Using Instruction-Embedded Formative Assessment to Predict State Summative Test Scores and Achievement Levels in Mathematics

Autor/inn/en	Zheng, Guoguo; Fancsali, Stephen E.; Ritter, Steven; Berman, Susan R.
Titel	Using Instruction-Embedded Formative Assessment to Predict State Summative Test Scores and Achievement Levels in Mathematics
Quelle	In: Journal of Learning Analytics, 6 (2019) 2, S.153-174 (22 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1929-7750
Schlagwörter	Formative Evaluation; Predictor Variables; Summative Evaluation; Scores; Mathematics Instruction; Accountability; Models; Intelligent Tutoring Systems; Mathematics Achievement; Grade 6; Grade 7; Grade 8; Public Schools; Achievement Tests; Standardized Tests; State Standards; Florida; Florida Comprehensive Assessment Test + Suchen Sie Ihr Suchwort? Prädiktor; Mathematics lessons; Mathematikunterricht; Verantwortung; Analogiemodell; Intelligentes Tutorsystem; Mathmatics sikills; Mathmatics achievement; Mathematical ability; Mathematische Kompetenz; School year 06; 6. Schuljahr; Schuljahr 06; School year 07; 7. Schuljahr; Schuljahr 07; School year 08; 8. Schuljahr; Schuljahr 08; Public school; Öffentliche Schule; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Standadised tests; Standardisierter Test
Abstract	If we wish to embed assessment for accountability within instruction, we need to better understand the relative contribution of different types of learner data to statistical models that predict scores and discrete achievement levels on assessments used for accountability purposes. The present work scales up and extends predictive models of math test scores and achievement levels from existing literature and specifies six categories of models that incorporate information about student prior knowledge, demographics, and performance within the MATHia intelligent tutoring system. Linear regression, ordinal logistic regression, and random forest regression and classification models are learned within each category and generalized over a sample of 23,000+ learners in Grades 6, 7, and 8 over three academic years in Miami-Dade County Public Schools. After briefly exploring hierarchical models of this data, we discuss a variety of technical and practical applications, limitations, and open questions related to this work, especially concerning to the potential use of instructional platforms like MATHia as a replacement for time-consuming standardized tests. (As Provided).
Anmerkungen	Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: http://learning-analytics.info/journals/index.php/JLA/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01