An Analysis of students’ performance using classification algorithms

Abstract

In recent years, the analysis and evaluation of students performance and retaining the standard of education is a very important problem in all the educational institutions. The most important goal of the paper is to analyze and evaluate the school students performance by applying data mining classification algorithms in weka tool. The data mining tool has been generally accepted as a decision making tool to facilitate better resource utilization in terms of students performance. The various classification algorithms could be specifically mentioned as J48, Random Forest, Multilayer Perceptron, IB1 and Decision Table are used. The results of such classification model deals with accuracy level, confusion matrices and also the execution time. Therefore conclusion could be reached that the Random Forest performance is better than that of different algorithms. I. Introduction Data Mining could be a promising and flourishing frontier in analysis of data and additionally the result of analysis has many applications. Data Mining can also be referred as Knowledge Discovery from Data (KDD).This system functions as the machine-driven or convenient extraction of patterns representing knowledge implicitly keep or captured in huge databases, data warehouses, the Web, data repositories, and information streams. Data Mining is a multidisciplinary field, encompassing areas like information technology, machine learning, statistics, pattern recognition, data retrieval, neural networks, information based systems, artificial intelligence and data visualization. The application of data mining is widely prevalent in education system. Educational data mining is an emerging field which can be effectively applied in the field of education. The educational data mining uses several ideas and concepts such as Association rule mining, classification and clustering. The knowledge that emerges can be used to better understand students' promotion rate, students' retention rate, students' transition rate and the students' success. The data mining system is pivotal and crucial to measure the students' performance improvement. The classification algorithms can be used to classify and analyze the students' data set in accurate manner. The students' academic performance is influenced by various factors like parents' education, locality, economic status, attendance, gender and result. The main objective of the paper is to use data mining methodologies to study and analyze the school students' performance. Data mining provides many tasks that could be used to study the students' performance. In this paper, the classification task is employed to gauge students' performance and deals with the accuracy, confusion matrices and the execution time taken by the various classification data mining algorithms. This paper is catalogued as follows. Section 2 enumerates a related work. Section 3 presents the idea of Classification and discusses the aspects of classification algorithm. Section 4 elaborates a Data Preprocessing. Section 5 explains the Implementation of model construction. Section 6 describes the results and discussions. Section 7 provides the conclusion. II. Related Work Alaael-Halees 2009 suggested that Data Mining is an emerging methodology used in educational field to enhance the understanding of learning process. The application of Data mining is widely spread in Higher Education system. In Education domain many researchers and authors have explored and discussed various applications of data mining in higher education. The authors had gone through the survey of the literature to understand the importance of data mining applications. In the year 2001 Luan al. suggested a powerful decision support tool called data mining. Data Mining is a powerful tool for academic purposes Alumni, Institutional effectiveness, marketing and enrollment can benefit from the use of data mining Data Mining is the most suited technology that can be used by lecturer, student, alumnus, manager and other educational staff and is a useful tool for decision making on their educational activities