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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
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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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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Barry O'Sullivan – Language Assessment Quarterly, 2023
This paper highlights as issues of concern the rapid changes in technology and the tendency to report on partial validation efforts where the work is not identified as forming part of a larger validation project. With close human supervision emerging technologies can have a significant and positive impact on language testing. While technology…
Descriptors: Technology Uses in Education, Computer Assisted Testing, Language Tests, Supervision
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Sami Baral; Eamon Worden; Wen-Chiang Lim; Zhuang Luo; Christopher Santorelli; Ashish Gurung; Neil Heffernan – Grantee Submission, 2024
The effectiveness of feedback in enhancing learning outcomes is well documented within Educational Data Mining (EDM). Various prior research have explored methodologies to enhance the effectiveness of feedback to students in various ways. Recent developments in Large Language Models (LLMs) have extended their utility in enhancing automated…
Descriptors: Automation, Scoring, Computer Assisted Testing, Natural Language Processing
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Rafner, Janet; Biskjaer, Michael Mose; Zana, Blanka; Langsford, Steven; Bergenholtz, Carsten; Rahimi, Seyedahmad; Carugati, Andrea; Noy, Lior; Sherson, Jacob – Creativity Research Journal, 2022
Creativity assessments should be valid, reliable, and scalable to support various stakeholders (e.g., policy-makers, educators, corporations, and the general public) in their decision-making processes. Established initiatives toward scalable creativity assessments have relied on well-studied standardized tests. Although robust in many ways, most…
Descriptors: Creativity, Evaluation Methods, Video Games, Computer Assisted Testing
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Tri Sedya Febrianti; Siti Fatimah; Yuni Fitriyah; Hanifah Nurhayati – International Journal of Education in Mathematics, Science and Technology, 2024
Assessing students' understanding of circle-related material through subjective tests is effective, though grading these tests can be challenging and often requires technological support. ChatGPT has shown promise in providing reliable and objective evaluations. Many teachers in Indonesia, however, continue to face difficulties integrating…
Descriptors: Artificial Intelligence, Computer Assisted Testing, Scoring, Tests
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Cathy Cavanaugh; Bryn Humphrey; Paige Pullen – International Journal on E-Learning, 2024
To address needs in one US state to provide a professional development micro-credential for tens of thousands of educators, we automated an assignment scoring workflow in an online course by developing and refining an AI model to scan submitted assignments and score them against a rubric. This article outlines the AI model development process and…
Descriptors: Artificial Intelligence, Automation, Scoring, Microcredentials
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Themistocleous, Charalambos; Neophytou, Kyriaki; Rapp, Brenda; Tsapkini, Kyrana – Journal of Speech, Language, and Hearing Research, 2020
Purpose: The evaluation of spelling performance in aphasia reveals deficits in written language and can facilitate the design of targeted writing treatments. Nevertheless, manual scoring of spelling performance is time-consuming, laborious, and error prone. We propose a novel method based on the use of distance metrics to automatically score…
Descriptors: Computer Assisted Testing, Scoring, Spelling, Scores
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Eran Hadas; Arnon Hershkovitz – Journal of Learning Analytics, 2025
Creativity is an imperative skill for today's learners, one that has important contributions to issues of inclusion and equity in education. Therefore, assessing creativity is of major importance in educational contexts. However, scoring creativity based on traditional tools suffers from subjectivity and is heavily time- and labour-consuming. This…
Descriptors: Creativity, Evaluation Methods, Computer Assisted Testing, Artificial Intelligence
Dongmei Li; Shalini Kapoor; Ann Arthur; Chi-Yu Huang; YoungWoo Cho; Chen Qiu; Hongling Wang – ACT Education Corp., 2025
Starting in April 2025, ACT will introduce enhanced forms of the ACT® test for national online testing, with a full rollout to all paper and online test takers in national, state and district, and international test administrations by Spring 2026. ACT introduced major updates by changing the test lengths and testing times, providing more time per…
Descriptors: College Entrance Examinations, Testing, Change, Scoring
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Wang, Wei; Dorans, Neil J. – ETS Research Report Series, 2021
Agreement statistics and measures of prediction accuracy are often used to assess the quality of two measures of a construct. Agreement statistics are appropriate for measures that are supposed to be interchangeable, whereas prediction accuracy statistics are appropriate for situations where one variable is the target and the other variables are…
Descriptors: Classification, Scaling, Prediction, Accuracy
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Becker, Kirk A.; Kao, Shu-chuan – Journal of Applied Testing Technology, 2022
Natural Language Processing (NLP) offers methods for understanding and quantifying the similarity between written documents. Within the testing industry these methods have been used for automatic item generation, automated scoring of text and speech, modeling item characteristics, automatic question answering, machine translation, and automated…
Descriptors: Item Banks, Natural Language Processing, Computer Assisted Testing, Scoring
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Christopher D. Wilson; Kevin C. Haudek; Jonathan F. Osborne; Zoë E. Buck Bracey; Tina Cheuk; Brian M. Donovan; Molly A. M. Stuhlsatz; Marisol M. Santiago; Xiaoming Zhai – Journal of Research in Science Teaching, 2024
Argumentation is fundamental to science education, both as a prominent feature of scientific reasoning and as an effective mode of learning--a perspective reflected in contemporary frameworks and standards. The successful implementation of argumentation in school science, however, requires a paradigm shift in science assessment from the…
Descriptors: Middle School Students, Competence, Science Process Skills, Persuasive Discourse
Bradley J. Ungurait – ProQuest LLC, 2021
Advancements in technology and computer-based testing has allowed for greater flexibility in assessing examinee knowledge on large-scale, high-stakes assessments. Through computer-based delivery, cognitive ability and skills can be effectively assessed cost-efficiently and measure domains that are difficult or even impossible to measure with…
Descriptors: Computer Assisted Testing, Evaluation Methods, Scoring, Student Evaluation
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