Abstract
This paper discusses exploratory research which computationally examines over one and a half million words presented by first-year students as part of weekly online assignments over the Fall 2020 academic term. This work aims to explore whether computational analyses of first-year engineering student vocabulary can be employed to understand the levels of student motivation when learning engineering in an online environment. The investigation uses NVivo 12 Plus (NVivo), a data analysis software, to track the overall sentiment of weekly student discussion board responses and apply text queries to determine the number of responses that include words related to the expectancy-value theory. Applying this theory reveals trends in overall student motivation, with weeks four to six and eight to ten having an overall positive sentiment. This positive sentiment reveals higher levels of student motivation during those weeks.
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