Literaturnachweis - Detailanzeige
Autor/inn/en | Lacefield, Warren E.; Applegate, E. Brooks |
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Titel | Data Visualization in Public Education: Longitudinal Student-, Intervention-, School-, and District-Level Performance Modeling |
Quelle | (2018), (20 Seiten)
PDF als Volltext |
Sprache | englisch |
Dokumenttyp | gedruckt; online; Monographie |
Schlagwörter | Public Education; Data; Visual Aids; Artificial Intelligence; Predictive Measurement; Academic Achievement; At Risk Students; Identification; Longitudinal Studies; Intervention; Cohort Analysis; Educational Environment; Elementary Secondary Education; Data Analysis; Schools; School Districts; Michigan; Ohio; Illinois Öffentliche Erziehung; Daten; Anschauungsmaterial; Künstliche Intelligenz; Schulleistung; Identifikation; Identifizierung; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; Kohortenanalyse; Lernumgebung; Pädagogische Umwelt; Schulumwelt; Auswertung; School; Schule; School district; Schulbezirk |
Abstract | Accountability seems forever engrained into the K-12 environment, as has been the expectation of delivering quality education to school aged children and adolescents. Yet, repeated failure of this expectation has focused the public's and policy maker's attention on the limitations of major accountability systems. This paper explores applications of machine learning, predictive analytics, and data visualization to student information available to educational decision makers. In particular, we demonstrate how to use individual academic performance histories to identify "at-risk" students in real time for advising, academic coaching, and other support services and how to aggregate longitudinal data at the school or district level for system modeling, profiling, comparison, and intervention evaluation. (As Provided). |
Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
Update | 2020/1/01 |