Macro-scale analysis of teaching assignment plans based on complex network metrics and graph visualizations

Macro-scale analysis of teaching assignment plans based on complex network metrics and graph visualizations

연구 분야: Networking

학회: Social Network Analysis and Mining

This paper presents a new approach to analyze the teaching assignment plan of a large-sized university department, combining different graph visualizations and complex network metrics. In this study, we propose the usage of a macro-scale analysis solution to offer an overall vision to the department chair and the teaching committee for effective decision making. The proposed method offers several advantages, such as helping the department to identify which faculty categories are overburdened, detect flaws and re-assign courses to faculty members according to their expertise, discover gaps in faculty expertise for future teaching recruitment, among other aspects. The proposed analysis approach is illustrated with a practical use case corresponding to a Spanish Computer Science department of more than 150 teachers and a total of nearly 310 courses taught. The analysis revealed, among other findings, that: there are no distinct subgroups (i.e., clusters) within the department under study; all faculty categories are assigned to at least two types of courses; each course type has at least one primary faculty category that assumes the majority of teaching hours; all faculty categories are interconnected through the course types; and no faculty category systematically avoids any particular course type, although certain course types are noticeably less preferred. All the data and code related to this project can be downloaded from the following GitHub repository: https://github.com/jfvelezserrano/TAP.