The potential of human-machine collaborative learning in educational virtual environments for knowledge retention in science learners: a decade-long meta-analysis

Educational virtual environments (EVEs), constructed through virtual reality (VR), augmented reality (AR), and mixed reality (MR), serve as digital learning spaces that provide crucial support for applying human-machine collaborative learning in science education. In science education, knowledge retention is the foundation for students to understand complex scientific concepts and to conduct further inquiry-based practices, directly influencing the long-term development of scientific literacy. However, there remains academic debate regarding whether EVE-based human-machine collaborative learning can effectively enhance knowledge retention, and its core mechanisms have yet to be systematically clarified. This study employs a meta-analysis approach to synthesize 80 experimental and quasi-experimental studies (including 88 effect sizes) conducted between 2014 and 2024, examining the impact of EVEs-based human-machine collaborative learning on science learners' knowledge retention. The results indicate that compared with traditional learning methods, EVEs-based human-machine collaborative learning has significant potential to improve knowledge retention. Although no statistically significant differences were found across moderating variables such as educational level and technology type, environments built with VR technology, high school contexts, operational learning, and presentation-based instructional approaches demonstrated more pronounced effects on knowledge retention. Subgroup analysis of instructional methods further revealed that their effectiveness is moderated by factors such as testing formats and educational levels. Accordingly, researchers and educational practitioners are encouraged to adopt systematic thinking in designing instructional practices, explore effective teaching methods, and apply diversified assessments to continuously monitor long-term impacts. This study aims to provide both theoretical insights and practical guidance for science educators to optimize EVEs-based instructional strategies and enhance knowledge retention.