This study aims to clearly define the concept of metaverse class and develop a framework for the design and implementation of effective and efficient metaverse class. This study developed a basic framework through a literature review and interviews of 5 experts with experience in implementing metaverse class. After that, the content validity was conducted twice using CVI (Content Validity Index), and the development of the last version was completed by correcting and supplementing the content validity and expert opinions. The significance of this study is as follows: First, the concept of the metaverse class was defined from a pedagogical perspective. Second, a systematic framework for instructional design and implementation was presented by sufficiently reflecting the concept and characteristics of the metaverse class. Third, it can be used effectively and efficiently for the design and implementation of metaverse class regardless of the school level. This result is expected to serve as a practical guide for instructors who want to design and implement the metaverse class, and as a reflection and monitoring tool for the metaverse class.
This study aims to analyze the self-directed learning abilities recognized by university students that are needed in a non-face-to-face class environment and to derive a plan to create an environment and conditions for students to demonstrate the self-directed learning ability. To do so, an online survey of university students in the region of Gangwon-do using questionnaires to measure their self-directed learning abilities from the perspective of the learning process was conducted. The responses of 357 students were analyzed using a paired-sample t-test, Borich needs assessment model, and the Locus for Focus Model. As a result, it was found that those students had a top priority for the ability to identify learning resources and manage learning in a non-face-to-face class environment. Next, it was confirmed that the students had a second-priority for choosing a learning strategy and attributing results to effort. Based on the analysis results, the study suggests the needs for support for learning spaces and smart devices, vitalization of online learning communities, provision of open learning resources, introduction of multifaceted evaluation methods, and support for individualized learning through online learning spaces, for students to demonstrate self-directed learning abilities in a non-face-to-face class environment.
A novel algorithm to detect road lanes in videos, called recursive video lane detector (RVLD), is proposed in this paper, which propagates the state of a current frame recursively to the next frame. RVLD consists of an intra-frame lane detector (ILD) and a predictive lane detector (PLD). First, we design ILD to localize lanes in a still frame. Second, we develop PLD to exploit the information of the previous frame for lane detection in a current frame. To this end, we estimate a motion field and warp the previous output to the current frame. Using the warped information, we refine the feature map of the current frame to detect lanes more reliably. Experimental results show that RVLD outperforms existing detectors on video lane datasets. Our codes are available at https://github.com/dongkwonjin/RVLD.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.