The Metaverse is a network of 3D virtual worlds supporting social connections among its users and enabling them to participate in activities mimicking real life. It merges physical and virtual reality and provides channels for multisensory interactions and immersions in a variety of environments [1]. Metaverse is considered the third wave of the Internet revolution, and it is built on new and emerging technologies such as Extended Reality (XR) and Artificial Intelligence (AI). Research on the impact of the Metaverse on education exploded in 2022. Here we explore learning across the Metaverse and propose a new and innovative theoretical framework by reviewing literature and synthesizing best practices in designing metaverse learning environments. This Ecosystem consists of four major hubs: A) Instructional design and performance technology hub, B) Knowledge hub, C) Research and technology hub, and D) Talent and training hub. Common to all four hubs are the factors in the three wheels (Fig. 8): a) Infrastructure, business industry, and communication: b) technology access and equity, and c) user rights, data security, and privacy policy. We believe that this framework can help guide emerging research and development on the applications of Metaverse in education. We also hope this paper can serve as a launch pad for the special issue on Metaverse and the Future of Education supported by the IEEE Education Society.

Modern online education has the capacity to provide intelligent educational services by automatically analyzing substantial amounts of student behavioral data. Knowledge Tracing (KT) is one of the fundamental tasks for student behavioral data analysis, aiming to monitor students' evolving knowledge state during their problem-solving process. In recent years, a substantial number of studies have concentrated on this rapidly growing field, significantly contributing to its advancements. In this survey, we will conduct a thorough investigation of these progressions. Firstly, we present three types of fundamental KT models with distinct technical routes. Subsequently, we review extensive variants of the fundamental KT models that consider more stringent learning assumptions. Moreover, the development of KT cannot be separated from its applications, thereby we present typical KT applications in various scenarios. To facilitate the work of researchers and practitioners in this field, we have developed two open-source algorithm libraries: EduData that enables the download and preprocessing of KT-related datasets, and EduKTM that provides an extensible and unified implementation of existing mainstream KT models. Finally, we discuss potential directions for future research in this rapidly growing field. We hope that the current survey will assist both researchers and practitioners in fostering the development of KT, thereby benefiting a broader range of students.

This research project investigates the impact of Prompt Engineering, a key aspect of ChatGPT, on college students' information retrieval in flipped classrooms. In recent years, an increasing number of students have been using AI-based tools such as ChatGPT rather than traditional research engines to learn and to complete course assignments. Despite this growing trend, previous research has largely overlooked the influence of prompt engineering on students' use of ChatGPT and effective strategies for improving the quality of information retrieval in learning settings. To address this research gap, this study examines the information quality obtained from ChatGPT in a flipped classroom by evaluating its effectiveness in task completion among 26 novice undergraduates from the same major and cohort. The experimental results provide evidence that proficient mastery of prompt engineering improves the quality of information obtained by students using ChatGPT. Consequently, by acquiring proficiency in prompt engineering, students can maximize the positive impact of ChatGPT, obtain high-quality information, and enhance their learning efficiency in flipped classrooms.

Even though existing studies have shown the positive effects of social network sites (SNSs) on learning behaviors and outcomes, how SNS can cause learners to develop negative perceptions about their learning activities is still understudied. Here we report a one-of-a-kind study that examines the negative impact of privacy concern on students’ learning behaviors online while using SNSs. Based on the stimulus-organism-response (SOR) paradigm, our study focuses on how privacy concern develops knowledge hiding perceptions of the learners, thereby affecting their online collaboration. Further, our study also explores the moderating role of perceived supervisory support between the relationship of privacy concern and knowledge hiding perceptions. Participants in the study were 282 students pursuing their studies in several higher educational institutions in China. Our findings show that two types of privacy concern (abuse of knowledge and unauthorized access to private data) can positively influence students’ knowledge hiding perception, which subsequently can negatively affect their participation in online collaborative learning. On the other hand, perceived supervisory support only moderates the relationship between privacy concern about abuse of knowledge and knowledge hiding perceptions. Based on these findings, we draw practice implications for supervisors and students in Universities.