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College of Education and Human Development wordmark.
Department of Educational Psychology wordmark.

Cognitive architecture lab

Focus

The Cognitive Architecture Lab takes a multidisciplinary approach to the fundamental mechanisms that support high-level cognition. We are currently addressing four research goals:

  • Undertaking behavioral experiments investigating how people understand abstract mathematical concepts such as irrational numbers, and also developing mesaures of mathematical insight, mathematical intuition, and other abilities that potentially predict success in STEM disciplines in college
  • Investigating the cognitive and neural mechanisms underlying language comprehension through the development of computational models of word and sentence comprehension that explain behavioral and brain imaging data in normal adults neuropsychological patients, and people with autism
  • Conducting lab- and classroom-based studies of how adults and children solve problems, think spatially, and reason scientifically
  • Applying findings from neuroscience and cognitive science to improve instruction in classrooms and in online environments

Projects

  • Experimental studies of abstract mathematical thinking
  • Neurocognitive models of sentence comprehension and spatial problem solving
  • Memory mechanisms supporting learning from text
  • Application of neuroscience findings to educational contexts

Sashank Varma headshot

Sashank Varma

  • Lab director
  • Associate professor of educational psychology in the psychological foundations of educcation program
  • Emphasis: Learning and cognition/educational technologies

Research group

Soo-hyum Im headshot

Soo-hyun Im Ph.D. student, psychological foundations: learning and cognition/educational technologies

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 Anderson M.A. student, psychological foundations: learning and cognition/educational technologies

Erik is developing semantic ontologies for expressing the assessment history of individual students over an entire school year.

Astrid Schmied Ph.D. student, psychological foundations: learning and cognition/educational technologies

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 Patel headshot

Purav Patel Ph.D. student, psychological foundations: learning and cognition/educational technologies

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 Michel headshot

Kasey Michel Ph.D. student, psychological foundations: learning and cognition/educational technologies

Kasey's research focuses on educational neuroscience.

Anne Rafferty headshot

Anna RaffertyVisiting scholar

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.

Recent publications

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.]