Selection and Assignment of STEM Adjunct Faculty Using Text Data Mining

Abstract

This paper presents the development and testing of a text data mining tool to assist in the selection and assignment of adjunct faculty to teach STEM courses. The tool scores the resume of a faculty member against course descriptions in a STEM graduate program. The tool returned a similarity score between a resume and course descriptions, which was then used as an indicator of faculty suitability to teach courses in the program. We enhanced the original tool with an improved user interface and deployed it to search for new faculty searches and in the process of assigning courses. A TD-IDF text analytic technique was used for similarity scoring. Our research question was to investigate whether a similarity-scoring tool for faculty resumes against course descriptions would be useful in the search and assignment process to hire faculty to teach specific courses. As part of our methods, we developed a friendly user interface to the existing tool using a student-centered coding contest. We applied the tool to the hiring and assignment of adjunct faculty. We measured success as the processing of a large number of open positions in a relatively short period of time and found a significantly high number of good fits between faculty and their course assignments. We investigated whether the scoring system positively correlated with the courses assigned to them.We successfully filled over 50 unassigned courses with appropriate faculty over a period of three months, where 30% were new hires. In the process, we discovered that the vast majority of the incumbent's similarity scores positively correlated to the courses assigned to them. This generated sufficient confidence that the description scoring system has been integrated as part of our faculty hiring and assignment processes in our programs.