Validity Evidence for the SUCCESS Survey: Measuring Non-Cognitive and Affective Traits of Engineering and Computing Students

摘要

This research paper examines the validity evidence from an exploratory factor analysis for a pilot of the SUCCESS survey (Studying Underlying Characteristics for Computing and Engineering Student Success). This survey was developed to measure underlying factors that may influence student success including personality, community, grit, thriving, identity, mindset, motivation, perceptions of faculty caring, stress, gratitude, self-control, mindfulness, and belongingness. We measure these underlying factors because each engineering and computing student admitted to a university has clear potential for academic and personal success in their undergraduate curriculum from admissions criteria, however, while some thrive academically, others flounder. In this project, we ask, "Why is it that highly credentialed and previously successful students do not see the same success in college?" We posit that some collection of characteristics—apparently not visible on their admission applications and perhaps not related to their talent or intelligence—is an important piece of the student performance puzzle. We developed a survey to measure various non-cognitive and affective factors that we believe are important for student achievement, academically, personally, and professionally. These non-cognitive and affective factors are representative of multifaceted aspects of undergraduate student success in prior literature. Each of the constructs we chose had validity evidence from prior studies, some within an engineering population. We piloted the survey across two different universities, one West Coast and one Midwest (n= 490), in Summer 2017. We used Exploratory Factor Analysis (EFA) to evaluate instrument performance to decide which items to include in the national release of the survey in Fall 2017. Our results provide preliminary validity evidence for items that measure various non-cognitive and affective factors. The wide-ranging constructs within the SUCCESS survey provide multiple pathways to understand students' likelihood for success in engineering and computing. Our future work includes distributing this survey to over a dozen universities across the U.S., yielding a broad dataset of non-cognitive profiles of engineering and computing students broadly. In parallel, we will link these results with students' registrar information at three study sites to develop predictive models for student success.