Recommendation System Algorithm Application to Increase Interdisciplinary Undergraduate Course Collaborations

A problem within many institutions is the lack of resources to create meaningful interdisciplinary partnerships together. Addressing this issue is imperative for enhancing the quality of projects and research opportunities for faculty members. Students involved in these opportunities gain a deeper understanding of how their field can be implemented in another field. Application X is a novel web application developed to expand resources for Institution X to break the boundaries of faculty specializations to create more collaborative learning opportunities across disciplines. Faculty can connect and engage with others from various disciplines who share common interests. This platform enables faculty to establish collaborations across their courses and inspire projects, bringing together students from different backgrounds, to work with one another to benefit a community partner. Completed projects can be uploaded by faculty members, which can then be reviewed and evaluated by other users of Application X to provide resources that potentially inspire new collaborations. Application X implements principles of human computation and collective intelligence to solve this issue. Reviews can help Application X become self-sustaining and maintain the reliability of the information. This poster will delve into the design of an algorithm that recommends faculty for potential course collaborations. The unique challenge encountered in this research was how to develop an algorithm that matches faculty from dissimilar disciplines but who have similar interests outside of their own discipline. The algorithm's success in addressing the challenge allows for further enriching interdisciplinary collaborations within the college course environment.