Learning as a Machine: Crossovers between Humans and Machines

Autor/in	Hildebrandt, Mireille
Titel	Learning as a Machine: Crossovers between Humans and Machines
Quelle	In: Journal of Learning Analytics, 4 (2017) 1, S.6-23 (18 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1929-7750
Schlagwörter	Behaviorism; Data Processing; Profiles; Learning Processes; Privacy; Confidential Records; Confidentiality; Intellectual History; Intervention; Identification; Prediction; Data Analysis; Relevance (Education); Behavior Theories + Suchen Sie Ihr Suchwort? Behaviourism; Behaviorismus; Datenverarbeitung; Charakterisierung; Profilanalyse; Learning process; Lernprozess; Privatsphäre; Geistesgeschichte; Identifikation; Identifizierung; Vorhersage; Auswertung; Relevance; Relevanz
Abstract	This article is a revised version of the keynote presented at LAK '16 in Edinburgh. The article investigates some of the assumptions of learning analytics, notably those related to behaviourism. Building on the work of Ivan Pavlov, Herbert Simon, and James Gibson as ways of "learning as a machine," the article then develops two levels of investigation (processing of personal data and profiling based on machine learning) to assess how data driven education affects privacy, non-discrimination, and the presumption of innocence. Finally, the article discusses how data minimization and profile transparency will contribute to the methodological integrity of learning analytics, while protecting the fundamental rights and freedoms of human learners thus safeguarding the creativity, humour, and contestability of human learning. [Paper presented at the LAK '16--International Conference on Learning Analytics and Knowledge (6th, Edinburgh, United Kingdom, April 25-29, 2016).] (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