Investigating student academic performance based on student demographic data using prediction analysis techniques: a case study of Ministry of Education Malaysia / Rabie Awaluddin Mohamad

Recently many researchers have been interested in conducting studies that relates to student academic performance. Student academic performance is an indicator for the student to indicate whether the student is performing well in their studies. To have a better prediction of student academic performance is an advantage for providing the counter measure for student who are not likely to succeed or dropout student. These counter measure can be translate in variety of action such as improving student retention, prioritization of attention, creating improvement programs, providing adequate teaching and learning and many more. Due to this reason, this study has been implemented to investigate the effect of student demographic data towards academic performance. This sample data are focused on the MARA University Of Technology (UiTM) students who pursue a bachelor degree in the computing discipline. The objective for this study are to identify the relationship between student demographic data and academic performance using prediction analysis techniques, evaluate appropriate prediction analysis technique for predicting student academic performance and propose student academic prediction model based on the prediction finding. This study was conducted using two techniques which are observation and interview session in order to collect data from the Ministry of Education (MOE). The prediction of student academic performance starting with identifying and evaluation the related demographic data that influences to academic performance, selection of the most accurate academic performance prediction techniques and analysis of the prediction finding to understanding the relationship between the demographic data with academic performance in term of graduation on time and final grade point average. The finding of this study can assist the institution of higher learning in the early detection of students who are most likely to be dropouts and remedial action taken. Thus, the arrangement of teaching and learning can be made to address the needs of potential dropout students. Improvement of student academic performance can also influenced the quality of academic landscape in a holistic manner.