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The Impact of Gender on Interest in Science Topics and the Choice of
Scientific and Technical Vocations
Grazia Buccheria; Nadja Abt Gürbera; Christian Brühwilera
a
Institute of Research on Teaching Profession and on Development of Competencies, University of
Teacher Education St.Gallen, St.Gallen, Switzerland
Online publication date: 11 January 2011
To cite this Article Buccheri, Grazia , Gürber, Nadja Abt and Brühwiler, Christian(2011) 'The Impact of Gender on Interest
in Science Topics and the Choice of Scientific and Technical Vocations', International Journal of Science Education, 33: 1,
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International Journal of Science Education
Vol. 33, No. 1, 1 January 2011, pp. 159–178
RESEARCH REPORT
The Impact of Gender on Interest in
Science Topics and the Choice of
Scientific and Technical Vocations
Grazia Buccheri*, Nadja Abt Gürber and Christian Brühwiler
Downloaded By: [Buccheri, Grazia] At: 11:02 12 May 2011
Institute of Research on Teaching Profession and on Development of Competencies,
University of Teacher Education St.Gallen, St.Gallen, Switzerland
10grazia.buccheri@phsg.ch
33
Ms.
000002010
GraziaBuccheri
International
10.1080/09500693.2010.518643
TSED_A_518643.sgm
0950-0693
Original
Taylor
2010
and
&
Article
Francis
(print)/1464-5289
Francis
Journal of Science
(online)
Education
Many countries belonging to the Organisation for Economic Co-operation and Development
(OECD) note a shortage of highly qualified scientific-technical personnel, whereas demand for
such employees is growing. Therefore, how to motivate (female) high performers in science or
mathematics to pursue scientific careers is of special interest. The sample for this study is taken
from the Programme for International Student Assessment (PISA) 2006. It comprises 7,819 high
performers either in sciences or mathematics from representative countries of four different education systems which generally performed well or around the OECD average in PISA 2006:
Switzerland, Finland, Australia, and Korea. The results give evidence that gender specificity and
gender inequity in science education are a cross-national problem. Interests in specific science
disciplines only partly support vocational choices in scientific-technical fields. Instead, gender and
gender stereotypes play a significant role. Enhancing the utility of a scientific vocational choice is
expected to soften the gender impact.
Keywords: Scientific literacy; Science career; Interest in science; Large-scale studies;
International comparisons
Countries belonging to the Organisation for Economic Co-operation and Development (OECD) note a shortage of highly qualified scientific and technical personnel
(High Level Group on Increasing Human Resources for Science and Technology in
Europe, 2004; OECD, 2008), whereas the demand for science and technology
graduates is growing (OECD, 2008). Studies on overall trends in higher education
*Corresponding author. Institute of Research on Teaching Profession and on Development of
Competencies, University of Teacher Education St.Gallen, Seminarstrasse 27, St.Gallen 9400,
Switzerland. Email: grazia.buccheri@phsg.ch
ISSN 0950-0693 (print)/ISSN 1464-5289 (online)/11/010159–20
© 2011 Taylor & Francis
DOI: 10.1080/09500693.2011.518643
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160 G. Buccheri et al.
enrolments show that although absolute numbers of science and technology students
have risen between 1985 and 2003 as access to higher levels of education expands in
OECD economies, the relative share of science and technology students among the
overall student population has been falling, with female students still lagging behind
males (OECD, 2008). To counteract the deficit of young academics in scientific and
technical fields, motivating students with high competencies in science or
mathematics to pursue scientific careers is particularly important.
Interest in science is not only seen as an important precondition for successful
learning, it may also have a major influence on students’ choice of the vocational
education or the field of study as well. Since it is often linked to enjoyment and
personal value, interest in science is also fundamental for lifelong learning and
continuing education in scientific subject areas (e.g. OECD, 2006; Prenzel, Schütte,
& Walter, 2007).
Todt, Drewes, and Heils (1994) distinguish between global interests and specific
interests in their heuristic model of interest. Specific interests are compatible with,
but not deducible from, general interests and are more characteristic of individuals
than general interests. According to the model of development of general interests
and for the first stages of specific interests, a progressive differentiation of interests
can be observed from birth to about 15 years of age. The engagement with future
careers starts at about the age of 10 years. At the age of 15 and beyond, awareness
and exploration of new fields of societal and social life become relevant. Already at
the age of three, boys and girls start choosing gender-adequate activities and interests.
Gender-inadequate activities will in all probability be dropped. While Todt et al.
(1994) focus on the object of interest, Krapp (1992, 2005) focuses on the person in
his theoretical person–object relation. The German Person–Object Theory of Interest
(Krapp, 1992, 2005) defines interest as a specific and unique relation between a
person and an object which is relatively stable. The development of general interests
depends increasingly on perceived competencies relating to self-concept (Todt et al.,
1994). A weak self-concept impairs the development of interests (Gottfredson, 1981;
Köller, Daniels, Schnabel, & Baumert, 2000). According to Möller and Köller
(2004), students tend to compare their marks across the different subjects. As females
usually have better marks in languages than in hard sciences, their self-concept in
science is low compared to that of males even if they have the same competencies
(Lips, 2004, as quoted in Murphy & Whitelegg, 2006, p. 7; Möller & Köller, 2004).
This gender effect might be strengthened by the fact that, at least in Finland, hard
sciences are marked more strictly than language arts (Hautamäki et al., 2000).
Hannover and Kessels (2006) propose another explanation for female students’ low
interest in hard sciences: The image of the hard sciences, especially physics, is male
related and interferes with the development of female gender identity. A lower selfconcept leads not only to less academic interest in science (Gottfredson, 1981; Köller
et al., 2000), but also to the avoidance of scientific vocations such as engineering or
computer science. Findings of the international comparative survey on the Relevance
of Science Education show that the perceptions of scientific and technical careers are
gender stereotyped and limited (Schreiner & Sjøberg, 2005). Generally, females tend
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Choice of Scientific and Technical Vocations 161
to choose science courses or subjects out of necessity, for instance because they want
to become doctors, and less because they are interested in the subjects themselves
(Miller, Slawinski Blessing, & Schwartz, 2006).
In the Programme for International Student Assessment (PISA) 2006, students’
general interest in science subjects was measured. Students were also asked what their
vocation might be at the age of 30 years. In most participating countries, no gender
differences were found in general interest in science (OECD, 2007). However, when
focusing on interests in specific science disciplines, gender differences can be
expected (Hannover & Kessels, 2006; Krapp, 1992, 2005; Todt et al., 1994). Taskinen, Asseburg, and Walter (2008) analysed the expected careers in PISA 2006 more
specifically by arranging them into four scientific-technical vocational fields. The
findings show that the choice of scientific-technical vocational fields differs greatly
between males and females. Females avoid vocational choices such as being engineers
or technicians even if they possess the same competencies in science as their male
counterparts.
Although educational gender differentiation is a fact, there are still women who
study physics, chemistry, or computer sciences, and men who study medicine
(Gilbert & Calvert, 2003). Interest seems to facilitate choices even within genderatypical vocational fields. This raises the question of whether choices of different
scientific-technical vocations involve different and specific interests and how this
applies to different countries, cultures, and education systems.
This study aims to investigate students’ interests in specific science disciplines and
their relationship to the indicated vocational intentions across selected countries that
participated in PISA 2006. The selection of the countries is based upon Fend’s country group typology (2003), which is both theory- and empirically driven (Baumert,
Blum, & Neubrand, 2004; Fend, 1998, 2002, 2003; Helmke, 2009). Education
systems are regarded as collective agents where individuals interact according to
normative policies on different organisational levels in order to achieve desired
effects. The four country groups1 are: (1) (North-)East Asian countries, (2) Nordic
countries, (3) Anglo-Saxon countries, and (4) German-speaking countries. In the
(North-)East Asian countries, education is generally characterised by an authoritative
style, high student discipline and a focus on collective achievement (Fend, 2003;
Helmke & Vo, 1999). Lessons are usually prepared in collaboration with other teachers. There is a strict external examination system; exams are administered throughout
schools simultaneously to identify the best performers. The Nordic countries, like the
(North-)East Asian countries, have comprehensive schools but differ notably in the
pedagogical culture and examination system. Performance-based ranking and
student selection only occurs late in students’ educational careers. The pedagogical
culture focuses on fostering students’ learning and the teaching of vital basic competencies. Cooperation between schools and parents is crucial, and teachers consider
themselves mainly as coaches for the optimal development of the individual child.
Anglo-Saxon countries are characterised by an external monitoring system whereby
tests are simultaneously administered throughout schools. They have intensive
training programmes and support systems, especially in early childhood. Educational
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162 G. Buccheri et al.
resources are directed particularly towards the beginning of schooling: Group sizes in
the early years are smaller, teachers receive special training to implement learning
programmes, and parental involvement is fostered. The education systems of the
German-speaking countries are characterised by their selective performance assessment, assigning students at a relatively early age to different streams of secondary
schooling. External monitoring systems are applied or under examination. The
curricula are aligned throughout the education system from primary school to university, and transitions are regulated either through entry exams or assigned marks.
Resources such as staffing and infrastructure are allocated either at the federal or state
level (Fend, 2003).
In PISA 2006, the four country groups mentioned above generally performed well
or around the OECD average in sciences.2 One representative of each country group
was chosen. The selected countries were: (a) Finland, as the best-performing country in PISA 2006; (b) Korea, as a typical (North-)East Asian country; (c) Australia,
as one possible representative of the Anglo-Saxon countries; and (d) Switzerland3 as
one possible representative of the German-speaking countries. The presented theories and empirical results indicate an interest-, gender-, and vocation-specific
approach. The following questions are investigated in this study:
(1) Is there a gender difference among highly competent students with regard to
their interests in specific science subjects in the selected countries?
(2) Are there differences between the expected careers indicated by highly competent
females and males, respectively, in the selected countries?
(3) Is there a correlation between interest in a specific science subject and the choice
of a vocational field?
(4) For highly competent students in the selected countries, how do students’
gender and their stated interests in different science disciplines impact on their
anticipated career at age 30?
Method
Participants
PISA assigns students’ performance to different levels of competence. This study
focuses on highly competent students achieving the two highest levels of performance
either in science or mathematics (Levels 5 or 6; OECD, 2007). The sample
comprises, in Korea, 619 females and 787 males, representing 155,577 total
weighted; in Finland, 613 females and 724 males, representing 15,590 total weighted;
in Australia, 1,084 females and 1,474 males, representing 43,524 total weighted; and
in Switzerland, 1,087 females and 1,431 males, representing 19,773 total weighted.
Instruments
The students’ interests in science, their expectation about their vocational career
at the age of 30 years, their economic, social, and cultural status (ESCS) and their
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Choice of Scientific and Technical Vocations 163
self-concept in science4 have been assessed by the PISA 2006 student questionnaire.
To differentiate general interest in science into interests in specific science subjects,
three Likert-scaled items were chosen out of the eight which made up the index of
general interest in science. These are namely: interest in physics, chemistry, and
human biology (e.g. ‘How much interest do you have in learning about topics in physics?’). The answer options are ‘no interest’, ‘low interest’, ‘medium interest’, and ‘high
interest’. The career expectations were assessed by an open question (‘What kind of
job do you expect to have when you are about 30 years old?’). From the responses, a
group of students who expect to pursue science-related careers was identified.
Students’ responses were classified using the International Standard Classification of
Occupations (ISCO-88; see OECD, 2007, Annex A10). For this study, academic and
non-academic science-related occupations are grouped into the following four vocational fields on the basis of Taskinen et al. (2008), but with a modified assignment to
the different vocational fields:5 (1) vocations in engineering, architecture, physics,
chemistry, and technology; (2) medical vocations; (3) vocations in computer sciences;
and (4) other technical-scientific vocations (e.g. pilot or cinema projectionist). The
ESCS index is derived from four variables related to family background: (1) the level
of parental education; (2) parental occupation status; (3) home possessions; and (4)
the number of books at home (OECD, 2009). ESCS was used as the control variable
in research question (4). The index self-concept in science is made up of six Likertscaled items: (1) I can usually give good answers to test questions on school science
topics; (2) when I am being taught school science, I can understand the concepts very
well; (3) I can learn science topics quickly; (4) I can easily understand new ideas in
school science; (5) learning advanced school science topics would be easy for me; and
(6) school science topics are easy for me. The answer options are ‘strongly disagree’,
‘disagree’, ‘agree’, and ‘strongly agree’. In PISA, indices are constructed where the
average OECD student is given an index value of 0. About two-thirds of the OECD
student population were between the values –1 and 1. This corresponds to a standard
deviation of 1 (OECD, 2007).
Methods of Analysis
To analyse gender differences according to the first two research questions, crosstable analyses (standardised residuals were considered, but not reported) and independent t-tests are used. In the third research question, point-biserial correlation
tests are used to examine the relationship between interest in a specific science
subject and the choice of a vocational field. The last research question analyses how
students’ gender and their stated interests in different science disciplines impact on
their anticipated career at age 30 by applying binary logistic regression. The reported
odds ratios are the ratio of the probability of occurrence of an event to the probability of the event not occurring (e.g. of choosing a vocational field). The percentage of
variance explained by the models is approximated with Nagelkerke’s R2.
PISA is a two-stage sample. In order to estimate correct standard errors, WesVar
v5.1.16 has been applied in addition to the software application PASW 17.
164 G. Buccheri et al.
Results
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Interests in Specific Science Subjects by Gender and Country
The interests of high-performing female and male students in specific science subjects
throughout the selected countries are summarised in Table 1 and Figure 1. The findings regarding the highly competent students in the four selected countries are characterised mainly by commonalities, but also unveil differences in interests in the
science disciplines physics, chemistry, and human biology. Finland and Switzerland
show gender-specific differences throughout the three science subjects, whereby
females are particularly interested in human biology (in both countries, 82% declare
high or medium interest). This preference for human biology is significantly higher
than for chemistry (49% and 67% high or medium interest, respectively), while
chemistry is significantly preferred to physics (47% and 54% high or medium interest,
respectively). In contrast, males in both countries are equally interested in chemistry
and physics and more interested in these than in biology. In Finland, 74% and 76%
declare high or medium interest in chemistry and physics, respectively, whereas in
Switzerland the corresponding figures are 77% and 79%. Human biology, on the
other hand, arouses significantly less interest (59% and 54% high or medium interest,
respectively) than chemistry or physics among males in both countries.
Korea and Australia differ from Finland and Switzerland, as there are no significant gender differences concerning interest in chemistry. This difference comes
because Australian females declare higher interest in chemistry than their female
counterparts in the other countries, and because Korean males report comparatively
lower interest in chemistry. On the other hand, there are similar gender-specific
findings concerning the preferences of female students in science. Australian and
Korean females significantly prefer human biology (84% and 78% high or medium
interest, respectively) versus chemistry (70% and 63% high or medium interest,
respectively), which in turn is significantly preferred to physics (56% and 40% high
or medium interest, respectively). The interests of the Australian males differ from
the interests of the Korean males. As in Switzerland and Finland, Australian males
are almost equally interested in physics (76% high or medium interest) and chemistry (72% high or medium interest), and are significantly less interested in human
biology (63% high or medium interest). In contrast, Korean males are most and
equally interested in human biology (63% high or medium interest) and chemistry
(64% high or medium interest), and are less interested in physics (54% high or
medium interest).
Expectation of a Vocational Career at the Age of 30 by Gender and Country
The expected occupations indicated by the high performers in sciences or mathematics in PISA 2006 reflect not only a shortage of scientific and technical personnel
with a majority of students choosing a non-scientific vocation, but also gender differentiation in educational careers (OECD, 2008). Sixty-four per cent of the Swiss,
2.56 (0.04)
2.43 (0.05)
2.62 (0.04)
2.30 (0.05)
3.10 (0.03)
3.10 (0.04)
3.11 (0.03)
2.60 (0.05)
M (SE)
M (SE)
M (SE)
2.83 (0.03)
2.53 (0.05)
2.95 (0.03)
2.77 (0.05)
−10.67***
−11.41***
−10.04***
−6.16***
Female
t-value
Difference
3.11 (0.04)
3.01 (0.03)
3.01 (0.03)
2.77 (0.04)
M (SE)
Male
−6.52***
−8.84***
−1.33ns
0.14ns
t-value
Difference
Interest in chemistry
3.23 (0.03)
3.12 (0.03)
3.32 (0.03)
3.13 (0.04)
M (SE)
Female
2.60 (0.04)
2.71 (0.03)
2.84 (0.03)
2.80 (0.03)
M (SE)
Male
12.75***
10.67***
11.94***
8.18***
t-value
Difference
Interest in human biology
*p < 0.05, **p < 0.01, ***p < 0.001.
Note. M = mean; SE = standard error; CHE = Switzerland; FIN = Finland; AUS = Australia; KOR = Korea; assumption of an interval
measurement scale; ns = not significant.
CHE
FIN
AUS
KOR
Male
Female
Interest in physics
Table 1. Means and differences in interests in physics, chemistry, and human biology by gender and country
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Choice of Scientific and Technical Vocations 165
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166 G. Buccheri et al.
Figure 1.
Percentage interests in physics, chemistry, and human biology by gender and country
65% of the Korean, and 70% of the Finnish high performers expect to work in a
non-scientific job at the age of 30. In Australia, comparatively few high performers
(46%) choose a non-scientific career.
The expected choice of vocational field in the four selected countries is partly
marked by gender specificity (Table 2): At least 91% of the highly competent
students indicating vocational choices within the field of computer sciences are
male. Male students also report far above average expectations for the vocational
fields of engineering, architecture, etc., with 79–82%. On the other hand, there is no
clear pattern for the field of medicine: While in Switzerland and in Finland females
dominate the medical field and men are clearly underrepresented, in Australia and
especially in Korea, gender representation is balanced. The rather heterogeneous
field which comprises ‘other’ scientific–technical occupations is dominated by
females in Finland and Switzerland (66% and 59%), whereas in Korea and Australia
it is dominated by males (both 59%).
Choice of Scientific and Technical Vocations 167
Table 2.
Percentage expected choice of vocational field by gender and country
Engineering, etc.
Female
CHE
FIN
AUS
KOR
Male
Medicine
Female
Computer sciences
Male
Female
Male
Other scientifictechnical occupations
Female
Male
%
%
(SE)
%
%
(SE)
%
%
(SE)
%
%
(SE)
20
21
21
18
80
79
79
82
(2.9)
(3.7)
(2.7)
(3.7)
74
67
55
50
26
33
45
50
(2.9)
(4.0)
(3.5)
(4.7)
4
3
5
9
96
97
95
91
(1.9)
(2.8)
(2.6)
(6.0)
59
66
41
41
41
34
59
59
(7.0)
(6.5)
(4.9)
(8.5)
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Note. SE = standard error; CHE = Switzerland; FIN = Finland; AUS = Australia; KOR = Korea.
Correlations between (Un)Specific Interests in Science and Expected Vocations Including
Vocational Fields
Based on the responses of the high performers who expect to pursue a scientifictechnical vocation at the age of 30, the relationship between general interest in science
and (non)scientific vocations were measured, as well as between specific interests in
science disciplines and four scientific-technical vocational fields. In Table 3 Switzerland has been selected to illustrate the differences in the correlations between interests
and vocations depending on the specificity of the measurement, as the correlations in
the various countries are comparable. The strongest correlations are found between
human biology and medicine (r = 0.46), vocations in engineering etc. (r = −0.31) and
in computer sciences (r = −0.31). In other words, high interest in human biology goes
along with an expected vocational choice in medicine and with the non-selection of
engineering, which for example implies interest in physics. A comparison of the correlations between general interest in science and (non)scientific vocations with the
correlations between interests in specific science disciplines and specific scientifictechnical vocational fields shows that the strength of the relation may be underestimated, and its direction is likely to change if generalised parameters are used for
measurement. This applies especially to the correlations between interest in human
biology and the vocational fields of engineering, etc. (r = 0.08 to −0.31), computer
sciences (r = −0.15 to −0.31) and medicine (r = 0.05 to 0.46).
Impact of Gender and Specific Interests in Science on Choosing Medicine or Engineering,
etc.
Tables 4 and 5 show the impact of gender alone (Model I) and in combination with
interests in specific science disciplines (Model II) on vocational fields like medicine—
which is mainly preferred by females—and engineering, architecture, physics, chemistry, and technology, which are mainly preferred by males. In Model I the binary
logistic regression gives evidence about the odds of one gender choosing a career in
168 G. Buccheri et al.
Table 3.
Correlations between (specific) interests and vocational fields
Engineering,
Technical or
architecture,
science-related
physics, chemistry, Computer
Other technicalvs. other
technology
sciences Medicine scientific vocations
vocations
General interest
Interest in physicsa
Interest in chemistrya
Interest in human
biologya
0.18***
0.14***
0.19***
0.15***
−0.15*** 0.05*
0.06** −0.24***
ns
ns
−0.31*** 0.46***
0.08***
0.27***
0.12***
−0.31***
−0.06***
−0.11***
−0.15***
0.07***
*p < 0.05; **p < 0.01; ***p < 0.001.
of an interval measurement scale; point-biserial correlations are applied.
Note. ns = not significant; correlations > 0.20 are printed in bold.
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aAssumption
medicine, and in Model II about the odds of choosing medicine, taking into account,
for example, high interest in physics versus no interest to medium interest in physics.
Throughout the four countries, Models I and II show a significant gender effect for
high performers in choosing medical vocations. The odds of males choosing medical
vocations in Model I are 0.11–0.30 times lower than for females. In Model II the
gender impact decreases after having accounted for specific interests, but is still
significant (odds ratios = 0.21–0.40). Interests in specific science disciplines have
different impacts on the choice of a medical career in the four countries. Generally,
no interest to medium interest in human biology leads to small odds of choosing a
medical vocation (odds ratios = 0.16–0.30), whereas in Switzerland and in Australia
low interest in physics is a crucial factor for choosing a medical career: No interest to
medium interest in physics heightens the odds of choosing such a vocation (odds
ratios = 2.13–2.55). On the other hand, interest in physics is not relevant in Finland
Table 4.
Career choice within the medical field by country
Model I
Predictors
B (SE)
CHE
Intercept
0.64 (0.19)**
Gender: female
−2.24 (0.23)***
Interest in human biology
No to medium interest; high interest
—
Interest in chemistry
No to medium interest; high interest
—
Interest in physics
No to medium interest; high interest
—
ESCS
—
R2
Model II
Odds ratio
B (SE)
Odds ratio
0.11
0.83 (0.31)**
−1.53 (0.24)***
0.21
−1.82 (0.30)***
0.16
−0.21 (0.26)ns
—
0.75 (0.30)*
0.13 (0.17)ns
2.13
—
0.445
0.306
Choice of Scientific and Technical Vocations 169
Table 4.
(Continued)
Model I
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Predictors
B (SE)
FIN
Intercept
0.57 (0.18)**
Gender: female
−1.43 (0.23)***
Interest in human biology
No to medium interest; high interest
—
Interest in chemistry
No to medium interest; high interest
—
Interest in physics
No to medium interest; high interest
—
ESCS
—
R2
AUS
Intercept
0.88 (0.12)***
Gender: female
−1.37 (0.16)***
Interest in human biology
No to medium interest; high interest
—
Interest in chemistry
No to medium interest; high interest
—
Interest in physics
No to medium interest; high interest
—
ESCS
—
R2
KOR
Intercept
0.97 (0.16)***
Gender: female
−1.22 (0.21)***
Interest in human biology
No to medium interest; high interest
—
Interest in chemistry
No to medium interest; high interest
—
Interest in physics
No to medium interest; high interest
—
ESCS
—
R2
Model II
Odds ratio
B (SE)
Odds ratio
0.24
0.93 (0.29)**
−1.23 (0.28)***
0.29
−1.08 (0.25)***
0.34
−0.20 (0.35)ns
—
0.07 (0.32)ns
0.41 (0.15)**
—
1.51
0.240
0.150
0.25
1.45 (0.19)***
−0.91 (0.16)***
0.40
−1.73 (0.14)***
0.18
−0.84 (0.20)***
0.43
0.94 (0.17)***
0.02 (0.11)ns
2.55
—
0.357
0.133
0.30
0.103
1.31 (0.28)***
−1.01 (0.18)***
0.37
−1.21 (0.25)***
0.30
−0.03 (0.22)ns
—
0.29 (0.32)ns
0.09 (0.13)ns
—
—
0.194
*p < 0.05; **p < 0.01; ***p < 0.001.
Note. CHE = Switzerland; FIN = Finland; AUS = Australia; KOR = Korea; reference categories
are printed in italics; ns = not significant; odds ratios > 1 heighten, odds ratios < 1 diminish the
odds of a choice within the medical field.
170 G. Buccheri et al.
Table 5.
Career choice within the vocational field ‘engineering, etc.’ by country
Model I
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Predictors
B (SE)
CHE
Intercept
−1.45 (0.20)***
Gender; female
1.72 (0.24)***
Interest in human biology
No to medium interest; high interest
—
Interest in chemistry
No to medium interest; high interest
—
Interest in physics
No to medium interest; high interest
—
ESCS
—
2
R
FIN
Intercept
−2.06 (0.26)***
Gender; female
1.66 (0.24)***
Interest in human biology
No to medium interest; high interest
—
Interest in chemistry
No to medium interest; high interest
—
Interest in physics
No to medium interest; high interest
—
ESCS
—
R2
AUS
Intercept
−1.71 (0.13)***
Gender; female
1.19 (0.16)***
Interest in human biology
No to medium interest; high interest
—
Interest in chemistry
No to medium interest; high interest
—
Interest in physics
No to medium interest; high interest
—
ESCS
—
R2
KOR
Intercept
−1.79 (0.18)***
Gender; female
1.35 (0.23)***
Interest in human biology
No to medium interest; high interest
—
Model II
Odds ratio
B (SE)
Odds ratio
5.57
−1.09 (0.30)***
1.07 (0.24)***
2.92
1.22 (0.30)***
3.39
−0.32 (0.18)ns
—
−0.71 (0.24)**
−0.26 (0.14)ns
0.49
—
0.286
0.189
5.27
−2.03 (0.36)***
1.34 (0.27)***
3.81
1.00 (0.33)**
2.71
−0.42 (0.38)ns
—
−0.16 (0.34)ns
−0.23 (0.16)ns
—
—
0.206
0.157
3.29
−1.85 (0.19)***
0.57 (0.16)**
1.77
1.52 (0.16)***
4.58
−0.08 (0.15)ns
−1.05(0.17)***
−0.09 (0.09)ns
0.083
3.85
—
0.35
—
0.250
−1.82 (0.31)***
1.13 (0.23)***
3.11
0.98 (0.28)**
2.67
Choice of Scientific and Technical Vocations 171
Table 5.
(Continued)
Model I
Predictors
Interest in chemistry
No to medium interest; high interest
Interest in physics
No to medium interest; high interest
ESCS
R2
B (SE)
Model II
Odds ratio
B (SE)
Odds ratio
—
−0.29 (0.26)ns
—
—
—
−0.39 (0.31)ns
−0.02 (0.15)ns
—
—
0.160
0.101
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*p < 0.05; **p < 0.01; ***p < 0.001.
Note. CHE = Switzerland; FIN = Finland; AUS = Australia; KOR = Korea; reference categories
are printed in italics; ns = not significant; odds ratios > 1 heighten, odds ratios < 1 diminish the
odds of a choice within the vocational field “engineering, etc.”
or Korea with regard to the choice of medical vocations. No interest to medium interest in chemistry (odds ratio = 0.43), in Australia only, leads to a small probability of
choosing a medical vocation. The ESCS of the students is only relevant in Finland.
An increase in the ESCS by about one point, which corresponds to one standard
deviation, increases the odds of choosing a medical career by 1.51.
Throughout the four countries, modelling the vocational field engineering, etc.,
shows, as in the model for choosing medical careers, a significant gender effect for
the high performers in choosing vocations like engineer, architect, physicist, chemist,
etc. (Table 5): in Model I, the odds of males choosing such a career are between
3.29 (Australia) and 5.57 (Switzerland) times higher than those of females. The
gender impact decreases when specific interests are taken into account (Model II),
but it still remains significant (odds ratios = 1.77–3.81). Again, the roles of interests
in specific science disciplines in choosing such a career differ in the four countries:
Generally, no interest to medium interest in human biology heightens the odds of
choosing a vocation in engineering, etc. (odds ratios = 2.67–4.58). Unexpectedly,
interests in physics or in chemistry are apparently not essential for choosing such a
career: Only in Australia and in Switzerland does no interest to medium interest in
physics diminish the odds of choosing a career in engineering, etc. (odds ratio = 0.35
and 0.49).
Summary and Conclusions
Todt’s model (1994) of the gender-specific development of general and specific
interests can mostly be applied to the findings in the present research. Although
interests in specific science subjects and vocational expectations at the age of 30 are
generally affected by gender, differences between countries were found. The interests of highly competent Swiss and Finnish students in physics, chemistry, and
human biology and their vocational expectations are marked by a complementary
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172 G. Buccheri et al.
gender specificity: Females are especially interested in human biology and are correspondingly overrepresented in medicine, whereas interests in chemistry and physics
are significantly less pronounced. This leads to an underrepresentation in vocational
fields such as engineering, architecture, physics, chemistry, and technology or
computer sciences. In contrast, males report being equally interested in chemistry
and physics, but significantly less interested in human biology. As a consequence,
males are underrepresented in medicine and overrepresented in vocations such as
engineering, architecture, physics, chemistry, technology, and computer sciences.
Compared to Switzerland and Finland, Korea and Australia differ partly in interests
and vocational expectations. There are no significant gender-specific differences
concerning interest in chemistry. This is due to the fact that Australian females
declare comparatively higher interest in chemistry than their female counterparts in
the other selected countries and that Korean males declare comparatively less interest in chemistry. On the other hand, there are similar gender-specific findings
concerning the preferences of female students in science. As in Switzerland or
Finland, Australian males are equally interested in physics and chemistry and are
significantly less interested in human biology. Korean males, by contrast, are equally
interested in human biology and chemistry. Their interest in physics is low in
comparison with male students in the other three countries.
With regard to vocational expectations, Australian and Korean males are clearly
overrepresented in the fields of engineering and computer sciences, whereas gender,
especially in Korea, is nearly perfectly balanced in the medical field. The specificity
of the relation between interest and job expectations described above supports
Krapp’s (1992, 2005) Person–Object Theory of Interest and explains why general
measurement of interest or vocational expectations at the age of 30 did not unveil
gender gaps in PISA 2006 (OECD, 2007). It thus illustrates the fundamental importance of specific measurement. The findings of Möller and Köller (2004) could be
replicated as well: Throughout the four countries investigated, female high performers have a significantly lower self-concept in sciences than their male colleagues (see
Note 4 below).
Among all the representatives of the four different education systems, interests
only partly compensate the gender impact on vocational choices—even if only high
performers in sciences or mathematics are considered. Gender seems to be a
crucial determinant of vocational choices: Women choose medical careers by preference; men, in turn, choose mostly vocational fields like engineering, etc., or
computer sciences. This paper gives evidence that gender equity in science education and working life is a cross-national problem. The fact that interests can only
partly compensate the gender impact on vocational choices can be corroborated by
the model of achievement-related choices developed by Eccles, Barber, Updegraff,
and O’Brien (1998) and the empirical findings related to it. This model links
educational, vocational, and other activity choices directly to two sets of beliefs:
the individual’s expectations for success (e.g. self-concept of ability) and the task
value (interest, importance/utility, costs) the individual attaches to the various
perceived options. The findings which are related to the prediction of physical
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Choice of Scientific and Technical Vocations 173
sciences course enrolment may be applied to this study. They show that gender
can only partly be mediated by interests and only if it is the sole mediator. In other
words, the impact of gender is mediated by interests, but it also predicts independently course enrolment (Eccles et al., 1998) and, as can be assumed, vocational
choices.
There are differences in the impact of gender among the representatives of the
four education systems on vocational choices: Modelling the two vocational fields
shows that gender has a stronger impact on vocational choices in Switzerland or
Finland compared to Australia or Korea. Interest in physics has a remarkable impact
in Switzerland and in Australia on choosing both vocational fields, but less so in
Finland or Korea. On the other hand, interest in human biology generally favours
the choice of a medical vocation and deters students from choosing a vocation in
engineering. These differences and commonalities could be related to how the various science disciplines, vocational fields and gender roles are viewed in the general
culture in these four countries and how these views are reflected, according to Fend
(2003), in their respective education systems. In order to improve understanding of
these connections, further in-depth studies are required. The characteristics of the
education systems described at the beginning of this paper give little explanation for
the findings of this study.
At first sight, the fact that gender determines specific scientific interests and vocational choices internationally even among high performers is discouraging, considering the political and educational efforts to enforce gender equity.6 The good news is
that gender impact seems to differ in strength between the countries: The aim of
closing the gender gap completely might be not achievable, but the gap may be
minimised, albeit indirectly. Eccles et al. (1998) found that the perceived importance/utility of a physical sciences course, rather than either self-concept of ability or
interest, was the strongest predictor of enrolment. Importance/utility had an independent relation to course enrolment and was weakly related to both gender and
ability. Emphasising the (rather gender-neutral) importance/utility of a choice of
scientific studies or vocations could be a possible approach to motivate (female)
students to pursue a scientific career.
In the following, empirical findings about how teachers (or parents) and teaching
methods could enhance interests, self-concept of ability in science, and the perceived
utility of the choice of a scientific vocation are summarised. The provision of various
experiences as early as preschool would offer children the opportunity to discover
their talents and interests and, therefore, to form different self-perceptions and task
values such as interest and utility (Eccles & Harold, 1991). These measures would
provide opportunities for girls or children with a modest social background to catch
up on background knowledge prior to primary school. Appropriate teaching methods such as a gender-neutral teaching style which integrates female needs but
equally fosters males are essential (Labudde, Herzog, Neuenschwander, Violi, &
Gerber, 2000; New Zealand Ministry of Education, 2002, as quoted in OECD,
2008, p. 103). This is especially so in secondary school where (female) interests in
science are experiencing a decline cross-nationally (Gardner, 1985).
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174 G. Buccheri et al.
Teachers, like parents, are not only the providers but also the interpreters of experiences; thus, they influence the inferences children make about their successes and
failures in two ways. Firstly, to the degree that the socialiser lets the children’s
gender influence their interpretations, they contribute to the emergence of children’s self-perception and expectations of success or the value of the task (Eccles &
Harold, 1991). Secondly, according to the attribution theory (Weiner, 1972), the
impact of failure and success on self-perceptions, expectations for future success
and self-concept of one’s ability depends on the causal attribution made for success
or failure. Girls are more likely than boys to attribute their failures to lacking talent,
which consequently lowers their expectations of success (Bird & Williams, 1980);
they are also less prone to attribute their successes to aptitude (Eccles-Parsons,
Meece, Adler, & Kaczala, 1982). Teachers could positively influence girls’ expectations of success by engaging in attributional retraining for girls.
The enhancement of the general importance and utility of sciences generally has a
positive impact on interest (Brühwiler, Kis-Fedi, Buccheri, & Mariotta, 2009), and
presumably on the individual perception of utility of the choice of scientific courses
or vocations. Teaching scientific concepts within their respective contexts allows
students not only to make links between their (prior) knowledge, the classroom experiences, and the science to be learned, but also to acknowledge the social relevance
and the aims of sciences (Miller et al., 2006; Osborne & Collins, 2000; Todt et al.,
1994). Given females’ interest in (human) biology, chemistry and physics teachers
could motivate females for these less-preferred domains by relating the subject matter
to (human) biology (Miller et al., 2006). Furthermore, cooperative learning situations and a partly single-sex setting are recommended (Labudde et al., 2000;
Osborne, Simon, & Collins, 2003).
It is suggested that by inviting (female) representatives of industry to give lectures
about their career, obsolete images of scientific and technical careers could be
updated and corrected. Additionally, companies could facilitate internships to gain
further and practical insights into scientific or technical careers.
On the level of teacher education, continuing empirical research on how to achieve
gender equity in sciences should be incorporated in teacher training (e.g. attributional
retraining), while ensuring the solid expertise of the teachers. According to Osborne
and Collins (2000), the recruitment and retention of able, bright, and enthusiastic
teachers guarantees the quality of science education, which will have a significant
impact on students’ attitudes towards sciences. In order to attract and keep especially
female professionals in science- and technology-related working environments such
as engineering, which are dominated by males, organisational structures and attitudes
need to be revised (Faulkner, 2006; Gilbert, 2007; Haffner, Könekamp, & Krais,
2006; Kanter, 1977).
Notes
1.
Originally, Fend (2003) used the terms Asian countries and Scandinavian countries instead of
(North-)East Asian countries and Nordic countries. These terms are not quite adequate. The
Choice of Scientific and Technical Vocations 175
2.
3.
4.
5.
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6.
comments on Asian education systems refer only to the countries of (North-)East Asia (e.g.
Korea or Japan), and Finland was included in the Scandinavian country group although
Finland is not a Scandinavian but a Nordic country.
Iceland and Norway performed significantly below the OECD average in sciences.
The Swiss sample considers the high performers of all three language regions (74.6%
German-speaking, 22.7% French-speaking, and 2.7% Italian-speaking students).
The index self-concept in science, although a crucial determinant of vocational choices,
has not been accounted for in research question (4), as the variances of self-concept for
high performers are small and would not appear as significant in the models. The means
of self-concept in science differ significantly by gender throughout the countries, in favour
of males.
The number of students choosing a scientific career reported in this paper is higher than the
number of students of the official international variable self science-related career at 30
(SRC_S). This difference is due to the application of a more detailed and comprehensive
assessment of scientific careers.
Australia implemented the whitepaper ‘Science for Australian Schools’ in the 1980s, which
comprises intercurricular aspects such as gender equity. The attendance and achievement of
female students in mathematics and science in secondary school have been supported by coeducation, mono-education, individual programs, mentoring programs, and female teacher
education in science subjects (Cook, 2003). In Korea, a gender programme for sciences known
as Women into Science and Engineering (WISE) was launched in 2001 and implemented from
(junior) high school to university (Ki Won & Heisook, 2007). In 2002, Korea also launched a
five-year plan to generally promote young people in science and engineering by facilitating
research and by providing students with science-experience opportunities outside regular
science classes (OECD, 2008). The plan included teacher training programmes. The national
project to improve skills in mathematics and natural sciences (LUMA programme, n.d.) was
launched in 1996 at the Finnish Ministry of Education. One further goal was to raise interest
in learning mathematics and natural sciences among girls and encourage them to pursue scientific careers. In Switzerland, gender equity has been an issue for the Swiss Conference of
Cantonal Ministers of Education (EDK) since the 1970s. Nevertheless, there are no
programmes to specifically foster equity in science education in primary or lower secondary
school (Grossenbacher, 2006).
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