Research Interest

(1) Learning sciences: technological and psychological factors influencing students’ effective learning and academic achievements; (2) Technology-rich learning environments; intelligent tutoring systems, simulations, AI tools, etc.; (3) Multimodal learning analytics: trace data, neurophysiological measures, facial expressions, self-report, etc.

 

Education

2020.09-2024.05 McGill University, Ph.D. in Learning Sciences

2017.09-2020.06 Tsinghua University, M.Ed. in Higher Education

2013.09-2017.06 China University of Mining and Technology, B.A. in Management

 

Work Experience

2024.09 – present Assistant Professor, Renmin University of China

 

Publications

Wang, T., Zheng, J., Huang, X., Ruiz-Segura, A., & Lajoie, S. P. (In press). Student engagement profiles in technology-rich environments: What they reveal about motivational beliefs, perceived task difficulty, and performance. Educational Technology & Society. (IF = 4.74, SSCI, Q1)

Wang, T., Ruiz‐Segura, A., Li, S., & Lajoie, S. P. (2024). The relationship between students' self‐regulated learning behaviours and problem‐solving efficiency in technology‐rich learning environments. Journal of Computer Assisted Learning. (IF = 5.1, SSCI, Q1)

Wang, T., Liu, S., Zheng, J., Ruiz-Segura, A., & Lajoie, S. (2024). Electrodermal activities during self-regulated learning relate to learning performance within a technology-rich environment. In Proceedings of the 18th International Conference of the Learning Sciences-ICLS 2024, pp. 1406-1409. International Society of the Learning Sciences.

Li, S., Huang, X., Wang, T., Zheng, J., & Lajoie, S. P. (2024). Using text mining and machine learning to predict reasoning activities from think-aloud transcripts in computer assisted learning. Journal of Computing in Higher Education, 1-20. (IF = 5.2, SSCI, Q1)

Zheng, J., Li, S., Wang, T., & Lajoie, S. P. (2024). Unveiling emotion dynamics in problem-solving: a comprehensive analysis with an intelligent tutoring system using facial expressions and electrodermal activities. International Journal of Educational Technology in Higher Education, 21(1), 33. (IF = 9.9, SSCI, Q1)

Wang, T., Li, S., Tan, C., Zhang, J., & Lajoie, S. P. (2023). Examining the relationship between cognitive load patterns and self-regulated learning within a technology-rich learning environment. Computers & Education, 104924. (IF = 12.0, SSCI, Q1)

Wang, T., & Lajoie, S. P. (2023). How does cognitive load interact with self-regulated learning? A dynamic and integrative model. Educational Psychology Review, 35(3), 69. (IF = 10.1, SSCI, Q1)

Wang, T., Zheng, J., Tan, C., & Lajoie, S. P. (2023). Computer‐based scaffoldings influence students’ metacognitive monitoring and problem‐solving efficiency in an intelligent tutoring system. Journal of Computer Assisted Learning, 39(5), 1652-1665. (IF = 5.1, SSCI, Q1)

Wang, T., Li, S., Huang, X., & Lajoie, S. P. (2023). Task complexity influences temporal characteristics of self-regulated learning behaviours in an intelligent tutoring system. Educational Technology Research and Development, 1-21. (IF = 4.8, SSCI, Q1)

Wang, T., Li, S., & Lajoie, S. P. (2023). The interplay between cognitive load and self-regulated learning in a technology-rich learning environment. Educational Technology & Society, 26 (2), 50-62. (IF = 4.74, SSCI, Q1)

Huang, X., Li, S., Wang, T., Pan, Z., & Lajoie, S. P. (2023). Exploring the co‐occurrence of students' learning behaviours and reasoning processes in an intelligent tutoring system: An epistemic network analysis. Journal of Computer Assisted Learning, 39(5), 1701-1713. (IF = 5.1, SSCI, Q1)

Huang, X., Ruiz-Segura, A., Tan, C., Wang, T., Sharma, R., & Lajoie, S. P. (2023). Social presence in technology-rich learning environments: how real we are feeling connected and how does it matter for learning? Information and Learning Sciences, 124(11/12), 396-424.

Zheng, J., Lajoie, S. P., Wang, T., & Li, S. (2023). Supporting self-regulated learning in clinical problem-solving with a computer-based learning environment: the effectiveness of scaffolds. Metacognition and Learning, 1-17. (IF = 4.7, SSCI, Q1)

Wang, T., Li, S., Huang, X., Pan, Z., & Lajoie, S. P. (2022). Examining students’ cognitive load in the context of self-regulated learning with an intelligent tutoring system. Education and Information Technologies, 28(5), 5697-5715. (IF = 4.8, SSCI, Q1)

Li, S., Huang, X., Wang, T., Pan, Z., & Lajoie, S. P. (2022). Examining the interplay between self-regulated learning activities and types of knowledge within a computer-simulated environment. Journal of Learning Analytics, 9(3), 152-168.

Liu, Y., Wang, T., Wang, K., & Zhang, Y. (2021). Collaborative learning quality classification through physiological synchrony recorded by wearable biosensors. Frontiers in Psychology, 12, 674369.

Li, Z., Xue, J., Li, R., Chen, H., & Wang, T. (2020). Environmentally specific transformational leadership and employee’s pro-environmental behavior: The mediating roles of environmental passion and autonomous motivation. Frontiers in Psychology, 11, 1408.

Li, Z., Wang, T., Liang,Y., & Wang, M. (2017). Mobile phone dependence and subjective well-being among college students: the mediating role of social anxiety. Studies of Psychology and Behaviors, 15(4), 7. (In Chinese)

Li, Z., Liang,Y., & Wang, T. (2017). The impact of mobile phone dependence and self-control on procrastination among college students. Psychological Research, 10(2), 7. (In Chinese)

 

Projects

Host the project funded by Société et culture - Fonds de recherche du Québec “Balancing self-regulated learning and cognitive load to improve clinical reasoning performance of medical students” (2022.04-2024.05)

 

Courses

NA

 

Professional Community Services

Reviewer for Computers & Education; Journal of Computer Assisted Learning; Education and Information Technology; British Educational Research Journal; Frontiers in Education, International Journal of Human-Computer Interaction, etc.

 

Contact Information

E-mail: tingtingwang2024@ruc.edu.cn

Office: Room 306, Guoxueguan Building