Hi, my name is Jinnie Shin. Nice to meet you.

Book chapters [1] Shin, J. & Demmans epp, C. (in press). Improving Academic Writing Analysis with Educational Data Science: Focusing on Cohesion in Academic Writing. Educational Data Science: Essentials, Approaches, and Tendencies – Proactive Education based on Empirical Big Data Evidence Peer-reviewed Journal [2] Banawan, M., Shin, J., Balyan, R., Leite, W. L., & McNamara, D. S (in press). Shared Language: Linguistic Similarity in an Algebra Discussion Forum. Computers.

Role: Co-PI Team: (PI) Wanli Xing, University of Florida & (Co-PI) Anthony Botelho, University of Florida Project Period: 2023-01-01 - 2023-12-31 Funding Agency: JAFF Foundation Amount: $96,000 This project focuses on creating an analysis pipeline that automatically grades students’ mathematics responses and reasoning skills using the image data. My focus will be on evaluating the responses accurately using the transformer language models and providing feedback on students’ mastery levels.

This study introduces a novel approach to item pre-calibration using item content coding. The current method associates the metadata gathered from the items —content code and examinee information — to predict item difficulty with high-performance accuracy. We demonstrated a novel application of a deep collaborative filtering system in the pre-calibration process. This study has the potential to contribute to the methodology and the current practices of test development by providing the benefits of item content coding combined with data-informed and machine-learning approaches for estimating item difficulty. The approach we illustrate helps overcome the costly process of item field testing and can be used to calibrate large numbers of items in both an efficient and cost-effective manner.