Planning Grant: Engineering Tools for Education Research (EnTER)

提案团队设想了一个工程研究中心，该中心将改变学习和教学，重点是STEM教育中的主动学习。目前，了解在这种环境中发生了什么的努力仅限于选择使用紧凑，简单形式的数据（如成绩或调查）的研究，以及使用学生作业的视频和书面记录的研究。 紧凑的数据提供了洞察力，但从课堂环境中删除，而详细的记录审查是小规模的，因为分析这些丰富的定性数据所需的劳动密集型方法。 与这两种策略相关的妥协严重限制了对课堂环境的科学理解以及利用这些信息影响现实世界课堂的进展。从根本上改变教育环境促进学习的方式需要多管齐下的方法，将具有认知和学习科学，生物传感，数据分析和计算机系统专业知识的研究人员聚集在一起。由此产生的工程系统将解决有关学生如何学习最好的个人，团队和课堂社区的基本研究问题。这些知识随后可以转化为实用的工具，为教师提供课堂上深入了解学生的思维和参与大规模的主动学习环境。 ERC计划拨款将促进团队和中心结构的发展，美国国家科学院和工程院认为这对下一个世纪推进个性化学习是必要的。 通过这些工具开发的知识将以一种包容性和道德敏感的方式，导致课程，教学法和支持学习和教学的工程系统设计的进步。具有多个并发对话和活动的主动学习环境是研究的挑战;然而，理解它们的动态是学习科学进步的核心。我们的工程团队将发明新的传感器，嵌入低功耗可穿戴计算平台，其数据将由下一代机器学习算法处理。 ERC规划补助金将通过研讨会和数据黑客马拉松将融合学科的研究人员聚集在一起，以了解研究问题并展示潜在的技术。 这些工具化的学习环境将使研究人员能够迅速地制定和评估假设，其规模远远超过今天所能实现的。ERC规划团队将直接与对下一代劳动力教育感兴趣的一系列利益相关者接触。这些利益相关者包括学生、教育工作者、研究人员、管理人员、行业和认证机构，他们将参与围绕拟议中心建立一个生态系统。 鉴于这项工作的性质，人类受试者的隐私和道德待遇将是该项目所有方面的核心。黑客松和研讨会活动将通过展示这些技术在教育研究中的潜力，影响塔夫茨大学以外更广泛的研究社区。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标准进行评估，被认为值得支持。

The proposing team envisions an Engineering Research Center that will transform learning and instruction with a focus on active learning in STEM education. Current efforts to understand what takes place in such environments are limited to choosing between studies using compact, simple forms of data (such as grades or surveys), and studies employing video and written records of students' work. Compact data provide insight but are removed from the classroom setting, while detailed record reviews are small-scale because of the labor-intensive methods required to analyze these rich, qualitative data. The compromises associated with both strategies have severely limited progress in developing a scientific understanding of the classroom environment and using this information to impact real-world classrooms. Radically altering the way educational environments foster learning requires a multi-pronged approach bringing together researchers with expertise in cognitive and learning sciences, bio-sensing, data analytics and computer systems. The resulting engineered system will address fundamental research questions concerning how students learn best as individuals, in teams, and in classroom communities. This knowledge can subsequently be translated into practical tools to provide instructors with in-classroom insight into students' thinking and engagement in active learning environments at scale. This ERC planning grant will facilitate the development of a team and Center structure that the National Academies of Science and Engineering believe is necessary to Advance Personalized Learning in the next century. Knowledge developed through these tools will, in an inclusive and ethically sensitive manner, lead to advances in curriculum, pedagogy, and the design of engineered systems in support of learning and instruction.Active learning environments with multiple concurrent conversations and activities are a challenge to study; however, understanding their dynamics is central to the advancement of learning science. Our engineering team will invent new sensors, embedded in low-power wearable computing platforms, whose data will be processed by next-generation machine learning algorithms. The ERC planning grant will bring together researchers in convergent disciplines through workshops and data hackathons to understand the research problems and demonstrate potential technologies. These instrumented learning environments will allow researchers to formulate and evaluate the hypotheses rapidly and at a scale well beyond that which can be achieved today. The ERC planning team will directly engage a range of stakeholders with an interest in the education of the next generation workforce. These stakeholders include students, educators, researchers, administrators, industry and accreditation agencies that will be engaged to build an ecosystem around the proposed center. Given the nature of the effort, privacy and ethical treatment of human subjects will be central to all aspects of the project. The hackathons and workshop activities will impact the broader research community beyond Tufts University by showing the potential of these technologies for educational research.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.