Design of Video Aided Retention Tool for the Health Care Professionals in Self-Directed Video-Based Learning

Abstract

Health Care Professionals (HCPs) depend on self-directed learning by watching medical videos. In the traditional video learning system, it is difficult to identify the important videos from the huge data set and to find the essential inside parts of a long video. In addition, it is hard to know learners' preferences inside the video parts, including duration and repetition of watching. If the system could know the attention and retention process of each learner, it could change the way to show the video. Accordingly, this research proposes to design the Video Aided Retention Tool (VART) system for analyzing video content to improve self-directed video-based learning among HCPs. The VART consists of a combination of video tracking, analyzing, and filtering tools, with the integration of domain model, learners' model, and e-teaching strategy model to aid in self-directed learning. The proposed VART will pick important videos on a single topic and put automatic indexes to represent the essential parts of video content. It will also track the learner's ID, content preference, monitor watching duration, and repetition of the content. Using such kind of data, attention, and retention will be determined and filtered reels, recommendations, interactive videos will be provided to the learners.

New Cluster of Readiness Affecting Perceived Usefulness of Open Distance Learning Readiness During Cov?d-19 Outbreak

“…The prior study seeks to see the influence of psychological readiness that affect perceived usefulness of ODL implementation and student engagement in ODL activities (Yu et al, 2020). Factors that influence student's psychological readiness included competent facilitator, better delivery by the lecturer, comprehensive guidance especially for the topic which require students to perform higher-order thinking skills (Morton et al, 2016) and effective digital content (Sagorika & Hasegawa, 2020) which assists learners to fill up the gap in their knowledge (Lunnay et al, 2015) as indicators which constantly measure self-directed learning. These indicators contributed to the high level of psychological readiness towards perceived usefulness of ODL implementation.…”

“…While the ODL implementation forces them to do self-manage and selfdirected learning to achieve the best academic achievement. Most probably factors such as competent lecturer, high academic performance monitoring by the academic advisor (Morton et al, 2016), in addition to developing effective digital content (Sagorika & Hasegawa, 2020) are expected to probably influence high level of perceived usefulness towards ODL implementation (Lunnay et al, 2015) and predicted as an indicator to measure psychological readiness towards perceived usefulness of ODL implementation.…”