A critical (theory) data literacy: tales from the field

Abstract

Purpose

This paper aims to explore how critical digital and data literacies are facilitated by testing different methods in the classroom, with the ambition to find a pedagogical framework for prompting sustained critical literacies.

Design/methodology/approach

This contribution draws on a 10-year set of critical pedagogy experiments conducted in Denmark, USA and Italy, and engaging more than 1,500 young adults. Multi-method pedagogical design trains students to conduct self-oriented guided autoethnography, situational analysis, allegorical mapping, and critical infrastructure analysis.

Findings

The techniques of guided autoethnography for facilitating sustained data literacy rely on inviting multiple iterations of self-analysis through sequential prompts, whereby students move through stages of observation, critical thinking, critical theory-informed critique around the lived experience of hegemonic data and artificial intelligence (AI) infrastructures.

Research limitations/implications

Critical digital/data literacy researchers should continue to test models for building sustained critique that not only facilitate changes in behavior over time but also facilitate citizen social science, whereby participants use these autoethnographic techniques with friends and families to build locally relevant critique of the hegemonic power of data/AI infrastructures.

Originality/value

The proposed literacy model adopts a critical theory stance and shows the value of using multiple modes of intervention at micro and macro levels to prompt self-analysis and meta-level reflexivity for learners. This framework places critical theory at the center of the pedagogy to spark more radical stances, which is contended to be an essential step in moving students from attitudinal change to behavioral change.

Citation

Markham, A. and Pronzato, R. (2023), "A critical (theory) data literacy: tales from the field", Information and Learning Sciences, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/ILS-06-2023-0087