Predicting Students' Performance Using ID3 And C4.5 Classification Algorithms
This addresses the problem for educational institutions needing to forecast student outcomes to support interventions, but it is incremental as it uses existing methods on new data.
The paper tackled predicting students' academic performance by applying ID3 and C4.5 classification algorithms to historical student data, achieving predictions for general and individual future performance.
An educational institution needs to have an approximate prior knowledge of enrolled students to predict their performance in future academics. This helps them to identify promising students and also provides them an opportunity to pay attention to and improve those who would probably get lower grades. As a solution, we have developed a system which can predict the performance of students from their previous performances using concepts of data mining techniques under Classification. We have analyzed the data set containing information about students, such as gender, marks scored in the board examinations of classes X and XII, marks and rank in entrance examinations and results in first year of the previous batch of students. By applying the ID3 (Iterative Dichotomiser 3) and C4.5 classification algorithms on this data, we have predicted the general and individual performance of freshly admitted students in future examinations.