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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.