Total views : 131
Predicting Analysis of Data Mining Extraction Technique in Secondary Education
Objectives: To implement the data mining techniques to evaluate the performance evaluation of tutor and compare the results obtained by adopting several data mining techniques. Methods/Statistical Analysis: The details regarding the results of the students are taken as the base parameter for the performance evaluation of the tutor. The classification data mining techniques are utilized to study the data developed based on the students performance. Findings: By employing the various data mining classification techniques on the student result data to evaluate the performance of the instructor, efficiency of the work carried out by the instructor and also the way in which the instructor is approaching the students point of view is highly impactful on the students results can be given as the output based on the numerical generated by the classification techniques. Application/Improvements: The result generated by the classification techniques can be more accurate by providing more accurate input and considering the other factors that would affect the outcome of the technique.
Artificial Neural Network, Category Algorithms, Linear Differentiate Breakdown, Outcome Foliage, Presentation Assessment.
- Abaidullah AM, Ahmed N, Ali E. Identifying hidden patterns in students’ feedback through cluster analysis. Int J Comput Theory Eng. 2015; 7(1):16–20.
- Delavari N, Phon-Amnuaisuk S, Beikzadeh MR. Data mining application in higher learning institutions. Inform Edu-Int J. 2007; 7(1):31–54.
- Goyal MVohra R. Applications of data mining in higher education. Int J Comput Sci Issue. 2012; 9(2):113–20.
- Luan J. Data mining and its applications in higher education.New Directions for Institutional Research. Wiley.2002; 113:17–36.
- Han J, Kamber M, Pei J. Data Mining: Concepts and Techniques. Waltham, MA, USA: Morgan Kaufmann; 2012.
- Luan J. Data mining and knowledge management in higher education-potential applications. Proc AIR Forum; Toronto, ON, Canada. 2002. p. 1–16.
- Minaei-Bidgoli B, Kashy DA, Kortemeyer G, Punch WF.Predicting student performance: An application of data mining methods with an educational Web-based system.Proc 33rd Annu IEEE Frontiers Edu. 2003 Nov; 1:T2A–13.
- Kumar V, Chadha A. An empirical study of the applications of data mining techniques in higher education. Int J Adv Comput Sci Appl. 2011; 2(3):80–4.
- Romero C, Ventura S. Educational data mining: A survey from 1995 to 2005. Expert Syst Appl. 2007 Jul; 33:135–46.
- Baradwaj BK, Pal S. Mining educational data to analyze students’ performance. Int J Adv Comput Sci Appl. 2011; 2(6):63–9.
- Ma J, Perkins S. Time-series novelty detection using oneclass Support Vector Machines. Proc of the International Joint Conference on Neural Network. 2003; 3:1741–5.
- Gupta D, Jindal R, Dutta Borah M. A knowledge discovery based decision technique in engineering education planning.Proc Int Conf on Emerging Trends and Technologies in Data Management; Institute of Management Technology Ghaziabad. 2011. p. 94–102.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.