LAUSR

In recent years, the education system has undergone a significant transformation due to technological advancements. E‐learning has become an essential part of the education system, especially during the COVID‐19 pandemic. However, one of the challenges faced by e‐learning is student retention and engagement. With the advent of Web 3.0, there is a potential for improving e‐learning environments through blended learning. Blended learning combines online learning with face‐to‐face instruction, allowing for a more personalized learning experience. Web 3.0, also known as the Semantic Web, utilizes advanced technologies such as artificial intelligence, machine learning, and the Internet of Things to create a more intelligent and interactive web environment. The integration of Web 3.0 and blended learning has the potential to revolutionize the e‐learning experience and increase student retention and engagement. This special cluster has provided a platform to explore the concept of Web 3.0 and blended learning, their potential benefits in the e‐learning environment, and the challenges faced in their implementation. This special cluster includes seven articles and the highlights of these contributions are described below. In the first paper titled “From Offline Learning to Online Learning: Moderating Effect of Relationship Inertia and Satisfaction,” the author uses the questionnaire method and structural equation model to analyze the influencing factors of switching intention and behavior from offline learning to online learning. The results show that the switching intention is significantly affected by perceived usefulness, perceived ease of use and computer self‐efficacy, and negatively affected by the perceived risk. To achieve the effectiveness of Web 3.0 with a blended learning process, the Improved Neighbor Propagation Algorithm (INPA) with a concept mind mapping tool is introduced in the second paper titled “Application of Improved Neighbor Propagation Algorithm in International Communication and Cooperation to Promote Internationalization of Higher Education.” The numerical results of the proposed INPA algorithm show the improvement of student performance by 98.9%, satisfaction ratio by 97.5%, engagement level by 95.6%, accuracy ratio by 98.1%, precision ratio by 96.2%, and recall ratio by 95.4% compared to other existing methods. The third paper titled “Holistic Transfer Educational Learning Approach for Higher Education” proposes the Holistic Transfer Educational Learning Approach to strengthen specific instructional priorities, general practices in transferring knowledge, and socialization of learning for higher education. Statistics show that the suggested framework is trustworthy, with the simulation analysis determining an accuracy ratio of 98.2%, validation in the secured score of 95.3%, assessment of numerous learners ratio of 98.4%, error rate analysis of 23.4%, and overall performance ratio of 94.1%. The fourth paper titled “A new model based on the extended COPRAS method for improving performance during the accreditation process of Indian Higher Educational Institutions” aims to perform an empirical evaluation to rank various quality parameters suggested