Ji-Eun LEE | Research Fellow

Biography

Ji Eun (Ji-Eun) Lee is a Research Fellow at the Lee Kuan Yew Centre for Innovative Cities (LKYCIC). She received her Ph.D. in Instructional Technology and Learning Sciences from Utah State University. Her research focuses on designing, developing, and evaluating technology-based interventions to enhance student learning and engagement. She applies learning analytics and advanced statistical methods to identify factors that contribute to positive learning outcomes.

Before joining LKYCIC, she worked as a Research Scientist at Worcester Polytechnic Institute in the U.S. for six years. There, she led and contributed to large-scale projects involving technology-enhanced learning environments. She has extensive experience leading interdisciplinary research teams, managing multiple projects, and mentoring students, bringing both strong data analytics expertise and a track record of impactful research to her work.

She is supporting King Wang, Thijs and team on the “Shaping the Innovators of Tomorrow: AI’s Role in Managing Failure and Innovation in Education” project.

Education

• Ph.D., Instructional Technology and Learning Sciences, Utah State University (2019)
• M.A., Educational Technology, Ewha Woman’s University (2010)
• B.A., Home Economics Education, Mass Communications (with Great Honor), Korea University (2006)

Publications

• Lee, J. E., Thorp, C. S., Kamberi, A., Ottmar, E. (in press). Unpacking strategy efficiency: Examining the relations between pre-solving pausetime and productivity in a digital mathematics game. Metacognition and Learning.
• Ottmar, E., Zhang, P., Lee, J. E., Bye, J. K., Colbert, M. A., Egorova, A., Yu, S., Closser, A. H., &  Hornburg, C. B. (in press). Data from the effects of perceptual cues on middle school students’ online mathematical reasoning and learning study. Journal of Open Psychology Data.
• Hornburg, C. B., Lee, J. E., Closser, A. H., Bye, J. K., Egorova, A., Reinhardt, M. A., Yu, S., Zhang, P., Valdivia, I., & Ottmar, E. (2025). Examining the effects of congruent spacing and color perceptual cues on middle school students’ order-of-operations performance. The Journal of Experimental Education. https://doi.org/10.1080/00220973.2025.2528691
• Kuhfeld, M., Robinson, G., Isaacs, J., Postell, S., Lee, J. E., & Ottmar, E. (2025). High school math course-taking: Shifts in access and achievement post-COVID-19. AERA Open, 11.  https://doi.org/10.1177/23328584251353514
• Lee, J. E., Ottmar, E., Chan, J. Y. C., Decker-Woodrow, L., & Booker, B. (2025). In-person vs. virtual: Learning modality selections and movement during COVID-19 and their influence on student learning. Learning Environments Research, 28(1), 149-169. https://doi.org/10.1007/s10984-025-09525-4
• Bye, J. K., Chan, J. Y.-C., Closser, A. H., Lee, J. E., Shaw, S. T., & Ottmar, E. (2024). Perceiving precedence: Order of operations errors are predicted by perception of equivalent expressions. Journal of Numerical Cognition, 10, e14103. https://doi.org/10.5964/jnc.14103
• Norum, R., Lee, J. E., Ottmar, E., & Harrison, L. (2024). Student profiles based on in-game performance and help-seeking behaviors in an online mathematics game. British Journal of Educational Technology, 1-22. https://doi.org/10.1111/bjet.13463
• Lee, J. E., Jindal, A., Patki, S. N., Gurung, A., Norum, R., & Ottmar, E. (2023). A comparison of different machine learning algorithms for predicting student performance in an online interactive mathematics game. Interactive Learning Environments, 1–15. https://doi.org/10.1080/10494820.2023.2212726
• Ottmar, E., Lee, J. E., Vanacore, K., Pradhan, S., Decker-Woodrow, L., & Mason, C. A. (2023). Data from the efficacy study of From Here to There!: A dynamic technology for improving algebraic understanding. Journal of Open Psychology Data. 1–15. https://doi.org/10.5334/jopd.87
• Decker-Woodrow, L., Mason, C. A., Lee, J. E., Chan. J. Y. C., Sales, A., Liu, A., & Tu, S. (2023). The impacts of three educational technologies on algebraic understanding in the context of COVID-19. AERA Open. 9(1), 1–17. https://doi.org/10.1177/23328584231165919
• Finstera, M., Decker-Woodrow, L., Booker, B., Mason, C. A., Tu, S., & Lee, J. E. (2023). Cost-effectiveness of algebraic technological applications. Journal of Research on Educational Effectiveness, 1–24. https://doi.org/10.1080/19345747.2023.2269918
• Lee, J. E., Chan, J. Y. C., Botelho, A., & Ottmar, E. (2022). Does slow and steady win the race?: Clustering patterns of students’ behaviors in an interactive online mathematics game. Educational Technology Research and Development. 70(5), 1575–1599. https://doi.org/10.1007/s11423-022-10138-4
• Lee, J. E. Hornburg, C. B., Chan, J. Y. C., & Ottmar, E. (2022). Perceptual and number effects on students’ solution strategies in an interactive online mathematics game. Journal of Numerical Cognition, 8(1), 166-182. https://doi.org/10.5964/jnc.8323
• Lee, J. E., & Recker, M. (2022). Predicting student performance by modeling participation in asynchronous discussions in university online introductory mathematical courses. Educational Technology Research and Development, 70(6), 1993–2015.  https://doi.org/10.1007/s11423-022-10153-5 [Doctoral dissertation]
• Lee, J. E., Stalin, A., Ngo. V., Drzewiecki, K., Trac, C., & Ottmar, E. (2022). Show the flow: Visualization of students’ solution strategies with Sankey diagrams in an online mathematics game. Journal of Interactive Learning Research, 33(2), 97-126.
• Chan J. Y., Lee, J. E., Mason, C., Sawrey, K., & Ottmar, E. (2022). From Here to There!: A dynamic algebraic notation system improves understanding of equivalence in middle school mathematics. Journal of Educational Psychology, 114(1), 56–71. https://doi.org/10.1037/edu0000596
• Chan J. Y., Ottmar, E., & Lee, J. E. (2022). Slow down to speed up: Longer pause time before solving problems relates to higher strategy efficiency. Learning and Individual Differences, 93, 102109. https://doi.org/10.1016/j.lindif.2021.102109
• Lee, J. E., & Recker, M. (2021). The effects of instructors’ use of online discussion strategies on student performance in university online introductory mathematics courses. Computers & Education, 104084. https://doi.org/10.1016/j.compedu.2020.104084 [Doctoral dissertation]
• Lee, J. E., Recker, M., & Yuan, M. (2020). The validity, reliability, and instructional value of a rubric for evaluating online course quality: An empirical study. Online Learning Journal, 24(1), 245–263. https://doi.org/10.24059/olj.v24i1.1949
• Lee, J. E., Recker, M., Choi, H., Hong, W. J., Kim, N. J., Lee, K., Lefler, M., Louviere, J., & Walker, A. (2015). Applying data mining methods to understand user interactions within learning management systems: Approaches and lessons learned. Journal of Educational Technology Development and Exchange, 8(2), 99–116. https://doi.org/10.18785/jetde.0802.06 

Conference Presentations/Proceedings

• Lee, S., Li, H., Zhang, S., Zhong, E., Lee, J. E., & Botelho, A. (2025). So what? Unpacking the complexities in collaboration analytics with AI-augmented sense-making. In A. I. Cristea, E. Walker, Y. Lu, O. C. Santos, & S. Isotani (Eds.), Proceedings of the 26th International Conference on Artificial Intelligence in Education (pp. 431-438). Springer Cham.
• Li, H., Zhang, S., Lee, S., Lee, J. E., Zhong, Z., Weitnauer, E., & Botelho, A. F. (2024). Math in motion: Analyzing real-time student collaboration in computer-supported learning environments. In B. Paaßen, & C. D. Epp (Eds.), Proceedings of the 17th International Conference on Educational Data Mining (EDM 2024). Atlanta, GA: ACM. https://zenodo.org/records/12729878
• Lee, J. E., Ottmar, E., Vanacore, K., Egorova, A., Botelho, A., & Gurung, A. (2024). Investigations into the effects of hint reading time and hint type in an online mathematics game through response time decomposition. In C. Hoadley, & X. C. Wang (Eds.), Proceedings of the 18th International Conference of the Learning Sciences - ICLS 2024 (pp. 83-90). Buffalo, NY: International Society of the Learning Sciences.
• Pradhan, S., Lee, J. E., Egorova, A., Jerusal, J., &  Ottmar, E. (2024). An application of data mining methods on in-game behaviors: Predicting student math performance. In C. Hoadley, & X. C. Wang (Eds.), Proceedings of the 18th International Conference of the Learning Sciences -ICLS 2024 (pp. 1007-1010). Buffalo, NY: International Society of the Learning Sciences.
• Egorova, A.,  Lee, J. E., Shaw, S., &  Ottmar, E. (2024). Clusters of math anxiety and performance in an online learning game: Differences in hint usage and pause time. In C. Hoadley, & X. C. Wang (Eds.), Proceedings of the 18th International Conference of the Learning Sciences -ICLS 2024 (pp. 1163-1166). Buffalo, NY: International Society of the Learning Sciences.
• Liu, A., Chan, J. Y. C., Lee, J. E., Decker-Woodrow, L. E., Tu, S., Sales, A., & Mason, C. A. (2022). Does where you start matter? The interaction between prior knowledge and effectiveness of game-based interventions. In C. Chinn, E. Tan, C. Chan, & Y. Kali (Eds.), Proceedings of the 16th International Conference of the Learning Sciences - ICLS 2022 (pp. 2182-2183). Hiroshima, Japan: International Society of the Learning Sciences.
• Norum, R., Lee, J. E., & Ottmar, E. (2022). Student profiling on behavioral patterns in an online mathematics game: Clustering using K-means. In C. Chinn, E. Tan, C. Chan, & Y. Kali (Eds.), Proceedings of the 16th International Conference of the Learning Sciences - ICLS 2022 (pp. 1942-1943). Hiroshima, Japan: International Society of the Learning Sciences.
• Lee, J. E., Stalin, A., Ngo. V., Drzewiecki, K., Trac, C., & Ottmar, E. (2021). Show the flow: Visualization of students’ solution strategies with Sankey diagrams in an online mathematics game. In de Vries, E., Hod, Y., & Ahn, J. (Eds), Proceedings of the International Conference on Learning Sciences (pp. 887-888). Bochum, Germany: International Society of the Learning Sciences.
• Lee, J. E. (2019). The effects of discussion strategies and learner interactions on performance in online mathematics courses: An application of learning analytics. In D. Azcona & R. Chung (Eds.), Companion Proceedings of the 9th International Learning Analytics & Knowledge (LAK) Conference. Tempe, AZ: Society for Learning Analytics Research.
• Lee, J. E., Recker, M., Bowers, A. J., & Yuan, M. (2016). Hierarchical cluster analysis heatmaps and pattern analysis: An approach for visualizing learning management system interaction data. In T. Barnes, M. Chi & M. Feng (Eds.), Proceedings of the 9th International Conference on Educational Data Mining (pp. 603-604). New York, NY: ACM.
• Choi, H., Lee, J. E., Hong, W. J., Lee, K., Recker, M., & Walker, A. (2016). Exploring learning management system interaction data: Combining data-driven and theory-driven approaches. In T. Barnes, M. Chi & M. Feng (Eds.), Proceedings of the 9th International Conference on Educational Data Mining (pp. 324-329). New York, NY: ACM.