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A Prediction for Student's Performance Using Decision Tree ID3 Method International Journal of Scientific & Engineering Research, Volume 5, Issue 7, July-2014 1329 ISSN 2229-5518 Data Mining: A prediction for Student's Performance Using Decision Tree ID3 Method D.BHU LAKSHMI, S. ARUNDATHI, DR.JAGADEESH Abstract— Knowledge Discovery and Data Mining (KDD) is a multidisciplinary area focusing upon methodologies for extracting useful knowledge from data and there are several useful KDD tools to extracting the knowledge. This knowledge can be used to increase the quality of education. But educational institution does not use any knowledge discovery process approach on these data. Data mining can be used for decision making in educational system. A decision tree classifier is one of the most widely used supervised learning methods used for data exploration based on divide & conquer technique. This paper discusses use of decision trees in educational data mining. Decision tree algorithms are applied on students’ past performance data to generate the model and this model can be used to predict the students’ performance. The most useful data mining techniques in educational database is classification, the decision tree (ID3) method is used here. Index Terms— Educational Data Mining, Classification, Knowledge Discovery in Database (KDD), ID3 Algorithm. 1. Introduction the students, prediction about students’ The advent of information technology in various performance and so on, the classification task is fields has lead the large volumes of data storage in used to evaluate student’s performance and as various formats like records, files, documents, there are many approaches that are used for data images, sound, videos, scientific data and many classification, the decision tree method is used new data formats. The data collected from here. Alaa El-Halees (2009) [4] applied the different applications require proper method of educational data mining concerns with developing extracting knowledge from large repositories for methods for discovering knowledge from data that better decision making. Knowledge discovery in come from educational environment. used databases (KDD), often called data mining, aims at educational data mining to analyze learning the discovery of useful information from large behavior. Student’s data has been collected from collections of data [1]. The main functions of data Database course. After preprocessing the data, we mining are applying various methods and applied data mining techniques to discover algorithms in order to discover and extract association, classification, clustering and outlier patterns of stored data [2]. The main objective of detection rules. In each of these four tasks, we this paper is to use data mining methodologies to extracted knowledge that describes students' study student’s performanceIJSER in end General behavior. appreciation. Data mining provides many tasks that could be used to study the student Mohammed M. Abu Tair and Alaa M. El-Halees performance. In this research, the classification (2012) [5] applied the educational data mining task is used to evaluate student's performance and concerns with developing methods for discovering as there are many approaches that are used for knowledge from data that come from educational data classification, the decision tree method is used domain. used educational datamining to improve here. graduate students’ performance, and overcome the 2. Related Work problem of low grades of graduate students and Han and Kamber (1996) [3] describes data mining try to extract useful knowledge from graduate software that allow the users to analyze data from students data collected from the college of Science different dimensions, categorize it and summarize and Technology. The data include fifteen years the relationships which are identified during the period [1993-2007]. After preprocessing the data, mining process. we applied data mining techniques to discover Brijesh Kumar Baradwaj and Saurabh Pal (2011) [1] association, classification, clustering and outlier describes the main objective of higher education detection rules. In each of these four tasks, we institutions is to provide quality education to its present the extracted knowledge and describe its students. One way to achieve highest level of importance in educational domain. quality in higher education system is by SonaliAgarwal, G. N. Pandey, and M. D. Tiwari discovering knowledge for prediction regarding (2012) [6] describes the educational organizations enrolment of students in a particular course, are one of the important parts of our society and detection of abnormal values in the result sheets of IJSER © 2014 http://www.ijser.org International Journal of Scientific & Engineering Research, Volume 5, Issue 7, July-2014 1330 ISSN 2229-5518 playing a vital role for growth and development of help to achieve the goal by extracting the discovery any nation. Data Mining is anemerging technique of knowledge from the end semester mark. Bhise with the help of this one can efficiently learn with R.B, Thorat S.S and Supekar A.K. (2013) [14] used historical data and use that knowledge for data mining process in a student’s database using predicting future behavior of concern areas. K-means clustering algorithm to predict students Growth of current education system is surely result. Varun Kumar and AnupamaChadha (2013) enhanced if data mining has been adopted as a [15] used of one of the data mining technique futuristic strategic management tool. The Data called association rule mining in enhancing the Mining tool is able to facilitate better resource quality of students’ performances at Post utilization in terms of student performance, course Graduation level.Pallamreddy. venkatasubbareddy development and finally the development of and VudaSreenivasarao (2010) [16] explained the nation's education related standards. Monika Decision trees are commonly used in operations Goyal and RajanVohra (2012) [7] applied data research, specifically in decision analysis, to help mining techniques to improve the efficiency of identify a strategy most likely to reach a goal and higher education institution. If data mining use of decision trees is as a descriptive means for techniques such as clustering, decision tree and calculating conditional probabilities. association are applied to higher education processes, it would help to improve students’ 3. Data Mining Definition and performance, their life cycle management, selection Techniques of courses, to measure their retention rate and the Data mining refers to extracting or "mining" grant fund management of an institution. This is knowledge from large amounts of data [3]. Data an approach to examine the effect of using data mining techniques are used to operate on large mining techniques in higher education. Surjeet volumes of data to discover hidden patterns and Kumar Yadav, BrijeshBharadwaj, and Saurabh Pal relationships helpful in decision making [1]. The (2012) [11] used decision tree classifiers are studied sequences of steps identified in extracting knowledge from data are: shown in Figure 1. and the experiments are conducted to find the best IJSERVarious algorithms and techniques like classifier for retention data to predict the student’s Classification, Clustering, Regression, Artificial drop-out possibility. Brijesh Kumar Baradwaj and Intelligence, Neural Networks, Association Rules, Saurabh Pal (2011) [12] Used the classification task Decision Trees, Genetic Algorithm, Nearest on student database to predict the students Neighbor method etc., are used for knowledge division on the basis of previous database. discovery from databases. These techniques and K.ShanmugaPriya and A.V.Senthil Kumar(2013) methods in data mining need brief mention to have better understanding. [13] applied a Classification Technique in Data Mining to improve the student's performance and IJSER © 2014 http://www.ijser.org International Journal of Scientific & Engineering Research, Volume 5, Issue 7, July-2014 1331 ISSN 2229-5518 Figure 1.The Steps of Extracting Knowledge students according to their behavior. According to from Data clustering, clusters distinguish student‘s performance according to their behavior and activates. In this paper, students are clustered into three groups according totheir academics, punctuality, exams and soon [8]. 3.3. Association rule Association analysis is the discovery of association rules showing attribute-value conditions that occur frequently together in a given set of data. Association analysis is widely used for market basket or transaction data analysis [9]. 3.4. Decision Trees Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal [10]. 4. Data Mining Process 3.1. Classification 4.1. Data Preparations Classification is the most commonly applied data The data set used in this study was obtained mining technique, which employs a set of pre- from a student's database used in one of the classified examples to develop a model that can educational institutions, on the sampling classify the population of records at large. This method of Information system department from approach frequently employs decision tree or session 2005 to 2010. Initially size of the data is neural network-based classification algorithms. 1547 records. In this step data stored in different The data classificationIJSER process involves learning tables was joined in a single table after joining and classification. In Learning the training data are process
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