Interactivity and signaling in Immersive Virtual Reality: Effects on EFL learning experiences and outcomes

Immersive Virtual Reality (IVR) is used in education due to its high level of immersion, interactivity, and engaging learning experience, particularly in situated learning scenarios such as foreign language acquisition. While IVR has been widely used in situated learning, its effectiveness, particularly in second language learning through interactivity and signaling, remains underexplored. According to the Cognitive Theory of Multimedia Learning (CTML) and the Cognitive-Affective Model of Immersive Learning (CAMIL), this study investigates how different signaling types (Traditional vs. Anti-Signaling) and levels of interactivity affect learners’ effectiveness in IVR environments. To explore their interrelationship, we conducted a mixed-factor experiment in which both factors served as independent variables, and the dependent variables included learning outcomes, cognitive load, and intrinsic motivation, among others. The results indicate that medium interactivity yields the most favorable learning outcomes, followed by high interactivity, with low interactivity performing worst across all measures. The proposed approach (Anti-Signaling) has been shown to be effective in mitigating the negative effects associated with increased interactivity. With Anti-Signaling, high interactivity can achieve similar learning outcomes to medium interactivity. However, no significant differences were observed between signaling types at medium or low levels of interactivity. These findings point to an interaction between interactivity and signaling, in which the benefits of signaling become more pronounced as interactivity increases. Besides, no significant negative effects were observed when applying Anti-Signaling, suggesting that it serves as a harmless strategy. Our findings offer valuable insights into the effects of varying interactivity and highlight the use of anti-signals in IVR learning, particularly in relation to learning efficiency and user experience.