Design and Use of Machine Learning Techniques Using DSS Model

Computer Science

The basic goal of every school is to provide its pupils with the most advanced knowledge and abilities possible. Understanding which pupils need more help and then providing that help is crucial to achieving the desired outcome. Lack of accuracy, poor analysis of the defining qualities, and an insufficient database provide challenges and impediments for information systems that cannot reap the full benefits of EDSS. In order to better the prediction process, it is essential to do a thorough literature research and gather the best predictive methods. The purpose of this research was to apply machine learning techniques to develop a classifier for predicting economics majors' long-term performance. This work uses machine learning to present a DSS model built on top of a knowledge base that can be used to assess students' progress during the semester. This paper applies data mining to the college student information management system, mines student evaluation information data, employs data mining technology in the design of student evaluation information modules, and uncovers the factors that affect student development and the various relationships between them. The foundation for effective education is predictive testing of students' knowledge and data-informed pedagogical choices.

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