IDENTIFYING STUDENT CAPACITY TO IMPROVE ACADEMICS PERFOMANCE USING CLASSIFICATION ALGORITHM

IT Skills Show & International Conference on Advancements in Computing Resources, (SSICACR-2017) 15 and 16 February 2017, Alagappa University, Karaikudi, Tamil Nadu, India. International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

The main objective of the research is to improve Students performance in academic and classify the students as slow learner and fast learner according to their marks. The research problem is identifying slow learner and fast leaner from the given data set using classification algorithms. Data mining is the process of extracting or mining hidden knowledge from huge amounts of data. The information and knowledge gained can be used for applications ranging from Financial Data Analysis, Retail Industry, Telecommunication Industry, Biological Data Analysis, Scientific Applications and Intrusion Detection [4]. Supervised learning is the machine learning task of inferring a function from labelled training data. Classification is the data mining technique. It is applied to our real time problems. Classification is the process of classify the data According to the features of the data with predefined set of classes [16]. It is difficult to analyse the large amount of data and make the decision based on that data. To solve this problem, data mining tools and techniques can be used. The weak tool is used for this research problem. Classification technique can be used for prediction of slow learners and fast learners for improving the performance. The student data set can be used for classification process. The research takes three algorithms or classifiers to solve the research problem. The algorithms are Naïve bayes, Multilayer perceptron and J48. Each algorithm gives the best result for this research process. The J48 algorithm gives high accuracy compared to naïve bayes and multilayer perceptron. The accuracy of the J48 algorithm is 98%.

References

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Keywords

Slow learner, Fast learner, Classifiers, Student Performance, Accuracy, Classification, Naïve bayes, Multilayer Perceptron and J48.