STUDENT’S PERFORMANCE PREDICTION USING EDUCATIONAL DATA MINING - QUAH, MIN QI (IRC, 2020)
Every educational institution generates a large amount of data related to the registered students. If that data is not analyzed and used for useful purposes, then all efforts will be wasted as there is no future use of the data occurring. Academic institutions such as universities, colleges and schools usually do not have tools or process flow that uses the big data from the systems (e.g. enrollment and performance systems) to perform prediction on student’s performance. By using the big data, academic institutions shall be able to predict student’s performance for strategic decision making (e.g. improve current teaching model, identify low performing students etc.). The most used technique for prediction is educational data mining. This study is conducted with the aim to better understand educational data mining. The main objective of this study is to appropriate educational data mining techniques and select suitable technique(s) to implement analyses and prediction on the big data obtained. The method that used to conduct this study is design science. The outcome of this study is acquiring which classification method has the highest accuracy in predicting student’s performance and visualizing the prediction results in a Power BI Dashboard. The findings of this study may contribute towards the improvement of educational institution’s teaching models and student performance.