A Systematic Literature Review of Undergraduate Data Science Education Research

TL;DR

This systematic review analyzes current undergraduate data science education research, highlighting gaps in empirical data, discipline coverage, and terminology consistency to guide future research and policy.

Contribution

It provides a comprehensive overview of existing literature, identifies knowledge gaps, and offers recommendations for improving research quality and visibility in undergraduate data science education.

Findings

Significant gaps in empirical research and reproducibility.

Underrepresented disciplines in the literature.

Need for standardized terminology and increased empirical studies.

Abstract

The presence of data science has been profound in the scientific community in almost every discipline. An important part of the data science education expansion has been at the undergraduate level. We conducted a systematic literature review to (1) portray current evidence and knowledge gaps in self-proclaimed undergraduate data science education research and (2) inform policymakers and the data science education community about what educators may encounter when searching for literature using the general keyword 'data science education.' While open-access publications that target a broader audience of data science educators and include multiple examples of data science programs and courses are a strength, significant knowledge gaps remain. The undergraduate data science literature that we identified often lacks empirical data, research questions and reproducibility. Certain disciplines are less visible. We recommend that we should (1) cherish data science as an interdisciplinary field; (2) adopt a consistent set of keywords/terminology to ensure data science education literature is easily identifiable; (3) prioritize investments in empirical studies.