Data Driven Identification and Selection Algorithms for At-Risk Students Likely to Benefit from High School Academic Support Services

Autor/inn/en	Lacefield, Warren E.; Applegate, E. Brooks; Zeller, Pamela J.; Van Kannel-Ray, Nancy; Carpenter, Shelly
Titel	Data Driven Identification and Selection Algorithms for At-Risk Students Likely to Benefit from High School Academic Support Services
Quelle	(2011), (18 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Academic Failure; At Risk Students; Identification; Information Systems; High School Students; Academic Support Services; Data; Educational Diagnosis; Intervention; Mathematics; Predictive Validity; Evaluation Research; Longitudinal Studies; Evidence; Program Effectiveness; Caseworker Approach; Michigan + Suchen Sie Ihr Suchwort? Identifikation; Identifizierung; High school; High schools; Student; Students; Oberschule; Schüler; Schülerin; Studentin; Daten; Pedagogical diagnostics; Pädagogische Diagnostik; Mathematik; Evaluationsforschung; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; Evidenz
Abstract	This study describes a well-defined data-driven diagnostic identification and selection procedure for choosing students at-risk of academic failure for appropriate academic support services. This algorithmic procedure has been validated both by historical quantitative studies of student precedents and outcomes as well as by current qualitative comparisons with existing school procedures and efforts to accomplish the same goal through committee work and recommendations. Results indicate it is both possible and feasible using readily available school student information system data to identify who appears to be at substantial academic risk, what some of those risks are, and who appears likely to benefit from specific academic support service interventions. (Contains 6 figures and 7 footnotes.) (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10