User-Centered Models for Adaptive Learner Journeys in Self-Paced Learning

Abstract

Self-paced learning in digital education endows learners with the autonomy to explore educational content aligned with their interests and ambitions. However, managing the learning effort poses a challenge, as learners must continually estimate and regulate their individual learning pace throughout the course. This paper introduces a model for time and effort management to support learners' self-regulated learning (SRL) skills in self-paced digital courses. The model provides a blueprint for developing digital courses focused on self-paced learning paradigms and includes initial implementations at the Schmalkalden University of Applied Sciences. Successful self-paced learning relies on learners' metacognitive self-regulation strategies. Learners must develop effective SRL skills to monitor their progress, employ appropriate strategies for comprehension and retention, and autonomously manage their learning journey. An adaptive model is proposed to offer personalized recommendations based on individual learner characteristics. It includes a practical indicator model featuring learning complexity indices, estimated time-to-completion indicators, learning milestones, learn controls, and adaptive recommendations. Feedback mechanisms and interactive elements are highlighted for enhancing engagement and reducing cognitive effort in learning. The paper emphasizes the importance of adaptive models, user-centricity, and the need to continuously understand and enhance learner performance in self-paced learning environments to support successful course completion.

Keywords: Self-paced Learning, Self-regulated Learning, Adaptive Models, Effort Management, Digital Education.

DOI: 10.54941/ahfe1004532