The Design of Self-Paced Learning for Structured Learning Environments

Abstract

This paper addresses self-paced learning within digital courses, focusing on instructional design paradigms to cater to the needs of individual learners. Through content structuring and leveraging large language models (LLMs), digital learning platforms can provide learners with a clear understanding of the subject matter while enabling flexible learning. However, challenges arise in measuring and managing learners’ study efforts and cognitive loads effectively. Strategies such as setting minimum reading times, recommending study breaks, and assigning individual Learning Complexity Indices (LCIs) help optimize the learning experience. The paper also explores the role of metacognition in self-regulated learning, emphasizing learners’ responsibility in managing their learning processes. Despite advancements in digital learning environments, challenges persist in supporting learners with diverse backgrounds and abilities, especially in chat-based learning settings. Examples from the prototypical implementation of a learning platform demonstrate how personalized learning experiences can be enhanced through data-driven insights and tailored recommendations.