Fraud Detection in Selection Exams Using Knowledge Engineering ...

making the analysis of the behavior of other CBR software or
others KE tools for a greater volume of data.
ACKNOWLEDGMENT
We want to thank Dr. Marcelo Sobottka of the Department
of Mathematics at the Federal University of Santa Catarina
(Brazil) for his valuable help in determining the probability of
IWA in a test of multiple-choice questions.
REFERENCES
Figure 6. Cases retrieved bythe CBR tool.
Even when the query was submitted with one more wrong
answer purposely introduced in the first column, it is clear that
case numbers 2 and 8 were recovered with maximum
similarity. The remaining cases that did not show a significant
number of IWA had low similarity, proving the efficiency and
effectiveness of the proposed model.
VIII. CONCLUSIONS AND FUTURE WORK
The methodology of CBR showed excellent performance in
the detection of IWA, identifying all the cases in the case base
used as a test. While there are some differences between the
values of the query with the values found in the case base, the
CBR software used in this study correctly retrieved all cases
that showed the highest similarity.
We conclude that the method proposed in this study can be
applied in detecting and preventing fraud in selection
examinations that use multiple-choice questions, allowing the
adoption of countermeasures in a timely manner, thanks to its
ease of modeling and application.
One limitation of this study is the lack of a more detailed
analysis of the behavior of the CBR software with respect to its
performance with a case base with thousands of answers from a
large group of applicants. However, based on other authors’
similar experiences with using bases of more than 200,000
cases [12], the CBR systems have a satisfactory performance.
Thus, it is expected that the CBR solution here proposed
presents a good performance even in the case of larger bases.
Future research could explore the application of the model
proposed in this study for other kinds of questions and tests,
adapting the methodology of CBR to the specificity of these
applications. Others investigators can also expand this study,
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