Jinnie Shin
Assistant Professor
About
Jinnie Shin, Ph.D., is an Assistant Professor of Research and Evaluation Methodology in the School of Human Development and Organizational Studies in Education within the College of Education at the University of Florida. She has expertise in application of theory-based natural language processing and learning analytics in education research. In her work, she has focused on investigating how to bridge the gap between psychometric analysis and artificial intelligence in education research. Dr. Shin has experience with various international industry projects with the Medical Council of Canada, American College Testing, and the New Zealand Qualifications Authority, which focus on providing effective solutions to complex education problems using deep learning and natural language processing research.
Affiliations
- School of Human Development and Organizational Studies in Education
- University of Florida- School of Human Development and Organizational Studies in Education
Research Interests
Educational Assessment and Measurement, Emerging Technologies, Methodological Research, Quantitative Research, Technology Trends and Issues
Education
- Ph.D. in Educational Psychology (Measurement, Evaluation, and Data Science), 2021 University of Alberta
- M.Ed. in Educational Psychology (Measurement, Evaluation, and Cognition), 2018 University of Alberta
- B.Ed. in Elementary Education (English Education), 2016 Gyeongin National University of Education
Professional Appointments
- Assistant Professor, School of Human Development and Organizational Studies in Education, College of Education, University of Florida, 2021 - Present
Selected Publications
- Shin, J., Guo, Q., & Gierl, M. J. (2020). Automated essay scoring using deep learning. In M. Khosrow-Pour (Ed.), Handbook of research on modern educational technologies, applications, and management (Vol. 2). IGI-Global. doi: 10.4018/978-1-7998-3476-2
- Shin, J., Chen, F., Chang, L., & Bulut, O. (in press). Analyzing students' performance in computerized formative assessments to optimize teacher's test administration decisions using deep learning frameworks. Journal of Computers in Education.
- Shin, J., & Bulut, O. (2021). Building an intelligent recommendation system for personalized test scheduling in computerized assessments: A reinforcement learning approach. Behavior Research Methods. https://doi.org/10.3758/s13428-021-01602-9
- Bulut, O., Cormier, D. C., & Shin, J. (2020). An intelligent recommender system for personalized test administration scheduling with computerized formative assessments. Frontiers in Education, 5, 1-11. https://doi.org/10.3389/feduc.2020.572612
- Shin, J., & Gierl, M. J. (2020). More efficient processes for creating automated essay scoring frameworks: A demonstration of two algorithms. Language Testing, 38(2), 247-272. https://doi.org/10.1177/0265532220937830
- Shin, J., Bulut, O., & Gierl, M. J. (2020). Development practices of trusted AI systems among Canadian data scientists. International Review of Information Ethics, 28. https://doi.org/10.29173/irie377
- Shin, J., Bulut, O., & Gierl, M. J. (2019). The effect of the most-attractive-distractor location on multiple-choice item difficulty. Journal of Experimental Education, 88(4), 643-659. https://doi.org/10.1080/00220973.2019.1629577
- Shin, J., Guo, Q., & Gierl, M. J. (2019). Multiple-choice item distractor development using topic modeling approaches. Frontiers in Psychology, 10, 825. https://doi.org/10.3389/fpsyg.2019.00825