Engineering and computing education plays an integral role in the competitiveness of U.S. industry by providing the necessary workforce for advanced technology development and innovation. Many academic institutions have deep pools of applicants with strong credentials based on traditional measures of academic success such as high school GPA or standardized tests. However, many students languish in their studies or leave their STEM academic programs altogether without completing degrees for many reasons. This project examined a large number of non-cognitive and affective (NCA) factors to understand what characteristics might predict student success, and to develop ways in which engineering and computing students can be supported that would lead to higher success. These NCA factors represent latent characteristics, attitudes, and beliefs of students that are not usually measured at entry into a University, including personality, approach to learning, motivation, stress and sense of belongingness in STEM. The work brought together over 20 researchers (professors, Ph.D. students and undergraduates) from three institutions: Purdue University, a large research focused public school, The University of Texas - El Paso (UTEP), a Hispanic serving research institution and California Polytechnic State University (Cal Poly), a large undergraduate focused public school with no Ph.D. program.

The main goals of the project were: 1) to determine the NCA profiles of engineering and computing students and characterize variations by institution, academic program or change over time; 2) to understand how these factors might correlate to academic performance; and 3) to test NCA-based interventions that might improve overall student success. Working together, we developed, piloted, and statistically validated a survey that measured 28 NCA factors related to fourteen constructs that research has shown correlate to academic success. This survey was validated and is a major outcome of the project. Over the first two years of the project, the survey was given to 2216 engineering and computing students at 17 different institutions across the United States including many students at the partner institutions. For 490 of these students we were able to connect survey responses to their academic records. From this, we determined that an element of student motivation and their approach to studying were more predictive of grade point average than standardized admission test scores. We next took the entire data set and looked for common groupings (or 'clusters') of student NCA factors. Using a Gaussian mixture modeling approach, we determined that 73% of the students fell into one of four distinct profiles that vary substantially from each other. The different clusters can be broadly characterized as: 1) the 'typical' cohort whose NCA profiles match the average factor score for each individual construct; 2) the High Positive cluster where student score strongly in NCA factors which individually strongly correlate to academic success; 3) the low motivation cluster whose students score lower than the norm on many factors associated with academic success; and 4) a group that generally feels very little support from faculty or peers and has many strongly negative NCA factors scores. Correlations of cluster membership to self-reported incoming academic performance measures were not strong, suggesting again that students' NCA factors (rather than traditionally used cognitive measures) better distinguish among students in engineering programs.

UTEP created a dataset of engineering and computing students from spring 2018 to spring 2022 that included students' academic performance over time and tracked student NCA profiles. Using the UTEP student survey data sets, we found that there was significant gender disparity in several NCA profiles, such as big five personalities and experience with life stress among engineering and computing students. We also found considerable differences in students? NCA profiles and changes within departments over time across departments. The results informed initiatives focused on student success and building a sense of belonging in the major, including an Allyship program that pairs first- and second-year students with senior female students to engage in professional development activities, provide resources, grow from shared experiences, and identfy opportunities to learn from professionals.  

The dataset is the central contribution of this work because it holds a unique combination of academic, survey, and ODOS data, as well as institutional data as derived from IPEDS. This dataset has enabled a wide range of modeling projects to be pursued that explore important questions around engineering and computing education. UTEP's student data linked to student records over the last five years allows the university to answer questions concerning NCA.

Last Modified: 02/01/2023
Modified by: Ann Q Gates