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

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...

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Bibliographic Details
Published in:International journal of emerging technologies in learning 2019-01, Vol.14 (5), p.64
Main Authors: Musabirov, Ilya, Pozdniakov, Stanislav, Tenisheva, Ksenia
Format: Article
Language:English
Subjects:
Academic achievement
Blended learning
Data science
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Summary: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.
ISSN:1863-0383
1863-0383
DOI:10.3991/ijet.v14i05.9512