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

Authors

  • Ilya Musabirov National Research University Higher School of Economics https://orcid.org/0000-0003-2246-0094
  • Stanislav Pozdniakov National Research University Higher School of Economics
  • Ksenia Tenisheva National Research University Higher School of Economics

DOI:

https://doi.org/10.3991/ijet.v14i05.9512

Keywords:

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

Abstract


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.

Author Biography

Ilya Musabirov, National Research University Higher School of Economics

Senior Lecturer, Department of Informatics

Downloads

Published

2019-03-14

How to Cite

Musabirov, I., Pozdniakov, S., & Tenisheva, K. (2019). Predictors of Academic Achievement in Blended Learning: the Case of Data Science Minor. International Journal of Emerging Technologies in Learning (iJET), 14(05), pp. 64–74. https://doi.org/10.3991/ijet.v14i05.9512

Issue

Vol. 14 No. 05 (2019)

Section

Papers

License

The submitting author warrants that the submission is original and that she/he is the author of the submission together with the named co-authors; to the extend the submission incorporates text passages, figures, data or other material from the work of others, the submitting author has obtained any necessary permission.
Articles in this journal are published under the Creative Commons Attribution Licence (CC-BY What does this mean?). This is to get more legal certainty about what readers can do with published articles, and thus a wider dissemination and archiving, which in turn makes publishing with this journal more valuable for you, the authors.
By submitting an article the author grants to this journal the non-exclusive right to publish it. The author retains the copyright and the publishing rights for his article without any restrictions.