Fuzzy Prediction: An Accurate Approach of Performance Prediction in Present Scenario of Higher Education
Due to ease of application and capability to provide accurate and gradual responses, neural networks have become very popular over the recent past when it comes to classification problems. Also, data mining has been used extensively with good effect for decision making in educational system. Improved assessment technique is of paramount importance in understanding, analysing and assessing the progress in performance of the candidates in higher education sectors. Availability of a prediction tool to asses such progression accurately can be boon to organisations. In our work we proposed a model called Fuzzy decision tree model which uses the data of the student to analyse and evaluate their performance. The data include various factors such as previous year results, academic performance, sports interest, social activities etc. to predict their success rate. Use of such model will enable the organisation to identify students who are at potential risk and help them to develop best course of action which would eventually enhance the performance of the whole organisation.
- Pooja T, Anil M, Manisha. Performance analysis and prediction in educational data mining: A research travelogue, International Journal of Computer Applications, Vol 110, No. 15, pp. 60-68, 2015.
- Rashmi A. K-Nearest Neighbour for uncertain data, Internal Journal of Computer Applications, Vol 105, No. 11, pp. 13-16, 2014.
- Monika G, Rajan V. Applications of data mining in higher education, International Journal of Computer Science Issues, Vol 9, No. 2, pp. 113-120, 2012.
- Brijesh K B, Saurabh P. Data mining: A prediction for performance improvement using classification, International Journal of Computer Science and Information Security, Vol 9, No. 4, 2011.
- Shanmuga P K. Improving the student’s performance using educational data mining, International Journal of Advanced Networking and Application, Vol 4, No. 4, pp. 1680-1685, 2013.
- Bhise R B, Thorat S S, Supekar A K. Importance of data mining in higher education system, International Journal of Humanities and Social Science, Vol. 6, No. 6, pp. 18-21, 2013.
- Varun K, Anupama C. Mining association rules in student’s assessment data, International Journal of Computer Science Issues, Vol 9, No. 5 (3), pp. 211-216, 2012.
- Pallamreddy V S R, Sreenivasarao V. The result oriented process for students based on distributed datamining, International Journal of Advanced Computer Science and Applications, Vol 1, No. 5, pp. 22-25, 2010.
- Tripti Mishra, Dharminder K, Sangeeta G. Mining student’s data for prediction performance, Proceedings of the 2014 Fourth International Conference on Advanced Computing & Communication Technologies, Pages 255-262, Feb 08-09, 2014.
- Fadhilah A, Nur H I, Azwa Abdul A. The prediction of student’s academic performance using clcassification data mininh techniques, Applied Mathematical Sciences, Vol 9, No. 129, pp. 6415-6426, 2015.
- Yehuala, MulukenAlemu. Application of data mining techniques for Student success and failure prediction (The case study Of Debre_Markos University), (2015).
- Kabra R R, Bichkar R S. Prediction performance of engineering students using decision trees, International Journal of Computer Applications, Vol 136, No. 11, pp. 8-12, 2011.
- Quinlan, J. R. C4.5: Programs for Machine Learning. San Mateo, CA: MorganKaufmann, 1993.
- Pratima G, Jyoti U. Computational models for performance prediction, Research Journal of Computer and Information Technology Sciences, Vol 5, No. 4, 2017.
- Pratima G.,Jyoti U.: Role of fuzzy set in students performance prediction,IOSR Journal of computer Engineering,Vol 1,Issue 18,2016.
Abstract Views: 1
PDF Views: 0