Literaturnachweis - Detailanzeige
Autor/inn/en | Kil, David; Baldasare, Angela; Milliron, Mark |
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Titel | Catalyzing a Culture of Care and Innovation through Prescriptive Analytics and Impact Prediction to Create Full-Cycle Learning |
Quelle | In: Current Issues in Education, 22 (2021) 1, (19 Seiten)Infoseite zur Zeitschrift
PDF als Volltext |
Sprache | englisch |
Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
Schlagwörter | Learning Analytics; Academic Achievement; College Students; Electronic Learning; Algorithms; Artificial Intelligence; Prediction; Educational Innovation; Caring; Decision Making; Evidence; Scores |
Abstract | Student success, both during and after college, is central to the mission of higher education. Within the higher-education and, more specifically, the student-success context, the core raison d'être of machine learning (ML) is to help institutions achieve their social mission in an efficient and effective manner. While there should be synergy among people, processes, and ML, this synergy is not often realized because ML algorithms do not yet connect the dots on fully understanding and strategically fostering student success. Transitioning from risk to impact prediction is a catalyst for institutional transformation, which can lead to continuous learning and student-success process innovation. This paper explores how ML can complement and facilitate organizational transformation in promoting a culture of care and innovation through virtuous full-cycle learning. (As Provided). |
Anmerkungen | Arizona State University, Mary Lou Fulton Institute and Graduate School of Education. Deans Office, P.O. Box 870211 Payne 108, Tempe, AZ 85287. Tel: 480-965-3306; Fax: 480-965-6231; e-mail: cie@asu.edu; Web site: https://cie.asu.edu/ojs/index.php/cieatasu |
Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
Update | 2024/1/01 |