Classifying the 1st Year Academic Performance of Nursing Students at Tobruk University via Data Mining with SQL and WEKA Tool - Mendoza, James Neil B; Buhat-Mendoza, Dorothy (Scientific Journal for Faculty of Science-Sirte University, 2024)
Data mining is a tool that can identify hidden patterns affecting academic success. The objective of this research is to investigate and classify the academic performance of first-year nursing students at Tobruk University. This study concentrates on the preliminary stage of data preprocessing and data mining classification. The methodology to classify academic performance includes data acquisition and preprocessing stage using SQL commands to extract student data from the university database and undergo basic cleaning and transformation. Initial classification and data analysis followed using the preprocessed data, further refined by the WEKA data mining tool algorithms including BayesNet, NaiveBayes, JRIP, and J48. Results of the preliminary data distribution and initial classification show that J48 is the most accurate model creator using regular classification (88.6619) and attribute selector (97.8261). Relative to the other three algorithms, J48 also recorded the highest precision, recall, F1 measure, and the lowest error measurement. The recorded Kapa stat of J48 (0.7779 and 0.9599) also proves the significance of the classification result, interpreted as substantial to near-perfect reliability scores respectively, which BayesNet and JRIP also attained. The results reveal that Finals (final exam result) attribute is the biggest factor in determining the descriptive Rating of a student’s grade at the university. The created model can serve as a classifier for future test sets and may provide a foundation for further research and model development. Further modification will help discover what factors contribute to student success and what applicable interventions are needed to improve the academic achievement of students in the nursing program.