Predicting Students Grades Using Artificial Neural Networks and Support Vector Machine

Predicting Students Grades Using Artificial Neural Networks and Support Vector Machine

Sajid Umair (National University of Sciences and Technology (NUST), Pakistan) and Muhammad Majid Sharif (National University of Sciences and Technology (NUST), Pakistan)
Copyright: © 2018 |Pages: 14
DOI: 10.4018/978-1-5225-2255-3.ch449
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Abstract

Prediction of student's performance on the basis of his habits has been a very important research topic in academics. Studies also show that selection of the correct data set also plays a vital role in these predictions. In this paper we took data from different schools that contains students habits and their comments, analyzed it using Latent Semantic Analysis to get out semantics and the used Support Vector Machine to classify data into two classes, important for prediction and not important, finally we used Artificial Neural Networks to predict the grades of students regression was also used predict data coming from Support Vector Machine, while giving only the important data for prediction.
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Background

In educational environments, it is very important to predict student’s performance. To amplify a students’ performance is a long-term goal in all academic institutions in their learning environment. Now a day, data mining technique ‘Educational Data Mining’ (EDM) is used on a large-scale to automatically analyze the student’s performance and his behavioral data with learning environments. The use of text mining is a new trend in EDM that extends data mining on text data. A lot of experiments have been done in past couple of years in areas to predict students’ academic performance. A couple of methods have also been applied in Machine learning area to obtain useful/important data and for the prediction of future data trends.

Key Terms in this Chapter

SVM: SVM stands for Support Vector Machines which is supervised learning model, which means some of our data which we intend to use as our training set. Support vector machines are used for categorization of hypertext and text and also categorize their applications. Support vector machines are also used for transductive settings and also for standard inductive settings.

EDM: EDM stands for Educational Data Mining. The educational data mining is an emerging field. In this field we use and developed different type of methods which are used to investigate the different data types.

LSA: LSA stands for Latent Semantic Analysis which is famous neural language processing techniques that is widely being used to find relationship between the words. It works on the fact that if words are similar in meaning it means they can be correlated.

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