Abstract

At present, with the continuous development of new technologies such as human-computer interaction, multimodal data collection, big data and knowledge mapping in online education, intelligent technologies are cross-fertilising with various fields such as learning psychology and cognitive recognition, rapidly driving the vertical development of self-adaptive learning, while self-adaptive learning systems have emerged to meet the personalised learning needs of students. It breaks the boundaries of learning time and space and provides learners with a diverse and free learning environment. Over the years, a great deal of research has been done on this subject, from practical theory to system implementation. Most of these studies have analysed self-adaptive learning systems at a macro or technical practical level, but less at a micro and learner experience level, and few have analysed self-adaptive learning systems from the perspective of the learning experience. Therefore, it is increasingly relevant to investigate the factors influencing students' learning experience in self-adaptive learning systems. In this paper, we explore the factors influencing students' learning experience in self-adaptive learning systems, and make recommendations for optimising students' experience, taking into account previous research on students' learning experience in intelligent environments and self-adaptive learning systems.