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Computational modeling skills are critical for the success of both engineering students and practicing engineers and are increasingly included as part of the undergraduate curriculum. However, students' belief in the utility of these skills and their ability to succeed in learning them can vary significantly. This study hypothesizes that the self-efficacy and motivation of engineering students at the outset of their degree program varies significantly and that engineering students pursuing some disciplines (such as computer, software, and electrical engineering) will begin with a higher initial self-efficacy than others (such as materials science and engineering and biomedical engineering). In this pilot study, a survey was used to investigate the motivational and efficacy factors of approximately 70 undergraduate students in their first year of engineering studies at a large public university. Surveys were implemented after students were introduced to MATLAB in their first-year engineering design course. The data was analyzed for variations in baseline motivation based on the students' intended major. The results of this survey will help determine whether efficacy and interest related to computational modeling are indeed lower for certain engineering disciplines and will inform future studies in this area.
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Developing undergraduate teaching materials in collaboration with pre-university students
ABSTRACT In this project we have involved four high-achieving pre-university summer placement students in the development of undergraduate teaching materials, namely tutorial videos for first year undergraduate Electrical and Electronic Engineering lab, and computer simulations of didactic semiconductor structures for an Electrical Science first year compulsory taught module. Here we describe our approach and preliminary results.
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- Award ID(s):
- 1663296
- PAR ID:
- 10028121
- Date Published:
- Journal Name:
- MRS Advances
- Volume:
- 2
- Issue:
- 31-32
- ISSN:
- 2059-8521
- Page Range / eLocation ID:
- 1713 to 1719
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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It is increasingly critical that engineering students develop proficiency with computational modeling tools, and many curricula include some introduction to such tools during their first year. It is clear that student interest and skill can vary significantly based on prior experiences, but it is less clear whether student motivation specifically related to computational modeling varies as well. This study hypothesizes that the self-efficacy and utility value related to computational methods varies significantly in students’ first year and that engineering students pursuing some disciplines (such as computer, software, and electrical engineering) will begin with a higher initial self-efficacy than others (such as chemical, materials, and biomedical engineering). A survey was used to investigate the utility value and efficacy of approximately 700 undergraduate students in their first year of engineering studies at both a large public institution and a small private institution. Data is analyzed for variations in baseline motivation based on the students’ intended major. This analysis also considers known confounding factors such as gender, race, and prior experience with programming. The results of this survey will help determine whether efficacy and interest related to computational methods vary based on intended major early in an engineering student’s academic career. Ultimately, it is hoped that this study can inform future studies related to what types of interventions might benefit students.more » « less
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1557/adv.2017.333
