Mind the gaps: Controversies about algorithms, learning and trendy knowledge

Abstract

This article critically explores the ways by which the Web could become a more learning-oriented medium in the age of, but also in spite of, the newly bred algorithmic cultures. The social dimension of algorithms is reported in literature as being a socio-technological entanglement that has a powerful influence on users’ practices and their lived world. They do not only govern what is visible (and inherently, what is obscured), what is valued and noteworthy, but also have the power to enable and assign meaningfulness in managing how information is perceived by users. This incurs a certain knowledge logic which is pervasive in algorithmic culture. This article posits that inquiry about the relation between algorithms and learning needs definitions as well as the stance of not extensively relying on them. When asking what an algorithm is, or how to define the process of learning and knowledge acquisition, one must also keep in mind that a definition is mostly blind to the ambiguity and slipperiness of contexts, hiding the gaps that hinder the objective circumscription of a concept. This article proposes to mind these ‘gaps’ through discussing controversies that may (or rather actually do) happen regarding contextual or theoretical differences in the interpretation of key concepts such as learning, knowledge and culture. To extend the discussion, I will expose alternative material which allows a wider consideration of the concept of learning and emphasises a dimension of learning seldom taken into account: contextual dependence. The chief characteristic of data processed by algorithms being their decontextualisation, I will discuss the agonistic relationship that is emerging from learning in the age of algorithmic cultures, to explore the possibilities of bridging the gaps and exploit the valuable resources the Web has to provide to enrich another dimension of learning in our lived world: its contextual relatedness.

Performativity, Big Data and Higher Education: The Death of the Professor?

“…The emergence of discourses on "evidence" in education (e.g., claims that teaching should be "evidence-based") is a further exemplification of these trends. It converges with the growing application of "big data" analytics and algorithmic thinking within education and more broadly (see further, Argenton, 2017;Attaran, Stark & Stotler, 2018;Ben-Porath & Shahar, 2017;Cope & Kalantzis, 2015;Daniel, 2015;Eichhorn & Matkin, 2016;Peters, 2012;Petrilli, 2018;Prinsloo, 2017;Schouten, 2017;Thompson, 2017;Wang, 2017;Williamson, 2017Williamson, , 2018. With the aid of powerful computers and search engines, vast quantities of data can now be sifted, sorted, packaged, and produced.…”

Living in Algorithmic Governance: A Study in the Digital Governance of Social Assistance in Ontario

“…Gerald Argenton (2017) argues that algorithms impact and govern what is visible and govern that which is not, that which is hidden. However, Ananny and Crawford (2016) caution that a focus on the lack of 'transparency' is not enough and write that "being able to see a system is sometimes equated with being able to know how it works and govern it -a pattern that recurs in recent work about transparency and computational systems" (p. 1).…”