EAGER: AI-DCL: Collaborative Research: Understanding and Overcoming Biases in STEM Education using Machine Learning

EAGER：AI-DCL：协作研究：利用机器学习理解和克服 STEM 教育中的偏见

基本信息

批准号：
1926925
负责人：
Flavio Calmon
金额：
$ 25.17万
依托单位：
Harvard University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1926925&HistoricalAwards=false
关键词：
EAGER AI DCL Collaborative Research

项目摘要

Diversity is the cornerstone of innovation and essential for the progress of science. However, the number of female students in engineering, computing, and physical sciences in the United States remains strikingly low. The lack of diversity in science, technology, engineering, and mathematics (STEM) education is, to a significant extent, due to biases at different stages of schooling (e.g., different perceptions of math achievements by male and female students, lack of encouragement for female student enrollment in advance placement classes, stereotypes influencing college course selection). These biases appear as early as middle school: a critical period when student's educational experience can significantly influence their academic choices in high school and, ultimately, in deciding whether or not to enroll in STEM majors in college. In order to broaden the participation of women in STEM, it is critical to identify factors and practices in middle school learning environments that may attract (or repel) students into science. This award will use machine learning (ML) to develop new, automated, and data-driven methods for discovering and monitoring biases in STEM classrooms, focusing on middle school and early adolescence science and mathematics education.The project combines methods from social psychology, machine learning, and information theory to create algorithmic tools that monitor middle school student, teacher, and school-level data for factors that impact students' engagement in STEM. These tools will (i) help identify pedagogical or socio-economic factors that have a disparate impact on the decisions made by female students, (ii) predict which students are most vulnerable to being discouraged from pursuing STEM fields, and (iii) inform effective interventions that help close the gender gap. Despite its potential, the use of ML in education is a double-edged sword: while ML algorithms may be able to flag discriminatory patterns, they can also propagate biases and have an unwarranted disparate impact if left unchecked. Thus, in parallel, this project also aims to characterize the fairness challenges involved in deploying ML in education settings. The proposed approach will be validated on a dataset collected during a five year period from middle school students from across the United States.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

多样性是创新的基石，对科学进步至关重要。但是，在美国，工程，计算和物理科学领域的女学生人数仍然非常低。由于不同的教育阶段的偏见，科学，技术，工程和数学（STEM）教育缺乏多样性（STEM）教育（例如，男性和女学生对数学成就的不同看法，缺乏鼓励，女学生的入学率提前安排课，刻板印象影响大学课程的选择）。这些偏见早在中学时就出现了：一个关键时期，学生的教育经验可以极大地影响他们在高中的学术选择，并最终决定是否决定是否参加大学的STEM专业。为了扩大妇女在STEM中的参与，至关重要的是，在中学学习环境中识别可能吸引（或击退）学生进入科学的因素和实践。该奖项将使用机器学习（ML）开发新的，自动化和数据驱动的方法，用于发现和监测STEM教室中的偏见，重点关注中学和青春期早期的科学和数学教育。该项目结合了社会心理学，机器的方法学习和信息理论创建算法工具，以监视中学的学生，老师和学校级别的数据，以影响学生参与STEM的因素。这些工具将（i）帮助确定对女学生做出的决定有不同影响的教学或社会经济因素，（ii）预测哪些学生最容易灰心，不愿意追求STEM领域，并且（iii）有效告知有效有助于缩小性别差距的干预措施。尽管具有潜力，但在教育中使用ML还是一把双刃剑：虽然ML算法可能能够标记歧视性模式，但它们也可以传播偏见，并且如果没有受到检查，它们也会产生无与伦比的不同影响。因此，同时，该项目还旨在表征在教育环境中部署ML所涉及的公平挑战。拟议的方法将在美国各地的中学学生的五年内收集的数据集中进行验证。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估评估的评估值得支持的。。