Socio Political Factors Affecting The Students Education Essay

Socio Political Factors Affecting The Students Education EssayEducation sector in India is a growing field that plays a pivotal mathematical function in improving the living status. The economic status or the rise of a country depends on the betterd education system. According to statistical survey, India after Independence gave more importance to primary education and expanded literacy rate to two thirds of its population. in that location ar several efforts made by the disposal to improve the literacy rate in India. Despite the educations sector growth, 25% of its population are still illiterate and the number of enumeration of students to higher education is still in decline. info tap deals with the process in which we notice and extract all the hidden in initializeion from data bases. Educational data mining plays a rattling important role in let outing, analyzing and visualizing the data to ring students performance, their academic achievements, providing feedback fo r supporting instructors and so on. There are so numerous factors that affect students enrolment to business office unessential education. So, the main aim of this research is to identify those factors using data mining techniques which depart help the educational institutions, academic heads and in like manner the policy makers of the giving medication schools to take required action.3. INTRODUCTIONA.DATA MINING entropy mining 6 7 is the emerging field of applying statistical and artificial intelligence techniques to the problem of finding novel, useful, and non-trivial patterns from large databases. information Mining is often defined as finding hidden information in a database8. Data mining provides many tasks that could help to study the students performance9. different data mining techniques are used in various fields of life such(prenominal) as medicine, statistical analysis, engineering, education, banking, marketing, sale, etc (MacLennan. 2005).B.EDUCATIONAL DATA MI NING (EDM)Educational Data Mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational correcttings, and using those methods to better understand students, and the settings which they learn in.1. Day by mean solar day the growth of the data is very rapid and that data need to transformed and converted into an useful information 2. Educational data mining (EDM) tends to focus on new tools and techniques for discovering patterns in the data. It also gains popularity in the new research areas in higher education. Recent research findings in educational data mining helps the students, institutions and government for improving the feeling of education. Inspite of the rapid growth in the education sector , 25% of its population is still illiterate , 15% of the students reach high school, and notwithstanding 7% graduate3. Statistics says according to the year 2011,out of 74% of the literacy rate, only 47% have at tained the diploma and attitude diploma courses4.Post secondary education plays a snappy role in countrys development. But the statistical data proves still major population in India are school dropouts. There are so many factors which affect the students enrolment to post secondary education such as family background, school infrastructure and facilities and their psychological behaviours and so on. The main aim of this paper is to identify the reasons for poor enrolment to post secondary education and the result will help the students, management and policy makers to give a better solution. Data mining techniques particularly miscellanea helps to analyze the input data and to develop a forge describing important data classes or to predict future data trends.4. belles-lettres SURVEYIn11, the author uses the data mining processes, particularly categorization to help in enhancing the quality of the higher educational system by evaluating student data to study the main attribute s that may affect the students performance in courses. Ayesha et.al 12 used clustering techniques in data mining to analyze students learning behaviour which helped the teachers to identify the drop out ratio to a significant level and improve the performance of the students. Liu Kan 13 designed a course management system on the base of operations of data mining methods such as classification, association rules and clustering. In 14, the author used different classification algorithms to get useful information to decision-making out of customers traffic behaviours. In 15, the author applies four different classification methods for crystaliseing students based on their final grade obtained in their courses. Dr. Surabh paul16, in his research used classification to rate previous years student dropout data using Bayesian classification method.5. STATEMENT OF THE PROBLEMThis minor research aims to study the socio-political factors affecting the students enrolment to post secondary education using data mining techniques. These attributes consist of 1)personal information such as age, gender, occupation of the parents, family income, highest educational qualification of the parents, stay, family size.2)institution related information such as type of learning, usage of precept aids, exposure to ICT, faculty qualification etc 3)psychological information such as social status, illness, disability etc are considered. These attributes were used to predict the students enrolment to post secondary education.6. CONCEPTUAL AND THEORETICAL FRAMEWORKTo build the classification, CRISP methodology is adopted. The proposed methodology is to build the classification model that leavens the factors which affect the students enrolment to post secondary education.DATA MINING PROCESSKnowing the reasons for not continuing their post secondary education can help the teachers and administrators to take necessary actions so that enrolment rate can be improved. Predicting the reason for students not enrolling to post secondary education needs a lot of parameters to be considered. divination models that include all personal, social, psychological and other environmental variables are necessitated for the effective prediction and decisions to be made.A.BUSINESS UNDERSTANDINGBusiness understanding focuses on the understanding of the send off objective and requirements from business perspective then converting it into a data mining problem definition and a plan is designed to accomplish those objectives.B.DATA UNDERSTANDINGData set is to get familiar with the data and to identify the problem to discover useful information out of it. Data understanding also helps to examine the quality of data in addressing the questions Is the data complete? or any missing determine?. The data set used in this study was obtained from the Gottigere Government postgraduate School, Karnataka. Initially size of the data is 110.C.DATA PREPARATIONData Preparation takes usually 90% of the time to collect, assess, clean and select the data required to construct, integrate and format the data. Identify data sources based on the data available to solve an identified business problem or objective. From the selected data sources, the actual data to be used must be determined 20.D.BUILDING THE CLASSIFICATION MODELThe collected attributes may have some irrelevant attributes that may degrade the performance of the classification model a feature selection approach is used to select the most appropriate set of features. Classification techniques are supervised learning techniques that classify data item into predefined class label 19. This technique in data mining is very useful from a data set to build the classification model that is used to predict future data trends. With classification, the generated model will be able to predict a class for given data depending on previously learned information from historical data. To explore knowledge discovery decision tree to pr oduce a model with rules in human decipherable way. The tree has the advantages of easy interpretation and understanding for decision makers to compare with their domain knowledge for validation and justify their decision 19. Some of decision tree classifiers are C4.5/C5.0/J4.8,ID3 and others.Generating the Classification rule by applying ID3 algorithmThe classifier identified to implement this model is ID3 algorithm. The decision tree building algorithm ID3 determines the classification of objects by testing the values of the their attributes. It builds the tree in a top down fashion, starting from a set of objects and a specification of properties. At each node of the tree, a holding is tested and the results are used to partition the object set. This process is recursively done till the set in a given sub tree is uniform with respect to the classification criteria in other words it contains objects belonging to the same category. This process then becomes a leaf node. At each node, the property to test is chosen based on information theoretic criteria that seek to maximize information gain and minimize entropy. In simpler terms, that property is tested which divides the candidate set in the most homogeneous subsets17. For this purpose the WEKA toolkit is used and the attributes are ranked and then the ranked attributes are eliminated by the feature selection approach.E. military rankEvaluation is to check whether we correctly built the model and determines how to proceed and whether to finish the project and move on to deployment mannikin. Evaluating the results assess the degree to which the model meets the business objectives and also unveils additional challenges, information or hints for future directions. Choosing the proper data mining method is a critical and difficult task in KDD process. To implement this model WEKA Toolkit is used which has a collection of machine learning algorithms for solving data mining problems implemented in Java. Weka has tools for data processing, classification, regression and association, clustering and visualization. It is an open source toolkit for machine learning.F.DEPLOYMENTDeployment phase is to determine how the evaluated results need to be utilized. The knowledge gained has to be organized and presented in the way it is applicable to the end user. This phase may be a final and comprehensive presentation of the data mining results. This CRISP provides a uniform framework for experimenting, analyzing, evaluating and predicting the result7. SPECIFIC OBJECTIVESThere are few objectives stated below1. This project is a preliminary attempt to help supporting the decision makers of the institution to improve their teaching methodology, and teaching aids and all other infrastructure facilities that they lack.2. The result evaluated out of this project will motivate the parents of BPL (Below poverty line) towards the values of post secondary education.3. This project will help the policy makers of our Indian government to help the children studying in government schools in a much better way towards their post secondary education.4. The model proposed as an academician can be useful to build a software model to provide a solution by formulating the result.