PREDICTING UPCOMING STUDENTS PERFORMANCE USING MINING TECHNIQUE

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1 PREDICTING UPCOMING STUDENTS PERFORMANCE USING MINING TECHNIQUE Madhan kumar R 1 and Rajesh N 2 1,2 Department of information science, The National Institute of Engineering, Mysuru Abstract- to improve the current trends in the higher education systems to understand from the outside which factors might create loyal students. The necessity of having loyal students motivates higher education systems to know them well, one way to do this is by using valid management and processing of the students database. Data mining methods represent a valid approach for the extraction of precious information from existing students to manage relations with future students. This may indicate at an early stage which type of students will potentially be enrolled and what areas to concentrate upon in higher education systems for support. For this purpose the data mining framework is used for mining related to academic data from enrolled students. The rule generation process is based on the decision tree as a classification method. The generated rules are studied and evaluated using different evaluation methods and the main attributes that may affect the student s loyalty have been highlighted. Software that facilitates the use of the generated rules is built using VB.net programming language which allows the higher education systems to predict the student s loyalty (numbers of enrolled students) so that they can manage and prepare necessary resources for the new enrolled students. Keywords- predicting, data mining, Student, performance. I. INTRODUCTION However the quality of education is judged by the success rate of students and to what extent an institute is capable of retaining its students. Predicting students performance can help identify the students who are at risk of failure and thus management can provide timely help and take essential steps to coach the students to improve his performance. Data mining techniques have been applied to predict the academic performance of the students based on their social economic condition and previous academic performances. This paper try to understand link between emotional skills of the students along with socio economic and previous academic performance parameters to predict academic performance using data mining techniques. The emotional skills like assertion, leadership, stress management etc are obtained, Emotional Skill. Data mining tasks can be either descriptive or predictive. Descriptive data mining uses techniques of association rule mining, clustering etc. to find patterns hidden in large data set and help in intelligent decision making. Predictive data mining constructs models using rule set, decision tree, neural nets, and support vectors etc. to predict the class of a new data set. The objective of this paper is to predict the performance of students. The rationale behind considering current semester for prediction is the observation that most of the students drop out of the course after first year and also students normally take a year to get integrated in an institute academic environment. Decision tree algorithms have been used to build the model and the main contribution of this paper is the model comparison along with finding the impact of various attributes on students performance. II. SYSTEM DESIGN Proposed paper is a web based designed which makes use of data mining technique for the extraction of useful information. Proposed system is a student management system which maintains DOI: /IJMTER GYPOL 38

2 all student admission details, course details, subject details, student marks details, attendance details etc. The proposed application allows the administrator of the application to take the student admission, while taking admission admin also collects the student behaviour details. The system uses Data mining approach which provides valid information from existing students to manage relationships with upcoming students and to identify the most effective factor to determine a student s test score. Data mining is described as a process of discovering or extracting interesting knowledge from large amounts of data stored in multiple data sources such as file systems, databases, data warehouses etc. This knowledge contributes a lot of benefits to business strategies, scientific, medical research, governments, and individual. Business data is collected explosively every minute through business transactions and stored in relational database systems. In order to provide insight into the business processes, data warehouse systems have been built to provide analytical reports that help business users to make decisions. Data is now stored in databases and/or data warehouse systems. Now there are four possible architectures of a data mining system. No-coupling: in this architecture, data mining system does not utilize any functionality of a database or data warehouse system. A no-coupling data mining system retrieves data from a particular data source such as file system, processes data using major data mining algorithms and stores results into the file system.the no-coupling architecture is considered a poor architecture for data mining system; however, it is used for simple data mining processes. Loose Coupling: in this architecture, data mining system uses the database or data warehouse for data retrieval. In loose coupling data mining architecture, data mining system retrieves data from the database or data warehouse, processes data using data mining algorithms and stores the result in those systems. This architecture is mainly for memory-based data mining system that does not require high scalability and high performance. Semi-tight Coupling: in semi-tight coupling data mining architecture, besides linking to database or data warehouse system, data mining system uses several features of database or data warehouse systems to perform some data mining tasks including sorting, indexing, aggregation etc. In this architecture, some intermediate result can be stored in database or data warehouse system for better performance. Tight Coupling: One of the main data mining system architectures is tight coupling; in tight coupling data mining architecture; database or data warehouse is treated as an information retrieval component of data mining system using integration. All the features of database or data warehouse are used to perform data mining tasks. This architecture provides system scalability, high performance, and integrated information. Figure 1. Data Mining All rights Reserved 39

3 There are three tiers in the tight-coupling data mining architecture: Data layer: as mentioned above, data layer can be a database and/or data warehouse systems. This layer is an interface for all data sources. Data mining results are stored in data layer so it can be presented to end-user in the form of reports or another kind of visualization. Data mining application layer is used to retrieve data from the database. Some transformation routine can be performed here to transform data into the desired format. Then data is processed using various data mining algorithms. Front-end layer provides intuitive and friendly user interface for end-user to interact with data mining system. Data mining result presented in visualization form to the user in the front-end layer. Association, Classification, Clustering, Prediction, Sequential patterns, Decision tree. III. DATA MINING TECHNIQUES A. Association Association is one of the best-known data mining techniques. In association, a pattern is discovered based on a relationship between items in the same transaction. That s the reason why association technique is also known as relation technique. The association technique is used in market basket analysis to identify a set of products that customers frequently purchase together. Retailers are using association technique to research customer s buying habits. Based on historical sale data, retailers might find out that customers always buy crisps when they buy beers, and, therefore, they can put beers and crisps next to each other to save time for customer and increase sales. B. Classification Classification is a classic data mining technique based on machine learning. Basically, classification is used to classify each item in a set of data into one of a predefined set of classes or groups. Classification method makes use of mathematical techniques such as decision trees, linear programming, neural network and statistics. In classification, we develop the software that can learn how to classify the data items into groups. For example, we can apply classification in the application that given all records of employees who left the company; predict who will probably leave the company in a future period. In this case, we divide the records of employees into two groups that named leave and stay. And then we can ask our data mining software to classify the employees into separate groups. C. Clustering Clustering is a data mining technique that makes a meaningful or useful cluster of objects which have similar characteristics using the automatic technique. The clustering technique defines the classes and puts objects in each class, while in the classification techniques, objects are assigned into predefined classes. To make the concept clearer, we can take book management in the library as an example. In a library, there is a wide range of books on various topics available. The challenge is how to keep those books in a way that readers can take several books on a particular topic without hassle. By using the clustering technique, we can keep books that have some kinds of similarities in one cluster or one shelf and label it with a meaningful name. If readers want to grab books in that topic, they would only have to go to that shelf instead of looking for the entire library. D. Prediction The prediction, as its name implied, is one of a data mining techniques that discover the relationship between independent variables and relationship between dependent and All rights Reserved 40

4 variables. For instance, the prediction analysis technique can be used in the sale to predict profit for the future if we consider the sale is an independent variable, profit could be a dependent variable. Then based on the historical sale and profit data, we can draw a fitted regression curve that is used for profit prediction. E. Sequential Patterns Sequential patterns analysis is one of data mining technique that seeks to discover or identify similar patterns, regular events or trends in transaction data over a business period. In sales, with historical transaction data, businesses can identify a set of items that customers buy together different times in a year. Then businesses can use this information to recommend customers buy it with better deals based on their purchasing frequency in the past. F. Decision trees The A decision tree is one of the most common used data mining techniques because its model is easy to understand for users. In decision tree technique, the root of the decision tree is a simple question or condition that has multiple answers. Each answer then leads to a set of questions or conditions that help us determine the data so that we can make the final decision based on it. For example, we use the following decision tree to determine whether or not to play tennis: Figure 2: Decision trees Starting at the root node, if the outlook is overcast then we should definitely play tennis. If it is rainy, we should only play tennis if the wind is the week. And if it is sunny then we should play tennis in case the humidity is normal. IV. SYSTEM ARCHITECTURE Figure 3. System architecture for Performance All rights Reserved 41

5 Data base: where previous years students attributes and result are stored and predicted result are stored. Data understanding: Data cleaning and Data integration are categorized as data understanding. Where data cleansing, it is a phase in which noise data and irrelevant data are removed from the collection and data integration stage, multiple data sources, often heterogeneous, may be combined in a common source. Data preprocessing: Data selection and Data transformation combined called as data preprocessing. At this step, the data relevant to the analysis is decided on and retrieved from the data collection and data transformation also known as data consolidation, it is a phase in which the selected data is transformed into forms appropriate for the mining procedure. Data mining: it is the crucial step in which clever techniques are applied to extract patterns and collecting useful information. Pattern evaluation: in this step, strictly interesting patterns representing knowledge are identified based on given measures. In this paper we make use of Classification Based mining technique and for predicting we make use of Naive Bayes algorithm. A Naive Bayes algorithm is one of the most effective methods in the field of text classification, but only in the large training sample set can it get a more accurate result. The requirement of a large number of samples not only brings heavy work for previous manual classification, but also puts forward a higher request for storage and computing resources during the computer post-processing. Naive Bayes classifier is a simple probabilistic classifier based on applying Bayes' theorem (from Bayesian statistics) with strong (naive) independence assumptions. Depending on the precise nature of the probability model, naive Bayes classifiers can be trained very efficiently in a supervised learning setting. In many practical applications, parameter estimation for naive Bayes models uses the method of maximum likelihood; in other words, one can work with the naive Bayes model without believing in Bayesian probability or using any Bayesian methods. Naïve Bayes Algorithm Steps for predicting performance Step 1: Scan the dataset (storage servers) Step 2: Calculate the probability of each attribute value.[n, n_c, m, p] Step 3: Apply the formulae P(attribute value(ai)/subject value(vj))=(n_c + mp)/(n+m) Where: n = the number of training examples for which v = vj nc = number of examples for which v = vj and a = ai p = 1/number of subject values m = the equivalent sample size [number of attributes] Step 4: Multiply the probabilities by p Step 5: Compare the values and classify the attribute values to one of the predefined set of All rights Reserved 42

6 Table 1: Attributes of Students STUDENT RELATED ATTRIBUTES FOR PERFORMANCE EVALUATION V. CONCLUSION Data mining is a powerful analytical tool that enables educational institutions to better allocate resources and staff, and proactively manage student outcomes. With the ability to uncover hidden patterns in large databases, community colleges and universities can build models that predict with a high degree of accuracy the behavior of population clusters. The goal of education is to help people, especially young people, to participate in the functions of society, to acquire knowledge and to develop skills that will help them to confront the needs of the future and to be productive and competitive in tomorrow s world. This product is intended to enhance the quality of the higher educational system by focusing on using the data mining techniques. Using this Building a Competitive Advantage for Higher Education., the University will have the ability to predict the students loyalty (numbers of enrolled students)so they can manage and prepare necessary resources for the new enrolled students. REFERENCES [1] Tripti Mishra, Dr. Dharminder Kumar, Dr. Sangeeta Gupta, Mining Students Data for Performance Prediction, IEEE 2014 Fourth International Conference on Advanced Computing & Communication Technologies. [2] Udeni Jayasinghe, Anuja Dharmaratne, Ajantha Atukorale, Students Performance Evaluation in Online Education System Vs Traditional Education System, IEEE th International Conference on Remote Engineering and Virtual Instrumentation All rights Reserved 43

7 [3] Krina Parmar, Prof. Dineshkumar Vaghela, Dr Priyanka Sharma, Performance Prediction of Students Using Distributed Data Mining, IEEE Sponsored 2nd International Conference on Innovations in Information Embedded and Communication Systems [4] Luan (eds.) Knowledge Management: Building a Competitive Advantage for Higher Education. New Directions for Institutional Research No.113.SanFrancisco, CA: Jossey Bass (2002). [5] Nigel culkin, norbert morawetz, university of hertfordshire, centre for innovation and enterprise, Andreas, Schnbrunn, Karoline, Schmode,Sophie."Student Relationship Management In Germany - Foundations and Opportunities, Management Revue, [6] Foundations-and-Opportunities. [7] Delavari N, Beikzadeh M. R. A New Method for Using Data Mining in Higher Educational System, 5thInternational Conference on Information Technology based Higher Education and Training: Istanbul, Turkey, May-2nd Jun [8] Mierle K, Laven K, Roweis S, Wilson G, Mining Student CVS Repositories for Performance Indicators, [9] Varapron P. et.al. Using Rough Set theory for Automatic Data Analysis. 29 th Congress on Science and Technology of Thailand [10] Cancer diagnosis using data mining technology Muhammad Shahbaz1, Shoaib Faruq2, Muhammad Shaheen1, Syed Ather All rights Reserved 44