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Autor/inn/en | Crimmins, Patricia Beron; Foster, Jonathan K.; Youngs, Peter A. |
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Titel | Automated Neural Network Dashboards for Teacher Feedback |
Quelle | (2023), (6 Seiten)
PDF als Volltext |
Sprache | englisch |
Dokumenttyp | gedruckt; online; Monographie |
Schlagwörter | Artificial Intelligence; Algorithms; Teacher Attitudes; Feedback (Response); Elementary School Teachers; Mathematics Teachers; English Teachers; Instructional Effectiveness; Virginia |
Abstract | Recent research suggests that neural networks, algorithms designed to reflect the human brain's behavior to recognize patterns, can be used to develop data dashboards that provide teachers with more specific and frequent feedback to improve their instruction (Jacobs et al., 2022). This qualitative case study examines six teachers' perceptions of the usability, utility, and accuracy of an interactive automated dashboard developed to provide teachers with detailed personalized feedback on their use of activity type (whole group, small group, independent work, and transition time), presented in a quantifiable and non-evaluative manner. Overall, our findings highlight the feasibility of automated tools to support meaningful feedback opportunities for teachers. Specifically, teachers found feedback on transition time during lessons to be particularly helpful. (As Provided). |
Anmerkungen | AERA Online Paper Repository. Available from: American Educational Research Association. 1430 K Street NW Suite 1200, Washington, DC 20005. Tel: 202-238-3200; Fax: 202-238-3250; e-mail: subscriptions@aera.net; Web site: http://www.aera.net |
Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
Update | 2024/1/01 |