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

多样性是创新的基石，也是科学进步的关键。然而，美国工程、计算机和物理科学专业的女学生数量仍然惊人地低。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学校不同阶段的偏见(例如，男女学生对数学成绩的看法不同，对女生预修班缺乏鼓励，影响大学选课的陈规定型观念)。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业选择，并最终决定是否进入STEM专业的大学。为了扩大妇女在STEM中的参与，关键是要确定中学学习环境中可能吸引(或排斥)学生进入科学的因素和做法。该奖项将使用机器学习(ML)来开发新的、自动化的和数据驱动的方法来发现和监控STEM课堂中的偏见，重点是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，创建了算法工具来监控中学生、教师和学校层面的数据，以寻找影响学生参与STEM的因素。这些工具将有助于(I)确定对女学生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被劝阻攻读STEM领域，以及(Iii)提供有助于缩小性别差距的有效干预措施。尽管ML有潜力，但在教育中使用ML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，它们也可能传播偏见，并产生不必要的不同影响。因此，同时，该项目还旨在描述在教育环境中部署ML所涉及的公平挑战的特征。建议的方法将在五年期间从美国各地中学生那里收集的数据集上得到验证。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.