The Bias-Variance Tradeoff: How Data Science Can Inform Educational Debates

Autor/in	Doroudi, Shayan
Titel	The Bias-Variance Tradeoff: How Data Science Can Inform Educational Debates
Quelle	In: AERA Open, 6 (2020) 4, (18 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	ORCID (Doroudi, Shayan)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2332-8584
Schlagwörter	Data Analysis; Learning Theories; Teaching Methods; Educational Research; Educational Attitudes; Artificial Intelligence; Discovery Learning; Direct Instruction; Epistemology; Prediction; Statistical Bias; Constructivism (Learning) + Suchen Sie Ihr Suchwort? Auswertung; Learning theory; Lerntheorie; Teaching method; Lehrmethode; Unterrichtsmethode; Bildungsforschung; Pädagogische Forschung; Educational attitude; Bildungsverhalten; Erziehungseinstellung; Künstliche Intelligenz; Entdeckendes Lernen; Direct instructional procedues; Direct instructional approach; Unterrichtsverfahren; Erkenntnistheorie; Vorhersage
Abstract	In addition to providing a set of techniques to analyze educational data, I claim that data science as a field can provide broader insights to education research. In particular, I show how the bias-variance tradeoff from machine learning can be formally generalized to be applicable to several prominent educational debates, including debates around learning theories (cognitivist vs. situativist and constructivist theories) and pedagogy (direct instruction vs. discovery learning). I then look to see how various data science techniques that have been proposed to navigate the bias-variance tradeoff can yield insights for productively navigating these educational debates going forward. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01