Using Causal Models to Bridge the Divide between Big Data and Educational Theory

Autor/inn/en	Kitto, Kirsty; Hicks, Ben; Shum, Simon Buckingham
Titel	Using Causal Models to Bridge the Divide between Big Data and Educational Theory
Quelle	In: British Journal of Educational Technology, 54 (2023) 5, S.1095-1124 (30 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Kitto, Kirsty) ORCID (Hicks, Ben) ORCID (Shum, Simon Buckingham)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0007-1013
DOI	10.1111/bjet.13321
Schlagwörter	Causal Models; Learning Analytics; Educational Theories; Artificial Intelligence; Educational Technology; Theory Practice Relationship; Statistical Inference; Prediction + Suchen Sie Ihr Suchwort? Kausalanalyse; Educational theory; Theory of education; Bildungstheorie; Künstliche Intelligenz; Unterrichtsmedien; Theorie-Praxis-Beziehung; Inferential statistics; Schließende Statistik; Vorhersage
Abstract	An extraordinary amount of data is becoming available in educational settings, collected from a wide range of Educational Technology tools and services. This creates opportunities for using methods from Artificial Intelligence and Learning Analytics (LA) to improve learning and the environments in which it occurs. And yet, analytics results produced using these methods often fail to link to theoretical concepts from the learning sciences, making them difficult for educators to trust, interpret and act upon. At the same time, many of our educational theories are difficult to formalise into testable models that link to educational data. New methodologies are required to formalise the bridge between big data and educational theory. This paper demonstrates how causal modelling can help to close this gap. It introduces the apparatus of causal modelling, and shows how it can be applied to well-known problems in LA to yield new insights. We conclude with a consideration of what causal modelling adds to the theory-versus-data debate in education, and extend an invitation to other investigators to join this exciting programme of research. (As Provided).
Anmerkungen	Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01