Enhanced constructive decision tree classification model for engineering students data

Abstract

Educational Data Mining (EDM) explains the exploration involved with the application of data mining, machine learning and statistical analysis to the enormous amount of data generated from educational institutions. At a high level, the sector seeks to develop and improve strategies for exploring this information, which frequently has multiple levels of significant hierarchy, so as to find new insights regarding the learning process of individuals in the context of such settings. Therefore, the EDM has contributed to theories of learning investigated by researchers in educational psychological science and the learning methodology. This sector is closely tied with the learning analytics, which are compared and contrasted. This work is a comparative analysis of various decision tree classification algorithms using Engineering students’ academic performance data. Educational Data Mining is the process which extracts knowledge through interesting patterns recognized from large amount of data from educational field. Learning related datasets with the performance of students obtained from educational institutions and processed before actual data mining or data analytics process. Data mining is one of the information discovering regions which is broadly used in the field of computer science. Furthermore is an inter-disciplinary area which has great impact on various other fields such as data analytics in prediction of risk factors in business organizations, medical forecasting and diagnosis, market basket analysis, statistical analysis and forecasting, predictive analysis in various other fields. Data mining has multiple forms such as text data mining, web data mining, visual data mining, spatial data mining and Educational data mining. As educational institutions is the source of generating quality students in order to tune them to become an eminent personality. All the educational institutions must be aware of the competency and academic level of every student in order to upgrade their performance. The implementation work is performed in Weka tool to compare the performance accuracy between the different types of decision tree classification algorithms namely J48, Entree and Enhanced Random Tree. These three classifier algorithms which are widely working with the Weka tool are used to classify this learning dataset and the result are obtained and has been evaluated & compared to identify the best decision tree classifier among them.