This dissertation illuminates patterns across disparate university data sets to identify the insights that may be gained through the analysis of large amounts of disconnected student data on first-year engineering (FYE) students and to understand how FYE instructors use data to inform their teaching practices. Grounded by the Academic Plan Model, which highlights student characteristics as an important consideration in curriculum development, the study brings together seemingly distinct pieces of information related to students' learning, engagement with class resources, and motivation so that faculty may better understand the characteristics and activities of students enrolled in their classes.

In the dissertation's first manuscript, I analyzed learning management system (LMS) timestamp log-files from 876 students enrolled in the FYE course during Fall 2013. Following a series of quantitative analyses, I discovered that students who use the LMS more frequently are more likely to have higher grades within the course. This finding suggests that LMS usage might be a way to understand how students interact with course materials outside of traditional class time. Additionally, I found differential relationships between LMS usage and course performance across different instructors as well as a relationship between timing of LMS use and students' course performance.

For the second manuscript, I connected three distinct data sets: FYE student's LMS data, student record data, and FYE program survey data that captured students' motivation and identity as engineers at two time points. Structural equation modeling results indicate that SAT Math was the largest predictor of success in the FYE course, and that students' beginning of semester engineering expectancy was the only significant survey construct to predict final course grade.

Finally, for the third manuscript I conducted interviews with eight FYE instructors on how they use student data to inform their teaching practices. Ten themes emerged which describe the limited explicit use of formal data, but many instructors use data on an informal basis to understand their students. Findings also point to specific, existing data that the university already collects that could be provided to instructors on an aggregate, class-level basis to help them better understand their students.