Characterizing How Engineering Undergraduate Students Define and Develop Data Proficiency

Godwyll Aikins, Catherine G.P. Berdanier, Kim Doang Nguyen

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This work in progress presents current findings from a funded mixed-methods investigation of the relationship between data proficiency and engineering identity among undergraduate students throughout their curriculum. This study aims to understand ways engineering undergraduate students conceptualize data proficiency and develop data skills over time. Through semi-structured interviews with four undergraduate engineering students from different class levels, we examined their understanding of data proficiency and the importance of data skills in engineering practice. The interviews were guided by the How People Learn framework, which provided a lens through which to investigate students' attitudes, beliefs, and experiences related to data and data analysis. The findings suggest that students view data proficiency as an important skill for their future careers but differ in their preferences for learning data skills through assignments, projects, or lectures. This research contributes to the understanding of how engineering students define and develop data proficiency, which can inform the design of effective data skills curricula in engineering education.

Original languageEnglish (US)
Title of host publication2023 IEEE Frontiers in Education Conference, FIE 2023 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350336429
StatePublished - 2023
Event53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023 - College Station, United States
Duration: Oct 18 2023Oct 21 2023

Publication series

NameProceedings - Frontiers in Education Conference, FIE
ISSN (Print)1539-4565


Conference53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023
Country/TerritoryUnited States
CityCollege Station

All Science Journal Classification (ASJC) codes

  • Software
  • Education
  • Computer Science Applications

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