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

利用课堂感知和机器学习促进工程课堂主动学习的综合教师专业发展模型

• 批准号: 2021118
• 负责人: Evrim Baran Jovanovic
• 金额: $ 29.99万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2021118&HistoricalAwards=false
• 关键词: Integrated; Faculty; Professional; Development; Model

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.

该项目旨在通过提高工程学院的教学效率来服务于国家利益。教师培训被认为是成功整合循证教学实践的关键因素，例如STEM课堂中的主动学习。然而，教师也需要机会频繁地反馈和反思他们使用积极学习策略的有效性。该项目将把培训和反馈结合在一种名为TeachActive的教师发展模式中。TeachActive模式将在多个学期内与30名工程学教师一起实施。TeachActive将使用EduSense开源平台自动收集学生和教师在课堂上行为的实时描述。这些课堂分析将显示在仪表板上，为教师提供有关课堂主动学习水平的反馈。预计这一反馈与教师的反思一起，将促进教师参与者在循证教学中的系统性改进。因此，该项目有可能促进工程教育者在工程课堂上采用和有效实施主动学习策略，这反过来可以积极影响学生的参与和学习。教师主动专业发展模式将在理论基础和基于证据的专业发展框架中嵌入自动课堂观察和分析。整合将包括三个连续的组成部分：(1)主动学习培训；(2)为期四周的自动化课堂观察，并进行为期一年的跟踪；以及(3)反馈和反思。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。该项目的数据收集将分四个阶段进行：(1)教学后信念调查；(2)课堂分析；(3)反思提示；(4)对教师的半结构化访谈。教师参与教师活动前后对教学策略的信念将被用来评估教师对教学策略的信念。由EduSense(美国国家科学基金会资助的卡内基梅隆大学开发的基于摄像头的课堂感知系统)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针对爱荷华州的特定背景和感兴趣的行为特征(例如，坐姿与站姿；手抬起；动觉模式)进行验证，从而通过TeachActive仪表板提供自动化的上下文敏感反馈。该项目由NSF IUSE：EHR计划支持，该计划支持研究和开发项目，以提高所有学生的STEM教育的有效性。通过参与的学生学习路径，该计划支持有前景的实践和工具的创建、探索和实施。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。

项目成果

Designing the TEACHActive Feedback Dashboard: A Human Centered Approach

设计 TEACHActive 反馈仪表板：以人为本的方法

• 发表时间: 2021
• 期刊: Companion Proceedings 11th International Conference on Learning Analytics & Knowledge (LAK21
• 作者: AlZoubi, D;Kelley, J;Baran, E;Gilbert, S B;Jiang, S;Karabulut-Ilgu, A
• 通讯作者: Karabulut-Ilgu, A

From Data to Actions: Unfolding Instructors’ Sense-making and Reflective Practice with Classroom Analytics

从数据到行动：通过课堂分析展开教师的意义建构和反思实践

• 发表时间: 2022
• 期刊: Proceedings of 12th International Conference on Learning Analytics and Knowledge (LAK22
• 作者: AlZoubi, D.

Leveraging Engineering Instructors’ Professional Development with Classroom Analytics

通过课堂分析利用工程教师的专业发展

• 发表时间: 2022
• 期刊: Proceedings of Society for Information Technology & Teacher Education International Conference
• 作者: Baran, E.;AlZoubi, D.;Karabulut-Ilgu, A.
• 通讯作者: Karabulut-Ilgu, A.

TeachActive Feedback Dashboard: Using Automated Classroom Analytics to Visualize Pedagogical Strategies at a Glance

TeachActive 反馈仪表板：使用自动化课堂分析使教学策略一目了然

• DOI: 10.1145/3411763.3451709
• 发表时间: 2021
• 期刊: CHI EA '21: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems
• 作者: Alzoubi, Dana;Kelley, Jameel;Baran, Evrim;B. Gilbert, Stephen;Karabulut Ilgu, Aliye;Jiang, Shan
• 通讯作者: Jiang, Shan