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Samuel S. Davidson – ProQuest LLC, 2024
Automated corrective feedback (ACF), in which a computer system helps language learners identify and correct errors in their writing or speech, is considered an important tool for language instruction by many researchers. Such systems allow learners to correct their own mistakes, thereby reducing teacher workload and potentially preventing issues…
Descriptors: Computer Assisted Testing, Automation, Student Evaluation, Feedback (Response)
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Wen Xin Zhang; John J. H. Lin; Ying-Shao Hsu – Journal of Computer Assisted Learning, 2025
Background Study: Assessing learners' inquiry-based skills is challenging as social, political, and technological dimensions must be considered. The advanced development of artificial intelligence (AI) makes it possible to address these challenges and shape the next generation of science education. Objectives: The present study evaluated the SSI…
Descriptors: Artificial Intelligence, Computer Assisted Testing, Inquiry, Active Learning
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Guher Gorgun; Okan Bulut – Educational Measurement: Issues and Practice, 2025
Automatic item generation may supply many items instantly and efficiently to assessment and learning environments. Yet, the evaluation of item quality persists to be a bottleneck for deploying generated items in learning and assessment settings. In this study, we investigated the utility of using large-language models, specifically Llama 3-8B, for…
Descriptors: Artificial Intelligence, Quality Control, Technology Uses in Education, Automation
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Tadd Farmer; Michael C. Johnson; Jorin D. Larsen; Lance E. Davidson – Advances in Physiology Education, 2025
Team-based learning (TBL) is an active learning instructional strategy shown to improve student learning in large-enrollment courses. Although early implementations of TBL proved generally effective in an undergraduate exercise physiology course that delivered an online individual readiness assurance test (iRAT) before class, the instructor…
Descriptors: Cooperative Learning, Active Learning, Undergraduate Students, Exercise Physiology
Das, Bidyut; Majumder, Mukta; Phadikar, Santanu; Sekh, Arif Ahmed – Research and Practice in Technology Enhanced Learning, 2021
Learning through the internet becomes popular that facilitates learners to learn anything, anytime, anywhere from the web resources. Assessment is most important in any learning system. An assessment system can find the self-learning gaps of learners and improve the progress of learning. The manual question generation takes much time and labor.…
Descriptors: Automation, Test Items, Test Construction, Computer Assisted Testing
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Casabianca, Jodi M.; Donoghue, John R.; Shin, Hyo Jeong; Chao, Szu-Fu; Choi, Ikkyu – Journal of Educational Measurement, 2023
Using item-response theory to model rater effects provides an alternative solution for rater monitoring and diagnosis, compared to using standard performance metrics. In order to fit such models, the ratings data must be sufficiently connected in order to estimate rater effects. Due to popular rating designs used in large-scale testing scenarios,…
Descriptors: Item Response Theory, Alternative Assessment, Evaluators, Research Problems
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Asrial, Asrial; Syahrial, Syahrial; Kurniawan, Dwi Agus; Aldila, Febri Tia; Iqbal, Muhammad – Journal of Technology and Science Education, 2023
This study aims to see the effect of student perceptions on web-based character assessment on the results of student character assessment. The population in this study was a junior high school in Batanghari Regency with a sample of 322 students using the purposive sampling technique. Quantitative methods are used in this study with descriptive and…
Descriptors: Junior High School Students, Foreign Countries, Values Education, Web Browsers
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Ormerod, Christopher; Lottridge, Susan; Harris, Amy E.; Patel, Milan; van Wamelen, Paul; Kodeswaran, Balaji; Woolf, Sharon; Young, Mackenzie – International Journal of Artificial Intelligence in Education, 2023
We introduce a short answer scoring engine made up of an ensemble of deep neural networks and a Latent Semantic Analysis-based model to score short constructed responses for a large suite of questions from a national assessment program. We evaluate the performance of the engine and show that the engine achieves above-human-level performance on a…
Descriptors: Computer Assisted Testing, Scoring, Artificial Intelligence, Semantics
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Ebadi, Saman; Goodarzi, Ahmad – Reading Psychology, 2023
This study presents the results of a sequential explanatory mixed-method approach to investigate the Iranian English language non-gainers of a computerized dynamic reading comprehension test (CDRT) by utilizing a learning style survey. Using an interventionist approach, the researchers used the CDRT software to explore English learners' perceptual…
Descriptors: Cognitive Style, English (Second Language), Second Language Learning, Reading Tests
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Xiong, Jiawei; Li, Feiming – Educational Measurement: Issues and Practice, 2023
Multidimensional scoring evaluates each constructed-response answer from more than one rating dimension and/or trait such as lexicon, organization, and supporting ideas instead of only one holistic score, to help students distinguish between various dimensions of writing quality. In this work, we present a bilevel learning model for combining two…
Descriptors: Scoring, Models, Task Analysis, Learning Processes
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Bolat, Yusuf Islam; Tas, Nurullah – Education and Information Technologies, 2023
The purpose of this meta-analysis was to examine the effects of Gamified-Assessment Tools (GAT) used in formal educational settings on student academic achievement. We used PRISMA systematic procedures to screen the articles across Web of Science, ERIC, Scopus, Pubmed, and PsycArticles databases. We identified 23 independent results from 17…
Descriptors: Gamification, Student Evaluation, Academic Achievement, Computer Assisted Testing
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Monahan, Michael; Shah, Amit – Research in Higher Education Journal, 2023
Academic dishonesty is a major issue in education. Perhaps more so in the online environment where may times students are on their honor to complete exams without the use of the Internet, notes, or other prohibited materials. The age range of 18-24 encompasses the traditional aged student body. The non-traditional students are over the age of 25…
Descriptors: Ethics, Cheating, College Students, Nontraditional Students
Kelli A. Freeman – ProQuest LLC, 2023
School psychologists often administer evidence-based academic interventions such as Explicit Timing in order to increase math fact fluency. However, little is known about the efficacy of adapting Explicit Timing interventions to be used in a virtual format. This dissertation examines the use of virtually delivered Explicit Timing interventions as…
Descriptors: Electronic Learning, Intervention, Time on Task, Mathematics Skills
Ben Seipel; Patrick C. Kennedy; Sarah E. Carlson; Virginia Clinton-Lisell; Mark L. Davison – Journal of Learning Disabilities, 2023
As access to higher education increases, it is important to monitor students with special needs to facilitate the provision of appropriate resources and support. Although metrics such as the "reading readiness" ACT (formerly American College Testing) of provide insight into how many students may need such resources, they do not specify…
Descriptors: Multiple Choice Tests, Computer Assisted Testing, Reading Tests, Reading Comprehension
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Dhini, Bachriah Fatwa; Girsang, Abba Suganda; Sufandi, Unggul Utan; Kurniawati, Heny – Asian Association of Open Universities Journal, 2023
Purpose: The authors constructed an automatic essay scoring (AES) model in a discussion forum where the result was compared with scores given by human evaluators. This research proposes essay scoring, which is conducted through two parameters, semantic and keyword similarities, using a SentenceTransformers pre-trained model that can construct the…
Descriptors: Computer Assisted Testing, Scoring, Writing Evaluation, Essays
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