Abstract
This paper discusses an explorative approach on strengthening critical data literacy using data science methods and a theoretical framing intersecting educational science and media theory. The goal is to path a way from data-driven to data-discursive perspectives of data and datafication in higher education. Therefore, the paper focuses on a case study, a higher education course project in 2019 and 2020 on education and data science, based on problem-based learning. The paper closes with a discussion on the challenges on strengthening data literacy in higher education, offering insights into data practices and the pitfalls of working with and reflecting on digital data.
Highlights
Algorithms, Big Data and Artificial Intelligence (AI) are the remarkable developments of recent years and have found their way into society as conceptual terms, they shape everyday actions to different degrees, and this has long since gone beyond basic recommendation systems for better online shopping experiences (Kitchin, 2021)
While we faced many challenges, the explorative joint project holds rich insights pathing a way towards critical data literacy in an interdisciplinary setting for higher education
Higher education meets these requirements and allows a tentative approach on this topic. Efforts such as explorative projects on that topic are limited in their reach. Neither do they replace a comprehensive agenda on critical data literacy, nor do they fully anchor into existing study programmes
Summary
Algorithms, Big Data and Artificial Intelligence (AI) are the remarkable developments of recent years and have found their way into society as conceptual terms, they shape everyday actions to different degrees, and this has long since gone beyond basic recommendation systems for better online shopping experiences (Kitchin, 2021). The idea of processing even big data sets is not new; the concept of AI is not an innovation of recent years. Recent developments like the increase of computing power combined with networked technologies have unleashed a new quality of gathering, processing and working with Big Data. The processes of automation and computation increase complexity instead of reducing it, they reproduce social inequalities and, thereby, punish the poor (Eubanks, 2017)
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