Predictors of Academic Achievement in Blended Learning: the Case of Data Science Minor
This paper studies the patterns of learning behaviour in connection with educational achievement in multi-year undergraduate data science minor specialisation for non-STEM students. In particular, this work focuses on analysing the predictors of academic achievement in blended-learning setting factors related to initial mathematics knowledge, specific traits of educational programs, online and offline learning engagement, and connections with peers. Robust linear regression and non-parametric statistical tests reveal a significant gap in the achievement of students from different educational programs and on the connection between their class attendance and achievement. The results indicate that achievement is not related to the communication on the Q&A forum while peers do affect academic success.