Identifying Students at Risk: An Examination of Computer-Adaptive Measures and Latent Class Growth Analysis

Autor/inn/en	Keller-Margulis, Milena; McQuillin, Samuel D.; Castañeda, Juan Javier; Ochs, Sarah; Jones, John H.
Titel	Identifying Students at Risk: An Examination of Computer-Adaptive Measures and Latent Class Growth Analysis
Quelle	In: Journal of Applied School Psychology, 34 (2018) 1, S.18-35 (18 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1537-7903
DOI	10.1080/15377903.2017.1328627
Schlagwörter	At Risk Students; Multivariate Analysis; Adaptive Testing; Computer Assisted Testing; Screening Tests; Academic Achievement; Grade 3; Grade 4; Grade 5; Elementary School Students; Measurement; Standardized Tests; Achievement Tests; Reading Tests; Reading Skills; Classification; Texas + Suchen Sie Ihr Suchwort? Multivariate Analyse; Screening-Verfahren; Schulleistung; School year 03; 3. Schuljahr; Schuljahr 03; School year 04; 4. Schuljahr; Schuljahr 04; School year 05; 5. Schuljahr; Schuljahr 05; Messverfahren; Standadised tests; Standardisierter Test; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Lesetest; Reading skill; Lesefertigkeit; Classification system; Klassifikation; Klassifikationssystem
Abstract	Multitiered systems of support depend on screening technology to identify students at risk. The purpose of this study was to examine the use of a computer-adaptive test and latent class growth analysis (LCGA) to identify students at risk in reading with focus on the use of this methodology to characterize student performance in screening. Participants included 3,699 students in Grades 3-5. Three time points of administration (fall, winter, and spring) of the computer-adaptive reading measure were selected. LCGA results indicated 6-7 classes, depending on grade, informed by level and growth in student performance that significantly predicted failure on the statewide test administered at the end of the year. The lowest-performing classes had failure rates above 90% across all grades. The results indicate that identifying homogeneous groups of learners through LCGA may be valuable as an approach to determining students who need additional instruction. Practical implications and future directions are discussed. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01