{"title":"Microlearning in Immersive Virtual Reality: A User-Centered Analysis of Learning Interfaces","authors":"Amarpreet Gill;Derek Irwin;Linjing Sun;Dave Towey;Gege Zhang;Yanhui Zhang","doi":"10.1109/TLT.2025.3533360","DOIUrl":null,"url":null,"abstract":"The rapid changes in technology available for teaching and learning have led to a wide variety of potential tools that can be deployed to support a student's education experience. This article examines the learning interfaces for pedagogical virtual reality (VR) environments, including immersive VR (iVR). It also looks at how microlearning (ML) can be employed for instructional design at the sticking points of these interfaces. ML is an approach in which learning materials are provided in small bite-sized quantities and has been embraced as an ideal learning format for the modern learner. This study explores the research gap in ML literature regarding the ideal length of materials and modality when ML is employed for iVR. It does so through two experiments: in the first, students gave feedback on different interfaces for content and in the second, different lengths of text, video, and presentation style were tested for optimal user preference and comprehension. The findings show that preferences must be balanced against expected learning outcomes or desired level of engagement, but that fixed-point interfaces and longer texts may best be avoided. The study can be used to inform technology-enhanced learning delivery and can be used to guide policy regarding effective digital content, particularly within a VR environment.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"165-178"},"PeriodicalIF":2.9000,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Learning Technologies","FirstCategoryId":"95","ListUrlMain":"https://ieeexplore.ieee.org/document/10852361/","RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
The rapid changes in technology available for teaching and learning have led to a wide variety of potential tools that can be deployed to support a student's education experience. This article examines the learning interfaces for pedagogical virtual reality (VR) environments, including immersive VR (iVR). It also looks at how microlearning (ML) can be employed for instructional design at the sticking points of these interfaces. ML is an approach in which learning materials are provided in small bite-sized quantities and has been embraced as an ideal learning format for the modern learner. This study explores the research gap in ML literature regarding the ideal length of materials and modality when ML is employed for iVR. It does so through two experiments: in the first, students gave feedback on different interfaces for content and in the second, different lengths of text, video, and presentation style were tested for optimal user preference and comprehension. The findings show that preferences must be balanced against expected learning outcomes or desired level of engagement, but that fixed-point interfaces and longer texts may best be avoided. The study can be used to inform technology-enhanced learning delivery and can be used to guide policy regarding effective digital content, particularly within a VR environment.
期刊介绍:
The IEEE Transactions on Learning Technologies covers all advances in learning technologies and their applications, including but not limited to the following topics: innovative online learning systems; intelligent tutors; educational games; simulation systems for education and training; collaborative learning tools; learning with mobile devices; wearable devices and interfaces for learning; personalized and adaptive learning systems; tools for formative and summative assessment; tools for learning analytics and educational data mining; ontologies for learning systems; standards and web services that support learning; authoring tools for learning materials; computer support for peer tutoring; learning via computer-mediated inquiry, field, and lab work; social learning techniques; social networks and infrastructures for learning and knowledge sharing; and creation and management of learning objects.