Collaborative Research [FW-HTF-RL]: Enhancing the Future of Teacher Practice via AI-enabled Formative Feedback for Job-Embedded Learning

协作研究 [FW-HTF-RL]：通过人工智能支持的工作嵌入学习形成性反馈增强教师实践的未来

• 批准号: 2326169
• 负责人: Sean Kelly
• 金额: $ 131.64万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-11-01 至 2027-10-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2326169&HistoricalAwards=false
• 关键词: Collaborative; Research; FW; HTF; RL

This project envisions a future of work where advanced technologies provide automated, job-embedded, individualized feedback to drive professional learning of the future worker. To achieve this goal, it addresses a fundamental question: Are evaluative or non-evaluative feedback systems more effective in driving professional learning? This question will be tested on professionals where objective, fine-grained feedback is especially critical to improvement--the teaching professions. This research will be situated within English and language arts (ELA) instruction in middle- and high school classrooms, where underperformance and inequality in literacy outcomes are persistent problems facing the U.S. Current methods of supporting teacher learning through feedback are sparse, cumbersome, subjective, and evaluative. Thus, a major reconceptualization is needed to provide feedback mechanisms that- meaningfully affect teacher practice and are accessible to all. In partnership with TeachFX, an industry leader in technology-enabled instructional feedback, this project will work with teachers to design and test systems of automated feedback. Insights from the study will lead to feedback systems that empower teaching professionals, generate continued professional learning, and ultimately, increase student achievement. The scientific merits of the project are centered around the foundational question of whether instruction can be construed entirely along a continuum of ineffective to more effective practice. The hypothesis is that the richest opportunities for on-the-job feedback in the professions are agnostic technologically-driven feedback systems, which offer choice, withhold evaluation, make room for varied teacher practices, and promote a greater locus of control. The project has several goals towards testing this hypothesis, including: (1) to work with a diverse panel of teachers to design and refine automated feedback systems; (2) to enhance the robustness and fairness of computational models that underlie automated feedback; and finally, (3) to test fundamental design principles of professional feedback. The project will begin by leveraging TeachFX's corpus of instructional observations from approximately 5,000 educators to develop automated, robust, accurate, unbiased, generalizable, and interpretable feedback models. Next, working with teacher participants, the feedback interfaces will be co-designed and iteratively refined. Further, a variety of observational and survey-based measures will be used to assess teacher responsiveness to feedback. The project will culminate in a longitudinal, experimental study contrasting the effects of evaluative- with non-evaluative feedback on teacher learning, empowerment, and student achievement outcomes with a sample of 300 teachers. The study will create a blueprint for effective and efficient professional observation and feedback, and working systems to implement that feedback, driving the next generation of advancement in the sciences, technology, engineering, and mathematics.This project is supported by two programs at NSF: Primary support comes from the Future of Work at the Human-Technology Frontier program which supports multi-disciplinary research to sustain economic competitiveness, promote worker well-being, lifelong and pervasive learning, and quality of life, and illuminate the emerging social and economic context and drivers of innovations that are shaping the future of jobs and work. Additional support is from the Discovery Research preK-12 program (DRK-12) which seeks to significantly enhance the learning and teaching of science, technology, engineering, and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models, and tools.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.

该项目设想了一个工作的未来，高级技术提供自动化的，工作的个性化反馈，以推动对未来工人的专业学习。为了实现这一目标，它解决了一个基本问题：评估或非评估反馈系统在推动专业学习方面更有效吗？这个问题将对客观，细粒度反馈对于改进（教学专业）特别关键的专业人员进行测试。这项研究将位于中学和高中教室中的英语和语言艺术（ELA）教学中，识字成果的表现不佳和不平等是美国当前通过反馈支持教师学习的持续问题，这是稀疏，麻烦，主观的，主观的和评估的。因此，需要进行重大的重新概念化来提供反馈机制，从而有意义地影响教师的实践，并且可以访问所有人。该项目与技术支持教学反馈的行业领导者TeachFX合作，将与教师合作设计和测试自动反馈的系统。研究的见解将导致反馈系统，以增强教学专业人员的能力，创造持续的专业学习，并最终提高学生的成就。该项目的科学优点围绕着基本问题，即是否可以完全解释教学能力完全无效地对更有效的实践解释。假设是，在职业中，最富有的在职反馈的机会是不可知的技术驱动的反馈系统，它们提供了选择，选择，预定评估，为各种教师的实践腾出空间，并促进更大的控制源。该项目具有测试这一假设的几个目标，包括：（1）与各种教师小组合作设计和完善自动反馈系统； （2）增强自动反馈基础的计算模型的鲁棒性和公平性；最后，（3）测试专业反馈的基本设计原理。该项目将从大约5,000名教育工作者那里利用TeachFX的教学观察表，以开发自动化，健壮，准确，无偏见，可解释的反馈模型。接下来，与教师参与者合作，反馈界面将共同设计并迭代精致。此外，将使用各种观察和基于调查的措施来评估教师对反馈的反应。该项目将在一项纵向，实验研究中达到最终形式，将评估性的影响与非评估反馈对教师学习，授权和学生成就成果的影响与300名教师的样本进行了对比。 The study will create a blueprint for effective and efficient professional observation and feedback, and working systems to implement that feedback, driving the next generation of advancement in the sciences, technology, engineering, and mathematics.This project is supported by two programs at NSF: Primary support comes from the Future of Work at the Human-Technology Frontier program which supports multi-disciplinary research to sustain economic competitiveness, promote worker well-being, lifelong and普遍的学习，生活质量，并阐明了新兴的社会和经济环境以及创新的驱动力，这些创新正在塑造工作和工作的未来。额外的支持来自Discovery Research PreK-12计划（DRK-12），该计划旨在通过PreK-12学生和教师的研究和发展创新资源，模型和工具的研究和开发来显着增强科学，技术，工程和数学（STEM）的学习和教学，这是NSF的法定任务和审查的范围，这是通过评估的范围来进行评估，这是由Infectiral and Infectirair进行了评估。