Video annotation and analytics in CourseMapper

Abstract

Over the past few years there has been an increasing interest to investigate the potential of Video-Based Learning (VBL) as a result of new forms of online education, such as flipped classrooms and Massive Open Online Courses (MOOCs) in order to engage learners in a self-organized and networked learning experience. However, current VBL approaches suffer from several limitations. These include the focus on the traditional teacher-centered model, the lack of human interaction, the lack of interactivity around the video content, lack of personalization, as well as assessment and feedback. In this paper, we investigate the effective design of VBL environments and present the design, implementation, and evaluation details of CourseMapper as a mind map-based collaborative video annotation and analytics platform that enables learners’ collaboration and interaction around a video lecture. Thereby, we focus on the application of learning analytics mainly from a learner perspective to support self-organized and networked learning through personalization of the learning environment, monitoring of the learning process, awareness, self-reflection, motivation, and feedback.