100 DAYS OF... Data for Learning | Journal Club | 27 September

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What does research tell us about data for learning?
 

In the “100 DAYS OF... Data for Learning” Journal Clubs we explore and discuss papers/research on data for learning in the context of (engineering) education.
The core question to be addressed is: What does research on data for learning tell us about (designing) our engineering education?

Following a flipped-classroom approach, participants read an article before each session and start an open discussion on the article, moderated by Marcus Specht, Professor for Digital Education at TU Delft and Director of the Leiden-Delft-Erasmus Center for Education and Learning. 

To continue our exploration of data for learning in (engineering) education, the Journal Club is discussing another interesting article on 27 September.

• Article: Knox, J., Williamson, B., & Bayne, S. (2020). Machine behaviourism: Future visions of ‘learnification’and ‘datafication’across humans and digital technologies. Learning, Media and Technology, 45(1), 31-45.

About the article

This paper examines visions of ‘learning’ across humans and machines in a near-future of intensive data analytics. Building upon the concept of ‘learnification’, practices of ‘learning’ in emerging big data-driven environments are discussed in two significant ways: the training of machines, and the nudging of human decisions through digital choice architectures. Firstly, ‘machine learning’ is discussed as an important example of how data-driven technologies are beginning to influence educational activity, both through sophisticated technical expertise and a grounding in behavioural psychology. Secondly, we explore how educational software design informed by behavioural economics is increasingly intended to frame learner choices to influence and ‘nudge’ decisions towards optimal outcomes. Through the growing influence of ‘data science’ on education, behaviourist psychology is increasingly and powerfully invested in future educational practices. Finally, it is argued that future education may tend toward very specific forms of behavioural governance – a ‘machine behaviourism’ – entailing combinations of radical behaviourist theories and machine learning systems, that appear to work against notions of student autonomy and participation, seeking to intervene in educational conduct and shaping learner behaviour towards predefined aims.

In sum

• Title: Machine behaviourism: future visions of ‘learnification’ and ‘datafication’ across humans and digital technologies
• Authors: Jeremy Knox, Ben Williamson, and Sian Bayne
• Event date: 27 September
• Time:12:30 - 13:30
• Location: t.b.d. Teaching Lab or Online
• Other events in this series 
  • Journal Club 29 November
  • Journal Club 17 January