Enhancing Undergraduate Business Student Performance through Education Data Mining: A Study on Classification, Regression, and Pattern Analysis
Abstract
This is a research paper that focuses on education data mining and its impact on improving the education system in Asian countries. The paper highlights the importance of using data mining to improve the quality of education, enhance students' performance, and bring positive changes to the educational system. It emphasizes the need for efficient pattern and related data to identify the most significant courses for the final result. The paper presents a problem statement that focuses on the performance of undergraduate learners in Asian countries. The authors state that there is no linear relevance that exists in the educational structure of these countries, and it generates a tremendous problem for faculties and learners to track their institutional performance. The authors aim to predict the academic results of these students and identify their pattern to sort out the problem and help the faculties and teachers judge their students in danger. The scope of the research is to use a generalized framework to predict the CGPA of business undergraduate students using a DNN approach. The authors use student data gathered from a reputed university in Bangladesh to predict student performance and apply the proposed method to improve the students' academic performance and the quality of teaching procedures. The objectives of the paper are to predict student performance, find efficient patterns, and related data to search for the most significant courses for the final result. The authors aim to use classification methods such as Random Forest, Gradient Boosted Tree, Tree Ensemble, Decision Tree, SVM, and KNN to predict the level of student performance. They use the DNN model to predict students' CGPA of business undergraduate students with a minimum error rate based on transcript data of the initial four semesters. Overall, the paper highlights the importance of education data mining and its impact on improving the education system in Asian countries. It provides a useful framework for predicting student performance and improving the quality of teaching procedures in educational institutions.
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