An Integrated Faculty Professional Development Model Using Classroom Sensing and Machine Learning to Promote Active Learning in Engineering Classrooms

本项目旨在提高工科院校的教学效率，服务于国家利益。众所周知，教师培训是成功整合循证教学实践（如STEM课堂中的主动学习）的关键因素。然而，教师也需要有机会对他们使用主动学习策略的有效性进行频繁的反馈和反思。这个项目将把培训和反馈结合在一个叫做TeachActive的教师发展模式中。TeachActive模式将在30个工程学院的多个学期中实施。TeachActive将使用EduSense开源平台自动收集课堂上学生和教师行为的实时描述。这些课堂分析将显示在仪表板上，向教师提供有关课堂主动学习水平的反馈。预计这些反馈与教师的反思一起，将促进教师参与者在循证教学方面的系统改进。因此，该项目有可能加速工程教育工作者在工程课堂上采用和有效实施主动学习策略，这反过来又可以积极影响学生的参与和学习。TeachActive专业发展模式将把自动化的课堂观察和分析嵌入到一个基于理论和证据的专业发展框架中。整合将包括三个连续的部分：(1)主动学习训练；(2)为期四周的自动化课堂观察，随访一年；(3)反馈与反思。研究小组将收集定量和定性数据，以监测教师在使用主动学习、教学信念、促进策略和反思实践方面的变化。项目数据的收集将分为四个阶段：(1)前后教学信念调查；(2)课堂分析；(3)反思提示；(4)教师半结构化访谈。教学方法量表将用于评估教师在参与TeachActive之前和之后对教学策略的看法。EduSense（由美国国家科学基金会资助、卡内基梅隆大学开发的基于摄像头的教室传感系统）跟踪的课堂分析将揭示课堂上主动学习促进策略的行为指标以及学生的行为参与数据。EduSense系统中的机器学习技术将根据爱荷华州立大学的特定环境和感兴趣的行为特征（例如，坐与站、举手、动觉模式）进行验证，从而通过TeachActive仪表板提供自动的上下文敏感反馈。该项目由美国国家科学基金会IUSE: EHR项目支持，该项目支持研究和开发项目，以提高所有学生STEM教育的有效性。通过参与学生学习轨道，该计划支持有前途的实践和工具的创建，探索和实施。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 aims to serve the national interest by enhancing the teaching effectiveness of engineering faculty. Faculty training is known to be a key factor for successful integration of evidence-based teaching practices such as active learning in STEM classrooms. However, faculty also need opportunities for frequent feedback and reflection on the effectiveness of their use of active learning strategies. The project will combine both training and feedback in a faculty development model it calls TeachActive. The TeachActive model will be implemented with 30 engineering faculty across multiple semesters. TeachActive will use the EduSense open source platform to automatically gather real time descriptions of student and teacher behaviors in the classroom. These classroom analytics will be displayed in a dashboard to provide faculty with feedback about the level of active learning in their classrooms. It is expected that this feedback, together with faculty reflection, will promote systematic improvements in evidence-based teaching by the faculty participants. As a result, the project has the potential to accelerate engineering educators’ adoption and effective implementation of active learning strategies in engineering classrooms, which, in turn, can positively influence student engagement and learning.The TeachActive professional development model will embed automated classroom observation and analysis within a theoretically grounded and evidence-based professional development framework. Integration will include three consecutive components: (1) active learning training; (2) four-week sessions of automated classroom observations with a one-year follow-up; and (3) feedback and reflection. The research team will collect quantitative and qualitative data to monitor changes in instructors’ use of active learning, teaching beliefs, facilitation strategies, and reflective practices. The data for the project will be collected in four stages: (1) pre-post teaching beliefs survey; (2) classroom analytics; (3) reflection prompts; and (4) semi-structured interviews of faculty. The Approaches to Teaching Inventory instrument will be used to assess faculty beliefs about pedagogical strategies before and after their participation in TeachActive. Classroom analytics tracked by EduSense (an NSF-funded camera-based classroom sensing system developed at Carnegie Mellon University) will reveal behavioral indicators of active learning facilitation strategies in classrooms as well as students’ behavioral engagement data. The machine learning techniques within the EduSense system will be validated for the specific context at Iowa State and behavioral features of interest (e.g., sit vs stand; hand raises; kinesthetic patterns) thus providing automated context-sensitive feedback via the TeachActive dashboard. This project is supported by the NSF IUSE: EHR Program, which supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices 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.