Minju Hong Source Confirmed

Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.

Assistant Professor

University of Arkansas at Fayetteville

faculty

minjuh1215@cau.ac.kr

4 h-index 28 pubs 65 cited

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Biography and Research Information

OverviewAI-generated summary

Minju Hong's research focuses on educational methodologies and the application of machine learning in educational contexts. Her work includes a meta-analysis of professional development programs in science education and the use of topic modeling techniques to enhance rubric development and reveal students' interdisciplinary understanding. Hong also investigates the predictive capabilities of machine learning models for student performance, such as analyzing key factors influencing U.S. students' scientific literacy and mathematics performance on standardized assessments like PISA. Additionally, her research extends to behavioral health, with studies on animal-assisted therapy for children with autism spectrum disorder, examining its effects on prosocial behavior and emotional regulation in relation to verbal abilities. She has published 28 works, accumulating 65 citations and an h-index of 4. Hong collaborates with several researchers at the University of Arkansas at Fayetteville, including Michele Kilmer, Emily Shah, Danielle Randolph, and Allison Reichel.

Metrics

  • h-index: 4
  • Publications: 28
  • Citations: 65

Selected Publications

  • Predictive insights into U.S. students’ mathematics performance on PISA 2022 using ensemble tree-based machine learning models (2025) DOI
  • Unveiling effectiveness: A meta‐analysis of professional development programs in science education (2024) DOI
  • Application of Topic Modeling Techniques in Meta-analysis Studies (2024) DOI
  • A comparisons of the covariate types in applications of SEMtree model to educational studies (2024) DOI
  • Enhancing Rubric Development in Science Education through Topic Modeling Techniques (2024) DOI
  • Looking Beyond Disciplinary Silos: Revealing Students’ Interdisciplinary Understanding by Applying the Topic Modeling Technique (2024) DOI
  • Relationship between caregiver adverse childhood events and age of autism spectrum diagnosis (2023) DOI
  • Revealing Students' Interdisciplinary Understanding of Carbon Cycling Using the Topic Modeling Technique (2023) DOI
  • Multilevel Reliabilities with Missing Data (2023) DOI

Collaborators

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