Analysis of clustering techniques for the detection of outliers in student performance in higher education institutions
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Abstract
In universities, data management faces significant risks due to widespread ignorance. However, the collection and analysis of this data is essential to ensure quality teaching and transparency in academic processes. Irregularities often occur in the recording of students' grades, whether due to involuntary errors, attempted fraud or acts of corruption. In this article, we apply clustering techniques, specifically the "K-means" method, to detect these potential cases in a timely manner. This methodology is based on the ability to identify anomalous values or data, which allows for improved incident detection and provides an effective tool for the control and supervision of academic records. Furthermore, these techniques can be used to optimize the accuracy of assessment processes, thus improving the quality of information available for decision-making. This facilitates the implementation of timely corrective measures, reducing the risk of errors or bad practices affecting both the academic performance of students and the reputation of the institution.
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