Notice of Violation of IEEE Publication Principles: Interpreting learners behavior by monitoring online tests through data visualization

Notice of Violation of IEEE Publication Principles

"Interpreting Learners Behavior by Monitoring Online Tests through Data Visualization"
by M. Anantha Lakshmi and P. Rambabu
in the Proceedings of the 2011 3rd International Conference on Electronics Computer Technology (ICECT), April 2011, pp. 172-176

After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.

This paper copied text and a figure from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.

Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:

"Monitoring Online Tests through Data Visualization"
by Gennaro Costagliola, Vittorio Fuccella, Massimiliano Giordano, Giuseppe Polese
in the IEEE Transactions on Knowledge and Data Engineering, Vol 21, No 6, June 2009, pp. 773-784


This paper presents an approach and a system to let tutors monitor several important aspects related to online tests, such as learner behavior and test quality. The approach includes the logging of important data related to learner interaction with the system during the execution of online tests and exploits data visualization to highlight information useful to let tutors review and improve the whole assessment process. This paper has focused on the discovery of behavioral patterns of learners and conceptual relationships among test items. For this Characterization and summarization has been used. The Characterization and summarization is implemented efficiently using Attribute Oriented Induction algorithm which discovers patterns for accessing learners behavior. By analyzing the data visualization charts, we have detected several previously unknown test strategies used by the learners. Last, we have detected several correlations among questions, which gave us useful feedbacks on the test quality.