论文标题
数学教师的数学分析
A Mathematical Analysis of Mathematical Faculty
论文作者
论文摘要
我们使用美国各个分层排名的十个公共和私人数学部门的终身任期和终身教师的数据，作为案例研究，以证明几个变量之间的统计和数学关系，例如出版物和引用的数量，教授职位和AM Senter。首先，我们对数学部门进行探索性数据分析。然后应用了各种统计工具，包括回归，人工神经网络和无监督的学习，并比较了从不同方法获得的结果。我们得出的结论是，借助更先进的模型，可以设计一种自动促进算法，该算法有可能比人类方法更公平，更高效，更一致。
We use the data of tenured and tenure-track faculty at ten public and private math departments of various tiered rankings in the United States, as a case study to demonstrate the statistical and mathematical relationships among several variables, e.g., the number of publications and citations, the rank of professorship and AMS fellow status. At first we do an exploratory data analysis of the math departments. Then various statistical tools, including regression, artificial neural network, and unsupervised learning, are applied and the results obtained from different methods are compared. We conclude that with more advanced models, it may be possible to design an automatic promotion algorithm that has the potential to be fairer, more efficient and more consistent than human approach.
