Effects of an intelligent cues recognition-based multilevel knowledge graphs generation method on students in online learning environments

Abstract

ABSTRACT Online learning has been greatly widespread since the explosion of COVID-19. However, due to the lack of interaction between teachers and students in online courses, it is very difficult for students to focus on course content and complete the learning. Therefore, we developed a novel intelligent multilevel knowledge graph to help students quickly and systematically grasp the framework and key content of video lectures. Specifically, our method introduced cues into the interactive knowledge mapping. This approach not only allows students to interact with the graphs but also displays different levels of knowledge based on its importance. In addition, a quasi-experiment was implemented to verify the effectiveness of our method. Specifically, we compare the effects of knowledge graphs in three weighting strategies for cues with traditional manual concept maps on students' learning effectiveness and learning perception. Our study shows that although static artificial concept maps do enhance student learning to a certain extent compared to traditional learning, its development efficiency and enhancement of student learning outcomes are not ideal. In contrast, interactive graphs presenting overlay cues can effectively help students to restructure and systematize their knowledge, direct their attention and thus improve their learning outcomes.