Jinnie Shin
Assistant Professor | University of Florida
Gainesville, FL, UNITED STATES
Jinnie Shin is an assistant professor of research and evaluation methodology.
Biography
Jinnie Shin is an assistant professor of research and evaluation methodology in the School of Human Development and Organizational Studies in Education in the College of Education. Jinnie has expertise in the application of theory-based natural language processing and learning analytics in education research. Her primary research interest is investigating how to bridge the gap between psychometric and artificial intelligence in education research.
Areas of Expertise (4)
Learning Analytics
Educational Assessment
Artificial Intelligence
Natural Language Processing
Articles (3)
Building an intelligent recommendation system for personalized test scheduling in computerized assessments: A reinforcement learning approach
Behavior Research MethodsJinnie Shin and Okan Bulut
2021-06-15
The introduction of computerized formative assessments in the classroom has opened a new area of effective progress monitoring with more accessible test administrations. With computerized formative assessments, all students could be tested at the same time and with the same number of test administrations within a school year. Alternatively, the decision for the number and frequency of such tests could be made by teachers based on their observations and personal judgments about students. However, this often results in rigid test scheduling that fails to take into account the pace at which students acquire knowledge.
More efficient processes for creating automated essay scoring frameworks: A demonstration of two algorithms
Language TestingJinnie Shin and Mark J. Gierl
2020-07-04
Automated essay scoring has emerged as a secondary or as a sole marker for many high-stakes educational assessments, in native and non-native testing, owing to remarkable advances in feature engineering using natural language processing, machine learning and deep-neural algorithms. The purpose of this study is to compare the effectiveness and the performance of two AES frameworks.
Development Practices of Trusted AI Systems among Canadian Data Scientists
AI, Ethics and SocietyJinnie Shin, et al.
2020-06-30
The introduction of Artificial Intelligence systems has demonstrated impeccable potential and benefits to enhance the decision-making processes in our society. However, despite the successful performance of AI systems to date, skepticism and concern remain regarding whether AI systems could form a trusting relationship with human users. Developing trusted AI systems requires careful consideration and evaluation of its reproducibility, interpretability and fairness.
