Engaging Learners in Synchronous Online Training Using Facial Expression Analysis Technology

Abstract

The rapid growth of digitalization and the rise in the number of remote work environments have amplified the importance of remote training using online learning platforms. The effectiveness of these online trainings heavily relies on various factors such as training content, methods and duration, trainer skills, and the technology platforms used in trainings. In addition to these internal, and generally, controllable factors, various uncontrollable factors also have a significant impact on the overall learning experience and outcomes. Consideration of cultural, generational, linguistic factors in addition to gender and race-related factors is essential in increasing the effectiveness of online training efforts. The purpose of this study is to investigate how facial recognition technology can aid in creating an engaging learning experience for diverse participants in online synchronous training. In particular, the study explores factors affecting the learning experience through an empirical analysis. Incorporating learners’ feedback, practical design methods are delineated to form a highly inclusive and engaging learning model using facial expressions analysis

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