Fleeing from Frankenstein's monster and meeting Kafka on the way: Algorithmic decision-making in higher education. (May 2017)

• Record Type: Journal Article
• Authors: Prinsloo, Paul
• Other Names: Jandrić Petar guest-editor.; Knox Jeremy guest-editor.; Macleod Hamish guest-editor.; Sinclair Christine guest-editor.
• Is Part Of: E-Learning and digital media. Volume 14:Number 3(2017)
• Page Start: 138
• Page End: 163
• Publication Date: 2017-05
• Subjects: Algorithms -- learning analytics -- student data -- surveillance; Internet in education -- Periodicals; Computer-assisted instruction -- Periodicals; Education -- Computer network resources -- Periodicals; Educational technology -- Periodicals; Distance education -- Periodicals; 371.33405
• DOI: 10.1177/2042753017731355
• Languages: English
• ISSNs: 2042-7530

Abstract

In the socio-technical imaginary of higher education, algorithmic decision-making offers huge potential, but we also cannot deny the risks and ethical concerns. In fleeing from Frankenstein's monster, there is a real possibility that we will meet Kafka on our path, and not find our way out of the maze of ethical considerations in the nexus between human and nonhuman agencies. In this conceptual article, I map seven dimensions of student surveillance on an experimental matrix of human-algorithmic interaction to consider some of the ethical implications of algorithmic decision-making in higher education. The experimental matrix of human-algorithmic decision-making uses the four tasks of 'sensing', 'processing', 'acting' and 'learning' to open up algorithmic-human agency as comprising a number of possibilities such as (1) where only humans perform the task; (2) where the task is shared between humans and algorithms; (3) where algorithms perform the task but with humans supervising; and (4) where algorithms perform the tasks with no human oversight. I use this matrix to engage with seven dimensions of how higher education institutions collect, analyse and use student data namely (1) automation ; (2) visibility; (3) directionality ; (4) assemblage ; (5) temporality ; (6) sorting ; and (7) structuring . The article concludes by proposing a number of pointers to be taken into consideration when implementing algorithms in a higher education context from a position of an ethics of care.