Application of Statistical Classification Analysis to Engineering Student Recruitment

统计分类分析在工科生招生中的应用

• 批准号: 0836028
• 负责人: Edward Anderson
• 金额: --
• 依托单位: Texas Tech University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-01-01 至 2010-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0836028&HistoricalAwards=false
• 关键词: Application; Statistical; Classification; Analysis; Engineering

This exploratory project will apply statistical classification analysis to college admission data to identify those variables that impact a student?s choice of engineering as the first and perhaps only major field of study. Variables, such as entrance exam scores, high school GPA, socio-economic status, ethnicity, gender, pre-engineering courses completed, and other factors that are suspected to influence an adolescent?s decision to study engineering, will be examined to determine their role in this decision. Specifically, statistical logistic regression, classification tree, and recently developed random forest techniques will be applied to a sample of students admitted to Texas Tech University. Once accomplished, these techniques will classify the variables and yield a predictive model for student recruitment to study engineering. These findings can then be used by the engineering community to design courses, curricula and recruitment programs that both attract students and also introduce students to the study of engineering in a manner more consistent with their expectations and abilities. Improved recruitment and retention strategies will help increase the number of engineering graduates needed by the nation?s workforce, and the results of this project will be a resource for guidance about recruiting students to engineering programs. This project will apply recently developed statistical classification techniques to a large data sample and can filter through many variables simultaneously and identify the significance of each variable. General recruitment of engineering students can be improved by taking advantage of this additional insight into why students select engineering prior to entering college. Using these results efforts and initiatives directed towards increasing the number of students enrolling in engineering can be focused on to the significant decision variables. The findings will also be important for college recruiters as they design and implement engineering student recruitment programs.

这个探索性的项目将应用统计分类分析大学录取数据，以确定这些变量，影响学生？他选择工程学作为第一个，也许是唯一的主要研究领域。变量，如入学考试成绩，高中GPA，社会经济地位，种族，性别，完成的工程预科课程，以及其他被怀疑会影响青少年的因素？的决定学习工程，将审查，以确定他们在这一决定中的作用。具体来说，统计逻辑回归，分类树，和最近开发的随机森林技术将被应用到一个样本的学生录取到得克萨斯理工大学。一旦完成，这些技术将对变量进行分类，并产生一个预测模型，用于学生招聘学习工程。然后，这些研究结果可以被工程界用来设计课程，课程和招聘计划，既吸引学生，也介绍学生以更符合他们的期望和能力的方式学习工程。改善招聘和保留战略将有助于增加国家所需的工程专业毕业生的数量？的劳动力，这个项目的结果将是一个资源的指导招聘学生的工程计划。该项目将把最近开发的统计分类技术应用于大量数据样本，并能同时过滤许多变量，确定每个变量的重要性。工程专业学生的一般招聘可以通过利用这一额外的洞察力来改善为什么学生在进入大学之前选择工程专业。使用这些结果的努力和举措，旨在增加学生注册工程的数量可以集中在重大的决策变量。这些发现对大学招聘人员设计和实施工程专业学生招聘计划也很重要。