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Tianlong Zu

Assistant Professor of Instruction - Director of Introductory Physics Labs

PhD, Purdue University, 2017

I am the Director of Introductory Physics Labs in the Department of Physics & Astronomy. I will be teaching 140-1,2,3 series in the coming 2022-2023 academic year. 

 

I got my undergraduate degree in physics from Nankai University and Ph.D. in physics from Purdue University. Before joining Northwestern in the fall of 2022, I have worked as a visiting Assistant Professor / HHMI educational specialist in the Department of Physics at Lawrence University in Wisconsin, followed by working as a tenure track Assistant Professor of Physics at Jacksonville State University in Alabama.

 

I am a DBER (discipline-based education research) researcher in physics. My research is mainly about exploring different ways to improve physics education at the college level. For example, I have studied how to use retrieval practice to improve physics students’ problem-solving skills and found that retrieval practice could not only improve physics problem-solving skills, but also correct typical metacognitive errors. I have also studied how to use eye-tracking technology to measure cognitive load(s) which have profound impacts towards learning. In my studies, I have found several eye-movement proxies indicating the three subtypes of cognitive load. More importantly, I found that these proxies do not depend on students’ working memory capacity indicating very robust relationships between the proxies and the corresponding cognitive loads.

 

Selected Publications

  1. Magana, A. J., Hwang, J., Feng, S., Rebello, S., Zu, T., & Kao, D. (2022). Emotional and cognitive effects of learning with computer simulations and computer videogames. Journal of Computer Assisted Learning, 38(3), 875-891.
  2. Zu, T., Munsell, J., & Rebello, N. S. (2021). Subjective measure of cognitive load depends on participants’ content knowledge level. Frontiers in Education, 6, 56.
  3. Zu, T., Hutson, J., Loschky, L.C., & Rebello, N.S. (2020). Using eye movements to measure intrinsic, extraneous, and germane load in a multimedia learning environment. Journal of Educational Psychology, 112(7), 1338–1352.
  4. Zu, T., Munsell, J., & Rebello, N. S. (2019). Comparing retrieval-based practice and peer instruction in physics learning. Physical Review Physics Education Research, 15(1), 010105.
  5. Rebello, N., Nguyen, M., Wang, Y., Zu, T., Hutson, J., & Loschky, L. (2018). Machine Learning Predicts Responses to Conceptual Questions Using Eye Movements. Proceedings of Physics Education Research Conference, Washington, DC.

 

My current research is supported by an NSF grant (NSF award # 211138: from Oct 01, 2022, to ~Sep 30, 2025) aiming at helping students develop superior problem-solving skills in introductory undergraduate physics courses. In this project, my collaborators and I will develop and test strategies that integrate two proven pedagogical practices -- retrieval practice and scientific argumentation -- to improve problem-solving in the introductory calculus-based physics course that is usually taken by students who intend to become scientists and engineers.