Computational thinking in education - epistemology, pedagogy and politics

Abstract

The paper discusses computational thinking (CT) in education, as a new curricular content, and as a technosolutionist project to reshape educational practice. Proponents of CT argue that all students should learn to ?think like computer scientists?, because that is a universal mental skill for solving problems. However, practical difficulties in teaching and assessing this skill show that CT is contextually specific to computer programming, which means that its educational universalisation unjustifiably marginalizes other forms of knowledge. At the same time, especially during the pandemic, CT is increasingly permeating education through machine learning softwares, with the pedagogical argument that, like on YouTube or Netfix, algorithmic processing of data on student activities on educational platforms will achieve the progressive ideal of personalized user experience - of learning in an adaptive environment. Thus, however, algorithms shape the curriculum, not institutions. Proponents of digitalization actually confirm that these algorithms are not politically neutral, and that personalization means further privatization of education, when they demand that traditional schools be gradually replaced by educational platforms, while advocating the neoliberal view of education as acquisition of ?human capital?, or skills needed by the digital economy. Hence, ?thinking like a computer scientist? really does not only mean programming, but also creating digital content and data, which is capital for large IT companies.