Higher Education Student's Academic Performance Analysis through Predictive Analytics

As both localization and globalization continue to demand for knowledgeable and skilled individuals, it is imperative that higher education institutions (HEIs) produce quality graduates that can cope-up with it. Since then, this has been the goal of many of these institutions in the Philippines. However, the efforts of these institutions to deliver quality education aimed to equip the students often fall short when students are found to be under-performing academically because of over-exposure and addiction to social media. The challenge now with educators is to assess the performance of these students at an early stage and do the necessary interventions. With this, the researcher saw an opportunity to create a predictive classification model that can be used in predicting the academic performance of higher education students. Through Support Vector Machine (SVM), the academic profile of past students are categorized into performing and under-performing and this formed part of the predictive classification model. The accuracy of the model have been measured through cross-validation. The test results revealed that the model were able to appropriately predict if a student needs intervention or not with respect to academic performance.

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