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Direct linkNita A. Tarchinski; Thomas Matthew Colclough; Ashley Atkinson; Emily Bonem; Nathan Emery; Victoria Farrar; Madeleine Gonin; Daniel Guberman; Eleanor Louson; Matthew Mahavongtrakul; Timothy McKay; Marco Molinaro; Meryl Motika; Lizette Alda Muñoz Rojas; Hurshal Pol; Kem Saichaie; Kelsey Smart; Megan Stowe; Natasha T. Turman; Jenna Marie Thomas Vest; Ryan Sweeder – To Improve the Academy, 2025
The STEM Equity Learning Community (SELC) project guided multidisciplinary teams from nine large, public research institutions through a yearlong experience. We aimed to support instructors and undergraduate students in developing equity-mindedness to drive structural change efforts on their campuses. The SELCs were supported by access to equity…
Descriptors: Undergraduate Students, Equal Education, STEM Education, Educational Change
Download full textXiner Liu; Andres Felipe Zambrano; Ryan S. Baker; Amanda Barany; Jaclyn Ocumpaugh; Jiayi Zhang; Maciej Pankiewicz; Nidhi Nasiar; Zhanlan Wei – Journal of Learning Analytics, 2025
This study explores the potential of the large language model GPT-4 as an automated tool for qualitative data analysis by educational researchers, exploring which techniques are most successful for different types of constructs. Specifically, we assess three different prompt engineering strategies -- Zero-shot, Few-shot, and Fewshot with…
Descriptors: Coding, Artificial Intelligence, Automation, Data Analysis
Nongluk Weerasiri; Pinanta Chatwattana – Journal of Education and Learning, 2025
The community-based learning model via game simulation to promote community public health diagnosis skills, or CBL model via game simulation, is a research tool that was devised based on the concepts of public health diagnosis using the seven community tools (geo-social mapping, genogram, community organization chart, local health system,…
Descriptors: Public Health, Educational Games, Computer Simulation, Game Based Learning
Jiang Li; Chen Zhu; Mark Goh – Research Evaluation, 2025
Data Envelopment Analysis (DEA) is a widely adopted non-parametric technique for evaluating R&D performance. However, traditional DEA models often struggle to provide reliable solutions in the presence of data uncertainty. To address this limitation, this study develops a novel robust super-efficiency DEA approach to evaluate R&D…
Descriptors: Foreign Countries, Research and Development, COVID-19, Pandemics
