ABSTRACT
The digitalization of education that began at the age of 4.0 was a major influence on the IT industry as a whole. Evaluation is an essential aspect of the learning process. It is crucial to remember that the success of the educational program as a whole has a far smaller impact on student learning than does the quality of the assessment itself. Teachers say that students’ ability to gain access to technological resources is a complex and robust aspect of development. Numerous academics have conducted a wide range of studies on the topic of improving educational quality through digitization. However, there is currently a lack of high-quality, digitized data regarding the evaluation. As a result, this research introduces a brand-new swarm-optimized bagging C4.5 (SOBC) technique to investigate how professors and deans may learn to use technology to evaluate their students effectively in the post-4.0 era. Samples of student work are collected as a dataset to begin this investigation. The raw data are preprocessed using the normalization approach to remove any irrelevant or duplicate information. Linear discriminant analysis (LDA) is used to retrieve the important features from the normalized data. The bagging C4.5 method is then applied to the pupils’ performance to categorize it while the performance of the bagging C4.5 method is enhanced by particle swarm optimization (PSO). The proposed strategy’s effectiveness is investigated, and it is contrasted with other strategies that are presently in use. It has been established that this lecture is digitally assessed in higher learning, making it available as a prospective academic reference.
