When Teachers "Aren't There": Detecting, Evaluating, and Learning from Rote Teaching Across Development
当教师“不在场”时:从发展过程中的死记硬背中检测、评估和学习
基本信息
- 批准号:2327447
- 负责人:
- 金额:$ 33.82万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-09-15 至 2025-08-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
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.
这个项目探讨了自动化教学对儿童学习和发展的影响。与现场教学相反,自动化教学发生在教师不与学生在一起的情况下,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情况。最近,特别是自COVID-19疫情以来,在教育中使用异步学习、预录讲座和虚拟教室的情况有所增加。鉴于此,了解自动化方法如何以及为什么会影响儿童的学习至关重要。这个项目迈出了第一步,解释了为什么幼儿可能会从“不在场”的老师那里学到不同的东西。这项工作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继续在教育中利用技术,同时确保儿童的学习成果不会因此受到影响。其次,通过科学交流和传播工作,该项目将向教育工作者、家长和研究人员等各种受众传播其研究结果。第三,该项目将为来自通常在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。通过使用多学科的方法,该项目将从多个不同的角度回答关于儿童从自动化教学中学习的问题,并对多个不同的领域产生影响。自动化方法在教育中的越来越多的使用使得了解其对儿童学习的影响势在必行。过去在教育和发展心理学方面的工作引起了人们的关注:有效的教学需要关注学生的实时学习目标和个人需求,这在大规模自动教学中可能很难实现。总的来说,这导致了一个令人不安的动态:学生发现老师或信息源“不是真的在那里”,与他们在一起,可能更有可能脱离它,忽视它,通常从它中学到的东西很少。甚至更基本的问题,即儿童是否理解社会伙伴一般可以更自动和照本宣科,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推理自动行为。因此,该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自动采取行动,以及这如何影响他们的教学评价。目标2(两项研究,N = 60)将询问自动教学与反思教学之间的学习差异,利用行为和神经学方法。目标3(两项研究,N = 180)将测试自动和反思性教学之间的学习差异是否可以用最小的干预来减轻。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内招募参与者(5至10岁),以了解这些过程如何在早期至中期儿童的形成期随着发展而变化。该项目由STEM教育博士后研究奖学金资助(干艾德PRF)计划,旨在提高研究知识,技能,并在干,干教育,教育,该奖项反映了NSF的法定使命,并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。
项目成果
期刊论文数量(0)
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专利数量(0)
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Ilona Bass其他文献
Partition dependence in consumer choice: Perceptual groupings do not reliably shape decisions
消费者选择中的分区依赖性:感知分组并不能可靠地影响决策
- DOI:
10.3758/s13423-017-1326-4 - 发表时间:
2018 - 期刊:
- 影响因子:3.5
- 作者:
Sheri Reichelson;Alexandra Zax;Ilona Bass;A. Patalano;H. Barth - 通讯作者:
H. Barth
Children's developing theory of mind and pedagogical evidence selection: Experiment 2
儿童心理发展理论与教学证据选择:实验2
- DOI:
- 发表时间:
2018 - 期刊:
- 影响因子:0
- 作者:
Ilona Bass;Patrick Shafto;E. Bonawitz - 通讯作者:
E. Bonawitz
Choosing to Learn: Evidence Evaluation for Active Learning and Teaching in Early Childhood
选择学习:幼儿期主动学习和教学的证据评估
- DOI:
- 发表时间:
2018 - 期刊:
- 影响因子:0
- 作者:
E. Bonawitz;Ilona Bass;Elizabeth Lapidow - 通讯作者:
Elizabeth Lapidow
Efficient Partial Simulation Quantitatively Explains Deviations from Optimal Physical Predictions
高效的部分模拟定量地解释了与最佳物理预测的偏差
- DOI:
- 发表时间:
2021 - 期刊:
- 影响因子:0
- 作者:
Ilona Bass;Kevin Smith;Elizabeth Bonawitz;T. Ullman - 通讯作者:
T. Ullman
Ilona Bass的其他文献
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