Literaturnachweis - Detailanzeige
| Autor/inn/en | Christie, S. Thomas; Jarratt, Daniel C.; Olson, Lukas A.; Taijala, Taavi T. |
|---|---|
| Titel | Machine-Learned School Dropout Early Warning at Scale [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (12th, Montreal, Canada, Jul 2-5, 2019). |
| Quelle | (2019), (6 Seiten)
PDF als Volltext kostenfreie Datei |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| Schlagwörter | Dropout Prevention; At Risk Students; Artificial Intelligence; Decision Support Systems; Prediction; Student Records; Enrollment; Information Systems; Models; Middle School Students; High School Students; Statistical Analysis |
| Abstract | Schools across the United States suffer from low on-time graduation rates. Targeted interventions help at-risk students meet graduation requirements in a timely manner, but identifying these students takes time and practice, as warning signs are often context-specific and reflected in a combination of attendance, social, and academic signals scattered across data sources. Extremely high caseloads for counselors compound the problem. At Infinite Campus, a large student information system provider, we modeled statistical relationships between student educational records and enrollment outcomes, using de-identified records and in-system analysis to guarantee student data privacy. The resulting risk scores are highly predictive, context-sensitive, nationally available, integrated into the existing student information system, and updated daily. [For the full proceedings, see ED599096.] (As Provided). |
| Anmerkungen | International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2020/1/01 |
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