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Zifeng Liu; Wanli Xing; Chenglu Li; Fan Zhang; Hai Li; Victor Minces – Journal of Learning Analytics, 2025
Creativity is a vital skill in science, technology, engineering, and mathematics (STEM)-related education, fostering innovation and problem-solving. Traditionally, creativity assessments relied on human evaluations, such as the consensual assessment technique (CAT), which are resource-intensive, time-consuming, and often subjective. Recent…
Descriptors: Creativity, Elementary School Students, Artificial Intelligence, Man Machine Systems
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Xiner 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
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Wei Dai; Jionghao Lin; Flora Ji-Yoon Jin; Yi-Shan Tsai; Namrata Srivastava; Pierre Le Bodic; Dragan Gasevic; Guanliang Chen – Journal of Learning Analytics, 2025
Supporting academically at-risk students has attracted much attention in the field of learning analytics. However, much of the research in this area has focused on developing advanced machine learning models to predict students' academic performance, which alone is insufficient to improve student learning without the implementation of timely…
Descriptors: Learning Analytics, Identification, At Risk Students, Feedback (Response)