Physics education research is a rapidly growing interdisciplinary branch of physics which studies the processes of how people learn and teach physics. This field of research is pursued in physics departments at a number of leading graduate and research institutions across the country and has attracted funding from major governmental agencies. The results of this research provide the basis for an increasing understanding of the learning process as it applies to physics, as well as the basis for improved curriculum development and evaluation and for the integration of new technologies into the instructional process. Research in this field requires both a deep understanding of the physics content and insights into the results of the learning sciences as they apply to instructional processes in physics.

As with other fields of physics, physics education depends for its progress on both experimental and theoretical research. The Ohio State University Physics Education Research Group has strength in both of these areas, and is pursuing them through a number of projects supported by Federal and State funding.

On the experimental side, we have developed a rich knowledge base about specific difficulties that students encounter in learning physics and we apply this knowledge to create innovative curriculumand assessment materials that help students become better learners. On the theoretical side, an important and challenging task is the development of formal theoretical and mathematical models of the learning process. This work relies on, and is applied to, experimental investigations of specific aspects of student learning in a wide range of physics topics in various education settings. Our research integrates advances in neural science, cognitive science and psychology to model the learning process in physics and to develop measurement instruments and data analysis methods. The research work is part of a continuous cyclic  process of scientific research and development in physics and science education. An overview of the research framework is shown below.

Research Interests and Fields

Measurement and assessment methods
    • Model Analysis – multi-dimensional modeling for assessing learning.
    • Dynamic models of learning  – probability frameworks for education measurement.
    • Development of quantitative instruments for assessing scientific reasoning ability.
    • Cross-cultural large scale data collection and targeted comparisons.
    • Structures of student knowledge and the context dependent cueing processes
Computational models of student learning processes
Context cues and biologically plausible neural network models
Context dependence of students’ learning
Technologies in education (e.g. in-class polling, internet, and virtual reality)

   • Scientific Reasoning: Assessment and Effective Teaching
   • STEM Learning: Assessment of concepts and problem-solving; Effective teaching methods
   • Large Scale Assessment Data Center and Research Community   
   • In-class polling methods – Voting Machines, Clickers, etc.
   • Virtual Reality Physics Experiments