Scholarlink

<p>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: <a href="https://github.com/jfvelezserrano/TAP">https://github.com/jfvelezserrano/TAP</a>.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月2日

查看全文
  • Victoria Ruiz-Parrado,
  • A. Belen Moreno,
  • Angel Sanchez,
  • Jose F. Velez

摘要

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.