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Militsa G. Ivanova; Hanna Eklöf; Michalis P. Michaelides – Journal of Applied Testing Technology, 2025
Digital administration of assessments allows for the collection of process data indices, such as response time, which can serve as indicators of rapid-guessing and examinee test-taking effort. Setting a time threshold is essential to distinguish effortful from effortless behavior using item response times. Threshold identification methods may…
Descriptors: Test Items, Computer Assisted Testing, Reaction Time, Achievement Tests
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Ute Mertens; Marlit A. Lindner – Journal of Computer Assisted Learning, 2025
Background: Educational assessments increasingly shift towards computer-based formats. Many studies have explored how different types of automated feedback affect learning. However, few studies have investigated how digital performance feedback affects test takers' ratings of affective-motivational reactions during a testing session. Method: In…
Descriptors: Educational Assessment, Computer Assisted Testing, Automation, Feedback (Response)
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Sedigheh Karimpour; Ehsan Namaziandost; Hossein Kargar Behbahani – Journal of Educational Computing Research, 2025
As an integral part of dynamic assessment, computerized dynamic assessment (CDA) offers learners computer-assisted automated mediation. Accordingly, the possible efficacy of corrective feedback seems to be enhanced with new technologies, such as artificial intelligence tools, that offer automatic corrective feedback. Using technology-enhanced…
Descriptors: Computer Assisted Testing, Feedback (Response), Language Acquisition, Electronic Learning
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Shujun Liu; Azzeddine Boudouaia; Xinya Chen; Yan Li – Asia-Pacific Education Researcher, 2025
The application of Automated Writing Evaluation (AWE) has recently gained researchers' attention worldwide. However, the impact of AWE feedback on student writing, particularly in languages other than English, remains controversial. This study aimed to compare the impacts of Chinese AWE feedback and teacher feedback on Chinese writing revision,…
Descriptors: Foreign Countries, Middle School Students, Grade 7, Writing Evaluation
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Guozhu Ding; Mailin Li; Shan Li; Hao Wu – Asia Pacific Education Review, 2025
This study investigated the optimal feedback intervals for tasks of varying difficulty levels in online testing and whether task difficulty moderates the effect of feedback intervals on student performance. A pre-experimental study with 36 students was conducted to determine the delayed time for providing feedback based on student behavioral data.…
Descriptors: Feedback (Response), Academic Achievement, Computer Assisted Testing, Intervals
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Ye Ma; Deborah J. Harris – Educational Measurement: Issues and Practice, 2025
Item position effect (IPE) refers to situations where an item performs differently when it is administered in different positions on a test. The majority of previous research studies have focused on investigating IPE under linear testing. There is a lack of IPE research under adaptive testing. In addition, the existence of IPE might violate Item…
Descriptors: Computer Assisted Testing, Adaptive Testing, Item Response Theory, Test Items
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Yi-Ling Wu; Yao-Hsuan Huang; Chia-Wen Chen; Po-Hsi Chen – Journal of Educational Measurement, 2025
Multistage testing (MST), a variant of computerized adaptive testing (CAT), differs from conventional CAT in that it is adapted at the module level rather than at the individual item level. Typically, all examinees begin the MST with a linear test form in the first stage, commonly known as the routing stage. In 2020, Han introduced an innovative…
Descriptors: Computer Assisted Testing, Adaptive Testing, Test Format, Measurement
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Ishaya Gambo; Faith-Jane Abegunde; Omobola Gambo; Roseline Oluwaseun Ogundokun; Akinbowale Natheniel Babatunde; Cheng-Chi Lee – Education and Information Technologies, 2025
The current educational system relies heavily on manual grading, posing challenges such as delayed feedback and grading inaccuracies. Automated grading tools (AGTs) offer solutions but come with limitations. To address this, "GRAD-AI" is introduced, an advanced AGT that combines automation with teacher involvement for precise grading,…
Descriptors: Automation, Grading, Artificial Intelligence, Computer Assisted Testing
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Beyza Aksu Dünya; Stefanie A. Wind; Mehmet Can Demir – SAGE Open, 2025
The purpose of this study was to generate an item bank for assessing faculty members' assessment literacy and to examine the applicability and feasibility of a Computerized Adaptive Test (CAT) approach to monitor assessment literacy among faculty members. In developing this assessment using a sequential mixed-methods research design, our goal was…
Descriptors: Assessment Literacy, Item Banks, College Faculty, Adaptive Testing
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Kylie Gorney; Mark D. Reckase – Journal of Educational Measurement, 2025
In computerized adaptive testing, item exposure control methods are often used to provide a more balanced usage of the item pool. Many of the most popular methods, including the restricted method (Revuelta and Ponsoda), use a single maximum exposure rate to limit the proportion of times that each item is administered. However, Barrada et al.…
Descriptors: Computer Assisted Testing, Adaptive Testing, Test Items, Item Banks
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Abdou L. J. Jammeh; Claude Karegeya; Savita Ladage – Education and Information Technologies, 2025
Clicker-integrated instruction is the current innovation in teaching and learning. Several studies used this technology to investigate learning processes, while others mainly used it to asses for learning, facilitation of group discussion and students' participation. All applications require creativity and analytical thinking and very much…
Descriptors: Chemistry, Science Instruction, Audience Response Systems, Computer Assisted Instruction
Joanna Williamson – Research Matters, 2025
Teachers, examiners and assessment experts know from experience that some candidates annotate exam questions. "Annotation" includes anything the candidate writes or draws outside of the designated response space, such as underlining, jotting, circling, sketching and calculating. Annotations are of interest because they may evidence…
Descriptors: Mathematics, Tests, Documentation, Secondary Education
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Xuefan Li; Marco Zappatore; Tingsong Li; Weiwei Zhang; Sining Tao; Xiaoqing Wei; Xiaoxu Zhou; Naiqing Guan; Anny Chan – IEEE Transactions on Learning Technologies, 2025
The integration of generative artificial intelligence (GAI) into educational settings offers unprecedented opportunities to enhance the efficiency of teaching and the effectiveness of learning, particularly within online platforms. This study evaluates the development and application of a customized GAI-powered teaching assistant, trained…
Descriptors: Artificial Intelligence, Technology Uses in Education, Student Evaluation, Academic Achievement
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Daniel Lupiya Mpolomoka – Pedagogical Research, 2025
Overview: This systematic review explores the utilization of artificial intelligence (AI) for assessment, grading, and feedback in higher education. The review aims to establish how AI technologies enhance efficiency, scalability, and personalized learning experiences in educational settings, while addressing associated challenges that arise due…
Descriptors: Artificial Intelligence, Higher Education, Evaluation Methods, Literature Reviews
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Sun-Joo Cho; Goodwin Amanda; Jorge Salas; Sophia Mueller – Grantee Submission, 2025
This study incorporates a random forest (RF) approach to probe complex interactions and nonlinearity among predictors into an item response model with the goal of using a hybrid approach to outperform either an RF or explanatory item response model (EIRM) only in explaining item responses. In the specified model, called EIRM-RF, predicted values…
Descriptors: Item Response Theory, Artificial Intelligence, Statistical Analysis, Predictor Variables
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