"Big Data" in Educational Administration: An Application for Predicting School Dropout Risk

Autor/in	Sorensen, Lucy C.
Titel	"Big Data" in Educational Administration: An Application for Predicting School Dropout Risk
Quelle	In: Educational Administration Quarterly, 55 (2019) 3, S.404-446 (43 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Sorensen, Lucy C.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-161X
DOI	10.1177/0013161X18799439
Schlagwörter	At Risk Students; Dropouts; Data Collection; Data Analysis; Public Schools; Grade 3; Grade 4; Grade 5; Grade 6; Grade 7; Grade 8; Predictor Variables; High School Graduates; Graduation Rate; Dropout Rate; Gender Differences; Socioeconomic Influences; Racial Differences; Ethnicity; English Language Learners; Student Characteristics; Student Records; Academically Gifted; Student Behavior; Behavior Problems; Educational Technology; Reading; Homework; North Carolina + Suchen Sie Ihr Suchwort? Drop-out; Drop-outs; Dropout; Early leavers; Schulversagen; Data capture; Datensammlung; Auswertung; Public school; Öffentliche Schule; School year 03; 3. Schuljahr; Schuljahr 03; School year 04; 4. Schuljahr; Schuljahr 04; School year 05; 5. Schuljahr; Schuljahr 05; School year 06; 6. Schuljahr; Schuljahr 06; School year 07; 7. Schuljahr; Schuljahr 07; School year 08; 8. Schuljahr; Schuljahr 08; Prädiktor; High school; High schools; Graduate; Graduates; Oberschule; Absolvent; Absolventin; Geschlechterkonflikt; Sozioökonomischer Faktor; Rassenunterschied; Ethnizität; Schülerakte; Student behaviour; Schülerverhalten; Unterrichtsmedien; Leseprozess; Lesen; Hausaufgabe
Abstract	Purpose: In an era of unprecedented student measurement and emphasis on data-driven educational decision making, the full potential for using data to target resources to students has yet to be realized. This study explores the utility of machine-learning techniques with large-scale administrative data to identify student dropout risk. Research Methods: Using longitudinal student records data from the North Carolina Department of Public Instruction, this article assesses modern prediction techniques, with a focus on tree-based classification methods and support vector machines. These methods incorporate 74 predictors measures from Grades 3 through 8, including academic achievement, behavioral indicators, and socioeconomic and demographic characteristics. Findings: Two of the assessed classification algorithms predict high school graduation and dropping out correctly for more than 90% of an out-of-sample student cohort. Findings reveal a shift toward lower dropout incidence in regions hit hardest by the economic recession of 2008, especially for male students. Implications for Research and Practice: Machine-learning procedures, as demonstrated in this study, offer promise for allowing administrators to reliably identify students at risk of dropping out of school so as to provide targeted, intensive programs at the lowest possible cost. (As Provided).
Anmerkungen	SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: http://sagepub.com
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01