Currently, I am a Research Scientist at Educational Testing Service (ETS). I am a member of the Cognitive and Learning Sciences Group and conduct use-inspired foundational research as part of ETS's Learning and Assessment Foundations and Innovations Center.

Prior to joining ETS, I was a Lead Scientist at Soar Technology where I was principle investigator on research projects spanning multiple DoD agencies. 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. 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

Use-Inspired Research

My research brings together theory on human learning and decision-making with my industry experience building technologies to support training across diverse learner populations. Broadly my work combines data and theory to model the state of the learner from the decisions they make and actions they take. From investigating student strategy use and proficiency in complex digital environments to capturing student preferences when applying to universities, I draw inferences about cognition from user behavior. This approach of use-inspired foundational research builds knowledge that can be used to meet the immediate needs of the project while also contributing insights that can be generalized across contexts.

Foundational Research

The aim of my foundational research program is to understand and model human learning and decision-making. 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 assessment development and instructional design 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 work uses multiple, diverse data sources, from behavioral data (e.g. reaction time, self-report,processs logs from digital tools) to physiological data(e.g. fMRI, MEG), 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? Working across different domains and learning environments, I use behavioral data mining methods to identify strategies and strategy use in student data.

At ETS I am continuing this work investigating how people learn and use the knowledge and skills they have acquired. In my research, I develop methods for modeling student learning, and explore how we can take advantage of the rich data streams new assessment technologies produce to gain insight into the problem solving process.

Modeling Pause Behavior

The goal of this research is to identify methods for characterizing pauses in the problem-solving process and establish what these pauses contribute to the measurement of inquiry skill.

Clustering Strategic Problem Solving

The goal of this research is to identify methods for identifying different problem-solving strategies from clickstream data generated within an assessment of inquiry skill.

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., & Sparks, J. (2023). Combining cognitive theory and data driven approaches to examine students’ search behaviors in simulated digital environments. Large-scale Assessments in Education . 11, article 28. (Article available through open access)
  • Forsyth, C., Tenison, C., & Arslan, B. (2023). The Current Trends and Opportunities for Machine Learning in Learning Analytics. In: Tierney, R., Rizvi, F., Erkican, K. (Eds.), International Encyclopedia of Education, 14, 404-416
  • Tenison, C., Ling, G. & McCulla, L. (2022). Supporting College Choice Among International Students through Collaborative Filtering.International Journal of AI in Education,1-29.(Journal publication) (Author prepublication)
  • Popov, V., Ostarek, M., & Tenison, C. (2018). Practices and pitfalls in inferring neural representations. NeuroImage, 174, 340-351. (bioRxiv Prepublication)
  • Anderson, J. R., Borst, J. P., Fincham, J. M., Ghuman, A. S., Tenison, C., & Zhang, Q. (2018). The Common Time Course of Memory Processes Revealed. Psychological science, 29(9), 1463-1474
  • 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.