Improving Student Learning While Decreasing Bias in Teaching Through Simulation

该项目旨在通过鼓励教师识别、反思和纠正已暴露的偏见，为未来更加公正和公平的社会做出贡献。这个为期三年的项目将实施一个可扩展的模式，通过模拟教学环境来发展公平、文化响应的教学实践。该项目将确定最佳做法，以帮助教师认识和减轻经常影响学生成功的隐性偏见。在教学实践中减少偏见对于使未来的领导者发挥其最大的先天和积极培养的潜力至关重要。2019冠状病毒病大流行凸显了学习方面的差异，并强调了社会情绪稳定对学生和教师长期福祉的重要性。项目团队将反复开发和测试“无偏见教学”课程，并在现有的模拟教学模块中添加一套人工智能驱动的偏见减少工具。该项目将使用数据驱动的决策来确认减少教学偏见的三阶段方法的可行性。通过随机分配的实验设计，该项目还将确认这三个阶段中的任何一个是单独、依次还是结合起来最有效。通过模拟教学环境，项目成果将形成一个发展公平、文化响应教学实践的模式。该项目将使用模拟教学环境来开发和完善计算模型，从而改进教学实践并为学习科学做出贡献。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。

This project aims to contribute to a more just and equitable society in the future by encouraging teachers to identify, reflect on, and correct revealed biases. This three-year project will implement a scalable model for developing equitable, culturally responsive teaching practices through a simulated teaching environment. The project will identify best practices to help teachers recognize and mitigate implicit biases that often impact student success. Bias reduction in teaching practices is crucial for enabling future leaders to achieve their highest innate and positively nurtured potential. The COVID-19 pandemic has highlighted disparities in learning and emphasized the importance of socio-emotional stability for the long-term well-being of students and teachers. The project team will iteratively develop and test a Teaching without Bias curriculum and add an AI-driven set of bias reduction tools to existing simulation instruction modules. The project will use data-driven decisions to confirm the feasibility of a three-phase approach to reducing bias in teaching. Through a randomized-assignment experimental design, the project will also confirm whether any of the three phases alone, in sequence, or in combination, is most effective. Through a simulated teaching environment, the project outcomes will lead to a model for developing equitable, culturally responsive teaching practices. The project will use the simulated teaching environment to develop and refine computational models that lead to improved teaching practices and contribute to the learning sciences.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.