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.

CHAID is one of the classification tree algorithms of the Automatic Interaction Detector that has been developed for categorical variables. In fact, CHAID is a technique that recursively partitions (or splits) a population into separate and distinct segments. These segments, called nodes, are split in such a way that the variation of the response variable (categorical) is minimized within the segments and maximized among the segments.

In their research before constructing the CHAID model they used feature selection techniques in reduction of computation time and enhances the predictive accuracy of the model. They used Pearson chi-square test method and it evaluated features individually by measuring their chi-squared statistic with respect to the classes.

In their analysis it was found that the overall model prediction accuracy of CHIAD prediction model was 44.69% and it indicated that the CHAID model could correctly classify for about half of the students population. The accuracy of the constructed  model was compared with other models and it was found to be higher than the accuracy of earlier models.

Finally they suggested that even though CHAID model handled small and unbalanced data set, it could be worked out effectively with better predictive accuracy and with some principle pre-processing techniques, the predictive accuracy can be further improved.

Reference: M. Ramaswami and R. Bhaskaran, “A CHAID based performance prediction model in educational data mining,” arXiv preprint arXiv:1002.1144, 2010.