Tianzheng (Mark) Mao
Research Associate, Measurement and Data Analysis; PhD student in MESA

Tianzheng (Mark) Mao is a PhD student and Research Associate. His academic and professional endeavors are rooted in the field of educational measurement and data analysis, with a particular focus on Item Response Theory (IRT) modeling.

In his current role, Mark is engaged in a comprehensive study to evaluate the efficacy of various linking methods for IRT modeling. Specifically, he is exploring techniques that facilitate the linking of a large number of independent calibrations. This includes an in-depth analysis of the simultaneous linking method proposed by Haberman and the Alignment method developed by Muthén et al. He is also investigating the potential of machine learning techniques to achieve similar linking objectives.

Mark’s goal is to enhance the precision and scalability of linking processes in IRT modeling by bridging the gap between classical statistical methods and modern machine learning techniques in educational measurement. Through his research, he aspires to contribute to the advancement of assessment methodologies, ensuring that they are both scientifically rigorous and practically applicable.