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

该项目展望了未来的工作，在那里，先进的技术提供自动化的、嵌入工作的个性化反馈，以推动未来员工的专业学习。为了实现这一目标，它解决了一个根本问题：在推动专业学习方面，评估性反馈系统还是非评估性反馈系统更有效？这个问题将在客观、细粒度的反馈对改进特别关键的专业人士--教师专业人员--身上进行测试。这项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，在这些课堂上，表现不佳和识字结果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、主观和具有评价性。因此，需要进行一次重大的概念调整，以提供反馈机制--对教师实践产生有意义的影响，并使所有人都能获得。与TeachFX合作，该项目将与教师合作设计和测试自动反馈系统。TeachFX是一家技术支持的教学反馈行业领先者。这项研究的见解将导致反馈系统，赋予教学专业人员权力，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，即教学是否可以完全按照从无效到更有效的实践的连续体来解释。这个假设是，在职业中，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，拒绝评估，为多样化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)与不同的教师小组合作设计和改进自动反馈系统；(2)增强作为自动反馈基础的计算模型的稳健性和公平性；以及(3)测试专业反馈的基本设计原则。该项目将首先利用TeachFX从大约5,000名教育工作者那里获得的教学观察语料库来开发自动化、健壮、准确、无偏见、可概括和可解释的反馈模型。下一步，将与教师参与者合作，共同设计和迭代改进反馈界面。此外，将使用各种观察性和基于调查的措施来评估教师对反馈的反应。该项目将以300名教师为样本，进行纵向的实验研究，对比评价性反馈和非评价性反馈对教师学习、赋权和学生成就结果的影响。这项研究将为有效和高效的专业观察和反馈创建蓝图，并建立实施反馈的工作系统，推动下一代科学、技术、工程和数学的进步。该项目由NSF的两个项目支持：主要支持来自人类技术前沿的未来工作计划，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未来就业和工作的创新驱动因素。其他支持来自发现研究预科-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，显著提高预科-12学生和教师的科学、技术、工程和数学(STEM)的学习和教学。该奖项反映了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。

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.