A virtual learning environment of the Chinese University of Hong Kong

This paper introduces a scalable virtual learning environment of the ChineseUniversity of Hong Kong;an explicitly geographical, immersive, and sharable 3Dlearning space with comprehensive social elements. It is characterized by multiuser collaborative modeling, group learning approaches of geo-collaboration,social space-oriented hierarchical avatars, and knowledge exchanging and sharingbased on virtual geographic experiments. Applications for the purpose of publiceducation and virtual geographic experiment, and indicated future works provethe possibility to offer a greater opportunity to foster interdisciplinary collaborations, revitalize teaching patterns and learning contents, improve learners’cognitive abilities to solve problems, and enhance their understanding of scientificconcepts and processes.

Collaborative Virtual Learning in Education in STEM Education

There has been a recent explosion of interest from academics across a wide range of disciplines in the use of Multi-User Virtual Environments for education,driven by the success of platforms such as Internet Communication Technology learning skills in higher education.While digital virtual worlds are used in the 21st century learning,advances in the capabilities and the spread of technology have fed a recent boom in interest in massively multi-user 3D virtual worlds for entertainment,and this in turn has led to a surge of interest in their educational applications.As these platforms are used more often as environments for teaching and learning,there is increased need to integrate them with other institutional systems,Web-based Virtual Learning Environments(VLE)in particular.In this paper,we briefly review the use of virtual worlds for education,from informal learning to formal instruction,and consider what is required to turn a virtual world from a Multi-User Virtual Environment into a fully fledged 3D Virtual Learning Environment(VLE).In this we focus on the development of Moodle—a system which integrates the popular 3D virtual world of Second Life with the open-source Virtual Learning Environment.

A Hybrid Virtual Cloud Learning Model during the COVID-19 Pandemic

The COVID-19 pandemic has affected the educational systems worldwide,leading to the near-total closures of schools,universities,and colleges.Universities need to adapt to changes to face this crisis without negatively affecting students’performance.Accordingly,the purpose of this study is to identify and help solve to critical challenges and factors that influence the e-learning system for Computer Maintenance courses during the COVID-19 pandemic.The paper examines the effect of a hybrid modeling approach that uses Cloud Computing Services(CCS)and Virtual Reality(VR)in a Virtual Cloud Learning Environment(VCLE)system.The VCLE system provides students with various utilities and educational services such as presentation slides/text,data sharing,assignments,quizzes/tests,and chatrooms.In addition,learning through VR enables the students to simulate physical presence,and they respond well to VR environments that are closer to reality as they feel that they are an integral part of the environment.Also,the research presents a rubric assessment that the students can use to reflect on the skills they used during the course.The research findings offer useful suggestions for enabling students to become acquainted with the proposed system’s usage,especially during theCOVID-19 pandemic,and for improving student achievementmore than the traditional methods of learning.

TELF-PSPOC: Three-layer Ensemble Learning Framework for Predicting Student Performance of Online Courses

Virtual learning environment(VLE)MOOC provides large-scale data of resources,activities,and interactions within a course structure for predicting student performance.But it is challenging to extract and learn efficient features from student behaviors.In this paper,a three-layer ensemble learning framework for predicting student performance of online courses(TELF-PSPOC)at an early phase is proposed to analyze data collected from Open University Learning Analytics Dataset(OULAD).First,feature augmentation of student behavior is proposed to enrich current features of student performance,including pass rate and grades of all staged tests,daily clicks of online resources.Second,three-layer ensemble feature learning with heterogeneous classifiers(TEFL-HC)is proposed to benefit the integration of tree model and neural network.Compared with current two-layer ensemble learning,pretraining of features prevents overfitting while using nonlinear regression.The experiment shows that our TELF-PSPOC performs better than several baseline models.Besides,the relationship of the learning results and student behavior via VLE is further discovered.