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.

该项目设想了未来的工作，其中先进技术提供自动化、嵌入工作的个性化反馈，以推动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：评估性反馈系统还是非评估性反馈系统在推动专业学习方面更有效？这个问题将针对专业人士（即教师职业）进行测试，因为客观、细致的反馈对于改进尤为重要。这项研究将针对初中和高中课堂的英语和语言艺术 (ELA) 教学，在这些课堂中，表现不佳和识字成绩不平等是美国面临的长期问题。目前通过反馈支持教师学习的方法稀疏、繁琐、主观、和评价性的。因此，需要进行重大的重新概念化，以提供对教师实践产生有意义的影响并且可供所有人使用的反馈机制。该项目与技术教学反馈领域的行业领导者 TeachFX 合作，将与教师合作设计和测试自动反馈系统。该研究的见解将形成反馈系统，为教学专业人员提供支持，产生持续的专业学习，并最终提高学生的成绩。该项目的科学价值集中在一个基本问题上：教学是否可以完全沿着无效实践到更有效实践的连续体来解释。我们的假设是，职业中最丰富的在职反馈机会是不可知的技术驱动的反馈系统，它提供选择，保留评估，为不同的教师实践腾出空间，并促进更大的控制点。该项目有几个目标来检验这一假设，包括：（1）与多元化的教师小组合作，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则。该项目将首先利用 TeachFX 约 5,000 名教育工作者的教学观察资料库来开发自动化、稳健、准确、公正、可概括和可解释的反馈模型。接下来，将与教师参与者合作，共同设计和迭代完善反馈界面。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程度。该项目最终将进行一项纵向实验研究，以 300 名教师为样本，对比评价性反馈和非评价性反馈对教师学习、赋权和学生成绩结果的影响。该研究将为有效和高效的专业观察和反馈以及实施该反馈的工作系统创建蓝图，从而推动科学、技术、工程和数学的下一代进步。该项目得到了美国国家科学基金会的两个项目的支持：主要支持来自人类技术前沿计划的“工作的未来”，该计划支持多学科研究，以维持经济竞争力，促进工人福祉、终身和普遍学习以及生活质量，并阐明新兴的社会和经济背景以及正在塑造的创新驱动力工作和工作的未来。额外的支持来自 Discovery Research preK-12 计划 (DRK-12)，该计划旨在通过研究和开发创新资源、模型和工具。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。