Indian Journal of Science and Technology

Abstract

Objective: Everyone has the right to education. For Higher Educational Institutions, students serve as its best asset. The prediction of students’ success in their academic performance is then vital for it will benefit both students and professors, enabling the latter to do proactive measures and find ways in helping students learn, ultimately improving their academic performance. Methods: This study utilized the Data mining technique, specifically; the J48 algorithm was used to create the Decision Tree Model in predicting the Student Performance in Data Structures and Algorithms. For model accuracy, K-fold cross-validation and Receiving Operating Characteristics Curve (ROC) was used. The datasets used were collected from the grades of 2nd year BSIT students enrolled during the school year 2015-2016. Findings: The generated Decision Tree Model results showed that Finals had the highest instance and in predicting student performance in the Data Structures and Algorithms subject. It also shows that Finals has the highest factor to receive either of the following remarks: Pass, Failed or Conditional. The model was also able to identify 85.31% accuracy for the attribute Pass, 79.41% accuracy for the attribute Conditional and 91.67% accuracy for the attribute Failed. Further, the Decision Tree Model likewise revealed that for the student to pass the Data Structures and Algorithms subject they should have a grade higher than 66.12% in Midterms and a grade higher that 72.30% in Finals. Application/Improvements: The use of the data driven system can be used by institutions to track student performance. Data analysis is a key component to further strengthen their policies and do intervention programs where it is highly needed. Further, for more improvement of this study additional data mining techniques can be applied. 

Keywords: Data Mining, Data Structures and Algorithms, Decision Tree Algorithm, Information Technology, Student Performance