Maria Knobelsdorf, University of Dortmund, Germany - Didaktik der ...

characteristics mean m
(regarding the
data
boys)
of the
interest in Informatics t0: 3.79
t1: 3.65
self-concept of ability in In- t0: 3.82
formatics
t1: 4.10
vocational orientation t0: 4.07
t1: 4.00
difference mean m
m1-m0 (regarding the
data
girls)
of the
-0.14 t0: 2.92
t1: 3.36
+0.28 t0: 3.11
t1: 3.35
-0.07 t0: 2.95
t1: 3.44
difference StdDev
m1-m0 (regarding the
data
boys)
of the
+0.44 t0: 0.80
t1: 1.08
+0.24 t0: 1.08
t1: 0.97
+0.49 t0: 1.17
t1: 1.24
Table 6: Scale mean values and standard deviations regarding student characteristics
and 8). The correlation coefficients are Spearman’s Rho coefficients
and provide a measure of the relationship between
two sets of data.
The correlation tables 7 and 8 show that apart from one
correlation between “context-orientation” and “interest” for
the boys there are no statistically significant correlations
to be found at the time of the first survey, the explanation
being that hardly any of the students had any teaching
in Informatics at the time of the survey and therefore
could not give an assessment. This is supported by free text
comments of some students. At the time of survey t1, all
students characteristics correlate with the teaching characteristic
“student-orientation”. Gender differences are to be
noticed only in the level of the correlation coefficients and
the level of significance. The correlations between the surveyed
characteristics are stronger regarding the data of the
girls. It must be taken into account, however, that there are
only 12 data sets of girls as opposed to 45 of boys. 12 data
sets do not allow any general statements.
studentorientationcontextorientation
interest in
Informatics
t0: 0.306
t1: 0.673*
t0: 0.439**
t1: 0.298**
selfconcept
of ability
t0: 0.297
t1: 0.543*
t0: 0.229
t1: 0.290
vocational
orientation
t0: - 0.145
t1: 0.347**
t0: 0.162
t1: 0.128
Table 7: Correlations regarding student characteristics
(*p < 0.01, **p < 0.05, other correlations are
non-significant). Only the data of the boys are taken
into account.
studentorientationcontextorientation
interest in
Informatics
t0: 0.500
t1: 0.773*
t0: -0.008
t1: 0.534
self-concept
of ability
t0: 0.172
t1: 0.874*
t0: -0.239
t1: 0.391
vocational
orientation
t0: 0.305
t1: 0.577**
t0: -0.136
t1: 0.330
Table 8: Correlations regarding student characteristics
(*p < 0.01, **p < 0.05, other correlations are
non-significant). Only the data of the girls are taken
into account.
Contrary to our expectations, only the boys’ data present
a low correlation between “context-orientation” and “interest
in Informatics”. Consequently these results show that
“context-orientation” has only little or no influence on the
41
StdDev
(regarding the
data of the
girls)
t0: 0.84
t1: 1.20
t0: 1.07
t1: 1.23
t0: 1.20
t1: 1.20
students’ characteristics. This outcome contradicts the results
of [17] (see section 2.2). Further investigation must
show if these findings will be confirmed.
Our next research question referred to the correlations
between the ascertained student characteristics. According
to analyses in natural sciences some correlations were to be
expected (see section 2.1). The correlations of the students’
characteristics are shown in table 9 and 10, differentiated by
gender. The correlations are calculated by means of the correlation
measure Spearmans Rho, which allows to calculate
the relationship of ordinal scaled data.
interest in
Informatics
self-concept
of ability
vocational
orientation
interest in
Informatics
1.00
t0: 0.204
t1: 0.534*
t0: 0.537*
t1: 0.599*
self-concept
of ability
1.00
t0: 0.365**
t1: 0.230
vocational
orientation
1.00
Table 9: Correlations regarding student characteristics
(*p < 0.01, **p < 0.05, other correlations are
non-significant). Only the data of the boys are taken
into account.
interest in
Informatics
self-concept
of ability
vocational
orientation
interest in
Informatics
1.00
t0: 0.763*
t1: 0.792*
t0: -0.023
t1: 0.332
self-concept
of ability
1.00
t0: 0.156
t1: 0.767*
vocational
orientation
1.00
Table 10: Correlations regarding student characteristics
(*p < 0.01, other correlations are nonsignificant).
Only the data of the girls are taken
into account.
Table 9 and 10 present that for the boys there are moderate
correlations between “interest in Informatics” and “vocational
orientation” (regardless of whether or not Informatics
lessons take place) as well as a moderate correlation between
“interest in Informatics” and “self-concept of ability in Informatics”
(after one year of Informatics lessons) and a low
correlation between “self-concept of ability in Informatics”
and “vocational orientation” (only at the beginning of the
project). For the girls there are strong correlations between