NSF Award
2025093
Engineers need to be able to use powerful computational tools in order to continue to address increasingly complex problems. This is particularly true for the field of Materials Science and Engineering (MSE), in which simulation tools are vital to the effort to develop and test cutting-edge new materials like new metal alloys, smart materials, and composites. Universities who train future engineers have responded by including this information in their curriculum. However, a portion of undergraduate engineering students have had particular difficulty learning to use these tools and do not develop the competence necessary during their studies. One possible reason for this is that many engineering students, particularly materials science and engineering students, do not think they are capable of learning these skills or do not believe they will need to use them in their career. This project will survey and interview a large number of students at two different universities, Campbell University and Ohio State University, to determine whether or not there are significant differences in students? motivation based on their program of study, and whether or not this difference in motivation is tied to student learning. By better understanding this relationship, engineering educators can help develop programs or curriculum that will improve students? learning in this vital area.<br/><br/>Computational thinking and skills are critical for the success of both engineering students and practicing engineers in the 21st century. The study will explore the extent to which key motivation factors for students learning computational skills ? such as efficacy, expectancy value, and utility value ? differ across engineering disciplines. Students from a large public university (Ohio State University) and a small private university (Campbell University) will be surveyed and interviewed during their first year of studies in engineering to determine their motivational beliefs. Links between different types of motivation, chosen specialty within the field of engineering, and student background will be investigated. The degree to which these factors correlate with each other and with students? performance in basic computational modeling assignments will determine whether the hypothesis ? that students in some sub-disciplines of engineering studies such as materials science and engineering will be less motivated to learn computational tools ? is supported. Ultimately, these findings can lead to appropriate and high-impact interventions to improve computational materials science education. We may find that it is easier to address the way students think about the importance of learning computational skills and whether or not they are able to succeed, and if so dramatic improvements in student learning could result.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
