Year: 2017
Type: article
Abstract: The student failure prediction at school has turn into a very complicated challenge owing to both the large number of factors which can influence the high performance of students and the balanced nature of student's databases which are maintaining by Educational Data Mining (EDM) techniques.The main goal of this study is to detect and remove the both irrelevant and redundant features that can be used to enhance the classification accuracy in predicting the student performance.This goal is achieved by establishing the novel technique for feature or attributes selection process by hybrid of Artificial fish swarm-Cuckoo Search optimization algorithm to remove the irrelevant features or obtaining relevant features.Also, Non-negative Matrix Factorization Clustering algorithm (NMFC) performs the removal of redundant feature or attributes which are presented in the relevant features.The performance of this technique is analyzed by using the student database which comprises the gathering of student's information from different colleges.For analyzing the performance of this technique, the comparative evaluation is carried out between the classifiers used in this research such as Prism and J48 without the feature selection and classifiers with our proposed technique.The experimental consequences illustrate that hybrid of artificial fish swarm-cuckoo search optimization feature selection along with NMFC approach is accomplishing high accuracy rate than other techniques.This study facilitates us to enhance the performance of the student's failure and dropout prediction.In other words, this helps to increase the accuracy of the classification result.
Authors: Sasi regha R., Dr Uma rani R.
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W2735040665