What Different Kinds of Stratification Can Reveal about the Generalizability of Data-Mined Skill Assessment Models

Autor/inn/en	Sao Pedro, Michael A.; Baker, Ryan S. J. d.; Gobert, Janice D.
Titel	What Different Kinds of Stratification Can Reveal about the Generalizability of Data-Mined Skill Assessment Models
Quelle	(2013), (5 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Educational Research; Data Collection; Data Analysis; Generalizability Theory; Models; Science Process Skills; Inquiry; Prediction; Experiments; Simulation; Validity; Coding; Performance Based Assessment; Hypothesis Testing; Generalization; Physical Sciences; Scientific Concepts; Grade 8; Massachusetts + Suchen Sie Ihr Suchwort? Bildungsforschung; Pädagogische Forschung; Data capture; Datensammlung; Auswertung; Analogiemodell; Vorhersage; Erprobung; Simulation program; Simulationsprogramm; Gültigkeit; Codierung; Programmierung; Leistungsermittlung; Hypothesenprüfung; Hypothesentest; Natural sciences; Naturwissenschaften; Naturwissenschaft; School year 08; 8. Schuljahr; Schuljahr 08; Master-Studiengang
Abstract	When validating assessment models built with data mining, generalization is typically tested at the student-level, where models are tested on new students. This approach, though, may fail to find cases where model performance suffers if other aspects of those cases relevant to prediction are not well represented. We explore this here by testing if scientific inquiry skill models built and validated for one science topic can predict skill demonstration for new students and a new science topic. Test cases were chosen using two methods: student-level stratification, and stratification based on the amount of trials ran during students' experimentation. We found that predictive performance of the models was different on each test set, revealing limitations that would have been missed from student-level validation alone. (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2018/2/05