Due to the nature of the data collected it was most
appropriate to use non-parametric statistical analyses. For this
we used Wilcoxon signed-rank test [10] that is suitable for
comparing two dependent conditions as the repeat samples
came from the same set of students. Table III shows the
analysis of the evaluation data from year one students and
Table IV shows the analysis of the data from year two students
using the non-parametric tests. These results are obtained using
the SPSS Statistics 18 software package. The table shows the
number of pre/post-test returns, i.e. sample sizes, and the p
values for each item of the tests. The items are labeled QI to
Q5. For this evaluation, values of p < 0.05 are regarded as
statistically significant. Apart from Q3 in Compiler
Technology tutorial and QI in CPU Pipeline Technology the
rest of the results indicate that the probability that the
differences between the pre and the post tests can be attributed
to the intervention by the simulations is significantly high. This
then disproves the null hypothesis and lends significant support
to the assumption that the simulations were instrumental in
helping the students to feel more confident in their
understanding and in their ability to explain and demonstrate
the theory covered during the lectures.