Progression analysis of students in a higher education institution using big data open source predictive modeling tool

This research work reports a systematic analysis of association of age with academic performance in a higher education institution. The correlation coefficient between key parameters of the student data were absorbed to derive the attributes that contributed strong positive influence on student results and also to identify the attributes that donated a negative impact. Further, a predictive model was developed to forecast the student performance in higher level modules based on the contextual factors. The outcome of the work showcased that negative correlation exists between age and the academic performance. On the contrary, positive correlation exists between lower level and higher level modules. Further, future research directions are discussed.