The use of virtual reality (VR) in formal education has burgeoned in recent years, with enthusiastic uptake by teachers and instructors across a wide range of subject areas and academic disciplines. We conducted a systematic meta-analysis of effects of VR on Science, Technology, Engineering, and Mathematics learning from middle school through postsecondary education. Eighteen published journal articles met inclusion criteria, yielding 52 effects from 2214 participants. VR has an overall positive effect on learning of g = .33, with the largest significant moderator effects for redesign of VR, classroom settings, science learning, desktop displays, and all types of learning outcomes (factual, conceptual, and transfer). Results depart somewhat from Howard's (2019) and Wu et al.'s (2020b) meta-analyses of VR across learning and treatment, multiple domains, and ages; in our study, desktop VR showed larger effects than head-mounted display, and we found positive effects for all learning outcome types. One trend within studies showing the largest effects is the inclusion of active learning techniques, which may shift learners' focus from interesting but irrelevant details to the most learning-relevant aspects of the VR learning environment.

Learning from multiple representations (MRs) is not an easy task for most -people, despite how easy it is for experts. Different combinations of representations (e. g., text + photograph, graph + formula, map + diagram) pose different challenges for learners, but across the literature researchers find these to be challenging learning tasks. Each representation typically includes some unique information, as well as some information shared with the other representation(s). Finding one piece of information is only somewhat challenging, but linking information across representations and especially making inferences are very challenging and important parts of using multiple representations for learning. Coordination of multiple representations skills are rarely taught in classrooms, despite the fact that learners are frequently tested on them. Learning from MRs depends on the specific learning tasks posed, learner characteristics, the specifics of which representation(s) are used, and the design of each representation. These various factors act separately and in combination (which can be compensatory, additive, or interactive). Learning tasks can be differentially effective depending on learner characteristics, especially prior knowledge, self-regulation, and age/grade. Learning tasks should be designed keeping this differential effectiveness in mind, and researchers should test for such interactions.