Research Interests and Fields


Information theory and intelligent agent modeling of learning and socioeconomic systems

Scientific Reasoning: modeling, assessment, and development

Knowledge Integration and Deep Learning in STEM: modeling, assessment, and instruction

Measurement and assessment methods

  • Information theory and intelligent agent modeling
  • Model Analysis – multi-dimensional modeling for assessing learning 
  • Dynamic models of learning and a unified probability framework for education measurement, which integrates Model Analysis, normalized gain and IRT under a single coherent theoretical frame
  • Development of quantitative assessment instruments and methods for assessing content knowledge, reasoning, and views and attitudes
  • Large scale quantitative assessment and targeted comparisons

Big Data Analysis of Assessment Data 

Computational models of student learning processes such as neural network models

Experimental technology and methods for measuring and modeling behavioral data of student learning (e.g. automatic group dynamics analysis and eye-tracking analysis of human interactions with computer simulations)


Technologies in education (e.g. in-class polling, web based interactive learning modules, technology enhanced science inquiry, virtual reality experiments, learning games)