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

Ilya Musabirov,Ksenia Tenisheva,Stanislav Pozdniakov

doi:10.3991/ijet.v14i05.9512

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.

Highlights

Introduction and BackgroundA ubiquitous proliferation of demand for data science-related competences poses new challenges for universities all over the world
Students study the basics of computer science and different methods and techniques related to data science, text mining, and social network analysis
The aim of this work is to determine the main predictors of student academic achievement in the blended learning model

Summary

Introduction

Introduction and BackgroundA ubiquitous proliferation of demand for data science-related competences (data literacy) poses new challenges for universities all over the world. High market demand makes it easy to attract students from different disciplines [1], including non-STEM disciplines [2]. This paper is a part of the project dedicated to studying the patterns of learning behaviour in connection with educational achievement in multi-year undergraduate data science minor specialization. Data science is a minor specialization that undergraduate students can choose to study for two years (2nd and 3rd year of a bachelor program). The specialization unites students from different non-STEM programs and departments ranging from economics to oriental studies. Students study the basics of computer science and different methods and techniques related to data science, text mining, and social network analysis. The first cohort of students started their studies in September 2015, and the second cohort started one year later in September 2016

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