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Francesca Patterson; Melina A. Kunar – Cognitive Research: Principles and Implications, 2024
Computer Aided Detection (CAD) has been used to help readers find cancers in mammograms. Although these automated systems have been shown to help cancer detection when accurate, the presence of CAD also leads to an over-reliance effect where miss errors and false alarms increase when the CAD system fails. Previous research investigated CAD systems…
Descriptors: Cancer, Computer Use, Identification, Screening Tests
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Po-Chun Huang; Ying-Hong Chan; Ching-Yu Yang; Hung-Yuan Chen; Yao-Chung Fan – IEEE Transactions on Learning Technologies, 2024
Question generation (QG) task plays a crucial role in adaptive learning. While significant QG performance advancements are reported, the existing QG studies are still far from practical usage. One point that needs strengthening is to consider the generation of question group, which remains untouched. For forming a question group, intrafactors…
Descriptors: Automation, Test Items, Computer Assisted Testing, Test Construction
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Grochowalski, Joseph H.; Hendrickson, Amy – Journal of Educational Measurement, 2023
Test takers wishing to gain an unfair advantage often share answers with other test takers, either sharing all answers (a full key) or some (a partial key). Detecting key sharing during a tight testing window requires an efficient, easily interpretable, and rich form of analysis that is descriptive and inferential. We introduce a detection method…
Descriptors: Identification, Cooperative Learning, Cheating, Statistical Analysis
Leon Katcharian – ProQuest LLC, 2023
Remotely proctored online examinations proliferate in academic and corporate learning environments (Grajek, 2020). Remote (virtual) proctoring allows organizations to efficiently offer tests globally while reducing the costs of proctored testing generally associated with traditional paper-and-pencil and computer-based testing center examinations.…
Descriptors: Computer Assisted Testing, Supervision, Distance Education, Information Security
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Chen, Jennifer J.; Perez, ChareMone' – Childhood Education, 2023
Assessment holds the key to unlocking for the teacher a child's past (what he already knows), present (what he is learning), and future (what he still needs to learn) to inform teaching. Despite the benefits of assessment for informing teaching practice and enhancing student learning, it remains one of the most challenging and time-consuming tasks…
Descriptors: Evaluation Methods, Individualized Instruction, Artificial Intelligence, Computer Assisted Testing
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He, Yinhong; Qi, Yuanyuan – Journal of Educational Measurement, 2023
In multidimensional computerized adaptive testing (MCAT), item selection strategies are generally constructed based on responses, and they do not consider the response times required by items. This study constructed two new criteria (referred to as DT-inc and DT) for MCAT item selection by utilizing information from response times. The new designs…
Descriptors: Reaction Time, Adaptive Testing, Computer Assisted Testing, Test Items
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Debarati Mukherjee; Supriya Bhavnani; Georgia Lockwood Estrin; Vaisnavi Rao; Jayashree Dasgupta; Hiba Irfan; Bhismadev Chakrabarti; Vikram Patel; Matthew K. Belmonte – Autism: The International Journal of Research and Practice, 2024
Current challenges in early identification of autism spectrum disorder lead to significant delays in starting interventions, thereby compromising outcomes. Digital tools can potentially address this barrier as they are accessible, can measure autism-relevant phenotypes and can be administered in children's natural environments by non-specialists.…
Descriptors: Autism Spectrum Disorders, Young Children, Evaluation, Technology
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Priti Oli; Rabin Banjade; Jeevan Chapagain; Vasile Rus – Grantee Submission, 2024
Assessing students' answers and in particular natural language answers is a crucial challenge in the field of education. Advances in transformer-based models such as Large Language Models (LLMs), have led to significant progress in various natural language tasks. Nevertheless, amidst the growing trend of evaluating LLMs across diverse tasks,…
Descriptors: Student Evaluation, Computer Assisted Testing, Artificial Intelligence, Comprehension
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Fu Chen; Chang Lu; Ying Cui – Education and Information Technologies, 2024
Successful computer-based assessments for learning greatly rely on an effective learner modeling approach to analyze learner data and evaluate learner behaviors. In addition to explicit learning performance (i.e., product data), the process data logged by computer-based assessments provide a treasure trove of information about how learners solve…
Descriptors: Computer Assisted Testing, Problem Solving, Learning Analytics, Learning Processes
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Ramnarain-Seetohul, Vidasha; Bassoo, Vandana; Rosunally, Yasmine – Education and Information Technologies, 2022
In automated essay scoring (AES) systems, similarity techniques are used to compute the score for student answers. Several methods to compute similarity have emerged over the years. However, only a few of them have been widely used in the AES domain. This work shows the findings of a ten-year review on similarity techniques applied in AES systems…
Descriptors: Computer Assisted Testing, Essays, Scoring, Automation
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Cui, Zhongmin – Measurement: Interdisciplinary Research and Perspectives, 2022
Although many educational and psychological tests are labeled as computerized adaptive test (CAT), not all tests show the same level of adaptivity -- some tests might not have much adaptation because of various constraints imposed by test developers. Researchers have proposed some indices to measure the amount of adaption for an adaptive test.…
Descriptors: Adaptive Testing, Computer Assisted Testing, Measurement Techniques
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Ingrisone, Soo Jeong; Ingrisone, James N. – Educational Measurement: Issues and Practice, 2023
There has been a growing interest in approaches based on machine learning (ML) for detecting test collusion as an alternative to the traditional methods. Clustering analysis under an unsupervised learning technique appears especially promising to detect group collusion. In this study, the effectiveness of hierarchical agglomerative clustering…
Descriptors: Identification, Cooperation, Computer Assisted Testing, Artificial Intelligence
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Cheng, Yiling – Measurement: Interdisciplinary Research and Perspectives, 2023
Computerized adaptive testing (CAT) offers an efficient and highly accurate method for estimating examinees' abilities. In this article, the free version of Concerto Software for CAT was reviewed, dividing our evaluation into three sections: software implementation, the Item Response Theory (IRT) features of CAT, and user experience. Overall,…
Descriptors: Computer Software, Computer Assisted Testing, Adaptive Testing, Item Response Theory
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Alhadi, Moosa; Zhang, Dake; Wang, Ting; Maher, Carolyn A. – Computers in the Schools, 2023
This research synthesizes studies that used a Digitalized Interactive Component (DIC) to assess K-12 student performance during Computer-based-Assessments (CBAs) in mathematics. A systematic search identified ten studies, including four that provided language assistance and six that provided response-construction support. We reported on the one…
Descriptors: Computer Assisted Testing, Mathematics Tests, Student Evaluation, Elementary Secondary Education
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Gilbert, Kacey; Benson, Nicholas F.; Kranzler, John H. – Contemporary School Psychology, 2023
Despite the fact that the digital administration format of Wechsler Intelligence Scale for Children-Fifth Edition (WISC-V) was published in 2016, no research to date has examined its factor structure using all 10 of the primary subtests to measure intellectual ability. The purpose of this study, therefore, was to use exploratory and confirmatory…
Descriptors: Computer Assisted Testing, Children, Intelligence Tests, Factor Structure
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