We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Reseach Article

A Novel Feature Clustering Algorithm for Evaluation of Descriptive Type Examination

by A. Krishna Mohan, M H M Krishna Prasad
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 98 - Number 9
Year of Publication: 2014
Authors: A. Krishna Mohan, M H M Krishna Prasad
10.5120/17214-7447

A. Krishna Mohan, M H M Krishna Prasad . A Novel Feature Clustering Algorithm for Evaluation of Descriptive Type Examination. International Journal of Computer Applications. 98, 9 ( July 2014), 35-41. DOI=10.5120/17214-7447

@article{ 10.5120/17214-7447,
author = { A. Krishna Mohan, M H M Krishna Prasad },
title = { A Novel Feature Clustering Algorithm for Evaluation of Descriptive Type Examination },
journal = { International Journal of Computer Applications },
issue_date = { July 2014 },
volume = { 98 },
number = { 9 },
month = { July },
year = { 2014 },
issn = { 0975-8887 },
pages = { 35-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume98/number9/17214-7447/ },
doi = { 10.5120/17214-7447 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:25:47.811229+05:30
%A A. Krishna Mohan
%A M H M Krishna Prasad
%T A Novel Feature Clustering Algorithm for Evaluation of Descriptive Type Examination
%J International Journal of Computer Applications
%@ 0975-8887
%V 98
%N 9
%P 35-41
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Objective type of Examination evaluation is easy in Computer world, but the descriptive type of question evaluation is more complex and there is no significant research has been taken place. So many descriptive type examinations like University Exams, GRE etc. , have been conducting from long time which is being evaluated manually by sending these types of questions and answers to the experts. This kind of exams needs automatic evaluation instead of manual correction to bring accuracy and reduce the evaluation time. In this paper, authors propose CosInfo algorithm a new solution to the above problem which can evaluate the papers automatically. This algorithm implemented the feature clustering for evaluation purpose that calculate the similarity between two documents and cluster the relevant documents in to different groups. Proposed algorithm uses the expected information function and parts of speech in English grammar as parameters to cluster the data, and also builds a model to classify the testing documents using SVM classification to assess the degree of similarity which will help to award the marks automatically. Experimental results show that the proposed method obtains better and accurate results to allocate marks compared with manual evaluation.

References
  1. Jung-Yi Jiang, et al. “A Fuzzy Self-Constructing Feature Clustering Algorithm for Text Classification” - IEEE, Vol. 23, March 20011, No 3, 335-348.
  2. Elsayed et al. “Automatic evaluation technique for certain types of open questions in semantic learning systems”. Journal of Human-centric Computing and Information, Springer Berlin Heidelberg, 2013, Vol.3(1), 1-15.
  3. Papri Chakraborty. “Developing an Intelligent Tutoring System for Assessing Students' Cognition and Evaluating Descriptive Type Answers”-International Journal of Modern Engineering Research (IJMER) Vol.2, May-June 2012, Issue.3.
  4. Archana et al..” A Tool for Managing Descriptive Type Examinations” – In: International Conference on Management Technology for Educational Practices. 29th & 30th July 2009, Jnana Jyothi Auditorium, Central College Camous, Bengaluru India.
  5. Wojciech et al. “Automatic evaluation of examination tasks in the form of function plot” - proc. MEMSTECH’2010, 20-23 April 2010, Polyana-Svalyava (Zakarpattya), UKRAINE.
  6. Rein P. “Prospects of automatic assessment of step-by-step solutions in algebra “ Proceedings of the ninth IEEE international conference on advanced learning technologies. IEEE Computer Society, Washington, DC, USA, 2009,535–537. 10.1109/ICALT.2009.123.
  7. Amarjeet et al. “Algorithm for Automatic Evaluation of Single Sentence Descriptive Answer” International Journal of Inventive Engineering and Sciences (IJIES) , August 2013, ISSN: 2319–9598, Volume-1, Issue-9.
  8. George Forman. “An Extensive Empirical Study of Feature Selection Metrics for Text Classification”- Journal of Machine Learning Research, Vol.3. 2003.1289-1305.
  9. Akinori et al. ”Order-based clustering using formal concept analysis” - World Automation Congress, TSI Press. 2010.
  10. Qinbao et al. “A Fast Clustering-Based Feature Subset Selection Algorithm for High-Dimensional Data “- IEEE transactions on knowledge and data engineering, vol. 25, no. 1, January 2013.
  11. H. Almuallim et al. “Algorithms for Identifying Relevant Features”- Proc. Ninth Canadian Conf. Artificial Intelligence, 1992, 38-45.
  12. L.D. Baker et al. “Distributional Clustering of Words for Text Classification”- Proc. 21st Ann. Int’l ACM SIGIR Conf. Research and Development in information Retrieval, 1998,96-103.
Index Terms

Computer Science
Information Sciences

Keywords

Text classification Document clustering Information retrieval Feature Clustering cosine similarity