Students dropout factor prediction using EDM techniques

This study analyses the factors affecting students' academic performance that contributes to the prediction of their failure and dropout using educational data mining. This paper suggests the use of various data mining techniques to identify the weak students who are likely to perform poorly in their academics. WEKA, an open source tool for data mining was used to evaluate the attributes predicting school failure. The data set comprised of 670 student records with 57 attributes of students registered between year 2011 and 2013 in a reputed school in Kerala, India. Various classification techniques like induction rules and decision tree have been applied to the data. The results of each of these approaches have been compared to select the one that achieves high accuracy.