Research in Engineering Education Symposium 2011 - rees2009

Universidad Politécnica de Madrid (UPM) Página 751 de 957
Figure 3. (a) Pearson correlation between MP, MR and LS, and (b) Pearson correlation between LS and
ratios of MP and MR
Thus the mathematical expression that better explain the correlation is (3):
As it can be appreciated in Figure 4, a linear correlation would be weak (R 2=.157). Though
a rough analysis could show that the number of observations responding to a direct
relation (quadrant I and III), is greater than those that do not respond to it (quadrants II
and IV). In first and third quadrant, is possible to find 61 students (65.3%), in the former
are gathered students with high scores in T1 (RM), and also the ones in the ratio MP/MR 2;
in the latter quadrant is reproduced a similar phenomena, but considering the low scores.
Figure 4. Scatter graph that shows the correlation between LS and MP/MR2
The results show that the designed instruments can have the potentiality for
discriminating individual capacities, mainly cognitive, that are supposed to influence
learning.
Discussion
This papers shows results of an intermediate stage of a research aimed at validating the
pertinence of learning dynamics variables as predictors of engineering student success .
Proceedings of Research in Engineering Education Symposium 2011
Madrid, 4 th - 7 th October 2011