Set in motion by data: Human and data intra-actions in educational governance

Abstract

With the increased use of data in the governance of education, studies on how human beings interact with data become increasingly important. Recent scholarship has conceptualized human responses to data through the notion of affectivity. While this conceptualization offers a viable alternative to previous rational choice theorizations of human responses to data that over-emphasize rational though, it in turn under-emphasizes rational thought. This paper theorizes human responses to data as a matter of being set in motion by data, a term that includes rational thought, affect, and other responses. Through the philosophy of agential realism and an empirical analysis of various actors engaging with Danish graduate unemployment data, the paper elaborates how data and human beings (among other entities) intra-act and how human beings are affected by data, both in terms of what the data articulate and in terms of a code of conduct for dealing with data.

Integrating deep learning techniques for personalized learning pathways in higher education

“…The potential of AI, particularly DL, in reshaping educational practices is immense. DL algorithms, which simulate the neural networks of the human brain, can analyse complex data sets and extract patterns that can be used to tailor educational content [ 4 ]. This approach has already demonstrated transformative impacts in sectors such as healthcare and finance, where personalized solutions based on DL have led to significant advancements [ 5 ].…”