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

该项目设想了未来的工作，先进的技术提供自动化，工作嵌入式，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，它解决了一个基本问题：评价或非评价反馈系统更有效地推动专业学习？这个问题将在专业人员中进行测试，在这些专业人员中，客观、精细的反馈对改进尤为重要--教师职业。这项研究将位于英语和语言艺术（ELA）教学中的初中和高中课堂，在识字成果的表现不佳和不平等是美国目前通过反馈支持教师学习的方法所面临的持久性问题是稀疏，繁琐，主观和评价。因此，一个重大的重新概念化是必要的，以提供反馈机制，有意义地影响教师的做法，并提供给所有人。该项目与TeachFX合作，TeachFX是技术支持的教学反馈的行业领导者，该项目将与教师合作设计和测试自动反馈系统。从研究的见解将导致反馈系统，使教学专业人员，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沿着一个从无效到更有效的实践的连续体来解释。该假设是，最丰富的机会，在职反馈的专业是不可知论的技术驱动的反馈系统，提供选择，保留评价，为不同的教师的做法，并促进更大的控制点。该项目有几个目标来测试这一假设，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）增强自动反馈基础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。该项目将开始利用TeachFX的语料库的教学观察约5,000名教育工作者开发自动化，强大的，准确的，公正的，可推广的，可解释的反馈模型。接下来，与教师参与者合作，反馈界面将被共同设计和迭代改进。此外，各种观察和调查为基础的措施将被用来评估教师对反馈的反应。该项目将最终在一个纵向的，实验性的研究对比评价性的效果-与非评价性的反馈对教师的学习，授权，和学生的成绩成果与300名教师的样本。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，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：主要支持来自未来的工作在人类技术前沿计划，支持多学科研究，以维持经济竞争力，促进工人福祉、终身和普及学习以及生活质量，并阐明正在塑造就业和工作未来的新兴社会和经济背景以及创新驱动因素。额外的支持来自发现研究preK-12计划（DRK-12），该计划旨在通过研究和开发创新资源，模型，该奖项反映了NSF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进行评估，被认为值得支持的搜索.

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.