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\title{Improving Academic Performance of Students of Defence University Based on Data Warehousing and Data mining}
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             \author[1]{Dr. Vuda Sreenivasarao  }

             \affil[1]{  singhania university}

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\date{\small \em Received: 6 April 2012 Accepted: 30 April 2012 Published: 15 May 2012}

\maketitle


\begin{abstract}
        


The student academic performance in Defence University College is of great concern to the higher technical education managements, where several factors may affect the performance. The student academic performance in engineering during their first year at university is a turning point in their educational path and usually encroaches on their general point average in a decisive manner. The students evaluation factors like class quizzes mid and final exam assignment are studied. It is recommended that all these correlated information should be conveyed to the class teacher before the conduction of final exam. This study will help the teachers to reduce the drop out ratio to a significant level and improve the performance of students. Statistics plays an important role in assessment and evaluation of performance in academics of universities need to have extensive analysis capabilities of student achievement levels in order to make appropriate academic decisions. Academic decisions will result in academic performance changes, which need to be assessed periodically and over span of time. The performance parameters chosen can be viewed at the individual student, department, school and university levels. Data mining is used to extract meaning full information and to develop significant relationships among variables stored in large data set/ data warehouse. In this paper is an attempt to using concepts of data mining like k-Means clustering, Decision tree Techniques, to help in enhancing the quality of the higher technical educational system by evaluating student data to study the main attributes that may affect the performance of student in courses.

\end{abstract}


\keywords{Data base, Data warehousing, Data mining, Academic Performance, Educational data mining, Student performance analysis and K-Means clustering algorith}

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\let\tabcellsep& 	 	 		 
\section[{INTRODUCTION}]{INTRODUCTION}\par
ata mining techniques have been applied in many application domains such as Banking, Fraud detection, Instruction detection and Communication. Recently the data mining techniques were used to improve and evaluate the engineering education tasks. Some authors have proposed some techniques and architectures for using data warehousing and data mining for higher technical education. Data mining is a process of extracting previously unknown, valid, potentional useful and hidden patterns from large data sets. As the amount of data stored in educational data bases in increasing rapidly. In order to get required benefits from such large data and to find hidden relationships between variables using different data mining techniques developed and used. Clustering and decision tree are most widely used techniques for future prediction. The aim of clustering is to partition students in to homogeneous groups according to their characteristics and abilities. These applications can help both instructor and student to improve the quality education. Analyze different factors effect a students learning behavior and performance during academic career using K-means clustering algorithm and decision tree in an higher educational institute. Decision tree analysis is a popular data mining technique that can be used to explain different variables like attendance ratio and grade ratio. Clustering is one of the basic techniques often used in analyzing data sets. This study makes use of cluster analysis to segment students in to groups according to their characteristics. Academic decisions may require extensive analysis of student achievement levels. Statistical data can also be used to see the results of important academic decisions. It is necessary to have measurements to make appropriate academic decisions on one hand; while on the other hand, there is a need to see the results of academic decisions by taking measurements.\par
The decision, implementation, measurement and evaluation mechanisms work like a chain one leading to the other. Their relationship is shown in Fig.  {\ref 1}. 
\section[{Academic Decisions}]{Academic Decisions}\par
Evaluation Implementation 
\section[{Measurement}]{Measurement} 
\section[{Academic Activities}]{Academic Activities}\par
Figure  {\ref 1} : Academic decision phases.\par
II. 
\section[{RELATED WORK a) Data Base}]{RELATED WORK a) Data Base}\par
A data base is a collection of data usually associated with some organization or enterprise. Unlike a simple set, data in a data base are usually viewed to have a particular structure or schema with which it is associated. For example,(ID, Name, Address, Salary, Job No) may be the schema for a personal data base. 
\section[{b) Data warehousing}]{b) Data warehousing}\par
Data warehouse is a data base devoted to analytical processing. Data warehouse to be a set of data that supports DSS and is subject-oriented, integrated, time-variant, and non-volatile. A complete repository of historical corporate data extracted from transaction systems that is available for ad-hoc access by knowledge workers. The processes of DW involve taking data from the legacy system together with corresponding transactions of the system's data base and transforming the data in to organized information in a user friendly format. The data warehouse market supports such diverse industries as manufacturing, retail, telecommunications and health care. It has access a warehouse includes traditional querying, OLAP, and data mining. Since the warehouse is stored as a data base, it can be accessed by traditional query languages. Example of data warehousing can be defined in any of your organization. Consider the case of a Bank; a bank will typically have current accounts and saving accounts, foreign currency account etc. The bank will have an MIS system for leasing and another system for managing credit cards and another system for every different kind of business they are in . However, nowhere they have the total view of the environment from the customer's perspective. The reason being, transaction processing systems are typically designed around functional areas, within a business environment. For good decision making you should be able to integrate the data across the organization so as to cross the LoB (Line of Business) . So the idea here is to give the total view of the organization especially from a customer's perspective within the data warehouse, as shown in below figure \hyperref[fig_1]{3}.   
\section[{CLUSTERING}]{CLUSTERING}\par
Clustering is a method to group data in to classes with identical characteristics in which the similarity of intra-class is maximized or minimized. Cluster analysis used to segment a large set of data in to subsets called clusters. Each cluster is a collection of data objects that are similar to one another are placed within the same cluster but are dissimilar to objects in other clusters. A cluster of data objects can be treated collectively as one group in many applications. Cluster analysis is an important human activity. Cluster analysis has been widely used in numerous applications, including pattern recognition, data analysis, image processing, and market research. Clustering is a descriptive task that seeks to identify homogeneous groups of objects based on the values of their attributes. Current clustering techniques can be broadly classified in to three categories; partitional, hierarchical and locality-based algorithms.\par
Definition: Given a data base D = \{ t 1 , t 2 ,--------t n \} of tuples and an integer value K, the clustering problem is to define a mapping f:D \{1,2,3--------K\} where each t i is assigned to one cluster K j , i ? j?k. A cluster K j , contains precisely those tuples mapped to it; that is K j = \{t i / f(t i )= k j ,1?i?n and t i ?D\}. 
\section[{a) K-Means Clustering}]{a) K-Means Clustering}\par
K-Means is one of the simplest unsupervised learning algorithms used for clustering. K-means partitions "n" observations in to k clusters in which each observation belongs to the cluster with the nearest mean. This algorithm aims at minimizing an objective function, in this case a squared error function.  
\section[{Global Journal of Computer}]{Global Journal of Computer} 
\section[{DECISION TREE}]{DECISION TREE}\par
Decision tree induction can be integrated with data warehousing techniques for data mining. A decision tree is a predictive node ling technique used in classification, clustering, and prediction tasks. Decision tree use a "divide and conquer" technique to split the problem search space in to subsets.\par
A decision tree is a tree where the root and each internal node are labeled with a question. The arcs emanating from each node represent each possible answer to the associated question. Each leaf node represents a prediction of a solution to the problem under consideration.\par
Given a data base D = \{t 1 ,t \hyperref[fig_2]{2 ,--------t} n \} where t i = (t i1 -----------t in ) and the data base schema contains the following attributes \{A1,A2,-------An\}. Also given is a set of classes C = \{C1, C2, --------Cm\}. A decision tree or classification tree is a tree associated with D that has the following properties:\par
1. Each internal node is labeled with an attribute, Ai. 2. Each arc is labeled with a predicate that can be applied to the attribute associated with the parent. 3. Each leaf node is labeled with a class, Cj.\par
The basic algorithm for decision tree induction is a greedy algorithm that constructs decision trees in a top-down recursive divide-and-conquer manner.\par
Each internal node tests an attribute, each branch corresponds to attribute value, and each leaf node assigns a classification.    After the pattern is classified from the decision tree we can obtain the specify knowledge discovery to form the knowledge base system. Similarly the same data mining process can be done to the professors for classifying their performance which help in improve Technical education system.\begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-2.png}
\caption{\label{fig_0}Figure 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3}\includegraphics[]{image-3.png}
\caption{\label{fig_1}Figure 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-4.png}
\caption{\label{fig_2}Figure 2 :}\end{figure}
   \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.22993019197207681\textwidth}P{0.045986038394415354\textwidth}P{0.5740837696335078\textwidth}}
January 2012\tabcellsep \tabcellsep \\
34\tabcellsep \tabcellsep \\
Student Roll number\tabcellsep Marks\tabcellsep Effort\\
DEC-01\tabcellsep 10-50\tabcellsep More Attention, Conducting Special Classes, Assigments,\\
\tabcellsep \tabcellsep Conducting more practical classes,\\
\tabcellsep \tabcellsep Daily tests and Parents and Faculty meeting.\\
DEC-02\tabcellsep 51-60\tabcellsep Conducting Special Classes,\\
\tabcellsep \tabcellsep Assignments and\\
\tabcellsep \tabcellsep Conducting more practical classes.\\
DEC-03\tabcellsep 61-75\tabcellsep Assignments and\\
\tabcellsep \tabcellsep Conducting more practical classes.\\
DEC-04\tabcellsep 76-85\tabcellsep Assignments and\\
\tabcellsep \tabcellsep Conducting classes for Interviews.\\
DEC-05\tabcellsep 86-100\tabcellsep Conducting classes for Interviews and\\
\tabcellsep \tabcellsep Giving exposure for career important.\\
\multicolumn{3}{l}{Formation of Decision tree from students marks table and applying effort depending on marks.}\end{longtable} \par
  {\small\itshape [Note: © 2012 Global Journals Inc. (US)]} 
\caption{\label{tab_1}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_2}Table 1}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.02689873417721519\textwidth}P{0.22594936708860758\textwidth}P{0.2887130801687764\textwidth}P{0.17573839662447258\textwidth}P{0.06993670886075949\textwidth}P{0.017932489451476793\textwidth}P{0.044831223628691984\textwidth}}
S NO\tabcellsep Student\tabcellsep Course\tabcellsep Professor\tabcellsep \multicolumn{3}{l}{Marks Grade Results}\\
1\tabcellsep Ferede Adugna\tabcellsep Cryptography\tabcellsep Dr. Rao\tabcellsep 86\tabcellsep A\tabcellsep YES\\
2\tabcellsep Rwibasira M\tabcellsep Interfacing Tech\tabcellsep Miss Maria\tabcellsep 75\tabcellsep B\tabcellsep NO\\
3\tabcellsep Makuei Nyok\tabcellsep Operating Systems\tabcellsep Genetu Yohannes\tabcellsep 85\tabcellsep A\tabcellsep YES\\
4\tabcellsep Daniel Tekif\tabcellsep Microprocessor\tabcellsep Michael\tabcellsep 55\tabcellsep D\tabcellsep NO\\
5\tabcellsep Mesfin Dadi\tabcellsep Distributed Systems\tabcellsep Lea\tabcellsep 95\tabcellsep A\tabcellsep YES\\
6\tabcellsep Debebe Shibeshi\tabcellsep Computer Networks\tabcellsep Genetu Yohannes\tabcellsep 90\tabcellsep A\tabcellsep YES\\
7\tabcellsep Gidey Abrha\tabcellsep Network security\tabcellsep Dr. Srinivas\tabcellsep 98\tabcellsep A\tabcellsep YES\\
8\tabcellsep Samuel Hagos\tabcellsep Web technology\tabcellsep Oliver\tabcellsep 45\tabcellsep F\tabcellsep NO\\
9\tabcellsep Desta Desisa\tabcellsep Compiler Design\tabcellsep Melissa\tabcellsep 73\tabcellsep B\tabcellsep NO\\
10\tabcellsep Tibabu Beza\tabcellsep Cloud computing\tabcellsep Praveen\tabcellsep 50\tabcellsep D\tabcellsep NO\end{longtable} \par
 
\caption{\label{tab_3}Table 1 :}\end{figure}
 			\footnote{© 2012 Global Journals Inc. (US) Global Journal of Computer Science and Technology Volume XII Issue II Version I} 			\footnote{January 2012} 		 		\backmatter  			 \par
VI. 
\subsection[{CONCLUSION}]{CONCLUSION}\par
In this study of research paper idea is a starting attempt to use data warehousing and data mining techniques to analyze and find out student academic performance and to improve the quality of the engineering system. The managements can use some techniques to improve the course outcomes according to the improve knowledge. Such knowledge can be used to give a good understanding of student's enrollment pattern in the course under study, the faculty and managerial decision maker in order to utilize the necessary steps needed to provide extra classes. Other hand, such type of knowledge the management system can be enhance their policies , improve their strategies and improve the quality of the system.  
\subsection[{Performance of Students by}]{Performance of Students by} 
\subsection[{RESULTS}]{RESULTS}\par
Both K-Means clustering, Decision tree algorithm were applied on the data set.  
\subsection[{Number of Node}]{Number of Node}			 			  				\begin{bibitemlist}{1}
\bibitem[
			V
		]{b9}\label{b9} 	 		\textit{},  		 			V 		.  		 	 
\bibitem[References et al. ()]{b7}\label{b7} 	 		‘A Research on result oriented learning process from university students based on distributed data mining and decision tree algorithm’.  		 			References 		,  		 			Vuda Sreenivasarao 		,  		 			. S Dr 		,  		 			G Vidyavathi 		,  		 			Sk Ramaswamy 		,  		 			Shabber 		.  	 	 		\textit{Journal of Advance research in Computer Engineering}  		2010. 4  (2)  p. .  	 
\bibitem[Else attach the node returned by generate-decision-tree(S i ,attribute-list-attribute)]{b6}\label{b6} 	 		\textit{Else attach the node returned by generate-decision-tree(S i ,attribute-list-attribute)},  		 	 
\bibitem[Grow a branch from node N for the condition test attribute = a i]{b3}\label{b3} 	 		\textit{Grow a branch from node N for the condition test attribute = a i},  		 	 
\bibitem[If Si is empty then 12. Attach a leaf labeled with the most common class in samples]{b5}\label{b5} 	 		\textit{If Si is empty then 12. Attach a leaf labeled with the most common class in samples},  		 	 
\bibitem[Label node N with test-attribute]{b2}\label{b2} 	 		\textit{Label node N with test-attribute},  		 	 
\bibitem[Let Si be the set of samples for which test-attribute = a i]{b4}\label{b4} 	 		\textit{Let Si be the set of samples for which test-attribute = a i},  		 	 
\bibitem[Return N as a leaf node labeled with the most common class in samples]{b0}\label{b0} 	 		\textit{Return N as a leaf node labeled with the most common class in samples},  		 	 
\bibitem[Select test-attribute, the attribute among attribute-list with the highest information gain]{b1}\label{b1} 	 		\textit{Select test-attribute, the attribute among attribute-list with the highest information gain},  		 	 
\bibitem[Subbareddy and Sreenivasarao (2010)]{b8}\label{b8} 	 		‘The result oriented process for process for students based on distributed data mining’.  		 			P V Subbareddy 		,  		 			Vuda Sreenivasarao 		.  	 	 		\textit{International journal of advanced computer science and applications}  		Nov-2010. 1  (5)  p. .  	 
\end{bibitemlist}
 			 		 	 
\end{document}
