Hui Shi
Position
PhD candidate
Introduction
Hui Shi is a doctoral student in Instructional Systems and Learning Technologies program at Florida State University. She earned her B.S. in Computer Education at Jiangnan University and M.S. in Education Information Technology at East China Normal University. She went to Auburn University as a short-term visiting student during her master’s program.
Research Interests
Collaborative problem-solving, Knowledge Building, Online Teaching/Learning, Self-regulated Learning.
Projects
Project title: Metacognitive process and scaffolding design for online ill-structured collaborative problem-solving
Brief description: This study aims to explore how students’ collaborative ill-structured problem-solving experiences can be improved through structured learning support with metacognitive scaffolding. The participants will be graduate students enrolled in an advanced instructional design course for nine weeks. The purpose of the scaffolding is to provide support during an instructional design task for the group members to plan, monitor, and evaluate their workings. The self-reported and video data of groups’ online conversations will be collected and analyzed by focusing on the impacts of the scaffolding on groups’ metacognitive regulation and problem-solving performance. This study will use a convergent design of mixed methods.
Team members: Hui Shi
Project title: Knowledge Construction and Problem-solving Phases in a Simulation Learning Environment
Brief description: This project developed and evaluated an AI-supported simulation environment designed to cultivate future educational leaders. The project is based on a 3D virtual interactive simulation environment where participants collaborate with avatars to analyze and solve problems related to justice, equity, diversity, and inclusion. The research will evaluate how participants engage in knowledge construction and application within a leadership context focused on equity through interactions with the AI system.
Team members: Secil Caskurlu, Daniel Moraguez, Hui Shi, Clarice Han
Project title: Automatic Classification of Teaching Presence, Cognitive Presence, and Social Presence in Fully Online Course Discussions
Brief description: This project focuses on exploring the automated classification of the COI framework using both students’ and instructors’ posts in online discussions through machine learning, deep learning, and LLM models.
Team members: Hailey Kuang, Hui Shi, Secil Caskurlu, Kadir Kozan, Clarice Han, Sihan Jian, Jaesung Hur
Project title: Self-regulated learning and digital distraction in online learning environments
Brief description: The proliferation of digital tools in education offers numerous benefits but also introduces significant challenges, notably digital distractions that hinder academic performance, especially in online learning contexts. This study employs unsupervised data mining techniques, specifically association rule mining and clustering analysis, to identify effective learning strategies associated with decreasing digital distractions among college students.
Team members: Hui Shi
Project title: Academic peer review and knowledge co-construction
Brief description: Knowledge co-construction within the academic peer review process is an area that has yet to be explored empirically. This line of studies analyzed peer-review reports. Preliminary findings reveal that Negotiating Meaning is the most frequent phase, contrary to previous assertions of dominance by lower complexity thinking phases in knowledge co-construction. This analysis of expert dialogues offers insights that could enhance student peer review processes.
Team members: Shiyao Wei, Hui Shi, Giovanna Tang
Publications
Peer-reviewed Journal Articles
Shi, H., Caskurlu, S., Zhang, N., & Na, H (2024). To What Extent Has Machine Learning Achieved in Predicting Online At-Risk Students? Evidence From Quantitative Meta-analysis. Journal of Research on Technology in Education, 1–20. https://doi.org/10.1080/15391523.2024.2437741 [SSCI, Impact Factor: 4.5]
Shi, H., Zhou, Y., Dennen, V. P., & Hur, J. (2024). From unsuccessful to successful learning: profiling behavior patterns and student clusters in Massive Open Online Courses. Education and Information Technologies, 29(5), 5509-5540. https://doi.org/10.1007/s10639-023-12010-1 [SSCI, Impact Factor: 4.8]
Shi, H., Hur, J., Tang, Y. M., & Dennen V. P. (2023). Instructional strategies for engaging online learners: Do learner-centeredness and modality matter? Online Learning, 27(4), 271-294. https://doi.org/10.24059/olj.v27i4.4038 [ESCI, Impact Factor: 2.8]
Na, H., Staudt Willet, K. B., Shi, H., Hur, J., He, D., & Kim, C. (2024). Initial discussions of ChatGPT in education-related subreddits. Journal of Research on Technology in Education, 1-18. https://doi.org/10.1080/15391523.2024.2338091 [SSCI, Impact Factor: 4.5]
Dennen, V. P., He, D., Shi, H., & Adolfson, D. (2023). College students, networked knowledge activities, and digital competence: Implications for online instructors. Online Learning, 27(4), 122-143. https://doi.org/10.24059/olj.v27i4.4046 [ESCI, Impact Factor: 2.8]
Dennen, V. P., Shi, H., Rutledge, S. A., Bagdy, L. M., Jung, D., Bunn, S., Cargill, C., Cosgrove, C., & Hedquist, A. (2022). Parenting Teenage Social Media Users: Perceptions and Permissions as the Pandemic Began. IADIS International Journal on WWW/Internet, 20(2), 80-98. http://www.iadisportal.org/ijwi/
Lin, X., Dai, Y., Shi, H. (2022). Comparing the sense of online classroom community between students from urban and rural areas at a Chinese university. Journal of Educators Online, 19(3). https://doi.org/10.9743/JEO.2022.19.3.9
Lin, X., Dai, Y., Shi, H., & Li, C. (2020). E-learners’ Satisfaction as Predictors of Online Classroom Community. Journal of Contemporary Education Theory & Research, 4(2) 12-19. https://zenodo.org/record/4256511
Peer-reviewed Manuscripts Under Review
Shi, H., Zhang, N., Caskurlu, S., & Na, H. (Major Revision). Applications of Machine Learning for At-Risk Student Prediction in Online Education: A Ten-Year Systematic Review of Literature. Journal of Computer-Assisted Learning (SSCI, Impact Factor: 5.1).
Shi, H., Bi, R., Lin, X., & Dai, Y. (under review). Decreasing Digital Distraction in College Students: Associated Online Learning Strategies Identified by Unsupervised Data Mining Approach. The Internet and Higher Education (SSCI, Impact Factor: 6.4).
Caskurlu, S., Yalcin, Y., Hur, J., Shi, H., & Klein, J. (Major Revision). Data-Informed Decision Making in Instructional Design: Instructional Designers’ Practices and Strategies. TechTrends. [SSCI, Impact Factor: 2.2]
Zhang, N., Barrett, A., & Shi, H. (under review). A Systematic Review of Computational Thinking Disposition: Definitions, Assessments, Roles, and Pedagogical Approaches. Computers & Education (SSCI, Impact Factor: 8.9)
Social Media
Google Scholar – https://scholar.google.com/citations?user=laSfCbIAAAAJ&hl
Research Gate – https://www.researchgate.net/profile/Hui-Shi-34?ev=hdr_xprf
Git Hub – https://github.com/huishi-code
CV
