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continuous evaluation of the efforts performed by
each student and a clear measure of their efficiency
in attaining the targeted starting values. More
specifically, these parameters can be appropriately
used to derive specific suggestion as rectify each
student’s path when calibration reveals necessary.
Finally, it is important to note that EVA analysis,
tools and charts would be a significant addition to
the educational roadmap developed in this paper.
5 CONCLUSIONS
In this paper we have presented an innovative
approach in using and applying Educational Data
Mining. An educational roadmap that reliably and
greatly endows both advisors and students a
straightforward manual for success. This roadmap is
built using Regression Trees with different types
elaborated in the frame of a continuous and close
follow-up of students’ performance. Correlation
Analysis and study of different splitting values of
the regression tress were extensively examined to
choose the best readable and useful tree/roadmap
while keeping a high rate of precision in the
estimation process. In this context, two types of trees
were developed and elaborately analyzed. The first
type identifies, on a yearly-basis, courses and their
respective grades which have greater impact on
students’ cumulative GPA upon graduation. The
second type is an iterative tree that re-injects the
current students’ GPA into the dataset as an
additional attribute. This proved to be a powerful
tool for decision making and endorsed the results
and the computed precision. The trees were trained,
cross-validated and tested. Calculation of the
precision and Correlation Analysis were achieved
based on untrained data to significantly enhance the
reliability of the outcome. Also a detailed
perspective of this study was presented under Earned
Value Analysis as a future scope of work and an
added tool for advising and monitoring students’
efforts, performance and evolution throughout their
engineering curricula.
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