STEM Ed PRF: Understanding and Improving Undergraduate Computer Science Regulation, Performance, and Motivation Using Digital Traces and Technologies

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This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).This postdoctoral fellowship research project will focus on leveraging educational data mining to understand how undergraduate students in introductory Computer Science courses draw on their motivational resources and regulate learning. The study seeks to explore ways in which students’ motivation interacts with self-regulated learning. Initial findings are then expected to inform the creation of a just-in-time learning intervention designed to improve study patterns. This project also aims to increase participation for women and students of color in Computer Science by enhancing student learning and motivation which may result in greater rates of persistence and degree attainment in the field. Student learning is a dynamic and context-specific process. Current interventions designed to promote persistence and performance in STEM capture student learning in broad contexts rather than in the moment students engage with their coursework. This project aims to gain a more accurate and comprehensive view of undergraduates’ learning in Computer Science using multiple data sources, including digital trace data. These data have the potential to capture the dynamic nature of student learning and motivation. The postdoctoral fellow has chosen to position her work within the framework of the “Metacognitive and Affective Model of Self-Regulated Learning” (Efklides, 2011). The fellow will then explore connections between motivation, behavior, and learning at both the task and person level and contribute new insights into that foundational theory that consider the interplay between motivation and self-regulated learning across levels of interaction. In addition, this project will use A/B methods to test an intervention created as part of the project that focuses on just-in-time, self-regulated learning support to improve study patterns. The A/B testing of motivation and self-regulated learning messages may allow for stronger claims to be made about the links between constructs and performance. Combining these methods within the same project has transformative potential for the effective use of learning analytics by Computer Science undergraduate students. The project responds to the STEM Education Postdoctoral Research Fellowship (STEM Ed PRF) program that aims to enhance the research knowledge, skills, and practices of recent doctorates in STEM, STEM education, education, and related disciplines to advance their preparation to engage in fundamental and applied research that advances knowledge within the field.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.