Predicting the E-Learners Learning Style by using Support Vector Regression Technique

Predicting student performance is a prominent key factor of Educational Data Mining, Support Vector Regression are exposed to be useful factor for assessing under studies presentation in an e-learning atmosphere. In E-learning platform, college scholars performance, Study and effectively take an interest in the learning management system. Support Vector regression have been undertaken to analysis, student id, gender, region, highest education, studied credits, Disability, final result. It is difficult to characterize the amount of important factors are in the Support Vector Regression, organizations gives the predicting of information factors. At enduring, various factors were exposed determined involvement in live class, involvement in undertaking regular assessments, and more involvement in the time contributed significantly to the anticipation profit variable. This paper aims to collect the larger data sets followed by the utilization of one of the machine learning concepts called support vector regression to predict the learner's learning style. Then, the final results will help to predict the student's performance.

I. Introduction

Nowadays, data mining applications are getting superior and more widespread. On the grounds that information can be put away in data bases and this information can be progressive rapidly. E-learning platform has been used in large number of learners these days. Different categories of educational information’s were applied in educational information mining statistics, data mining algorithms and Machine learning. Predicting student execution in internet learning conditions according to student perusing information is significant regarding expanding the advantages of these conditions.