Journal of Web Engineering

Leveraging the Synergy of IPv6, Generative AI, and Web Engineering to Create a Big Data-driven Education Platform

2023-11-26T10:39:52+01:00

The rapid advancement of network technology in China has significantly accelerated the implementation of information technology in higher education. Through the utilization of computer technology, multimedia technology, big data technology, artificial intelligence technology, and network communication technology, the integration of these technologies in university teaching has become widespread. This paper presents an analysis and discussion on the utilization of the latest IPv6 network transmission protocol technology to enhance the application of data collection in university education, with a specific focus on gathering information related to university faculties. By leveraging web engineering and multimedia technology as fundamental components, the network facilitates the sharing of educational resources among students, thereby enabling the reform of management approaches, fostering educational progress in China, and establishing a comprehensive big data-driven education platform specifically tailored to colleges and universities. Additionally, the incorporation of big data visualization and analysis tools allows for easy retrieval of existing university educational information, facilitates the creation of data charts, and expedites the utilization of data for its inherent value. Finally, the proposed approach employs generative AI to collect and analyze feedback from students and educators, followed by the application of web engineering techniques to continuously enhance the online education platform based on this feedback.

Enhancing English Language Education Through Big Data Analytics and Generative AI

2023-12-05T08:39:25+01:00

This research paper provides a comprehensive examination of the significant impact of big data analytics and generative artificial intelligence (GAI) on the field of English language education. Utilizing a meticulous framework rooted in the evolutionary network influence of big data, our study critically analyzes several aspects of student engagement, learning motivation, self-efficacy, and the existing disparities among learners. Our primary objective is to enhance students’ active participation, intrinsic interest, and self-confidence in the context of English language learning, thus advancing their overall linguistic competence. To achieve these objectives, our study systematically integrates the concept of practice education with a multidisciplinary approach, leveraging the power of big data analysis and GAI, and reveals profound insights into student learning behaviors, preferences, and personalized educational needs. We employ advanced techniques for meticulous data processing and interpretation, empowering educators to make data-informed decisions and tailor pedagogical strategies to meet the unique requirements of each student. This data-driven pedagogical approach not only facilitates the implementation of effective teaching methodologies but also effectively addresses the disparities stemming from diverse student backgrounds, thereby fostering a more inclusive and personalized learning environment.

Music Curriculum Research Using a Large Language Model, Cloud Computing and Data Mining Technologies

2023-11-20T10:41:42+01:00

This paper presents a method to enhance the scientific nature of the music curriculum model by integrating a large language model, cloud computing and data mining technology for the analysis of the music teaching curriculum model. To maintain the integrity of the mixing matrix while employing the frequency hopping frequency, the paper suggests dividing the mixing matrix into a series of sub-matrices along the vertical time axis. This approach transforms wideband music signal processing into a narrowband processing problem. Additionally, two hybrid matrix estimation algorithms are proposed in this paper using underdetermined conditions. Furthermore, utilizing the estimated mixing matrix and the detected time-frequency support domain, the paper employs the subspace projection algorithm for underdetermined blind separation of music signals in the time-frequency domain. This procedure, along with the integration of the estimated direction of arrival (DoA), enables the completion of frequency-hopping network station music signal sorting. Extensive simulation teaching demonstrates that the music curriculum model proposed in this paper, based on a large language model, cloud computing and data mining technologies, significantly enhances the quality of modern music teaching.

Transformative Technologies in the Evaluation of a Vocational Education System

2023-12-25T06:59:02+01:00

The increasing demand for vocational education has necessitated the presence of highly skilled teachers. This study presents a novel framework for the effective management of vocational college instructors’ professional development through the utilization of advanced technologies. The system utilizes deep learning technology to analyze many data points, including academic achievements, teaching experience, student comments, and professional activities, in order to assess the performance and potential of teachers. The system evaluates both the positive and negative aspects, offers customized training programs, and enhances the delivery of instruction through the utilization of a generative language model. The effectiveness of the system is supported by a case study, which demonstrates enhancements in talent management, professional development, teaching quality, and student happiness. This proposed solution aims to improve vocational education by empowering educators and transforming the processes of evaluation, support, and guidance throughout their professional trajectories.

Flight Price Prediction Web-based Platform: Leveraging Generative AI for Real-time Airfare Forecasting

2023-12-26T05:24:48+01:00

The aviation business encounters difficulties in correctly and swiftly predicting flight fares due to the dynamic nature of the sector. Factors such as variations in demand, fuel costs, and the intricacies of various routes have an impact on this. This work presents a new method to tackle this issue by utilizing generative artificial intelligence (GAI) approaches to accurately forecast airfares in real-time. This paper presents a novel framework that integrates generative models, deep learning architectures, and historical pricing data to improve the precision of future flight price predictions. The study employs a GAI within a cutting-edge web engineering framework. This approach is designed primarily to gather knowledge about complex patterns and relationships present in historical airline data. Through the utilization of this methodology, the model is able to accurately perceive complex connections and adjust to ever-changing market conditions. Our model utilizes deep neural networks to effectively handle various circumstances and extract vital information, so facilitating a comprehensive comprehension of the intricate elements that impact flight cost. Moreover, the suggested approach places significant emphasis on precisely predicting upcoming occurrences in real-time, facilitating prompt reactions to market volatility and offering a valuable resource for airlines, travel agents, and customers alike. In order to enhance the accuracy of real-time forecasts, we utilize a web-based platform that allows for smooth interaction with live data streams and guarantees swift updates. The results demonstrate the model’s capacity to adjust to dynamic market conditions, rendering it an attractive option for stakeholders in search of precise and current forecasts of flight prices.