Technology-Enhanced Collaborative and Blended Learning Ensemble Learning: An Approach in Artificial Intelligence

Abstract

Progressive teaching methods are methods of improving teaching and learning performance. A variety of innovative teaching methods are now in use around the world. In addition to face-to-face classes, hybrid classes also include e-learning. The use of technology and multimedia is detailed. It covers the use of smart devices for various tasks such as teaching, designing surveys, assessing students, providing feedback, and research methodology. The application of innovative teaching and learning methods is very important if we want to inspire and arouse students' desire and enthusiasm for learning. The role of education is not only for faculty to teach, but to make it understandable to students from diverse cultural and linguistic backgrounds and to quickly familiarize them with the standards expected of them. It's common for students to drop grades because they don't know what level they're at or what their instructor expects of them. Teachers should therefore strive to use innovative methods in such a way that the student's learning process is as fluid as possible and the methodologies used are conducive to learning. Innovative teaching and learning methods such as short lectures, simulations, role-plays, portfolio development, and problem-based learning (PBL) are helping to keep pace with rapid technological advances and develop the jobs needed in the near future. Decades ago, in the field of machine learning and data mining, the development of methods of ensemble learning received significant attention from the science community. Machine integration techniques incorporate multiple learning acquisition skills and better performance of guesswork than you would find in any available learning skills alone. Combining multiple learning models is demonstrated in thought and experimentation providing better performance than single-foundation students. In a book, mix learning algorithms form a dominant and high-level approach to high throughput performance, thus applied to real-world problems ranging from face-face-to-facetional recognition through classification and medical diagnosis in financial forecasting.