afforded an opportunity to gather thoughts, opinions, and perceptions from 24 secondary science educators as well…
Descriptors: Secondary School Teachers, Science Teachers, Teacher Attitudes, Artificial Intelligence
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Tay McEdwards; Greta R. Underhill – Online Journal of Distance Learning Administration, 2025
Online learning has steadily increased since well before the COVID-19 pandemic (Seaman et al., 2018), but research has yet to explore online students' perceptions of online exam proctoring methods. The purpose of this exploratory study was to understand the perceptions of fully online students regarding types of proctoring at a large state…
Descriptors: Supervision, Computer Assisted Testing, Electronic Learning, Student Attitudes
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Mohamed Kara-Mohamed – Journal of Educational Technology Systems, 2025
(1) Context: The growing accessibility of Artificial Intelligence (AI) technology, such as ChatGPT, poses a challenge to the integrity of online assessments in higher education. As AI becomes more integrated into academic contexts, educators face the complex task of maintaining assessment standards particularly within modern Virtual Learning…
Descriptors: Artificial Intelligence, Virtual Classrooms, Computer Assisted Testing, Universities
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Andreea 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
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Gurvinder Kaur; Stephanie Stroever; Megh Gore; Bridget Vories; Vaughan H. Lee; Keith N. Bishop; Brandt L. Schneider – Discover Education, 2025
Background: Formative assessments build a positive learning environment and provide feedback to enhance learning. This study examined the impact of online formative and low-stake summative assessments on medical students' learning outcomes in the Clinically Oriented Anatomy course from 2016 to 2020. We aimed to demonstrate that formative…
Descriptors: At Risk Students, Identification, Prediction, Anatomy
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Tiana P. Johnson-Clements; Guy J. Curtis; Joseph Clare – Journal of Academic Ethics, 2025
Concerns over students engaging in various forms of academic misconduct persist, especially with the post-COVID-19 rise in online learning and assessment. Research has demonstrated a clear role of the personality trait psychopathy in cheating, yet little is known about why this relationship exists. Building on the research by Curtis et al.…
Descriptors: Pandemics, COVID-19, Cheating, Electronic Learning
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Wesley Morris; Langdon Holmes; Joon Suh Choi; Scott Crossley – International Journal of Artificial Intelligence in Education, 2025
Recent developments in the field of artificial intelligence allow for improved performance in the automated assessment of extended response items in mathematics, potentially allowing for the scoring of these items cheaply and at scale. This study details the grand prize-winning approach to developing large language models (LLMs) to automatically…
Descriptors: Automation, Computer Assisted Testing, Mathematics Tests, Scoring
New York State Education Department, 2022
The instructions in this manual explain the responsibilities of school administrators for the New York State Testing Program (NYSTP) Grades 3-8 English Language Arts and Mathematics Field Tests, and the Elementary-level (Grade 5) and Intermediate-level (Grade 8) Science Field Tests. School administrators must be thoroughly familiar with the…
Descriptors: Testing Programs, Mathematics Tests, Test Format, Computer Assisted Testing
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