A emergência do ensino orientado por dados digitais- data driven na educação: o dispositivo de hibridização algorítmico na docência universitária

Abstract

With the onset of the Covid-19 pandemic, the need for social isolation led Brazilian universities to implement Emergency Remote Teaching (ERT) and Emergency Hybrid Teaching (EHT). During this period of suspended in-person classes, university faculty were required to use digital platforms and applications powered by Artificial Intelligence (AI) algorithms to carry out their teaching activities. This shift generated a series of debates, doubts, questions and uncertainties. From a post-critical perspective, this scenario was problematized, leading to the following doctoral research question: How are knowledge and subjectivities produced in university teaching under algorithmic governmentality? To address this question, I used the following theoretical-analytical tools from philosopher Michel Foucault: power, knowledge, government, governmentality, device, truth, and modes of subjectivation. Narrative interviews were conducted in the second semester of 2022 with ten professors from different areas of knowledge at a Brazilian public university. As a methodollogy, I developed the strategy of methodological pointillism, which juxtaposed narrative interviews, the concept of experience, and Foucauldian discourse analysis. This approach aimed to examine the strategies, mechanisms, procedures, techniques, tactics, and self-governance practices implemented in the production of algorithmic governmentality in university teaching when faculty members were called upon to operate through digital platforms. The central argument of this research is that algorithmic governmentality has emerged in response to an urgent demand to integrate data economy in higher education and the development of data-driven education. To address this demand, the algorithmic hybridization device was activated during the Covid-19 pandemic, leveraging emergency remote and hybrid teaching to normalize the use of digital platforms in university teaching, accelerate the production of educational digital data, and train and perfect algorithms and models for data-driven education. In remote teaching, it has displaced the student body as the locus of the constitution of knowledge in university teaching practice, towards the production of data-driven teaching knowledge on digital platforms. It also worked with the mechanisms of didactic transcription and transcreation, which helped translate teaching experiences into digital formats. These processes reinforced the use of digital platforms both during and after the pandemic. As a result, the algorithmic hybridization device continues to shape university teaching in the post-pandemic era, promoting the idea that AI supports rather than replaces educators. It sustains its influence by activating games of substitution, trust, personalization, and educational improvement. This device has resulted in the emergence of new modes of teaching modes: vigilate digitalis, critica digitalis, and intellectual digitalis. The study concludes that the Covid-19 pandemic functioned as a laboratory for the advancement of artificial intelligence, the data economy, and the automation of teaching activities, all of which contributed to the rise of data-driven education in university teaching.