learning analytics

AKWI-Tagungsband zur 35. AKWI-Jahrestagung. Jahrgang, 2022, Seite S. 316–320 The purpose of methods of fairness auditing is to uncover to what extent Learning Analytics algorithms are fair. Fairness auditing methods often rely on pre-existing test data. In the context of Learning Analytics auditing, learning data is needed for testing. However, learning data might not be available (in large quantities) due to privacy concerns. Our poster shares our findings on how relational data for fairness audits of Learning Analytics systems can be synthesized from little pre-existing data, using the most promising available data synthesizers.

In German education, digitalization is evolving, and the COVID-19 pandemic has accelerated this process due to closed schools and universities. While these external conditions were almost the same for all institutions, they have dealt with remote teaching in different ways and developed various new learning concepts. We conducted 27 interviews with headmasters, e-learning managers, and teachers from Germany to gain insights into varying attitudes towards learning management systems, as well as the motivations and concerns about future teaching with digital support. Using an inductive method, we identify different obstacles, including lack of digital competencies, necessity of technical equipment, concerns about replacing traditional lessons, but also enthusiasm for new opportunities. We argue that a meaningful use of learning management systems and learning analytics depends on an open and impartial debate on data protection and equal opportunities. Overall, this paper contributes to ...