Semantic Segmentation of Educational Videos for Micro Learning Objects in Adaptive E-Learning

Abstract

E-Learning is gaining prominence, especially in lifelong learning, primarily through lecture videos. However, these videos often encompass multiple topics or serve various instructional roles within a single subject. In adaptive e-Learning, the smaller and granular the units, the more versatile presentations and personalized lectures are composed. Such units are known as Micro Learning Objects (MLOs). Consequently, the necessity emerges to segment these lecture videos into multiple MLOs, each fulfilling a distinct instructional role in a lecture. This article presents an automatic model leveraging advanced language models to segment lecture videos semantically into Micro Learning Objects (MLOs). Additionally, a new wellsegmented dataset of educational videos (YT-EV) was introduced, in which the video is segmented according to a pre-defined timestamped agenda. The model is trained on general text datasets to understand LO segments and subsequently fine-tuned using transfer learning on video datasets to achieve better segmentation results. The experimental results showed an F1-score of value 0.657, which is considered promising and emphasizes the significance of text transcript-based video segmentation for enhancing adaptive e-Learning.