When Teachers "Aren't There": Detecting, Evaluating, and Learning from Rote Teaching Across Development

这个项目探讨了自动化教学对儿童学习和发展的影响。与现场教学相比，自动化教学发生在教师不在学生身边的情况下，例如异步学习、预先录制的讲座和虚拟教室。自动化教学还可以包括教师不积极参与或不考虑个别学习者的需求和信念的面对面教学。最近，特别是自2019冠状病毒病大流行以来，异步学习、预先录制的讲座和虚拟教室在教育中的使用呈上升趋势。鉴于此，理解自动化方法如何以及为什么影响儿童的学习是至关重要的。这个项目迈出了第一步，解释了为什么幼儿可能与“不在那里”的老师学得不同。这项工作还有许多更广泛的影响。首先，这项研究的结果将有助于解释如何在教育中继续利用技术，同时确保儿童的学习成果不受影响。第二，通过科学沟通和传播工作，该项目将向教育工作者、家长和研究人员等不同受众传播其研究结果。第三，该项目将为来自STEM领域代表性不足的背景的学生提供研究机会。最后，该项目的研究方法将借鉴并整合许多不同的学科，包括幼儿教育、认知发展、神经科学和计算建模。通过采用多学科的方法，该项目将从多个不同的角度和对多个不同领域的影响来回答有关儿童从自动化教学中学习的问题。在教育中越来越多地使用自动化方法，因此有必要了解它们对儿童学习的影响。过去在教育和发展心理学方面的工作引起了人们的关注：有效的教学需要与学生的实时学习目标和个人需求相结合，这在大规模的自动化教学中可能很困难。总而言之，这导致了一种令人不安的动态：那些发现老师或信息来源“并不真正存在”的学生，在那一刻与他们互动，可能更有可能脱离它，忽略它，通常从中学到的东西更少。我们对儿童如何推理教学中的自动性知之甚少；甚至连更基本的问题——孩子们是否明白，一般来说，社会伙伴要么更自动、更照本不动，要么更善于反思、更投入——也没有得到很好的理解。为了设计未来有效利用自动化教学方法的教育体验，首先必须了解儿童在向他人学习时如何推理自动行为。因此，这个项目有三个具体的目的。目标1（3项研究，N = 430）将调查学习者是否注意到教师的自动行为，以及这如何影响对教师教学的评价。目标2（两项研究，N = 60）将探讨利用行为和神经学方法，自动教学与反思性教学之间的学习差异。目的3（两项研究，N = 180）将测试自动教学和反思性教学之间的学习差异是否可以通过最小的干预来缓解。这些问题将通过行为实验和神经测量来回答，同时也会受到教育和认知科学研究的影响。该项目将从广泛的目标年龄范围（5岁至10岁）招募参与者，以了解这些过程如何随着儿童早期至中期形成时期的发展而变化。该项目由STEM教育博士后研究奖学金（STEM Ed PRF）计划资助，旨在提高STEM， STEM教育，教育和相关学科的近期博士的研究知识，技能和实践，以促进他们从事基础和应用研究的准备，从而推进该领域的知识。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 explores the effects of automated teaching on children’s learning and development. In contrast to live, engaged teaching, automated teaching occurs when a teacher is not with the student, such as in cases of asynchronous learning, pre-recorded lectures, and virtual classrooms. Automated teaching can also include in-person cases when the teacher is not actively engaged or thinking about the individual learner’s needs and beliefs. Recently, and particularly since the COVID-19 pandemic, the use of asynchronous learning, pre-recorded lectures, and virtual classrooms in education has been on the rise. Given this, it is crucial to understand how and why automated approaches affect children’s learning. This project takes a first step in explaining why young children might learn differently from teachers who are “not really there”. There are many broader impacts of this work. First, results from this research will help explain how to continue to leverage technology in education while making sure children’s learning outcomes do not suffer as a result. Second, through science communication and dissemination efforts, the project will spread the word about its findings to a diverse audience of educators, parents, and researchers. Third, this project will provide research opportunities for students from backgrounds that are typically underrepresented in STEM fields. Finally, the project’s research approach will draw from and integrate across many different disciplines, including early childhood education, cognitive development, neuroscience, and computational modeling. By using a multidisciplinary approach, the project will answer questions about children’s learning from automated teaching from multiple different perspectives and with implications for multiple different fields.The increasing use of automated approaches in education makes it imperative to understand their impact on children’s learning. Past work in education and developmental psychology raises one cause for concern: Effective teaching requires engaging with students’ real-time learning goals and individual needs, which may be difficult in large-scale automatic teaching. Put together, this leads to a troubling dynamic: Students who detect that a teacher or source of information is “not really there”, engaging with them in the moment, may be more likely to disengage from it, ignore it, and generally learn less from it. Very little is known about how children reason about automaticity in teaching; even the more basic question of whether children understand that social partners in general can either be more automatic and scripted, versus reflective and engaged, is not well understood. In order to design future educational experiences that effectively utilize automated teaching approaches, how children reason about automatic behavior when learning from others must first be understood. Therefore, this project has three specific aims. Aim 1 (three studies, N = 430), will investigate whether learners notice when teachers are acting automatically and how this affects evaluations of their teaching. Aim 2 (two studies, N = 60) will ask how learning differs between automatic versus reflective teaching, leveraging behavioral and neurological methods. Aim 3 (two studies, N = 180) will test whether differences in learning between automatic and reflective teaching could be mitigated with minimal intervention. These questions will be answered using behavioral experiments and neurological measures, while also drawing influence from research in education and cognitive science. The project will recruit participants from a broad target age range (5- to 10-year-olds), in order to understand how these processes change with development during the formative years in early- to middle-childhood.This project is funded by the STEM Education Postdoctoral Research Fellowship (STEM Ed PRF) program that aims to enhance the research knowledge, skills, and practices of recent doctorates in STEM, STEM education, education, and related disciplines to advance their preparation to engage in fundamental and applied research that advances knowledge within the field.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.