丁香园AV

Research in the Learning Sciences

January 24, 2025

Dr. John Nesbit is a professor in the Faculty of Education at 丁香园AV who conducts primary research and meta-analyses on fundamental and applied topics within the learning sciences. He leads two research groups that have published SSHRC-funded research in areas such as the evaluation of digital learning resources, individual and collaborative concept mapping, effects of prompting self-explanation, cognitive tools for supporting self-regulated learning, the relationship between the need for cognition and academic achievement, and efficacy of intelligent tutoring systems. In recent years, his research has focused on two topics 鈥� interactive guidance in scientific inquiry learning and development and evaluation of an argument mapping tool to support critical thinking.

One of Dr. Nesbit鈥檚 research groups has published five articles on simulation-based scientific inquiry learning with several more under review or in progress. The research, intended to inform the design of AI-based tutoring agents, examines the benefits of interactive or 鈥渏ust-in-time鈥� guidance during inquiry tasks. It investigates which types of tutor guidance are most effective and the conditions under which they have beneficial effects. The results reported in the publications were obtained from analyzing over 230 hours of video in which human tutors used instructional heuristics and scripts to guide learners鈥� interactions with scientific simulations. The research was featured in a keynote presentation by Dr. Nesbit at the recent .  

A second topic of recent research by Dr. Nesbit and colleagues is the development and evaluation of a web-based argument visualization tool called the Dialectical Map (DMap). Originally conceptualized and investigated at 丁香园AV in PhD thesis research by Dr. Hui Niu, the DMap is a cognitive tool designed to support students鈥� construction of key argument features known to be difficult for secondary and postsecondary learners, such as counterarguments, rebuttals, and warrants.  Hundreds of undergraduate students have used the DMap tool at 丁香园AV and the British Columbia Institute of Technology (BCIT) in biology, education, criminology, computing science, psychology and communications courses.

Dr. Nesbit is a dedicated mentor who actively supports emerging scholars by collaborating as co-investigator on multiple SSHRC-funded Insight Development Grants (IDGs). One such project, titled 鈥淐ollaborative Argument Visualization in Post-secondary Education鈥� (awarded 2023), is led by Dr. Daniel Chang. This research explores how argument visualization technologies can enhance students鈥� ability to collaboratively generate, defend, examine, modify, and evaluate arguments. Another IDG project, titled 鈥淎dvancing Argumentation and Critical Thinking: Research Syntheses of Argument Visualization Pedagogy and Technology鈥� (awarded 2024), is led by Dr. Qing Liu. This project combines research evidence on argument visualization by gathering, cataloging, coding, and assessing all available studies on that topic.

Bibliography (recent publications)

Fukuda, M., Nesbit, J. C., & Winne, P. H. (2024). Effects of just-in-time inquiry prompts and principle-based self-explanation guidance on learning and use of domain texts in simulation-based inquiry learning. Frontiers in Education, 1鈥�15.

Liu, Q., & Nesbit, J. C. (2024). The relation between need for cognition and academic achievement: A meta-analysis. Review of Educational Research, 94(2), 155鈥�192. https://doi.org /10.3102/00346543231160474

Liu, Q., Zhong, Z., & Nesbit, J C. (2024). Argument mapping as a pre-writing activity: Does it promote writing skills of EFL learners? Education and Information Technologies, 29(7), 7895鈥�7925. https://doi.org /10.1007/s10639-023-12098-5

Nesbit, J., Liu, Q., Sharp, J., Cukierman, D., Hendrigan, H., Chang, D., Shahabi, B., Deng, Q., & Pakdaman Savoji, A. (2024). Argument visualization with DMaps: Cases from postsecondary learning. Journal of Interactive Learning Research, 35(2), 223鈥�253.

Obaid, T., Nesbit, J. C., Mahmoody Ghaidary, A., Jain, M., & Hajian, S. (2023). Explanatory inferencing in simulation-based discovery learning: Sequence analysis using the edit distance median string. Instructional Science 51, 309鈥�341.

Fukuda, M., Hajian, S., Jain, M., Liu, A. L., Obaid, T., Nesbit, J. C., & Winne P. H. (2022) Scientific inquiry learning with a simulation: Providing within-task guidance tailored to learners鈥� understanding and inquiry skill. International Journal of Science Education, 44, 1021鈥�1043.

Hajian, S., Jain, M., Liu, A. L., Obaid, T., Fukuda, M., Winne, P. H., & Nesbit, J. C. (2021). Enhancing scientific discovery learning by just-in-time prompts in a simulation-assisted inquiry environment. European Journal of Educational Research, 10, 989鈥�1007.

Liu, A. L., Hajian, S., Jain, M., Fukuda, M., Obaid, T., Nesbit, J. C., & Winne, P. H. (2022). A microanalysis of learner questions and tutor guidance in simulation鈥恆ssisted inquiry learning. Journal of Computer Assisted Learning, 38, 638鈥�650.