AD ALTA
JOURNAL OF INTERDISCIPLINARY RESEARCH
Figure 11: Clusters composed of the whole sample
Table 3: Cluster centroids and standard deviation
Cluster
Total score
during the test
Time used for task
solution
1
N
45
45
M
1378.36
14.51
SD
84.658
3.841
2
N
38
38
M
961.000
11.21
SD
128.830
3.024
3
N
70
70
M
455.79
8.93
SD
166.251
2.994
Total
N
153
153
M
852.61
11.14
SD
419.463
4.023
Each of the three groups contain students having reached low as
well as high scores, however, the tendency is what is described
by the regression analysis.
Reliability here was checked by the K-means procedure and was
found sufficient. The data of the cluster centroids are
summarized in Table 3.
Figure 12: Belonging to the clusters by the type of training
Figure 13: Belonging to clusters by specialization
Table 4: Description of clusters
Cluster 1
Cluster 2
Cluster 3
Time used for
task solution
less
medium
more
Achieved result
5 – 20 points
6 – 20 points
7 – 22 points
Type of training
correspond-ence
full-time and
correspond-ence
full-time
Specialization
nursery school
teachers
pedagogy and
public education
teacher students,
nursery school
teacher students
We examined the composition of the clusters in terms of the
background variables for the whole sample (Figure 12-13). The
higher rate of full-time students belongs to cluster C3 while that
of the correspondence students to C1. Most of the teacher
students belong to C3, most students of pedagogy and public
education to C2 while nursery school teacher students mainly
belong to C1 and C3. We proved it by Chi-square test that there
is significant correlation between the type of training and
classification into clusters (F= 18.473; p<0.05) as well as
between the specialization and belonging to a cluster (F= 15.138;
p<0.05). Table 4 presents the summary of these by describing
the clusters.
6 Conclusions
The objective of our research implemented with the participation
of 204 first-grade teacher students was to (1) determine the
development level of their inductive, and within that abstract and
analogue as well as diagrammatic reasoning and their rule
induction; (2) respond to the question whether it is possible to
draw conclusions from time consumption regarding the
performance expected in the inductive text; (3) identify the
background variables by means of which significant differences
can be detected between the student groups. We have found the
following answers.
(1)
The students’ analogue reasoning and rule induction are
much more developed than their diagrammatic reasoning.
(2)
One of the preconditions of achieving a good result in the
inductive test is the utilization of the whole time available,
however, maximal time utilization does not necessarily
bring about outstanding performance. Each of the students
having gained high scores used the available time fully.
Introducing the notion of specific performance, we found
that the students with the best results are involved in full-
time teacher training, live in cities and their parents have a
degree. Specific performance was mainly deteriorated by the
high amount of time used for the diagrammatic exercises.
(3)
Students can be well grouped by time consumption: (a)
neglectful and superficial, (b) considered but not persistent
enough, (c) persistent and diligent. Knowing the type of
training and specialization of the student helps us
understand the clusters.
The deficiencies in diagrammatic reasoning are less problematic
in teacher training; developed rule induction and analogue
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Full-time students
Correspondence students
Claster 1
Claster 2
Claster 3
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Teacher students
Nursery school teachers
Pedagogy and public education
students
Claster 1
Claster 2
Claster 3
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