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

该项目设想了一个未来的工作，先进的技术提供自动化的、嵌入工作的、个性化的反馈，以推动未来工人的专业学习。为了实现这一目标，它提出了一个基本问题：在推动专业学习方面，是评估性反馈系统更有效还是非评估性反馈系统更有效？这个问题将在客观的、细粒度的反馈对改进尤其重要的专业人士（教学专业）中进行测试。本研究将在初中和高中课堂的英语和语言艺术（ELA）教学中进行，在这些课堂中，表现不佳和读写能力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是稀疏、繁琐、主观和评估性的。因此，需要进行重大的重新概念化，以提供反馈机制，从而对教师实践产生有意义的影响，并使所有人都能获得反馈机制。该项目将与技术支持教学反馈的行业领导者TeachFX合作，与教师一起设计和测试自动反馈系统。从这项研究中获得的见解将导致反馈系统，从而增强专业教师的能力，产生持续的专业学习，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，即教学是否可以完全沿着一个从无效到更有效的连续体来解释。假设是，在职业中，最丰富的在职反馈机会是不可知论的技术驱动的反馈系统，它提供选择，保留评估，为各种教师实践腾出空间，并促进更大的控制点。该项目有几个目标来验证这一假设，包括：(1)与不同的教师小组合作，设计和完善自动反馈系统；(2)增强作为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的教学观察语料库，从大约5000名教育工作者那里开发自动化的、健壮的、准确的、无偏见的、可推广的和可解释的反馈模型。接下来，与教师参与者合作，共同设计并迭代改进反馈界面。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。该项目最终将开展一项纵向实验研究，对比评估性反馈和非评估性反馈对教师学习、赋权和学生成绩的影响，研究对象为300名教师。这项研究将为有效和高效的专业观察和反馈以及实施反馈的工作系统创建蓝图，推动下一代科学、技术、工程和数学的进步。该项目由美国国家科学基金会的两个项目提供支持：主要支持来自人类技术前沿项目的未来工作，该项目支持多学科研究，以维持经济竞争力，促进工人福祉，终身和普及学习，提高生活质量，并阐明正在形成未来就业和工作的新兴社会和经济背景和创新驱动因素。额外的支持来自探索研究preK-12项目（DRK-12），该项目旨在通过研究和开发创新资源、模型和工具，显著提高preK-12学生和教师对科学、技术、工程和数学（STEM）的学习和教学。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 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.