Maximizing Individual Learning Goals Through Customized Student-Project Matching (SPM) in CS Capstone Projects

This full innovative practice paper describes a computational tool designed to optimally match students to industry-sponsored capstone projects in a software engineering capstone course for Computer Science undergraduates at an R1 University (R1U). In the context of these capstone courses, where students stand at the culmination of their academic journey, aligning students' personal learning goals and existing computing skills with team formations becomes critical. This paper presents the Student-Project Matching Tool (SPMT), created to help students find the best available industry-sponsored projects based on their desired learning outcomes, project requirements, and their interests in each project. To choose the learning outcomes they aim to achieve, students can select from a list of predefined software engineering categories and the skills needed to achieve proficiency in each category. The initial list of technical skills for each category was recorded from job postings on a variety of well-known job-search websites, and was further refined by the capstone program's industry partners. Allowing students to select the skills they will work on ensures that they have opportunities and exposure to the skill sets required for employment while still working on one of their most appealing projects. We have developed and piloted the SPMT, which utilizes student vectors to represent their interests and experiences across various software engineering skill sets. Similarly, this tool uses vectors to represent the skills required by each available project, aligning with the exact dimensions as those of the student vectors. The SPMT calculated Euclidean distances between the student interest and project requirement vectors. Next, the resulting Euclidean distances were multiplied with weights associated with students' level of interest in each industry-sponsored project. Subsequently, we framed the student-project matching process as a linear sum assignment problem, aiming to minimize the total sum of Euclidean distances between each student-project pair. The output of the SPMT process consistently matched students with teams that met their software engineering interests and project priorities. Our results reveal increased engagement and growth toward students' desired learning outcomes and computing skills. Specifically, after the first term of the capstone sequence, most students self-reported higher levels of proficiency growth in the skills within their desired software engineering category. This suggests that the SPMT effectively provides students with valuable learning experiences relevant to their career interests and representative of real-world settings.