Year of Graduation
Academic Motivation, Digital Traces of Self-Regulated Learning and Academic Achievement: The Case of Data Science Minor
The connection between motivation, learning behaviour and academic achievement, while topical for any educational settings, is especially important in contemporary blended educational environments. These environments allow to investigate actual learning behaviour of students in great detail. Current work aims to examine students’ academic motivation, digital traces of self-regulation and academic achievement in the blended course in Data Science. Exploratory analysis of the connection between these constructs is analyzed with partial correlation networks. Patterns of self-regulation are revealed using archetypal analysis algorithms, resulting groups of students are analyzed in connection with their academic motivation and academic achievement. Current work is focused on the connection between traces of self-regulation with the motivational components and between these traces and academic achievement. Additionally, to see that learning behavior measures are important for students with different level of academic achievement quantile regression analysis is employed.