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Faculty & Research

Elizabeth Bonawitz

David J. Vitale Associate Professor of Learning Sciences

Elizabeth Bonawitz

Email:  [javascript protected email address]
Personal Site:   Link to Site
Vitae/CV:   Elizabeth Bonawitz.pdf
Faculty Assistant:  Claire Goggin

Profile

Elizabeth Bonawitz is the David J. Vitale Associate Professor of Learning Sciences at Harvard University. Her work focuses on the basic science theories of learning with the broader goal of informing educational practice. Her research bridges two research traditions: cognitive development and computational modeling. Specifically, Bonawitz’s empirical approach focus on the structure of children's early causal beliefs, how evidence and prior beliefs interact to affect children's learning, the developmental processes that influence children's belief revision and curiosity, and the role of social factors (such as learning from others) in guiding learning. Bonawitz received her Ph.D. from MIT in the brain in cognitive sciences in 2009 working with Dr. Laura Schulz. She then completed a post-doctoral fellowship at University of California, Berkeley with Thomas Griffiths and Alison Gopnik (2009-2013). She was an Assistant and Associate professor of psychology at Rutgers University, Newark from 2013 until 2020 when she moved to Harvard. Bonawitz is the recipient of the James McDonnell Foundation Understanding Human Cognition Scholar Award and the Jacobs Early Career Research Fellowship. Her work is additionally currently funded by several NSF grants, the Caplan Foundation, and the Templeton Foundation. Her research has been published in top journals in psychology, cognitive science, and education. Additionally, she has served as Associate Editor for Cognitive Science (journal) and is on the governing board of the Cognitive Development Society and Children Helping Science

Areas of Expertise
Research

Research
Elizabeth Bonawitz focuses on the basic science theories of learning with the broader goal of informing educational practice. Her research bridges two research traditions: Cognitive Development and Computational Modeling. Specifically, BonawitzÂ’s empirical approach focus on the structure of children's early causal beliefs, how evidence and prior beliefs interact to affect children's learning, the developmental processes that influence children's belief revision and curiosity, and the role of social factors (such as learning from others) in guiding learning.

Sponsored Projects

 

The Mind Lab: Thought Experiments as a Means to Teaching Science Effectively and Efficiently (2021-2022)
Caplan Foundation for Early Childhood

The goal of this project is to develop a curriculum for teaching physical sciences to young children (K-2, ages 5 to 7) that relies exclusively on thought experimentation. Thought experiments are built on the principles of active learning and incorporate imaginative learning through the mental activities of prediction, model building, analogical reasoning, and counterfactual reasoning. This proposal identifies several different problems that are not adequately addressed by existing K-2 curricula: (a) Targeting only older populations: Although misconceptions about the structure of matter develop in early childhood, they are not targeted by curricular interventions until middle-school. The reliance on imagination – an activity that children naturally engage in – will allow us to target the precursors to many misconceptions about the structure of matter in an age-appropriate manner but beginning at a very early age. (b) Shallow encoding, distracting materials: Traditional approaches that highlight the use of materials and passive observation of data run two principle risks: that children will fail to consider the deep implications of the evidence, and that they will get distracted by superficial features of the materials. Contrary to traditional approaches, thought experiments include idealized situations that ask children to actively produce mental representations of the data and apply active learning strategies such as making predictions, representing contradictions, mental model building, analogical reasoning, and counterfactual reasoning, among others. These strategies have already been shown to effectively support learning. (c) Inequity. Although providing equal educational opportunities should be the gold standard of any educational system, the reality is that many schools and families have inadequate access to resources. Because thought experiments do not require expensive equipment and materials, the curriculum will be accessible to all students. This will be particularly beneficial to students from underserved populations. We discuss each of these problems and how the current proposal will address them in more detail below.

 

Learning in Early Childhood: A Computational Cognitive Developmental Approach (2021-2024)
James S. McDonnell Foundation, Inc.

The success of every course of action we choose in our daily lives depends on the accuracy of our intuitive beliefs. Given the importance of these beliefs to our survival, one would think that we would be strongly motivated to get our picture of the world right. When there is an opportunity to learn, we should explore. Yet, by adulthood, it seems that the desire to feel right often outweighs the drive to explore new ideas so that we can be accurate. For example, typical adults will choose to give up a chance at more money in order to avoid reading information that conficts with their beliefs; in fact, adults rate the experience of simply listening to viewpoints that confict with their own as comparable in pain to getting a tooth pulled (Frimer, Skitka, Motyl, 2017). Epistemic indifference does not extend all the way back into early childhood, however. My work has demonstrated that young children are sophisticated learners who are highly motivated to explore. Yet we currently know very little about the nature of the individual drives and early childhood experiences that maintain and foster this love of learning. Instead, our culture has created many experiences that diminish it. For example, as children progress through the American school system, they rate themselves as less engaged and less interested in learning new things (Gallup, 2017). It thus becomes critical to ask, how do we foster and maintain minds that love to learn? My research program aims to provide answers to these questions. Computational models of learning provide a powerful approach to specify and explore these questions. Furthermore, combining the tools of computational modeling and developmental psychology creates a unique research program that can characterize the role of early childhood experiences in shaping the epistemic stance taken throughout the lifespan.

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