课题基金 / 基金详情

Improving Decision Making and Equity in Engineering Admissions

Improving Decision Making and Equity in Engineering Admissions
改善工程招生的决策和公平性
批准号:
1329217
负责人:
Michael Bastedo
金额:
$27.87万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2013
资助国家:
美国
项目状态:
已结题
起止时间:
2013-09-01 至 2018-08-31

项目摘要

项目成果

Michael Bastedo的其他基金

相似基金

相关文献

中文摘要
翻译
点击翻译按钮获取中文摘要
英文摘要
This engineering education research project seeks to explore whether college admissions files can be structured to improve the decision making of admissions officers. Psychological biases may hinder the ability of admissions officers to make ideal decisions, which could lead to the rejection of well-qualified low-income and minority students. This experimental study will examine whether changing the information included with the application, along with the order in which that information appears, will affect admissions officers' decisions about which applications to accept. The interventions developed here have the potential to be adopted within college admissions processes for engineering majors and for all students.The broader significance and importance of this project arises through the potential for simultaneously improving the quality and diversity of incoming engineering students. This project may inform not only practices in engineering admissions offices, but also the activities of important national entities such as the College Board and Common Application. This project overlaps with NSF's strategic goal for performing as a model organization by helping colleges and universities attain both excellence and inclusion. Additionally NSF's goal of innovating for society is enabled by facilitating research that informs educational policies and practices.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
Research: Do Test-Optional Policies in Engineering Admissions Improve Decision Making and Equity? Empirical Research using Experimental Simulations
Research: Predicting Achievement and Improving Equity in Engineering Using Contextualized High School Performance
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis