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Learning & Cognition/Educational Technology M.A. & Ph.D.

Make improvements in education through psychology, science, and technology

As a student in the program, you’ll study the psychological processes critical to education. Research in our program focuses on cognitive and social-emotional development, including high-level cognition and factors shaping and enhancing learning throughout the lifespan. You'll use this knowledge to develop better educational processes and procedures for schools and other educational agencies, businesses, human and social service organizations, health care providers, government agencies, and more.

How to apply


Degrees & coursework

A master's degree is not required to apply for the doctoral program.

Master's of Arts (M.A.)

M.A. curriculum (33-34 credits)

Doctor of Philosophy (Ph.D)

Ph.D. curriculum (72 credits)

What you'll study

  • Cognitive processes
  • Scientific reasoning
  • Problem solving
  • Teaching effectiveness
  • Intelligence
  • Dynamic-based simulations
  • Learning from texts
  • Software and courseware design
  • Reading and language
  • Adaptive learning systems
  • Technology-enhanced learning
  • Conceptual change
  • Mathematical cognition

  • Faculty

    Learning and cognition / educational technology

    Headshot of Willian Bart

    William M. Bart

    Email | Lab

    • Relationships among cognition, instruction, and testing
    • Gifted and talented education
    • Chess expertise
    Headshot of Panayiota Andrea Kendeou

    Panayiota Andrea Kendeou

    Email | Lab

    • Cognitive processes in reading comprehension
    • Change of pre-existing beliefs and misconceptions
    • Technology-based comprehension interventions and assessments
    Headshot of Robert D. Tennyson

    Robert D. Tennyson

    Email | Lab

    • Cognitive processes in reading comprehension
    • Change of pre-existing beliefs and misconceptions
    • Technology-based comprehension interventions and assessments

    Keisha Varma

    Email | Lab

    • Cognitive processes in science learning
    • Scientific visualizations and student learning outcomes
    • Teacher knowledge development and effective teaching practice
    Headshot of Sashank Varma

    Sashank Varma

    Email | Lab

    • Understanding of abstract mathematical concepts
    • Long-term memory and language understanding
    • Computational models of mathematical reasoning, language understanding, and spatial problem solving
    Headshot of Steve Yussen

    Steve Yussen

    Email | Lab

    • Memory and comprehension of stories
    • Narrative exchanges between children and parents (e.g., storybook reading, reminiscence)
    • The role of culture on narrative storytelling and memory


    Headshot of Martin Van Boekel

    Martin Van Boekel


    “The professors and everyone involved in the program are amazing. The department fosters a community of researchers where students are encouraged and supported in exploring their own interests.”

    • Interests: How people reason when confronted with information that contradicts their prior knowledge
    • Research: Hindsight bias, knowledge revision when reading refutation texts, and reasoning styles in scientific contexts
    • Work: Conducts research at the University on factors that support student academic and social success in school
    Headshot of Soo-hyum Im

    Soo-hyum Im


    "I help build a meaningful foundation of support for educators’ instructional strategies and the development of students’ learning skills using neuroscience.”

    • Interests: Applying neuroscience findings to educational practice
    • Research: Examining young children’s cognitive process of solving arithmetic problems; studying the influence of arithmetical flexibility on future mathematics achievement and brain development
    • Work: His current research focus: Improving educators’ neuroscience literacy; developing mathematical flexibility items