Detecting Unusual Student Academic Grades Using the Isolation Forest Method
Keywords:
Anomalies, Detection, Isolation Forest, Student Grades, educationAbstract
This study aims to detect anomalies in the final grades of students in the Computer Networks course using the Isolation Forest algorithm. The data used comes from student grades for the current semester, including variables such as Discipline, Practice, Final Semester Exam (UAS), and Final Grades. The Isolation Forest algorithm was applied to identify students with grades that are inconsistent compared to their peers. The analysis results show that 10 students were detected to have final grades considered anomalous. These anomalies can be caused by various factors, including personal factors, differences in study effort, external influences, assessment methodology, and possible data errors. The distribution of grades was analyzed using histograms and boxplots, which indicated the presence of outliers in several variables. Correlations between variables were identified through a heatmap, showing that UAS has the highest correlation with Final Grades. Anomaly identification through scatter plot visualization helps in understanding the distribution and detection of anomalies in students' final grades. The conclusions of this study highlight the importance of further investigation to understand the causes of anomalies and provide appropriate interventions. This study also emphasizes the benefits of using data mining techniques in education to improve the quality of assessment and monitoring of students’ academic performance.
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Copyright (c) 2024 Hendra Nusa Putra, Nanda Tommy Wirawan (Author)
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