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Yingbin Zhang; Luc Paquette; Nigel Bosch – International Journal of Artificial Intelligence in Education, 2025
Understanding the transitions among affective states during computer-based learning may guide the design of affect-responsive learning environments. Current studies have focused on the marginal strength of an affect transition, which is the average transition tendency over possible affective states preceding the transition. However, marginal…
Descriptors: Affective Behavior, Emotional Response, Electronic Learning, Learning Experience
Giulia Cosentino; Jacqueline Anton; Kshitij Sharma; Mirko Gelsomini; Michail Giannakos; Dor Abrahamson – British Journal of Educational Technology, 2025
As AI increasingly enters classrooms, educational designers have begun investigating students' learning processes vis-à-vis simultaneous feedback from active sources--AI and the teacher. Nevertheless, there is a need to delve into a more comprehensive understanding of the orchestration of interactions between teachers and AI systems in educational…
Descriptors: Artificial Intelligence, Learning Processes, Instructional Design, Design
Hendra Y. Agustian; Bente Gammelgaard; Muhammad Aswin Rangkuti; Jonas Niemann – Science Education, 2025
Affect and emotions matter to science learning. They also matter because they are integral to science identity formation and sense of belonging. This study aims to foreground the epistemic and affective character of laboratory work in higher science education by conceptualizing it as epistemic practice, in which students activate their body and…
Descriptors: College Students, Chemistry, Science Instruction, Laboratory Experiments

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