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

多样性是创新的基石，也是科学进步的必要条件。然而，在美国，工程、计算机和物理科学专业的女生数量仍然非常少。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（例如，男女学生对数学成绩的不同看法，缺乏对女学生参加预修课程的鼓励，影响大学课程选择的刻板印象）。这些偏见早在中学就出现了：这是一个关键时期，学生的教育经历会显著影响他们在高中的学术选择，并最终决定是否在大学里注册STEM专业。为了扩大女性对STEM的参与，确定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。该奖项将使用机器学习（ML）开发新的、自动化的、数据驱动的方法，用于发现和监控STEM课堂中的偏见，重点是中学和青少年早期科学和数学教育。该项目结合了社会心理学、机器学习和信息论的方法，创建了算法工具，用于监控中学生、教师和学校层面的数据，以寻找影响学生参与STEM的因素。这些工具将(i)帮助识别对女学生决策产生不同影响的教学或社会经济因素，（ii）预测哪些学生最容易被劝阻从事STEM领域，以及（iii）告知有助于缩小性别差距的有效干预措施。尽管具有潜力，但ML在教育中的应用是一把双刃剑：虽然ML算法可能能够标记出歧视性模式，但如果不加以控制，它们也会传播偏见，并产生不必要的差异影响。因此，与此同时，该项目还旨在描述在教育环境中部署ML所涉及的公平性挑战。所提出的方法将在从美国各地的中学生收集的五年期间的数据集上进行验证。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.