An Application of J48 Classification Algorithm in Predicting Students’ Academic Performance
Hershey R. Alburo

Hershey R. Alburo, Faculty, College of Computer Studies, State-Owned University in Eastern Samar, Philippines.

Manuscript received on January 13, 2020. | Revised Manuscript received on January 24, 2020. | Manuscript published on February 10, 2020. | PP: 1172-1176 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1531029420/2020©BEIESP | DOI: 10.35940/ijitee.D1531.029420
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Abstract: This paper sets out to use J48 classification algorithm to predict students’ academic performance towards the end of the semester in the Data Structure course under the Computer Science Program. This algorithm aimed to help faculty in forecasting who among the students would likely to fail and who would make it until the end of the semester. In this way, the faculty could make remedial measures to help those struggling students pass the subject and advance to the next level, thus, increasing students’ success rate and retention in a Higher Education Institutions (HEI). This research employed a descriptive correlational design using Exploratory Data Analysis (EDA) for Data Mining in testing and verifying data to generate new information. Data mining is part of the Knowledge Discovery in Databases (KDD) process where it follows six steps: data selection, data pre-processing, data transformation, data mining, interpretation, and knowledge discovery. Step 1 includes gathering and selecting data for the study and for this purpose, a total of 103 students’ records were collected from the instructors for a period of two semesters, S.Y. 2014 -2015 & 2015 – 2016. Different evaluative criteria contained in the class records were utilized as attributes in predicting students’ academic performance. Steps 2 and 3 is pre-processing and transforming the data where it involves discarding those students who dropped/withdrawn from the semester, and converting the excel file into a comma separated values or .csv file, respectively. After these steps, step 4 or the application of J48 classification algorithm was utilized to discover classification rules. Step 5 refers to the tree visualization results where it identified the strongest predictor that most likely influence the students’ final average grade. Finally, step 7 shows the extracted information from the tree or the extracted rules that can be used by the administration, faculty and other stakeholders to improve the academic performance of the students. In particular, they might consider redesigning and restructuring teaching pedagogies to assist and focus more on struggling students. 
Keywords: Academic Performance, Classification Algorithm, Educational Data Mining, J48 Decision Tree
Scope of the Article:  Data Mining