Model of Video Aided Retention Tool for Enhancing Disaster Survival Skills on Earthquake among International Students

Abstract

The purpose of this research is to design an integrated video-based learning support system to provide training on earthquake Disaster Survival Skills (DSS) among the international students in the universities in Japan. The available literature shows a significant gap in the field of earthquake disaster survival skills between Japanese and international students. There are different types of contents used in DSS education and training. Among them, video contents received broad interest from the students in a self-directed video-based learning environment. However, in Japan, DSS video content specially designed for international students is limited. Often many topics are included in a single video, making the content long volume. So, arranging the essential parts inside the video with meaningful indexes and representing them in a logical order to keep the students’ motivation is important. Also, filtering and recommending more important content to fill the empty knowledge of the students is essential. In addition, tracking and analyzing students’ learning behavior inside video parts including the attention and retention process to support them during learning is equally important. Accordingly, the research designed the essential models of the VART system to accomplish such requirements. The proposed VART system is the combination of video fractioning, indexing, tracking, analyzing, and filtering tools, with the integration of domain model, students’ model, and eteaching strategy model to assist in self-directed video-based learning. The system organizes and makes fractions of each video into different important parts and put automatic indexes for each fraction. It also tracks each student’s ID, content preferences, duration, repetition of the content, most watching parts, etc. Based on the detailed watching history data VART analyzes and determines each student’s learning needs and filters and delivers essential video parts sequentially to support the video-based learning process.