Performance Prediction Models on Educational Data

In educational systems like Learning Management Systems Students’ academic performance depends on diverse factors like personal, socio-economic, psychological and other environmental variables. Each of these factors can affect the student overall performance in different weights. Based on the level how each of these factor is appearing in the student education several learning patterns can be identified on each of these students. Based on these learning patterns prediction models can be implemented such that they include all these variables for the effective prediction of the performance of the students. The prediction of student performance with high accuracy is beneficial to identify the students with low academic achievements which enable the educators to assist those students individually.

In M. Ramaswami and R. Bhaskaran [2010] research they argued that the student performance could depend on diversified factors such as demographic, academic, psychological, socio-economic and other environmental factors. Based on these factors they constructed a CHAID prediction model with highly influencing predictive variables obtained through feature selection technique to evaluate the academic achievement of students.