EAGER: Developing Teaching Assistant Expertise with a Sensor-Based Learning System

虽然大学学位对许多工作或职业道路至关重要，但许多大学生接受的教育并不理想。多年来，研究表明，远离大型讲座，提高学生的参与度和参与度可以显著提高学习效果。然而，大多数大学仍然依赖于讲座，学生被动地从教授那里获得信息，教师继续以他们自己被教导的方式授课。该项目通过调查如何支持助教（TA）在过渡到教师之前获得以学生为中心的教学技能，解决了理解和支持教师学习的大规模社会问题。这个探索性的研究项目将调查一个先进的学习技术系统的开发，该系统集成了多模态传感器数据，以提供近实时的教学实践数据。结合反思和培训系统将提供快速和频繁的反馈和良好的策略指导，以支持以学生为中心的教学信念和行为。反过来，该项目将进一步了解教师如何利用个人信息学来影响与教师专业发展相关的信念和行为。在这个探索性项目中，将开发一个由一组相互关联的模块组成的助教学习系统，通过提供一个包含近实时多模态课堂数据的仪表板，支持他们获得以学生为中心的教学实践。该系统借鉴了传感阵列，计算机视觉，智能环境和个人信息学方面的技术和社会技术进步，以及高等教育专业发展框架。该项目通过扩展最先进的多模态传感方法的能力来推进计算，以实现教室规模的非侵入式传感。通过与教授STEM大学课程的助教进行一系列基于设计的研究，该项目调查：a）该系统可以呈现个人数据以促进生产性自我怀疑的方式，B）与个人数据的接触如何导致改变的动机，以及c）数据驱动的教学如何培养教学中的自我效能。

While a college degree is essential for many jobs or career paths, many college students receive a less than ideal educational experience. For years, research has shown that moving away from large lectures and increasing student engagement and participation significantly improves learning. However, most colleges still rely on lectures where students passively receive information from professors, and instructors continue to teach in the ways in which they themselves were taught. This project addresses this large-scale societal problem of understanding and supporting instructor learning by investigating how to support teaching assistants (TAs) to acquire student-centered teaching skills before they transition to faculty. This exploratory research project will investigate the development of an advanced learning technology system that integrates multimodal sensory data to deliver near real-time data on teaching practices. The combined reflection and training system will provide rapid and frequent feedback and instruction on good strategies to support student-centered teaching beliefs and behaviors. In turn, the project will advance understanding of how instructors engage with personal informatics to impact beliefs and behaviors related to teacher professional development.In this exploratory project, a learning system for TAs will be developed, consisting of an interconnected set of modules that support them in acquiring student-centered teaching practices through the provision of a dashboard with near-real-time multimodal classroom data. The system draws upon technical and socio-technical advances in sensing arrays, computer vision, intelligent environments, and personal informatics, together with frameworks of professional development in higher education. The project advances computing by expanding the capabilities of state of the art multimodal sensing approaches to achieve non-invasive sensing at classroom-scale. Through a series of design-based research studies with TAs who are teaching STEM college courses, the project investigates: a) the ways in which this system can present personal data to foster productive self-doubt, b) how engagement with personal data leads to a motivation to change, and c) how data-driven instruction can foster self-efficacy in teaching.