Examining Different Affective Factors in Learning with Virtual Reality

Abstract

This study aims to examine how prior knowledge and affective factors of virtual reality environments predict science learning achievement through the mediation of learning engagement. Ninety-two sixth-grade students in Taiwan were recruited in this study. Data were analyzed through partial least squares structural equation modeling (PLS-SEM). The results showed that prior knowledge negatively predicted presence and control and active learning. Presence, control and active learning positively predicted learning engagement (behavioral engagement, cognitive engagement, emotional engagement). Cognitive fatigue was found to negatively predict emotional engagement and science learning achievement. Implications and suggestions for future research were addressed in the study.