Predictors of Academic Achievement in Blended Learning: the Case of Data Science Minor

Ilya Musabirov, Stanislav Pozdniakov, Ksenia Tenisheva


This paper is dedicated to studying patterns of learning behavior in connection with educational achievement in multi-year undergraduate Data Science minor specialization for non-STEM students. We focus on analyzing predictors of aca-demic achievement in blended learning taking into account factors related to initial mathematics knowledge, specific traits of educational programs, online and of-fline learning engagement, and connections with peers. Robust Linear Regression and non-parametric statistical tests reveal a significant gap in achievement of the students from different educational programs. Achievement is not related to the communication on Q&A forum, while peers do have effect on academic success: being better than nominated friends, as well as having friends among Teaching Assistants, boosts academic achievement.


hybrid learning, blended learning, data science, non-STEM students, social learn-ing analytics

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Copyright (c) 2019 Ilya Musabirov, Stanislav Pozdniakov, Ksenia Tenisheva

International Journal of Emerging Technologies in Learning (iJET) – eISSN: 1863-0383
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