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Leveraging Large Language Models to Generate Course-Specific Semantically Annotated Learning Objects
Peer reviewed
Direct linkDominic Lohr; Marc Berges; Abhishek Chugh; Michael Kohlhase; Dennis Müller – Journal of Computer Assisted Learning, 2025
Background: Over the past few decades, the process and methodology of automatic question generation (AQG) have undergone significant transformations. Recent progress in generative natural language models has opened up new potential in the generation of educational content. Objectives: This paper explores the potential of large language models…
Descriptors: Resource Units, Semantics, Automation, Questioning Techniques
Chander Diwaker; Neha Miglani – Journal of Computer Assisted Learning, 2025
Background: The ever-increasing computational demands call for optimal resource utilisation and system performance in the cloud environment. Organisations are increasingly migrating workloads to cloud platforms, mandating the need for efficient resource distribution. Load balancing, a critical cloud component, ensures equitable distribution of…
Descriptors: Internet, Information Storage, Educational Practices, Computation
Chung Kwan Lo; Khe Foon Hew; Simin Xu; Yanjie Song; Gaowei Chen; Morris Siu-Yung Jong – Journal of Computer Assisted Learning, 2025
Background: In the aftermath of the COVID-19 pandemic, it is critical to reflect holistically on the experiences gained in the past few years. We thus review research on remote mathematics teaching in Pre-K-12 contexts, utilising Activity Theory as a theoretical lens for research synthesis. Objectives: Drawing from a comprehensive overview of the…
Descriptors: Preschool Education, Elementary Secondary Education, Distance Education, Mathematics Instruction
