Prediction of MUET Results Based on K-Nearest Neighbour Algorithm

Abstract

The machine learning based prediction has been applied in various fields to solve different kind of problems. In education, the research on the predictions of examination results is gaining more attentions among the researchers. The adaptation of machine learning for the prediction of students’ achievement enables the educational institutions to identify the high failure rate, learning problems, and reasons for low student performance. This research is proposing the prediction of the Malaysian University English Test (MUET) results based on the K-Nearest Neighbour Algorithm (KNN). KNN is a powerful algorithm that has been applied in various prediction problems. The prediction of the MUET results would help the students and lecturers to be more well prepared and could improve the required English language skills accordingly before the actual examination. The MUET result prediction is based on the student’s English courses grades and there are 516 data of students’ results that have been collected from Universiti Teknologi MARA (UiTM) Dungun campus. The performance measurement that has been used are the mean accuracy, percentage error and mean squared error (MSE). In this research, the KNN prediction model has generated an acceptable performance with 65.29% accuracy. For future work, KNN could be modified or hybridized to further improve its performance. Furthermore, other algorithms could also be explored into this problem to further validate the best predictive model for the prediction of the MUET results.