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Autor/inn/enWongvorachan, Tarid; Lai, Ka Wing; Bulut, Okan; Tsai, Yi-Shan; Chen, Guanliang
TitelArtificial Intelligence: Transforming the Future of Feedback in Education
QuelleIn: Journal of Applied Testing Technology, 23 (2022), S.95-116 (22 Seiten)
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Spracheenglisch
Dokumenttypgedruckt; online; Zeitschriftenaufsatz
SchlagwörterArtificial Intelligence; Feedback (Response); Learning Analytics; Natural Language Processing; Information Retrieval; Technology Uses in Education
AbstractFeedback is a crucial component of student learning. As advancements in technology have enabled the adoption of digital learning environments with assessment capabilities, the frequency, delivery format, and timeliness of feedback derived from educational assessments have also changed progressively. Advanced technologies powered by Artificial Intelligence (AI) enable teachers to generate different types of feedback supporting student learning. Despite the rapid uptake of digital technologies in education, previous studies on educational feedback primarily focused on the theoretical underpinnings of feedback practices, which are limited in terms of their coverage of AI-based technologies. This paper aims to inform both researchers and practitioners about the present and future of AI applications in feedback practices, identify and organize potential areas for the use of AI for feedback purposes, and establish venues for AI research and practice in educational feedback. Furthermore, the role of the three branches of AI (i.e., natural language processing, educational data mining, and learning analytics) in feedback practices and potential areas for their future development are discussed. (As Provided).
AnmerkungenAssociation of Test Publishers. 601 Pennsylvania Avenue NW, South Building Suite 900, Washington DC 20004. Tel: 866-240-7909; Fax: 717-755-8962; e-mail: wgh.atp@att.net; Web site: https://jattjournal.net/index.php/atp/index
Erfasst vonERIC (Education Resources Information Center), Washington, DC
Update2024/1/01
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