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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.
The importance of high-quality STEM education cannot be overemphasized given our worlds increasing dependence on science and technology. There are at least two goals of high-quality STEM programs.First, to effectively deliver the current understanding of the core concepts in respective fields and second to create and nurture life-long active learners. Although query-based instruction is a teaching style that seems to effectively deliver core STEM concepts, while also nurturing active and inquisitive learners,there is disagreement regarding how these programs compare to direct instruction. One reason for this contention is that very little is known about the: i) nature of the cognitive and motivational mechanisms that support different kinds of learning in formal educational contexts, ii) how those cognitive and motivational mechanisms operate over longer time periods, and iii) how they operate across different communities. Here, we propose to address the gaps in our understanding by conducting a longitudinal study that will directly compare the consequences of a question-led guided learning curriculum with a direct instruction curriculum , in communities with varying SES levels. The proposed study will focus on childrens understanding of matter and its interactions in the early elementary school years. Elementary students struggle to learn matter concepts, yet these concepts are foundational for more complex core STEM concepts. Effective interventions must therefore be developed to support this acquisition. We deploy a longitudinal design that will include pre- and post-training data collection waves. We will experimentally compare the effects of a baseline condition that will receive no training, to the effects of two different teaching styles of curriculum that take place over six-modules. The Guided Query Learning curriculum will include pedagogical questions, self-explanation inquiries, and thought experiments. The Direct Instruction curriculum will only include direct instructions. Importantly, to better understand the underlying mechanisms engaged by the different types of instruction, we will measure childrens acquisition of a) factual; b) causal-explanatory; and c) conceptual knowledge, as well as their d) curiosity; e) perseverance; and f) attitudes toward self and learning. These follow-up measures will take place across several years to provide insight into short and longer-term effects of the different training styles. Furthermore, to better understand the role of different community and contextual factors, we will assess these factors incommunities with varying SES levels.
The Jacobs foundation CRISP opportunity supports Dr. Bonawitz and her research program in four primary areas. First the funding will support a postdoctoral fellow who will help build high-risk, high-reward research projects to build on the science of learning with educational applications. Within this first goal, there are two primary research projects. The first project is towards work developing clearer basic science understanding of the role of prediction, thought experiment, pedagogical questions, and explanations to support belief revision in early childhood (with a focus on science concepts). The second project builds on this work to investigate the impact of science interventions in early elementary school years that are low cost and can be applied globally. Second, the funding will support scientific capacity building by helping support a visiting doctoral student from a nation under-represented in sciences to collaborate on the above research projects. Third, funds will support scientific outreach, including open science publication and pay for staff to collaborate on writing a public-facing white-paper on our project fundings. Finally, funding will support the time that Dr. Bonawitz will dedicate to Jacobs and the LEAP foundation, providing advice on projects related to belief revision in early childhood, curiosity, and the role of home and school settings in pedagogical learning.
Now, more than ever, developmentalists have recognized the need for reaching representative samples of participants, reaching parents across the country, and creating opportunities to engage in science beyond traditional University lab-locked research (Sheskin et al, in press). Concurrently, there has been a reckoning in psychology, with an increasing emphasis on examining the degree to which our research is reproducible, beyond upper SES, white populations. In addition, with the COVID pandemic, researchers have realized the need for developing robust tools for online testing. Thus, online testing and replicability have been two huge trends in psychology for the past decade, but they have been previously hard to implement in children because the idea was that they had to be in lab. In this proposal we tackle these major themes of the developmental sciences with three Aims. (1) We will create a transformative online infrastructure that will support a big science approach to research with children through virtual participation options from anywhere in the county. (2) We will conduct a massive, multi-site online replication and extension of several classic studies in cognitive development. (3) We will leverage this platform to build a model for future online collaborative developmental science, including conducting new experiments and exploring new approaches to research. Together with collaborators from seven other institutions, I will advise on the implementation of these goals, while also supporting the development, recruitment, and testing of participants through the tobe built online infrastructure. Costs will also support the research for undergraduates internship, where student from underrepresented groups will be able to collaborate in carrying out the research and gain valuable experiences in STEM. The proposed research projects are synergistic to ongoing experiments in the lab and thus also serve to buoy concurrent research programs.
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.
A broader scientific understanding of why children play could lead to better educational tools and experiences that leverage fun
How educators and parents can help children's natural curiosity emerge — in the classroom and at home