Relationship of Grades in Electronic Subjects to Electronic Board Exam Performance

Abstract

This research involved the use of educational data mining methodologies to forecast and find relationships between electronic subjects and BSECE electronic board examination. Experiments based on real-world data from Cavite State University were presented in this paper. Based on earlier academic performance data, machine learning models were used to assess the ranking of topics that are crucial for electronic students to pass. WEKA, a modeling technique, was employed in this study. Furthermore, electronic grades had a significant impact on the academic achievement of undergraduate electronic communication students. This study contributes to the ongoing discussion on the relationship between essential BS electronics subjects and the Electronic Licensure Examination by evaluating the proposition of this research. During the data creation and manipulation process, challenges cannot be avoided, but they were filtered out until the data were suitable for processing. Overall, the study is beneficial since its findings can be applied in the future, and the model can be utilized as a reference and source of information for students and participants.