Research Interests

Research

Our work is motivated by a pursuit to understand cognitive change. That is, how and by what means does the human mind change over time? Research is conducted on how people acquire, represent, and use complex knowledge. Topics of particular interest include: abstraction, analogy, conceptual change, implicit versus explicit knowledge formation, problem solving, skill acquisition, and transfer. Both experimental and formal modeling methodologies are used to investigate these phenomena. Other interests include the application of dynamical systems theory to learning processes, educational and technological applications of cognitive theory, and the philosophy of science.

Below are descriptions of the three main areas of work done in the lab.

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Knowledge Transfer

A central aim of cognitive science is to develop a general theory of transfer to explain how people use and apply their prior knowledge to solve new problems. Previous work has identified multiple mechanisms of transfer including (but not limited to) analogy, knowledge compilation, and constraint violation. The central hypothesis investigated in the current work is that the particular profile of transfer processes triggered for a given situation depends on both (a) the knowledge present and how it is represented, and (b) the processing demands of the transfer task. We hypothesize that there is a trade-off between the mechanisms in terms of their scope of application (i.e., near vs. far transfer) and the amount of cognitive processing required to transfer the knowledge. Current laboratory studies examine when these mechanisms are triggered and their interaction in complex learning environments. Initial results show that people can adaptively shift between transfer mechanisms depending on their prior knowledge and the characteristics of the transfer task. The goal of this work is to develop a general theory of transfer based on a multiple mechanisms approach.

Conceptual Learning

A central focus of our work is to investigate and understand the mechanisms of conceptual learning. We are interested in determining the cognitive processes that lead to the development of deep conceptual knowledge – knowledge that is flexible and transfers to new contexts and situations. Much work focuses on the role of explanation and analogical comparison in conceptual learning in probability and physics. We are also interested in how the form of the learning materials (e.g., principles or worked examples) affects learning and transfer. One issue is how the level of abstraction impacts the representation of the acquired knowledge. Other topics include the role of meta-cognition and interest in conceptual learning.

A major theme of the lab (that runs across both the transfer and conceptual learning strand) is to understand the dynamic interaction of multiple cognitive processes in learning and transfer.

Projects:
Conceptual Analysis and Student Learning in Physics
Bridging Examples and Principles through Analogy and Self-Explanation
Analogical Transfer in Physics

Collaborative Learning and Problem Solving

In this work we examine the effects of expertise on collaborative problem solving. Specific questions include: What cognitive and social processes lead to successful collaborative performance? What impact does domain expertise have on one’s ability to effectively cross-cue and share information in a collaborative context? To examine these questions we are conducting laboratory experiments where novice and expert pilots are asked to come up with possible solutions for simple and complex flight problem scenarios either working individually or in collaboration with another person (with the same level of expertise). Initial results show a larger collaborative benefit for experts than for novices on the complex problems. We are currently analyzing verbal protocols to identify the types of collaborative interactions associated with this advantage.

Projects:
Analogical Scaffolding in Collaborative Learning


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