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

EAGER：利用基于传感器的学习系统培养助教专业知识

• 批准号: 1747997
• 负责人: Amy Ogan
• 金额: $ 30万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1747997&HistoricalAwards=false
• 关键词: EAGER; Developing; Teaching; Assistant; Expertise

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.

虽然大学学位对许多工作或职业道路来说都是必不可少的，但许多大学生的教育经历并不理想。多年来，研究表明，远离大型讲座，增加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课，学生被动地从教授那里接受信息，教师继续用他们自己被教导的方式教学。本项目通过调查如何支持助教（助教）在转变为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这一大规模的社会问题。这个探索性研究项目将研究一种先进的学习技术系统的开发，该系统集成了多模态感官数据，以提供近乎实时的教学实践数据。反思和训练相结合的系统将提供快速和频繁的反馈和指导，以良好的策略来支持以学生为中心的教学信念和行为。反过来，该项目将促进对教师如何参与个人信息学的理解，以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，将为助教开发一个学习系统，该系统由一组相互关联的模块组成，通过提供具有近实时多模式课堂数据的仪表板，支持助教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，以及高等教育中的专业发展框架。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，以实现课堂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，该项目调查了：a)该系统如何呈现个人数据以培养富有成效的自我怀疑，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感。

项目成果

Towards a Framework for Smart Classrooms that Teach Instructors to Teach

建立一个教教师教学的智能教室框架

• 发表时间: 2018
• 期刊: International Conference of the Learning Sciences
• 作者: Gerritsen, David;Zimmerman, John;Ogan, Amy
• 通讯作者: Ogan, Amy