A4 Article in conference proceedings
Course Satisfaction in Engineering Education Through the Lens of Student Agency Analytics (2020)

Heilala, V., Saarela, M., Jääskelä, P., & Kärkkäinen, T. (2020). Course Satisfaction in Engineering Education Through the Lens of Student Agency Analytics. In FIE 2020 : Proceedings of the 50th IEEE Frontiers in Education Conference. IEEE. Conference proceedings : Frontiers in Education Conference. https://doi.org/10.1109/FIE44824.2020.9274141

JYU authors or editors

Publication details

All authors or editors: Heilala, Ville; Saarela, Mirka; Jääskelä, Paivikki; Kärkkäinen,Tommi

Parent publication: FIE 2020 : Proceedings of the 50th IEEE Frontiers in Education Conference

Conference:

Place and date of conference: Uppsala, Sweden, 21.-24.10.2020

ISBN: 978-1-7281-8962-8

eISBN: 9781728189611

Journal or series: Conference proceedings : Frontiers in Education Conference

ISSN: 1539-4565

eISSN: 2377-634X

Publication year: 2020

Publisher: IEEE

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1109/FIE44824.2020.9274141

Publication open access: Other way freely accessible online

Publication channel open access: 

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/73023

Web address where publication is available: https://www.fie2020.org/abstracts-and-papers/

Abstract

This Research Full Paper presents an examination of the relationships between course satisfaction and student agency resources in engineering education. Satisfaction experienced in learning is known to benefit the students in many ways. However, the varying significance of the different factors of course satisfaction is not entirely clear. We used a validated questionnaire instrument, exploratory statistics, and supervised machine learning to examine how the different factors of student agency affect course satisfaction among engineering students (N = 293). Teacher’s support and trust for the teacher were identified as both important and critical factors concerning experienced course satisfaction. Participatory resources of agency and gender proved to be less important factors. The results provide convincing evidence about the possibility to identify the most important factors affecting course satisfaction.

Keywords: technological fields; study; students; contentment; study performance; machine learning; student affairs offices

Free keywords: course satisfaction; student agency; exploratory statistics; supervised machine learning

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2020

JUFO rating: 1