Student Usage Predicts Treatment Effect Heterogeneity in the Cognitive Tutor Algebra I Program [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (9th, Raleigh, NC, Jun 29-Jul 2, 2016).

Autor/inn/en	Sales, Adam C.; Wilks, Asa; Pane, John F.
Titel	Student Usage Predicts Treatment Effect Heterogeneity in the Cognitive Tutor Algebra I Program [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (9th, Raleigh, NC, Jun 29-Jul 2, 2016).
Quelle	(2016), (8 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Algebra; Mathematics Instruction; Educational Technology; Technology Uses in Education; Intelligent Tutoring Systems; High School Students; Secondary School Mathematics; Program Effectiveness; Middle School Students + Suchen Sie Ihr Suchwort? Mathematics lessons; Mathematikunterricht; Unterrichtsmedien; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Intelligentes Tutorsystem; High school; High schools; Student; Students; Oberschule; Schüler; Schülerin; Studentin; Middle school; Middle schools; Mittelschule; Mittelstufenschule
Abstract	The Cognitive Tutor Algebra I (CTAI) curriculum, which includes both textbook and online components, has been shown to boost student learning by about 0.2 standard deviations in a randomized effectiveness trial. Students who were assigned to the experimental condition varied substantially in how, and how much, the used the online component of CTAI, but original analyses of the experimental data focused on estimating average effects, and did not examine whether the CTAI treatment effect varied by the amount of style of usage. This study leverages log data from the experiment to present a more nuanced analysis. It uses the framework of Principal Stratification, which estimates the varying CTAI treatment effect as a function of "potential" usage--either how students used the program, or how they would have used it had they been assigned to the treatment condition. With experimental data, Principal Stratification does not require that we assume that all relevant variables have been measured. With this framework, we find that students who receive a medium amount of assistance from the software (in the form of hints and error feedback) experience the largest effects, with lower effects for students who receive a lot or a little; and evidence that students who do not follow the curriculum order experience smaller treatment effects. [For the full proceedings, see ED592609.] (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	2022/4/11