Of ‘black boxes’ and algorithmic decision-making in (higher) education – A commentary

Datos Bibliográficos

ID7667908
AutoresPaul Prinsloo (0000-0002-1838-540X, University of South Africa, autor de correspondencia)
Año2020
Volumen7
Número1
Páginas205395172093399
Fecha de publicación2020-01-01
Open AccessSí
TipoARTICLE
RevistaBig Data & Society (JOURNAL)
Identificadores de la revistaISSN: 2053-9517 • E-ISSN: 2053-9517
EditorialSAGE Publications (PUBLISHER • US)
DOI10.1177/2053951720933994
OpenAlexW3036777423
IdiomaEN
Citas recibidas12
Referencias citadas17

Higher education institutions have access to higher volumes and a greater variety and granularity of student data, often in real-time, than ever before. As such, the collection, analysis and use of student data are increasingly crucial in operational and strategic planning, and in delivering appropriate and effective learning experiences to students. Student data – not only in what data is (not) collected, but also how the data is framed and used – has material and discursive effects, both permanent and fleeting. We have to critically engage claims that artificial intelligence and the ever expansive/expanding systems of algorithmic decision-making provide speedy, accessible, revealing, panoramic, prophetic and smart analyses of students' risks, potential and learning needs. We need to pry open the black boxes higher education institutions (and increasingly venture capital and learning management system providers) use to admit, steer, predict and prescribe students’ learning journeys

Black box · Data science · Expansive · Higher education · Knowledge management · Political science · Public relations · Silver bullet · Sociology · Artificial Intelligence in Healthcare and Education · Big Data and Business Intelligence · Computer Science · Law · Online Learning and Analytics · Artificial Intelligence

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