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Jinnie Shin - University of Florida. Gainesville, FL, US

Jinnie Shin

Assistant Professor | University of Florida

Gainesville, FL, UNITED STATES

Jinnie Shin is an assistant professor of research and evaluation methodology.


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 Methods

Jinnie Shin and Okan Bulut


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.

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More efficient processes for creating automated essay scoring frameworks: A demonstration of two algorithms

Language Testing

Jinnie Shin and Mark J. Gierl


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.

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Development Practices of Trusted AI Systems among Canadian Data Scientists

AI, Ethics and Society

Jinnie Shin, et al.


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.

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