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

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.