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JOURNAL OF INTERDISCIPLINARY RESEARCH
However, latest researches have found contrary results. Nagdi et
al. (2018) identified cooperation, flexibility, the knowledge of
pupils’ needs and openness to equality and inclusion as the key
elements of the personal characteristics of STEM (science,
technology, engineering and mathematics) teachers. The notions
conform to the literature on teacher identity (Akkerman, Meijer,
2011; Franzak, 2002; Schutz et al., 2018) where the nature of
teacher identity is considered a dialogue concept in which
personal and professional experiences interact with the so-called
STEM-skill. Searching the problem from the students’ aspect,
one will find that the pupils’ STEM knowledge, skills and
abilities can be supported by the informal learning environment
(Denson et al., 2015), which can have a positive impact on
pupils’ interest in STEM (Denson et al., 2015; Mohr-Schroeder
et al. 2014) and may increase the probability of the continuation
of a STEM career during higher education studies (Kitchen et al.
2018; Kong et al., 2014).
2 Aim of research
The main objective of the empiric research was to (1) determine
the development of the inductive, and within that the abstract
and analogue thinking of teacher students starting their studies in
higher education, (2) find the background variables by means of
which significant differences can be detected between the
various groups of students and (3) respond to the question
whether any conclusions regarding the performance expected in
the inductive test can be drawn based on the time spent on
solving the exercises.
3 Materials and methods in research
We face a question here: how could it be possible to measure
reliably the students’ inductive, and within that abstract
reasoning without the specific subject knowledge and
competences (e.g. in mathematics or physics). There are several
methods available, like some intelligence tests (e.g. Raven), tests
measuring abstract reasoning or measuring tools focusing at the
given competence component.
During our research we applied the measurement tools
elaborated by Psychometric Success WikiJob Ltd. (UK, London)
that lays great stress on labour market expectancies (Newton,
Bristoll, s.a.). These tests were built on single- and multiple-
factor intelligence theories (Mackintosh, 1998).
Spearman, for example, was of the opinion that there is one or
several common factors existing in terms of the solution of each
intellectual task that is a pledge of success (Mackintosh, 1998).
He divided the g-factor of intelligence into two parts: (1)
inductive logical (eductive) and (2) reproductive skills related to
storing and recalling information. The Raven-test, for example,
connects to the previous one, while vocabulary test belongs to
the latter (Kane, Brand, 2003).
Eductive competences refer to logical operations based on
conclusion by means of which, through the recognition and
comprehension of interconnections and the consideration of the
contextual content, new knowledge is created from the perceived
information. To understand the whole of the problem, holistic
approach is needed while its solution demands the ability to
recognize the relations and interdependences between the parts.
Understanding the problem is more than comprehensive pattern
recognition (Gestalt); it is also necessary to highlight the essence
and neglect unimportant elements. In most cases, these are not
possible to be verbalized, therefore, the measuring tools mainly
consist of geometrical figures (squares, polygons, circles etc.).
The perception of these geometrical forms, the recognition of
their typical characteristics and the comprehension of the
relations between them is dependent on the existing knowledge
on one hand and certain cultural effects on the other (Kane,
Brand, 2003). The previous one is in harmony with the inductive
operations (Klauer, Phye, 2008). As for the latter, one of the
main advantages of the test must be stressed: it is, to a certain
extent, culture-independent.
Paul Newton and Helen Bristoll (s.a.) elaborated an inductive
reasoning test building on the Raven eductive skills
measurement test but paying more attention to career aspects in
nature sciences. To examine cognition based on inductive
reasoning and thinking, they developed the skills structure
presented in Figure 1.
The problem for the solver lies in the difficulty to realize the
logical relations hiding behind the patterns in the tasks. The
problems root in the difficulty to recognize the changes or the
iteration of the following characteristics: (1) form, (2) size), (3)
colour and (4) pattern. The tasks consist of visual patterns and
geometrical figures, and the series (one-or two-dimension
matrices) must be continued, or the elements not fitting be
found, relying on the recognition of the logical interrelations
behind them.
Figure 1: The task system examining inductive reasoning and
thinking
In our research, we used an inductive and abstract reasoning
online test made of 30 items where the certain types of exercises
contained 6 items:
Continuation of one-dimension series
Recognition of the (’odd-one-out’) elements not fitting in
the one-dimension series
Recognizing an analogue
Recognizing regularity – unknown operation (examination
of diagrammatic reasoning)
Recognizing regularity – known operation (examination of
diagrammatic reasoning)
One- (series) and two-dimension matrices demand the capability
to recognize various interconnections that in many cases are not
evident at the first instance. The recognition of connections
between geometric figures can be isolated from the identification
of single figures. This latter one must be clear-cut for each
person in the experiment. According to Spearman (1927), the
perception of the geometrical forms immediately elicits
knowledge created about the connections, and this is true vica
versa, as well. All this means that perception, observation and
abstract thinking make one whole during cognition. When
solving the problem, each characteristic of the geometrical
figures must be observed simultaneously, their interconnections
must be understood and perception must be precise to the
smallest details. No good solution can be born without
recognizing the “whole”, however, identifying the “parts” is of
decisive importance, as well (Georgiev, 2008).
In terms of identifying analogues, already Sternberg pointed to
the fact that the difficulty of the problems lies in the recognition
of regularities originating in the change of the characteristics of
the certain objects (A, B, C, D) for which the relation(s) (R) in
terms of A and B must be recognized and then applied in terms
C and D to identify D (Sternberg, 1977):
A – R – B :: C – R – D
He found that the experimentee may follow two strategies in
selecting object D: (1) (s)he considers the potential D candidates
one by one, and examining each of their characteristics chooses
the object fitting in the recognized relation the most (sequential
search), or (2) examines the characteristics one by one in terms
of each potential object D and then selects the one in the case of
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