Currently, I am a Research Scientist at Soar Technology in the Intelligent Training group. I received my Ph.D. from Carnegie Mellon University in 2016 and was advised by Dr. John Anderson. As a graduate student, I was a member of the Program for Interdisciplinary Education Research as well as the Pittsburgh Science of Learning Center.

Before coming to CMU, I worked at Stanford University in the Cognitive Systems Neuroscience Lab with Vinod Menon. As a research assistant at SCSNL, I led a large-scale intervention study investigating the effects of tutoring on children with developmental dyscalculia.

I completed my B.A. at University of Texas, Austin where I majored in Psychology and Plan II Honors. I worked with Dr. David Schnyer.

You can contact me by email at caitlin.tenison@soartech.com

The aim of my recent research is to understand and model learning. To break down this complex problem, I focus on two processes critical to learner performance: 1) the acquisition and decay of cognitive skills and 2) the use of problem solving strategies. By building an understanding of these basic processes, we can better support instructional designs so that learners can have a better education.

Teachers and students alike accept the mantra, 'practice makes perfect'. How exactly does practice make perfect? I use data to track changes that occur as students practice problem solving. My recent work uses multiple data sources, behavioral (e.g. reaction time, self-report) and brain ( fMRI, which measures changes due to blood saturation in the brain, and MEG, which measures changes due to neurons firing), to build and validate models of cognitive processing.

If practice of procedural skills provides students with the 'tools' for solving problems, then how do students learn which 'tool' is best suited for solving a problem? Collaborating on different data sets, I used a mix of brain measures and behavioral data mining, to make some advances in identifying strategies and strategy use in student data.

At SoarTech I am continuing this work investigating how people learn, exploring methods for supporting learning, and building technologies that improve training through evidence-based approaches. I am well positioned with my experience with brain imaging methodologies, applying machine learning techniques, and performing experiments across a diverse range of participants and environments, to contribute to this area of research.

Impact of Spacing on Skill Acquisition

How does the spacing of practice impact skill learning and retention? In this study we look at the effects of different practice frequency manipulations on both learning and retention three days later.

Modeling behavioral indications of skill acquisition

What changes occur when people practice skills? In this study we use behavioral modeling to detect both continuous and discrete changes in cognitive processes associated with practice.

Modeling neural states of skill acquisition

In this study we explore how the ACT-R cognitive architecture can be used to improve our model of skill acquisition. Using this method we gain more insight into the process by which skills are acquired.

From computation to automization: The timecourse of skill acquisition

In this third study of my dissertation we examine the timecourse that distinguishes the three phases of skill acquisition using MEG which provides better insight into quick neural responses.

Strategic Flexibility in Algebra Intelligent Tutoring System

When faced with multiple methods to solve a problem, which way do students choose? Here we explore a method for clustering students in distinct groups based on the strategies they use when faced with different algebra problems.

Help seeking strategies in an Intelligent Tutoring System

Help! We manipulated the type of help students received when they asked for help in an intelligent tutoring system, and found that it changed their strategies for help-seeking.

Identifying problem solving strategies with brain imaging

In these studies we explored how classifying neural responses could provide insight into the strategies people used to solve problems.

Field based experience

As part of my PIER fellowship, I spent time observing math classes being taught in a local High School.

Stanford Math Intervention

Math tutoring changes the structure and function of children's brains. These changes are even more dramatic for students who are low performing in mathematics.

  • Tenison, C., Fincham, J. & Anderson, J. (2016). Phases of learning: How skill acquisition impacts cognitive processing.Cognitive Psychology, 87,1-28.(Journal publication) (Author prepublication)
  • Jolles, D., Supekar, K., Richardson, J., Tenison, C., Ashkenazi, S., Rosenberg-Lee, M., Fuchs, L., & Menon, V. (2016). Reconfiguration of parietal circuits with cognitive tutoring in elementary school children. Cortex, 83, 231-245.
  • Jolles, D., Wassermann D., Chokani, R., Richardson, J., Tenison, C., Bammer, R., Supekar, K. Menon, V. (2016). Plasticity of left perisylvian white-matter tracts is associated with individual differences in math learning. Brain Structure and Function,221(3), 1337-1351.
  • Tenison, C., & Anderson, J. (2015). Modeling the distinct phases of skill acquisition. Journal of Experimental Psychology: Learning, Memory, and Cognition. 42 (5), 749-767. (Journal publication) (Author prepublication)
  • Iuculano, T., Rosenberg-Lee, M., Richardson, J., Tenison, C., Fuchs, L., Supekar, K., & Menon, V. (2015). Cognitive tutoring induces widespread neuroplasticity and remediates brain function in children with mathematical learning disabilities. Nature Communications, 6.
  • Tenison, C., Fincham, J., & Anderson, J. (2014). Detecting math problem solving strategies. An investigation into the use of retrospective self-reports, latency and fMRI data. Neuropsychologia, 54, 41-52.(Journal publication) (Author prepublication)
  • Supekar, K., Swigart, A.,Tenison, C., Jolles, D., Rosenberg-Lee, M., Fuchs, L., & Menon, V. (2013). Neural predictors of individual differences in response to math tutoring in primary-grade school children. PNAS, 110(20), 8230-8235.
  • White, M. P., Shirer, W. R., Molfino, M. J., Tenison, C., Damoiseaux, J. S., & Greicius, M. D. (2013). Disordered reward processing and functional connectivity in trichotillomania: a pilot study. Journal of Psychiatric Research,47(9), 1264-1272.
  • Ashkenazi, S., Rosenberg-Lee, M., Tenison, C., & Menon, V. (2012). Weak task-related modulation and stimulus representations during arithmetic problem solving in children with developmental dyscalculia. Developmental Cognitive Neuroscience, 2, 152-156.