Yükseköğretim öğrencilerinin 2 boyutlu ve 3 boyutlu metaverse öğrenme ortamlarındaki bilişsel yük ve akışlarının incelenmesi

By using an explanatory sequential mixed-methods design, this study examines the cognitive load and flow experiences of higher education students who attended the same lectures in either a 2D (n = 38) or 3D (n = 43) desktop virtual reality (DVR)-based metaverse learning environment. Quantitative data were collected through the cognitive load scale and flow scale; the qualitative data were collected via reflection forms and semi-structured interviews. Results indicated a statistically significant difference in intrinsic cognitive load, and the 3D environment caused higher intrinsic cognitive load. However, no significant differences were found in extraneous load, germane load, and flow of groups. Thematic analysis indicated that interactivity, simulations, immersive visuals, and collaborative tasks helped optimize cognitive load and enhanced flow. However, technical difficulties, verbal lecture style, digital distractions, and visual complexity caused cognitive overload and decreased flow. In the 3D group, technical issues were the primary reason for cognitive overload, while immersive visuals, realism, and presence supported cognitive load optimization and flow. In contrast, in the 2D group, cognitive overload increased due to verbal lecture style, lack of face-to-face interaction, and technical limitations, while the use of visual simulations, simplicity, and collaborative activities facilitated more efficient cognitive processing and supported flow. In metaverse-based education, the study emphasizes the significance of instructional strategies, collaboration, interaction, and technical reliability. Recommendations and future research are provided to optimize cognitive load and support flow in metaverse-based education.