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Guoqian Luo; Hengnian Gu; Xiaoxiao Dong; Dongdai Zhou – Education and Information Technologies, 2025
In the realm of e-learning, supporting personalized learning effectively necessitates recommending sequences of learning items that maximize learning efficiency while minimizing cognitive load, all tailored to the learner's goals. These recommendations must account for the prerequisite relationships among learning items and the learner's…
Descriptors: Electronic Learning, Individualized Instruction, Sequential Learning, Learning Processes
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Gyeonggeon Lee; Xiaoming Zhai – TechTrends: Linking Research and Practice to Improve Learning, 2025
Educators and researchers have analyzed various image data acquired from teaching and learning, such as images of learning materials, classroom dynamics, students' drawings, etc. However, this approach is labour-intensive and time-consuming, limiting its scalability and efficiency. The recent development in the Visual Question Answering (VQA)…
Descriptors: Artificial Intelligence, Computer Software, Teaching Methods, Learning Processes
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Hassan Hessari; Ankit Agarwalla; Mohammad Bai; Morteza Vahedian; Ali Bai – International Journal on E-Learning, 2025
This study investigates the multifaceted impact of generative AI tools (e.g., ChatGPT, Gemini) on higher education. Employing grounded theory, we analyzed the experiences of eight students using these tools in their academic work. Our findings reveal both significant benefits, including enhanced learning, efficiency, and accessibility, and…
Descriptors: Artificial Intelligence, Technology Uses in Education, College Students, Learning Processes
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Mikko Hyttinen; Ville Isomöttönen – International Journal of Technology in Education, 2025
This qualitative study investigates the integration of Artificial Intelligence (AI) to support students' independent learning within a flipped classroom (FC) pedagogy. The main challenge of FC has been students' insufficient preparation for classroom activities. To address the challenge the Essentials of Business Law course was implemented with FC…
Descriptors: Artificial Intelligence, Technology Uses in Education, Technology Integration, Independent Study
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Zhizezhang Gao; Haochen Yan; Jiaqi Liu; Xiao Zhang; Yuxiang Lin; Yingzhi Zhang; Xia Sun; Jun Feng – International Journal of STEM Education, 2025
Background: With the increasing interdisciplinarity between computer science (CS) and other fields, a growing number of non-CS students are embracing programming. However, there is a gap in research concerning differences in programming learning between CS and non-CS students. Previous studies predominantly relied on outcome-based assessments,…
Descriptors: Computer Science Education, Mathematics Education, Novices, Programming