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

• 批准号: 1926930
• 负责人: Muriel Medard
• 金额: $ 2.4万
• 依托单位: Massachusetts Institute of Technology
• 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 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1926930&HistoricalAwards=false
• 关键词: EAGER; AI; DCL; Collaborative; Research

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，也是科学进步的关键。然而，美国工程、计算机和物理科学领域的女学生数量仍然少得惊人。科学、技术、工程和数学（STEM）教育缺乏多样性，在很大程度上是由于不同教育阶段的偏见造成的（例如，男女学生对数学成绩的不同看法，缺乏对女学生入学预科班的鼓励，影响大学课程选择的刻板印象）。这些偏见早在中学时就出现了：在这个关键时期，学生的教育经历可以显着影响他们在高中的学术选择，并最终决定是否在大学就读 STEM 专业。为了扩大女性对 STEM 的参与，找出中学学习环境中可能吸引（或排斥）学生进入科学领域的因素和做法至关重要。该奖项将利用机器学习 (ML) 开发新的、自动化的数据驱动方法，用于发现和监控 STEM 课堂中的偏见，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、机器学习和信息论的方法，创建算法工具来监控中学生、教师和学校层面的数据，以了解影响学生参与 STEM 的因素。这些工具将 (i) 帮助确定对女学生的决策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受到劝阻，放弃追求 STEM 领域，以及 (iii) 提供有效的干预措施，帮助缩小性别差距。尽管机器学习具有潜力，但在教育中的使用是一把双刃剑：虽然机器学习算法可能能够标记歧视模式，但如果不加以控制，它们也可能传播偏见并产生不必要的不​​同影响。 因此，与此同时，该项目还旨在描述在教育环境中部署机器学习所涉及的公平挑战。拟议的方法将在五年期间从美国各地中学生收集的数据集上进行验证。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。