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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
Jonathan K. Foster; Peter Youngs; Rachel van Aswegen; Samarth Singh; Ginger S. Watson; Scott T. Acton – Journal of Learning Analytics, 2024
Despite a tremendous increase in the use of video for conducting research in classrooms as well as preparing and evaluating teachers, there remain notable challenges to using classroom videos at scale, including time and financial costs. Recent advances in artificial intelligence could make the process of analyzing, scoring, and cataloguing videos…
Descriptors: Learning Analytics, Automation, Classification, Artificial Intelligence
Crossley, Scott A.; Karumbaiah, Shamya; Ocumpaugh, Jaclyn; Labrum, Matthew J.; Baker, Ryan S. – Journal of Learning Analytics, 2020
This study builds on prior research by leveraging natural language processing (NLP), click-stream analyses, and survey data to predict students' mathematics success and math identity (namely, self-concept, interest, and value of mathematics). Specifically, we combine NLP tools designed to measure lexical sophistication, text cohesion, and…
Descriptors: Elementary School Mathematics, Blended Learning, Self Concept, Audience Response Systems

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