Fair Learning Analytics: Design, Participation, and Trans-discipline in the Techno-structure

Abstract:

The digitalisation of education is increasingly embracing intensive data collection practices. Sentiment analysis – the semantic analysis of social networks and human-computer interaction models among other data-driven practices, helps to understand human behaviour. However, it also poses a great dilemma regarding the invasion of privacy and triggers a reflection on the ethics for the use of data for improving learning. Consequently, the developers of artificial intelligence must engage in active dialogue with educators, sociologists, psychologists, educational technologists, pedagogues, communicators, and experts in data privacy to understand how their solutions have an impact on this educational practice. In this context, an approach based on human rights and ethics must be considered. This chapter presents an interdisciplinary work in an institutional project focused on the adoption of learning analytics. The experience was carried out by the Group of Open and Accessible Educational Resources from the University of the Republic of Uruguay. The two main pillars of fair learning analytics are design-by-privacy and the relationship between learning analytics and open and inclusive education. Furthermore, such phenomena are discussed in an attempt to generate recommendations for this practice in the regional context, and based on it, to contribute to the international debate.

Año de publicación:

2023

Keywords:

• learning analytics
• Open education
• Interdisciplinary
• Privacy-by-design

Fuente:

scopus