An Enhanced Machine Learning-Based Analysis of Teaching and Learning Process for Higher Education System

As the use of AI in colleges and universities becomes increasingly common, more studies on its impact on classroom instruction are likely to be conducted. However, this study highlights the lack of research on AI in pre-service teacher HES programs. The ML-based neural network model used in this study was an improvement on the standard classification neural network, and it successfully solved a multi-classification problem with multiple constraints as a supervised ML model. Further empirical research is needed to explore the use of AI by pre-service teachers, as it could facilitate the implementation of AI-based teaching in future classrooms if pre-service teachers have greater awareness and competency in AI. Our analysis suggests that the development of technically and pedagogically capable AI systems that can enhance quality education in various TLM environments is still a work in progress. Future work should focus on incorporating two equally important technologies into the model: blockchain technology, which can protect the institution's and students' data and business processes, and the internet of things, which can include devices that collect data to enable ongoing improvement of the TLM.