The Cognitive Architecture Lab takes a multidisciplinary approach to the fundamental mechanisms that support high-level cognition. We are currently addressing four research goals:
Soo-hyun is investigating how (1) pre-service teachers and (2) the general population reason about the educational relevance of neuroscience findings and how their reasoning can be improved by coursework in educational psychology. He is also developing instructional sequences to build the arithmetic fluency of elementary school children.
Erik is developing semantic ontologies for expressing the assessment history of individual students over an entire school year.
Astrid is investigating how (1) pre-service teachers, (2) undergraduates majoring in STEM fields, and (3) the general population reason about the ethics of applying neuroscience findings to improve educational outcomes.
Purav is investigating the mental representation of irrational numbers (such the square root of 2). He is also developing instructional sequences for teaching algebraic properties such as associativity, identity, inverse, and commutativity.
Kasey's research focuses on educational neuroscience.
Anna Rafferty is an Assistant Professor of Computer Science at Carleton College, and her work combines ideas from computer science, education, and cognitive science. Her research focuses on applying and developing machine learning and artificial intelligence techniques to improve educational technologies and better understand human learning. One current project focuses on developing algorithms to automatically assess learners’ misunderstandings from their actions and using these assessments to provide personalized feedback. She has applied the core technologies in this project to several domains, including game-based assessments for experiments about concept learning and interpreting learners’ algebra solving strategies. Other recent projects include exploring how reinforcement learning algorithms can be used for experimentation within online courses and materials in a way that meets the goals of both teachers and researchers, and examining how middle school students use and interpret interactive models about science content. Other general areas of interest include automated scoring and feedback for students, especially about strategies and non-written work; individualizing instruction in educational technologies; and how to draw on the strengths of both human teachers and machine learning to most effectively help students learn.
Varma, S. (2014). The subjective meaning of cognitive architecture: A Marrian analysis. Frontiers in Psychology, 5, e440.
Varma, S., & Karl, S. R. (2013). Understanding decimal proportions: Discrete representations, parallel access, and privileged processing of zero. Cognitive Psychology, 66, 283-301.
Dubinsky, J. M., Roehrig, G., & Varma, S. (2013). Infusing neuroscience into teacher training. Educational Researcher, 42, 317-329. [Featured as an Editor's Choice (“When Neuroscience Guides Education”) in Science, 342, 671.]