Minju Hong
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Assistant Professor
Faculty Researcher
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Minju Hong's research investigates the application of animal-assisted therapy (AAT) for children with autism spectrum disorder (ASD). Her work explores the impact of AAT on social behavior, prosocial behavior, and emotional regulation in this population. Hong has conducted pilot studies examining how AAT affects these outcomes in autistic children with varying verbal abilities. She also utilizes machine learning techniques to analyze large datasets, contributing to predictive insights into student performance in science and mathematics on national assessments like PISA. Her publication record includes meta-analyses on professional development programs in science education and studies on topic modeling for enhancing rubric development and revealing interdisciplinary understanding in students. Hong has a h-index of 4 and has published 28 works, with 72 citations. She collaborates with several researchers at the University of Arkansas at Fayetteville, including Michele Kilmer, Emily Shah, Danielle Randolph, and Sarah Huetter.
Metrics
- h-index: 4
- Publications: 28
- Citations: 72
Selected Publications
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Predictive insights into U.S. students’ mathematics performance on PISA 2022 using ensemble tree-based machine learning models (2025)
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Unveiling effectiveness: A meta‐analysis of professional development programs in science education (2024)
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Application of Topic Modeling Techniques in Meta-analysis Studies (2024)
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A comparisons of the covariate types in applications of SEMtree model to educational studies (2024)
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Enhancing Rubric Development in Science Education through Topic Modeling Techniques (2024)
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Animal-assisted therapy in pediatric autism spectrum disorder (2024)
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Looking Beyond Disciplinary Silos: Revealing Students’ Interdisciplinary Understanding by Applying the Topic Modeling Technique (2024)
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Relationship between caregiver adverse childhood events and age of autism spectrum diagnosis (2023)
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Revealing Students' Interdisciplinary Understanding of Carbon Cycling Using the Topic Modeling Technique (2023)
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Multilevel Reliabilities with Missing Data (2023)
Collaboration Network
Top Collaborators
- Unveiling effectiveness: A meta‐analysis of professional development programs in science education
- Predictive insights into U.S. students’ mathematics performance on PISA 2022 using ensemble tree-based machine learning models
- Looking Beyond Disciplinary Silos: Revealing Students’ Interdisciplinary Understanding by Applying the Topic Modeling Technique
- Enhancing Rubric Development in Science Education through Topic Modeling Techniques
- Animal-assisted therapy in pediatric autism spectrum disorder
- Relationship between caregiver adverse childhood events and age of autism spectrum diagnosis
- Unveiling effectiveness: A meta‐analysis of professional development programs in science education
- Application of Topic Modeling Techniques in Meta-analysis Studies
- Multilevel Reliabilities with Missing Data
- Relationship between caregiver adverse childhood events and age of autism spectrum diagnosis
- Animal-assisted therapy in pediatric autism spectrum disorder
- Animal-assisted therapy in pediatric autism spectrum disorder
- Animal-assisted therapy in pediatric autism spectrum disorder
- Animal-assisted therapy in pediatric autism spectrum disorder
- Animal-assisted therapy in pediatric autism spectrum disorder
- A comparisons of the covariate types in applications of SEMtree model to educational studies
- Unveiling effectiveness: A meta‐analysis of professional development programs in science education
- Predictive insights into U.S. students’ mathematics performance on PISA 2022 using ensemble tree-based machine learning models
- Predictive insights into U.S. students’ mathematics performance on PISA 2022 using ensemble tree-based machine learning models
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