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。与现场参与式教学不同，自动化教学发生在教师不与学生在一起的情况下，例如在异步学习、预先录制的讲座和虚拟教室的情况下。自动化教学还可以包括当教师没有积极参与或考虑个别学习者的需求和信念时的面对面案例。最近，特别是自新冠肺炎大流行以来，在教育中使用异步学习、预先录制的讲座和虚拟教室的情况一直在上升。有鉴于此，了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，解释了为什么年幼的孩子可能会与“不在那里”的老师学习不同的东西。这项工作还有更广泛的影响。首先，这项研究的结果将有助于解释如何继续在教育中利用技术，同时确保儿童的学习结果不会因此受到影响。其次，通过科学传播和传播努力，该项目将把关于其发现的信息传播给不同的教育工作者、家长和研究人员。第三，该项目将为来自STEM领域典型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和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。通过使用多学科方法，该项目将从多个不同角度回答关于儿童从自动化教学中学习的问题，并涉及多个不同领域。随着自动化方法在教育中的日益使用，了解其对儿童学习的影响是当务之急。教育学和发展心理学的过去研究提出了一个令人担忧的问题：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，这在大规模自动化教学中可能很难做到。总而言之，这导致了一种令人不安的动态：如果学生发现一位老师或信息源“不在那里”，并在那一刻与他们接触，那么他们可能更有可能脱离老师或信息源，忽视它，通常从中学到的东西更少。关于儿童如何在教学中推理自动性，我们知之甚少；甚至更基本的问题--儿童是否理解社会伴侣通常可以更自动和更照本宣科，而不是反思和参与--也没有得到很好的理解。为了设计有效利用自动化教学方法的未来教育体验，必须首先了解儿童在向他人学习时如何对自动行为进行推理。因此，这个项目有三个具体目标。目标1(三项研究，N=430)将调查学习者是否注意到教师的自动行为，以及这对他们的教学评价有何影响。目标2(两项研究，N=60)将利用行为和神经学方法，询问自动教学和反思性教学之间的差异。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在最小限度的干预下得到缓解。这些问题将通过行为实验和神经学测量来回答，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至10岁)招募参与者，以了解这些过程如何在儿童早期至中期的形成阶段随发育而变化。该项目由STEM教育博士后研究奖学金(STEM Ed PRF)项目资助，该项目旨在提高STEM、STEM教育、教育、这一奖项反映了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。

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.