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Prognostication of Students Performance and Suggesting Suitable Learning Style for Under Performing Students

Publication Type : Conference Paper

Publisher : 2nd International Conference on Computational Systems and Information Technology for Sustainable Solution.

Source : 2nd International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS-2017), R.V. College of Engineering, Bengaluru , 2017.

Campus : Bengaluru

School : Department of Computer Science and Engineering, School of Engineering

Department : Computer Science

Year : 2017

Abstract : Data analysis has the vital influence in the present world and it helps many organizations in making critical decisions. In our present research, we are working with educational organizations. The increased stress on the students leads to the increase in failure rate, which not only affects student's career, but also degrades the fame of the institution. The stress on students can be eased by suggesting an appropriate learning style to students. In this particular database, it preserves the student data in the expression of numeric data which stores and retrieves the data that is present in the database which leads to the inability of analyzing the data. We have created a web based application which uses a Naive Bayesian classifier for prognostication of student's performance. Around 500 students' with 26 attributes have been tested at Amrita University, Bangalore. The academic performance of each and every individual is given as input to the system which leads to the prediction of students' upcoming performances in his current semester. A suitable learning style is proposed for underperformed students. This system helps in enhancing the performance of the students and increasing the university's reputation.

Cite this Research Publication : Sasikala T and Krishna, K. Sandeep, “Prognostication of Students Performance and Suggesting Suitable Learning Style for Under Performing Students ”, in 2nd International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS-2017), R.V. College of Engineering, Bengaluru , 2017.

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