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Performance Assessment of Faculties of Management Discipline From Student Perspective Using Statistical and Mining Methodologies
Chandrani Singh , Arpita Gopal, Santosh Mishra
Pages - 63 - 69     |    Revised - 31-01-2011     |    Published - 08-02-2011
Volume - 1   Issue - 5    |    Publication Date - January / February  Table of Contents
Data Analysis, Mining, Clustering, Trend Extraction, Performance Predictio
This paper deals with Faculty Performance Assessment from student perspective using Data Analysis and Mining techniques .Performance of a faculty depends on a number of parameters (77 parameters as identified) and the performance assessment of a faculty/faculties are broadly carried out by the Management Body ,the Student Community ,Self and Peer faculties of the organization .The parameters act as performance indicators for an individual and group and subsequently can impact on the decision making of the stakeholders. The idea proposed in this paper is to perform an analysis of faculty performance considering student feedback which can directly or indirectly impact management’s decision, teaching standards and norms set by the educational institute, understand certain patterns of faculty motivation, satisfaction, growth and decline in future. The analysis depends on many factors, encompassing student’s feedback, organizational feedback, institutional support in terms of finance, administration, research activity etc. The data analysis and mining methodology used for extracting useful patterns from the institutional database has been used to extract certain trends in faculty performance when assessed on student feedback.
CITED BY (2)  
1 Duong, T. V. T., Do, T. D., & Nguyen, N. P. (2015, July). Exploiting faculty evaluation forms to improve teaching quality: An analytical review. In Science and Information Conference (SAI), 2015 (pp. 457-462). IEEE.
2 Shah, P. R., Vaghela, D. B., & Sharma, P. (2015, March). Faculty performance evaluation based on prediction in distributed data mining. In Engineering and Technology (ICETECH), 2015 IEEE International Conference on (pp. 1-5). IEEE.
1 Google Scholar 
2 CiteSeerX 
3 Scribd 
4 SlideShare 
5 PdfSR 
A.K. Jain and R. C. Dubes. [1988]. Algorithms for Clustering Data. Prentice Hall.
Amy Wong and Jason Fitzsimmons [2008] Student Evaluation of Faculty: An Analysis of Survey Results. U21GlobalWorking Paper Series, No. 003/2008.
Breiman, L., Friedman, J.H., Olshen, R., and Stone, C.J., 1984. Classification and RegressionTree Wadsworth & Brooks/Cole Advanced Books & Software, Pacific California.
Chandrani Singh ,Dr. Arpita Gopal Performance Analysis of Faculty Using Data Mining Techniques,IJFCSA-2010,1st edition.
Chiu, T., Fang, D., Chen, J., and Wang, Y. 2001. A Robust and scalable clustering algorithm for mixed type attributes in large database environments. In Proceedings of the 7th ACM SIGKDD,263-268, San Francisco, CA.
Cristóbal Romero, Sebastián Ventura, Pedro G. Espejo and César Hervás.[2008], Data Mining Algorithms to Classify Students. The 1st International Conference on Educational Data Mining Montréal, Québec, Canada, June 20-21, 2008 Proceedings.
Emmanuel N. Ogor.[2007] Student Academic Performance Monitoring and Evaluation Using Data Mining Techniques. Electronics, Robotics and Automotive Mechanics Conference, 2007.CERMA 2007 Volume, Issue, 25-28 Sept. 2007 Page(s): 354 – 359 Digital Object Identifier 10.1109/CERMA.2007.4367712.
Fathi Elloumi, Ph.D., David Annand. [2002] Integrating Faculty Research Performance Evaluation and the Balanced Scorecard in AU Strategic Planning: A Collaborative Model.
Ganti, V., Gehrke, J. and Ramakrishnan, R. 1999a. CACTUS-Clustering Categorical Data Using Summaries. In Proceedings of the 5th ACM SIGKDD, 73-83, San Diego, CA.
GUHA, S., RASTOGI, R., and SHIM, K. 1999. ROCK: A robust clustering algorithm for categorical attributes. In Proceedings of the 15th ICDE, 512-521, Sydney, Australia.
Karin Sixl-Daniell, Amy Wong, and Jeremy B. Williams.[2004] The virtual university and the quality assurance process: Recruiting and retaining the right faculty. Proceedings of the 21st ASCILITE Conference.
Luan J. [2002] “Data Mining and Knowledge Management in higher Education” Presentation at AIR Forum, Toronto, Canada.
M.R.K. Krishna Rao. [2004] Faculty and Student Motivation: KFUPM Faculty Perspectives
R Agrawal, R Srikant Fast Algorithms for Mining Association rules in Large Databases (1994)by Proceedings of the VLDB.
Raoul A. Arreola, Michael Theall, and Lawrence M. Aleamoni [2003] Beyond Scholarship:Recognizing the Multiple Roles of the Professoriate." Paper presented at the Annual Meeting of the American Educational Research Association (Chicago, IL, April 21-25, 2003).
Zaki, M.J. Scalable algorithms for association mining Knowledge and Data Engineering, IEEE Transactions on Volume 12, Issue 3, May/Jun 2000 Page(s):372 390 Digital Object Identifier 10.1109/69.846291
Dr. Chandrani Singh
- India
Dr. Arpita Gopal
- Iran
Mr. Santosh Mishra
- India