The development of the Metaverse nowadays has sparked widespread emotions among researchers, and correspondingly, many technologies have been derived to improve the human's sense of reality in the Metaverse. Especially, Extended Reality (XR), as an indispensable and important technology and research direction in the study of the metaverse, aims to bring seamless transformation between the virtual world and the real-world immersion to the experiential world. However, the technology we currently lack is the ability to simultaneously separate, classify, and locate dynamic human sound information to enhance human sound perception in complex noise environments. This article proposes a framework that utilizes FCNN for separation, algebraic models for positioning to obtain estimated distances, and SVM for classification. The dataset is built to simulates distance-related changes with accurate ground truth labels. The results show that our method can effectively separate, separate, and locate mixed sound data, providing users with comprehensive information about the content, gender, and distance of the speaking object in complex sound environments, enhancing their immersive experience and perception ability. Our innovation lies in the combination of three audio processing technologies and the framework proposed may well inspire future work on related topics.

Enhancing audio perception in augmented reality: a dynamic vocal information processing framework

LSTM-SHAP based academic performance prediction for disabled learners in virtual learning environments: a statistical analysis approach

The field of artificial intelligence is drastically advancing. This study aims to provide an overview of the integration of artificial intelligence into learning management systems. This study followed a bibliometric review approach. Specifically, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, 256 documents from the Scopus and Web of Science (WoS) databases over the period of 2004–2023 were identified and examined. Besides an analysis of the documents within the existing literature, emerging themes and topics were identified, and directions and recommendations for future research are provided. Based on the outcomes, the use of artificial intelligence within learning management systems offers adaptive and personalized learning experiences, promotes active learning, and supports self-regulated learning in face-to-face, hybrid, and online learning environments. Additionally, learning management systems enriched with artificial intelligence can improve students’ learning outcomes, engagement, and motivation. Their ability to increase accessibility and ensure equal access to education by supporting open educational resources was evident. However, the need to develop effective design approaches, evaluation methods, and methodologies to successfully integrate them within classrooms emerged as an issue to be solved. Finally, the need to further explore education stakeholders’ artificial intelligence literacy also arose. 

Impact of Artificial Intelligence on Learning Management Systems: A Bibliometric Review

Research in various fields has highlighted the benefits of using extended reality (XR) in many applications. However, the use of musical instruments in this context remains largely unexplored. This article presents a systematic review in which the existing literature is compiled and analyzed to identify the current state of research. A systematic search of four electronic databases of articles published in English since 2010 was conducted. After merging duplicates, a total of 2849 unique records were identified, of which 74 were included in the analysis. The review reveals a diverse landscape of XR applications, ranging from music learning to rehabilitation therapies. Diverse interaction modalities and factors affecting the musician's experience have been identified. XR technologies offer unprecedented opportunities for musicians; however, technological limitations pose challenges to creating fully satisfying experiences. This review is intended to guide future research that supports the development of new forms of musical expression in XR. 

Musical Instruments in Extended Reality: A Systematic Review

Abstract Academic performance (AP) is crucial for lifelong success. Unfortunately, many students fail to meet expected academic benchmarks, leading to altered career paths or university dropouts. This issue is particularly pronounced in the early stages of higher education, highlighting the need for the instructors of these foundational courses to have access to simple yet effective tools for the early identification of students at high risk of academic failure. In this study, we propose a streamlined conceptual model inspired by the Model of Human Behavior (MHB) to which we have incorporated two dimensions: capacity and willingness. These dimensions are assessed through the definition of three variables: Prior Academic Performance (PAP), Personality and Academic Engagement, whose measurements can easily be obtained by the instructors. Furthermore, we outline a Machine Learning (ML) process that higher education instructors can use to create their own tailored models in order to predict AP and identify risk groups with high levels of transparency and interpretability. The application of our approach to a sample of 322 Spanish undergraduates studying two mathematical subjects at a Spanish university demonstrates its potential to detect failure early in the semester with a precision that is comparable with that of more complex models found in literature. Our tailored model identified that capacity was the primary predictor of AP, with a gain-to-baseline improvement of 21%, and the willingness variables increasing this to 27%. This approach is consistent over time. Implications for instructors are discussed and an open prediction and analysis tool is developed. 

Study regarding the influence of a student’s personality and an LMS usage profile on learning performance using machine learning techniques

Predicting university student graduation is a beneficial tool for both students and institutions. With the help of this predictive capacity, students may make well-informed decisions about their academic and career paths, and institutions can proactively identify students who may not graduate and offer tailored support to ensure their success. The use of machine learning for predicting university student graduation has drawn more attention in recent years. Large datasets of student academic performance data can be used to train machine learning algorithms to identify patterns that are applicable in predicting future outcomes. In accordance with some studies, this approach predicts student graduation with an accuracy rate as high as 90%. Many systematic literature reviews (SLRs) have been conducted in this field, but there are still limitations, including not discussing the predictive models and algorithms used, a lack of coverage of the machine learning algorithms applied, small database coverage, keyword selection that does not cover all synonyms relevant to the investigation, and less specific data collection transparency. By delving into the limitations of existing SLRs on this topic, this research not only enhances the understanding of machine learning applications in forecasting student graduation but also fills a crucial gap in the literature. The inclusion of weaknesses in current SLRs provides a foundation for justifying the need for this study, emphasizing the necessity of a more nuanced and comprehensive review to advance the field and guide future research efforts in smart learning environments. This research conducts a thorough systematic review of the existing literature on machine learning-based student graduation prediction models from 70 journal articles from 2018 through 2023 that are pertinent. This review includes the various machine learning algorithms that have been implemented, the various academic performance data that was obtained from students, and the effectiveness of the models that have been developed. It also discusses the difficulties and potential advantages of utilizing machine learning to predict student graduation. The review indicates that the most common approach employed is the prediction of students’ academic performance, which relies on data obtained from the Learning Management System and Student Information System. The primary data utilized for prediction purposes consists Student retention and time of academic and behavioral information. Among the various algorithms employed, Support Vector Machine and Random Forest are the most commonly utilized. This study makes a significant contribution to the advancement of learner modules within the smart learning environment.

Predicting University Student Graduation Using Academic Performance and Machine Learning: A Systematic Literature Review

Machine learning for insulator: A systematic literature review

This research designs and implements an information system to manage web-based Semester Learning Plan using cloud computing technology, software, computers, and repositories as well as a serverless architecture. So far, the preparation of Semester Learning Plan in Department of Electrical Engineering UIN Sunan Gunung Djati Bandung is still done manually using a template in Microsoft Excel format. This method is considered ineffective because filling in takes a long time and the output is sometimes not uniform, both between lecturers and between courses. Based on this background, the author proposed the idea of creating a web-based Semester Learning Plan management information system. The method used to develop this system is the waterfall method which consists of five stages, namely requirements analysis, system and software design, implementation, testing, operation and maintenance. This information system using cloud computing technology and serverless architecture. The result of this research is a web-based Semester Learning Plan management information system. Through this system, lecturers no longer type manually into doc or xls format, but lecturers can directly input into the system or the web. The output is that the Semester Learning Plan can be downloaded and is in accordance with the applicable format and is in pdf format. The existence of this system can increase the effectiveness of lecturers in managing Semester Learning Plan.

Design and Implementation a Web-Based Semester Learning Plan Management Information System

Distance learning is a learning method used during the COVID-19 pandemic. In implementing distance learning, the lecturer uses two learning approaches, namely synchronous and asynchronous learning. Each of these learning approaches cannot be separated from challenges and obstacles from the lecturer and students. One of them is distance learning causes students to become less focused, even absent from the learning process. In this study, a virtual classroom monitoring service will be designed to help teachers monitor and evaluate the learning process with a synchronous approach. A virtual classroom monitoring service will require participants to meet face-to-face via video teleconferencing. The solution offered is to utilize Artificial Intelligence (AI) technology in the form of face detection, which will monitor the presence and facial expressions of learning participants through a camera installed on their computer. The research methods used and mapped are Design Research Methodology as the primary methodological basis and System Engineering as a supporting methodology for the Prescriptive Study stage. The result of this research is that the design of the smart online class monitoring service is successful by using a combination of two methodologies which consists of two applications, namely a desktop app for students and a website application for teachers. This service system can make distance learning with a synchronous approach more effective.

Smart Online Class Monitoring Service Design

Evaluating Trustworthiness in BYOD Online Exams via Smartphone Screenshots

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