Embedding Experiments: Staking Causal Inference in Authentic Educational Contexts

Autor/inn/en	Motz, Benjamin A.; Carvalho, Paulo F.; de Leeuw, Joshua R.; Goldstone, Robert L.
Titel	Embedding Experiments: Staking Causal Inference in Authentic Educational Contexts
Quelle	In: Journal of Learning Analytics, 5 (2018) 2, S.47-59 (13 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	ORCID (Motz, Benjamin A.) ORCID (Carvalho, Paulo F.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1929-7750
Schlagwörter	Causal Models; Statistical Inference; Inferences; Educational Experiments; Educational Research; Educational Improvement; Research Design; Ethics; Teacher Participation; Intervention; Data Collection; Data Analysis; Theory Practice Relationship; Educational Benefits; Scaling + Suchen Sie Ihr Suchwort? Kausalanalyse; Inferential statistics; Schließende Statistik; Inference; Inferenz; Schulversuch; Bildungsforschung; Pädagogische Forschung; Teaching improvement; Unterrichtsentwicklung; Forschungsdesign; Ethik; Data capture; Datensammlung; Auswertung; Theorie-Praxis-Beziehung; Bildungsertrag; Scale construction; Skalenkonstruktion
Abstract	To identify the ways teachers and educational systems can improve learning, researchers need to make causal inferences. Analyses of existing datasets play an important role in detecting causal patterns, but conducting experiments also plays an indispensable role in this research. In this article, we advocate for experiments to be embedded in real educational contexts, allowing researchers to test whether interventions such as a learning activity, new technology, or advising strategy elicit reliable improvements in authentic student behaviours and educational outcomes. Embedded experiments, wherein theoretically relevant variables are systematically manipulated in real learning contexts, carry strong benefits for making causal inferences, particularly when allied with the data-rich resources of contemporary e-learning environments. Toward this goal, we offer a field guide to embedded experimentation, reviewing experimental design choices, addressing ethical concerns, discussing the importance of involving teachers, and reviewing how interventions can be deployed in a variety of contexts, at a range of scales. Causal inference is a critical component of a field that aims to improve student learning; including experimentation alongside analyses of existing data in learning analytics is the most compelling way to test causal claims. (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