Recommendations for the University of Virginia’s Computer Science Curriculum to Support Student Well-being; An Analysis of the Pedagogies Used to Teach Computer Science and Their Impact on Mental Health

To improve a curriculum so that it better supports the mental health of undergraduate computer science (CS) students, one must understand the impact of the current pedagogies available to teach CS. The technical research report details the specific improvements that may be made to the University of Virginia’s (UVA’s) CS program to promote student well-being while still effectively teaching course content. The purpose of this proposal is to address the growing number of students at UVA that are seeking help with mental health challenges. The STS research paper provides an analysis of various pedagogies that may be used to teach CS and their effects on the well-being of students. This identifies methods of teaching that may be added or removed from the classroom to promote positive mental health. The analysis performed in the STS research paper serves as the foundation for the technical research report by providing evidence for teaching methods not present in UVA’s CS curriculum that may better support students. Pedagogies selected from the STS paper may then be integrated with modifications into specific CS classes at UVA, as described in the technical report.

The technical research report discusses pedagogies that have been shown to support the well-being of students and describes the specific ways in which these teaching methods can be integrated into UVA’s CS curriculum. This supplies curriculum designers and educators with a foundation for a more student-centered approach to teaching CS at UVA. A literature review was conducted to obtain information on pedagogies and stress-reduction methods that aim to promote positive well-being. An analysis of the methods reviewed allowed for those that best fit into UVA’s curriculum to be chosen for integration into the CS program.

From this analysis, the best methods for improving CS student well-being at UVA were found to be the implementation of a pass/fail grading system, infusing the curriculum with education on health and well-being, and the inclusion of mindfulness-based stress reduction into courses. These methods have been shown to increase intrinsic motivation in students, decrease overall stress levels, and have promoted better relationships among peers. Following the implementation of these pedagogies, it is necessary to monitor their effectiveness in the CS curriculum and to further modify them as needed to maximize positive well-being.

The STS research report aims to understand how a CS program taught at the undergraduate level can be modified to better support student learning and positive mental health. This was done through a literature review of both pedagogies whose purpose is to promote well-being in students and those that describe novel ways of teaching CS to minimize stress and increase learning. The report addresses the prevalence of mental illness and high stress levels in undergraduate students, especially when these challenges are caused by the academic environment, by analyzing pedagogies that place students at the forefront.

This analysis found that Scalable Game Design, Spiral Design, and pass/fail grading have been used to promote positive well-being both indirectly by teaching CS in a way that aids understanding and directly by targeting sources of stress in students. These methods were shown to increase student understanding of CS material, decrease competition among peers, and promote self-regulation in learning. While it was found that these pedagogies are highly likely to benefit students both in and out of the classroom, a larger scale evaluation of these methods is needed to truly understand their benefit. This includes analyzing these pedagogies in the context of the environment in which they are used, as well as gathering qualitative student feedback regarding their effectiveness.

School of Engineering and Applied Science

Bachelor of Science in Computer Science

Technical Advisor: Briana Morrison

STS Advisor: Pedro Francisco