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  1. This Work-In-Progress study investigates differences in freshman and junior engineering students’ valuation of and self-efficacy for computational work in engineering. We administered a survey to N=58 total students and performed a mixed-methods analysis to better understand what factors may influence students’ attitudes in this area. We found that freshmen’s intended major (CS or non-CS) was strongly correlated to differences in their response patterns across survey items. Interestingly, while MSE juniors had significantly higher self-efficacy scores for computational work than those of freshmen, their valuation scores were slightly lower than those of freshmen, despite their much greater experience in the area. We are currently conducting and analyzing follow-up interviews with survey participants to investigate the causes of these outcomes. 
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    Free, publicly-accessible full text available June 1, 2024
  2. 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. 
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  3. 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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