The Datafication of Higher Education: Discussing the Promises and Problems

Autor/inn/en	Selwyn, Neil; Gaševic, Dragan
Titel	The Datafication of Higher Education: Discussing the Promises and Problems
Quelle	In: Teaching in Higher Education, 25 (2020) 4, S.527-540 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Selwyn, Neil) ORCID (Gaševic, Dragan)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1356-2517
DOI	10.1080/13562517.2019.1689388
Schlagwörter	Criticism; Data Analysis; Higher Education; Dialogs (Language); Electronic Mail; Interpersonal Communication; Prediction; Accuracy; Interdisciplinary Approach; Researchers; Computer Science; Social Sciences; Problems + Suchen Sie Ihr Suchwort? Kritik; Auswertung; Hochschulbildung; Hochschulsystem; Hochschulwesen; Dialog; Dialogs; Dialogue; Dialogues; Elektronischer Briefkasten; Interpersonale Kommunikation; Vorhersage; Fächerübergreifender Unterricht; Fächerverbindender Unterricht; Interdisziplinarität; Researcher; Forscher; Informatik; Social science; Sozialwissenschaften; Gesellschaftswissenschaften; Problemsituation
Abstract	A common recommendation in critiques of datafication in education is for greater conversation between the two sides of the (critical) divide -- what might be characterised as sceptical social scientists and (supposedly) more technically-minded and enthusiastic data scientists. This article takes the form of a dialogue between two academics representing these different perspectives -- Dragan Gasevic (a data scientist who is one of the founders of the 'learning analytics' field) and Neil Selwyn (a social scientist and long-time critic of educational technology). Over the course of a six-month email exchange, we explore various arguments for and against the datafication of higher education. Thus, we are able to add a computational dimension to well-worn social criticisms of data representativeness, reductionism and injustice, as well as exploring social tensions inherent in technical claims to data-based precision, clarity and predictability. Crucially, these exchanges allow us to explore ways in which our two viewpoints converge, and therefore highlight opportunities for productive inter-disciplinary exchange and collaboration. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01