Total views : 821
A Comparative Analysis on the Evaluation of Classification Algorithms in the Prediction of Students Performance
Objectives: Data mining techniques are implemented in many organizations as a standard procedure for analyzing the large volume of available data, extracting useful information and knowledge to support the major decision-making processes. Data mining can be applied to wide variety of applications in the educational sector for the purpose of improving the performance of students as well as the status of the educational institutions. Educational data mining is rapidly developing as a key technique in the analysis of data generated in the educational domain. Methods: The aim of this study presents an analysis of final year results of UG degree students using data mining technique, which carried out in three of the private colleges in Tamil Nadu state of India. The primary objective of this research work is to apply the classification techniques to the prediction of the performance of students in end semester university examinations. Particularly, the decision tree algorithm C4.5 (J48), Bayesian classifiers, k Nearest Neighbor algorithm and two rule learner’s algorithms namely OneR and JRip are used for classifying the performance of students as well as to develop a model of student performance predictors. Results: The result of this study reveals that overall accuracy of the tested classifiers is above 60%. In addition classification accuracy for the different classes reveals that the predictions are worst for distinction class and fairly good for the first class. The JRip produces highest classification accuracy for the Distinction. Classification of the students based on the attributes reveals that prediction rates are not uniform among the classification algorithms. Also shows that selected data attributes have found to be influenced the classification process. The results showed to be satisfactory. Improvements: The study can be extended to draw the performance of other classification techniques on an expanded data set with more distinct attributes to get more accurate results.
Classification Algorithm, Classifiers, Comparative Analysis, Educational Data Mining (EDM), Predicting Student Performance
- Galit S, Patel NR, Bruce PC. Data mining for business intelligence: concepts, techniques, and applications in Microsoft Office Excel with XLMiner. John Wiley & Sons; 2007.
- Romero C, Ventura S. Educational data mining: A survey from 1995 to 2005. Expert systems with applications. 2007;33(1)135–46.
- Baker RSJD. Data mining for education. International encyclopedia of education. 2010; 7:112–8.
- Pal AK, Pal S. Analysis and Mining of Educational Data for Predicting the performance of Students. International Journal of Electronics Communication and Computer Engineering. 2013; . 4(5):1560–5.
- Rathee A, Mathur RP. Survey on Decision Tree Classification algorithm for the Evaluation of Student Performance. International Journal of computers & Technology. 2013;4(2):244–7.
- Aher SB, Lobo LMRJ. Data mining in educational system using Weka. IJCA Proceedings on International Conference on Emerging Technology Trends (ICETT). 2011; 3:20–5.
- Ajith P,, Tejaswi B, Sai MSS. Rule Mining Framework for Students Performance Evaluation. International Journal of Soft Computing and Engineering. 2013; 2(6):201–6.
- Ogunde AO, Ajibade DA. A Data Mining System for Predicting University Students’ Graduation Grades Using ID3 Decision Tree Algorithm. Journal of Computer Science and Information Technology. 2014; 2(1):21–46.
- Trivedi A. Evaluation of Student Classification Based On Decision Tree. Int Journal of Advanced Research in Computer Science and Software Engineering. 2014 Feb;4(2):111–2.
- Agrawal BD, GuravBharti B. Review on Data Mining Techniques used For Educational System. Int Journal of Emerging Technology and Advanced Engineering. 2014 Nov; 4(11):325–9.
- Suman, Pooja Mittal P. A Comparative Study on Role of Data Mining Techniques in Education: A Review. International Journal of Emerging Trends & Technology in Computer Science. 2014 Jun; 3(3):65–9.
- Dinesh KA, Radhika V, A Survey on Predicting Student Performance. Int Journal of Computer Science and Information Technologies. 2014; 5(5):6147–9.
- Shanmuga PK. Improving the student’s performance using Educational data mining. International Journal of Advanced Networking and Application. 2013; 4(4):1680–5.
- DorinaKabakchieva. Predicting Student Performance by using Data mining Methods for Classification. Bulgarian Academy of Science, Cybernetics and Information Technologies. 2013; 13(1):61–72.
- Longbing C. Data mining and multi-agent integration.Springer Science & Business Media; 2009.
- Tan, Pang-Ning, Steinbach M, Kumar V. Introduction to data mining. Boston: Pearson Addison Wesley; 2006; 1.
- Al-Barrak MA, Al-Razgan M. Predicting Students Final GPA Using Decision Trees: A Case Study. International Journal of Information and Education Technology. 2016 Jul; 6(7):528–33.
- Han J, Kamber M. Data Mining: Concepts and Techniques, New Delhi: Morgan Kaufmann Publishers; 2006. ISBN: 978-81-312-0535-8.
- Baradway BK, Pal S. Mining Educational Data to Analyze Students Performance. Int Journal of Advances in Computer Science and Applications. 2011; 2(6):63–9.
- Chirumamilla V, BhagyaSruthi T, Velpula S, Sunkara I. A Novel approach to predict Student Placement Chance with Decision Tree Induction. International Journal of Systems and Technologies. 2014; 7(1):78–88.
- Masethe MA, Masetha HD. Prediction of work integrated Learning placement using Data mining Algorithms. Proceedings of the World Congress on Engineering and Computer Science. 2014 Oct; 1:22–4.
- Sundar PVP. A Comparative Study For Predicting Students Academic Performance using Bayesian Network Classifiers. IOSR Journal of Engineering. 2013 Feb; 3(2):37–42.
- Kulkarni P, Ade R. Prediction of Students Performance based on Incremental Learning. International Journal of Computer Applications. 2014 Aug; 99(14):10–6.
- Ross QJ. C4. 5: programs for machine learning. Elsevier;2014.
- Stuart R, Norvig P. Artificial Intelligence: A Modern Approach. EUA: Prentice Hall; 2003.
- Altman NS. An introduction to kernel and nearest neighbor non-parametric regression. The American Statistician. 1992; 46(3):175–85.
- Witten IH, Frank E. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann; 2005.
- Cohen WW. Fast effective rule induction. Proceedings of the twelfth international conference on machine learning;1995. p. 115–23.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.