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vised by one experimenter in order to ensure silence. Students did not
have access to the game outside the playing sessions. In week 3 a third
and last game session took place in order to reach quarter 7 in the game.
The day after, another test session was scheduled which included administration
of the MSLQ, KMQUESTions, the transfer questions and an
exit-questionnaire. In week 4 a debriefing lecture was organised during
which students shared their experiences about the game. Also the grades
(post-test scores) were disclosed.
6.2.5 Statistics and a-priori comparisons
When comparing two or more groups one can use a number of statistical
analysis techniques such as an analysis of covariance or a General
Linear Model (GLM) for repeated measures (Stevens, 2002).
In an analysis of covariance, one measure is used in order to control for
another measure. For instance, post-test scores of learning can be controlled
for pre-test scores (the covariate), especially when pre-test scores
differ between participants. This can be important when differences between
conditions occur. Using a randomized design prevents systematic
differences occurring. In principle, the terms for using this technique are
met. However, the disadvantage of this technique is that learning effects
in terms of an increase from pre- to post-test are not taken into account
as such. One can compare the adjusted post-test scores.
The alternative technique, GLM (repeated measures) is used in this
study. This is an analysis of variance in which within-subject variables
and between-subject variables are taken into account. Several withinsubject
variables exist, namely the pre- and post-test measures of learning;
which are dependent variables. Between-subject variables divide
the students into groups. In this study there were two between subject
groups: ‘no-model’ and ‘model’ conditions. In order to test the hypothesized
interaction effect between condition and metacognition, the
measure of metacognition is used as an independent variable. As such it
needs to be transformed into a dichotomous variable. This reduces the
power of the analysis. The power is the probability of making a correct
decision (or rejecting the null hypothesis when it is false). The main advantage
of this analysis technique is that learning effects are more likely
to be clearly identified.
High and low scorers on each measure of metacognition were distinguished
by using the median in order to split the group. The median is
a robust measure for central tendency that is unaffected by possible outliers
or extreme scores (Stevens, 1994). The measures of metacognition
are: prospective self-reported use of metacognitive skills, retrospective
self-reported use of metacognitive skills (both measures on a 6-point