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AYELET BARAM-TSABARI and ANAT YARDEN
QUANTIFYING THE GENDER GAP IN SCIENCE INTERESTS
Received: 11 May 2009; Accepted: 18 January 2010
ABSTRACT. Nearly 5,000 self-generated science-related K–12 students’ questions,
classified into seven science subjects, were used to quantitatively measure the gender gap
in science interests and its change with age. In this data set, a difference between boys’
and girls’ science interests did not exist during early childhood, but increased over 20-fold
by the end of high school. Furthermore, the gap widened in a stereotypical manner, with
girls being increasingly interested in biology and boys more interested in physics and
technology. This method could be applied for identifying and comparing the gender gap in
science interests between different populations based on different data sources.
KEY WORDS: biology, gender gap, interest, physics, quantitative, students’ questions
methodology
INTRODUCTION
The wealth of data regarding boys’ and girls’ interests in science suggests
that boys, in general, are more interested in science than girls (Gardner,
1998; Miller, Slawinski Blessing, & Schwartz, 2006). However, the
sweeping generalization of boys being interested in science while girls are
not is rather superficial. An analysis of MadSci.org, an Internet-based
Ask-A-Scientist site, for example, demonstrated a decade-long (1996–
2006) dominance of female interest in science among kindergarten to
grade 12 students (Baram-Tsabari, Sethi, Bry, & Yarden, 2009); in fact,
in many developing countries, girls have the same positive attitudes and
interest in science that boys do (Sjøberg & Schreiner, 2005).
A persistent stereotypical gender gap was reported, however, between
girls’ and boys’ interests within science: numerous studies have shown
that, while physics and technology prove significantly less interesting to
girls than to boys, biology is of greater interest to girls than to boys and
chemistry is liked, to a similar extent, by both genders. These findings (or
parts thereof) have been repeated in several countries, including Scotland
(Stark & Gray, 1999), Australia (Dawson, 2000; Kahle, Parker, Rennie, &
Riley, 1993;Woodward&Woodward,1998), the USA (Burkam, Lee, &
Smerdon, 1997; Farenga & Joyce, 1999; Jones, Howe, & Rua, 2000),
England (Murphy & Whitelegg, 2006; Osborne & Collins, 2001; Spall,
Barrett, Stanisstreet, Dickson, & Boyes, 2003), Italy (Falchetti, Caravita, &
International Journal of Science and Mathematics Education 2010
#
National Science Council, Taiwan (2010)
Sperduti, 2007), Israel (Baram-Tsabari & Yarden, 2005;Friedler&Tamir,
1990; Trumper, 2006), Turkey (Yerdelen-Damar & Eryılmaz, 2009),
Germany (Hoffmann, 2002), and Japan (Scantlebury, Baker, Sugi, Yoshida,
&Uysal,2007), and in international studies, such as TIMSS (Mullis, Martin,
Fierros, Goldberg, & Stemler, 2000), “Science and Scientists” (Sjøberg,
2000), and “Relevance of Science Education” (Busch, 2005; Jenkins &
Nelson, 2005; Lavonen, Juuti, Uitto, Meisalo, & Byman, 2005; Schreiner,
2006; Sjøberg & Schreiner, 2002). This gender gap in focus of interest is also
apparent among female students who are interested in science and intend to
continue studying it (Murphy & Whitelegg, 2006;Zohar,2003). Interest
affects the ability to learn. Research indicates positive relationships between
interest and a wide range of learning indicators (Pintrich & Schunk, 2002)
through its contribution to students’ connection with the content, as well as
maintenance of that connection for a sufficient time (Ainley, Hidi, &
Berndorff, 2002).
Interest also affects the willingness to learn. Adolescents’ decisions
concerning the content and direction of their educational training are
strongly influenced by the topic-related interests they have developed
(Krapp, 2000). Therefore, along with other reasons, girls’ lack of interest in
physics results in their under-representation in advanced physics classes.
Indeed, despite nearly 30 years of effort to engage girls in physical sciences
and engineering, the choice of a science discipline remains highly gender-
dependent (OECD, 2006; Osborne & Dillon, 2008), and girls rarely choose
a career in these disciplines.
Some researchers have suggested that the basis for these stereotypically
gendered interests is an inborn trait rendering most girls hard-wired for
empathy, while most boys are predominantly hard-wired for understanding
and building systems (Baron-Cohen, 2003). Other studies, however, have
not found any such difference (Barres, 2006; Guiso, Monte, Sapienza, &
Zingales, 2008; Haworth, Dale, & Plomin, 2008; Linn & Hyde, 1989;
Spelke, 2005). A recent analysis of the Program for International Student
Assessment results, for example, suggests that the gender gap in math
scores, which historically favors boys, disappears in countries with more
gender-equal cultures (Guiso et al., 2008
).
Kelly (1978) divided the expla nations for gender differences in
achievements into three categories, which are also suitable for classifying
engagement-related differences: cultural, attitudinal, and educational.
Cultural explanations include the masculine image of science,
especially physics, and lack of female role models and their image in
science in the media, lack of experiences outside of school, parent-
gendered beliefs, peers’ views during puberty, girls’ perceived low
AYELET BARAM-TSABARI AND ANAT YARDEN
self-efficacy (Eccles, 1994; Handelsman, Cantor, Carnes, Denton, Fine,
Grosz, Hinshaw, Marret t, Rosser, Sha lala, & Sheridan, 2005), and
institutional discrimination (Wenneras & Wold, 1997). An example of
this latter category is evidenced in a recent report by Hewlett, Luce, &
Servon (2008) based on data from 2,493 science workers and hundreds
more interviewed in focus groups. It paints the portrait of a macho culture
where women are very much outsiders and where 52% of those who do
enter this field are likely to eventually leave (Hewlett et al., 2008).
Attitudinal explanat ions refer to girls’ negative attitudes towards
science and the pursuit of a science-related career (Osborne, Simon, &
Collins, 2003), whereas educational explanations include school-related
parameters, such as enrollment and achievement in mathematics classes,
class atmosphere, teaching and assessment methods traditionally used in
physics classes, gender-related differences as to the notion of what it
means to understand physics, and physics curricula which are heavily
biased towards boys’ interests, knowledge, and abilities (Hoffmann, 2002;
Zohar & Bronshtein, 2005).
Other explanations rely on gender-related differences in the sources for
the development of self-efficacy beliefs, for both adults pursuing Science,
Technology, Engineering, and Mathematics (ST EM) careers (Zeldin,
Britner, & Pajares, 2008) and school students (Britner, 2008). Men’sself-
efficacy beliefs are created primarily as a result of their interpretations of
their ongoing achievements and successes. Females usually rely on relational
episodes (Zeldin et al., 2008), social persuasions, vicarious experiences, and
physiological states to develop their science self-efficacy (Britner, 2008).
Age is another variable that influences students’ interest in science.
Students, especially girls, tend to lose interest in science as they grow
older, mainly in the middle school and high school years (George, 2006;
Greenfield, 1998), both in formal (Shakeshaft, 1995) and free-choice
science-learning environments (Baram-Tsabari et al., 2009). American
girls’ attitudes to science were found to become increasingly negative
with age (Kahle & Lakes, 1983), a finding that was repeated among
Israeli students (Friedler & Tamir, 1990; Shemesh, 1990). Furthermore,
the gender gap in science interest also widens with age (Baram-Tsabari &
Yarden, 2008, 2009). This study aims to quantify the widening of this gap
in the context of a web-based free-choice science-learning environment.
Research Approach and Assumptions
Interest in science has been traditionally identified using written
questionnaires (e.g., Christidou, 2006; Dawson, 2000; Qualter, 1993;
QUANTIFYING THE GENDER GAP IN SCIENCE INTERESTS
Sjøberg, 2000; Sjøberg & Schreiner, 2002; Stark & Gray, 1999) that rely
on adult-centric views of what subjects should be meaningful to students.
Asking a question is a self-regulated act (Deci, Vallerand, Pelletier, &
Ryan, 1991) and, as such, it should be a stronger measure of interest than
responses to a questionnaire, which is externally regulated. It is the
assumption of this study that relying on student’s questions may be a
better measure of their interests than their responses to an adult-written
questionnaire and may enable progress towards incorporating their views
into the school curriculum.
Students’ questions are an important part of the ongoing scientific
research process and have an important educational role (Biddulph,
Symington, & Osborne, 1986; Brill & Yarden, 2003; Scardamalia &
Bereiter, 1992). By asking questions, students express the foreign, science
terminology in their own words , using their own experiences and
previous knowledge, while searching for the authority of science as a
structured body of public knowledge (Aguiar, Mortimer, & Scott, 2009).
By studying students’ questions, one can learn what students are
interested in knowing about a given topic (Chin & Osborne, 2008).
However, despite the capacity of students’ questions for learning
enhancement, much of this potential still remains untapped (Chin &
Osborne, 2008). It is hard to use children’s questions for research in a
classroom setting, since they are so rare and seldom give evidence of
genuine intellectual curiosity (Dillon, 1988). Researchers attribute this
situation to a classroom atmosphere in which revealing a misunderstand-
ing may render the student vulnerable, open to embarrassment, censure,
or ridicule (Pedrosa de Jesus, Teixeira-Dias, & Watts, 2003; Rop, 2003).
However, students do pose science questions in free-choice science-
learning enviro nments. Therefore, studies have used student’s self-
generated science-related questions, submitted to Ask-A-Scientist sites
and TV shows, as a tool to probe their scientific interests (Baram-Tsabari
& Kaadni, 2009; Baram-Tsabari, Sethi, Bry, & Yarden, 2006, 2009;
Baram-Tsabari & Yarden, 2005, 2007, 2008; Baram-Tsabari & Yarden,
2009; Falchetti et al., 2007; Yerdelen-Damar & Eryılmaz, 2009). Student
questions submitted to a children ’s scie nce magazine (Cakmakci,
Sevindik, Pektas, Uysal, Kole, & Kavak, 2009) have been used as
indicators of interest as well.
This research approach relies on the assumption that the number of
questions regarding a certain science topic reflects, to some degree, the
interests of children, from a similar age group and from the same gender,
in that topic. This assumption rests on two factors. The first is the
understanding that, in a natural setting, people usually ask a question
AYELET BARAM-TSABARI AND ANAT YARDEN
when they seek information that they lack, rather than raise rhetorical
questions, suggestive questions, or questions that are asked in order to
please someone (Flammer, 1981): the latter groups of questions are those
which are usually asked by students in class (Dillon, 1988). Therefore,
self-generated questions, asked in a free-choice science-learning environ-
ment, can help reveal the asker’s interests and needs.
The second basis of this assumption is the retrospective compatibility
of results obtained using this method with findings from independent
studies, using controlled samples in a school setting. This agreement with
findings described in the literature, where data were gathered using
control samples, serves to bolster confidence in the new findings, such as
those presented here, which have not been described previously.
The data set used in this study was collected from five sources
(described in the “Methodology” section) based in the US and Israel.
Gendered construction of science differs between cultures. Female high
school students in Greece, for example, are less likely than boys to pursue
a computer science degree. Girls view computer science as a self-
referencing, machine-oriented, and programming-oriented discipline to a
greater extent than boys do and hold less positive views of the
information technology profession (Papastergiou, 2008). In Malaysia, on
the other hand, there are large numbers of women in computer science.
This reflects an understanding whereby femininity is constructed by
association with office work, commonly recognized as a woman-friendly
space because it is seen as more safe and protected (Lagesen, 2008).
Data from 44 societies have demonstrated that sex typing of curricular
fields is stronger in more economically developed contexts (Charles &
Bradley, 2009). Similar findings have been described with regard to self-
generated science questions sent to an Ask-A-Scientist site (Baram-
Tsabari et al., 2009). However, these differences might be much smaller
within Western countries. Lyons (2006), for example, found a remarkable
similarity in the experiences of school science reported by high school
students in Sweden, England, and Australia. Likewise, Cakmakci et al.
(2009) and Baram-Tsabari & Yarden (2005) found highly comparable
characteristics of children’s self-generated science questions in Turkey
and Israel, respectively.
Israel and the US are quite similar with regard to demographic
indicators such as economic level, percentage of women in the labor
force, and percentage of females among graduates. The two countries
have a similar sex segregation index describing female representation in
different fields of study (Charles & Bradley, 2009). This demographic
resemblance permitted the aggregation of data collected in these two
QUANTIFYING THE GENDER GAP IN SCIENCE INTERESTS
countries. The similarities notwithstanding, the authors would like to
point to the words of Roth (2008), who praises heterogeneity: difference
and heterogeneity are the norm in science education, not something less
than sameness and purity. Accepting heterogeneity as a norm in science
education is the rationale behind aggregating data from various informal
and formal data sources, thereby enabling the highest possible diversifi-
cation of our sample.
M
ETHODOLOGY
Data Sources
Five sets of students’ self-generated questions were used in this research.
Logi. Lechu hapsu (roughly translated as “Go and find out”) was an
Israeli TV program for children broadcast on Logi, a cable channel
available upon subscription. This data source was described in detail in an
earlier paper (Ba ram-Tsabari & Yarden, 2005) and is only briefly
described here.
Lechu hapsu might have been described as a hybrid of two formats:
“Ask the Experts” and a competition to find information. Approximately
90% of the children sent their questions via the specified Internet site, the
remainder via the telephone (all of the questions were sent in Hebrew).
Only the questions submitted via e-mail were used in this study. The
children were advised that the answers would be broadcast.
The program was first broadcast in August 2003 and, by early January
2004, over 3,100 questions had been accumulated in an e-mail database.
Of these, 1,676 questions fell into the following science and technology-
related categories: animals, health and medicine, how stuff works, nature
and science, earth and space, computers and Internet, and inventors and
inventions. Of these, 1,486 questions that provided the age of the askers
and that had a science-related topic were used in the current study.
Madsci Network. MadSci Network is an independent, award-winning
nonprofit organization operating from a server in Scottsdale, Arizona
(http://www.madsci.org). This data source was described in detail in an
earlier paper (Baram-Tsabari et al., 2006) and it is only briefly described
here.
MadSci Network receives 90 to 150 questions daily, most of which are
answered automatically by the site’ s search engine. Fewer than 20% of the
questions are answered by nearly 800 globally distributed volunteering
AYELET BARAM-TSABARI AND ANAT YARDEN
scientists, usually within 2 weeks. All questions submitted to the MadSci
Network by fourth to 12th graders from August to October 2004 were
collected, resulting in a sample number of 1,525. Questions automatically
answered by the archive’s search engine were not included, since the
system did not record them.
1
Bashaar. Bashaar is an Israeli Ask-A-Scientist Internet site operated by
the Bashaar academic network (http://www.bashaar.org.il), a nonprofit
organization established in 1998 by a group of faculty members drawn
from all of the universities in Israel. This Ask-A-Scientist service is
primarily aimed at answering teachers’ questions, especially those who
live in the periphery of the country. However, 42% of the questions
submitted to the site actually originate from school students.
All questions submitted to the Bashaar website by school students
from October 2003 to January 2007 were collected, resulting in a sample
number of 962. Of these, 795 questions that provided the age of the asker
and that dealt with a science-related topic were included in the analysis.
NEWTON. NEWTON BBS
2
is an Internet-based Ask-A-Scientist service
which has been operated by Argonne National Laboratory (http://www.
newton.dep.anl.gov) since November 1991. This Ask-A-Scientist service
is primarily aimed at answering questions of kindergarten to grade 12
educators and their students. At the time of the data collection, it was
receiving an average of between 85 and 125 questions daily; of which,
2.5% to 5% were being sent to scientists. Today, due to funding cuts,
NEWTON webmasters have reduced the amount of time the window is
open for questions. In 2007, about 12% of the questions were submitted
by people outside the US (personal communication with N. Unterman,
November 4, 2007).
All questions submitted to NEWTON from June 22 to November 5,
2006, excluding questions arriving between August 20 and September 1
of that year, were collected,
3
resulting in 6,348 questions. This sample
was reduced by excluding all users who did not provide status and grade
level, who were not kindergarten to grade 12 students, whose gender
could not be identified, or who did not provide country of origin.
4
Overall, 1,693 questions that were asked by gender-identifiable school
students who provided their grade group and who asked about a science-
related topic were included in the analysis.
School. In order to include self-generated questions asked in a formal
setting, 526 questionnaires filled in by Israeli fourth to 12th graders were
QUANTIFYING THE GENDER GAP IN SCIENCE INTERESTS
collected during the 2006/2007 school year in 17 classes from five
different schools. The questionnaire contained two sections. In the first
section, students were prompted to raise their own science questions and to
explain why they thought these questions were interesting. In the second
section, which was distributed only after the first section had been collected,
students were asked to mark questions they were interested in learning about
from a mixed list of student-generated and textbook questions. Only the
questions that were self-generated by the students in the first section and
were on a science-related topic were included in this analysis, resulting in a
sample of 446 questions. According to international comparisons, Israeli
students are less interested overall in science compared to the OECD average
(Israeli National Institute for Evaluation in Education, 2007).
Classifying the Questions
User Characteristics
Grade. In some databases, Internet surfers used free writing to indicate
their age, while in others, they had to choose their grade level from a
fixed list. In order to compare data from different sources, we sorted all of
the age-related information into four grade groups: kindergarten to third
graders, fourth to sixth graders, seventh to ninth graders (junior high
school), and tenth to 12th graders (senior high school).
Gender. Hebrew is a gender-identifying language. As a result, some of
those submitting questions automatically revealed their sex through the
use of verb gender indicators; for example, “I’m checking” translates as
ani bodeket (feminine) or ani bodek (masculine). Children’s names
provided a further indication of the sex of the questioner, although some
names (e.g., “Liron”) could be associated with either a boy or a girl. For
the English questions, gender identification was based on the asker’sfirst
name. For the NEWTON database, initial classification was done semiauto-
matically using an English name gender finder.
5
Next, the names that were
not automatically classified were analyzed individually using a baby name
guesser,
6
which operates by analyzing popular usage on the Internet. All the
names from the MadSci Network database were identified manually.
Question Characteristics
Different classifications were used to describe the content and
cognitive level of the questions, as well as the motivation for asking
AYELET BARAM-TSABARI AND ANAT YARDEN
them. Categories were developed in the course of earlier studies (Baram-
Tsabari et al., 2006; Baram-Tsabari & Yarden, 2005, 2009), based on the
data itself.
Topic of the Question: Subject and subsubject. Questions in this coding
scheme were placed in one of the following categories: “Biology,”
“Physics,”“Chemistry,”“Earth sciences,”“Astrophysics,”“Nature of
science (NOS),” and “Technology.”“Earth sciences” and “Astrophysics”
were kept as distinct categories since each accommodated a significant
number of questions. “NOS” refers to the epistemology and sociology of
science, science as a way of knowing, or the values and beliefs inherent in
scientific knowledge and its development (Lederman, 1992) without
reference to a specific scientific context. “Technology” questions were
categorized by defining technology as the development, production, and
maintenance of artifacts in a social context, as well as the artifacts
themselves (Gardner, Penna, & Brass, 1996). Questions in the field of
mathematics and questions that did not have a science topic were not
included in our sample. Each of the categories (except for NOS) was
further divided into subsubject, resulting in a total of 54 categories: 17 in
biology, five in physics, nine in chemistry, eight in earth sciences, six in
astrophysics, and eight in technology and NOS. All subsubjects are listed
in Appendix 1 . For examples of the application of the categories in this
coding scheme, see Table 1.
Type of Information Requested. Typology was influenced by Bloom’s
taxonomy (Bloom, Engelhart, Furst, Hill, & Krathwohl, 1956) and
Bybee’s classification for research questions (Biological Sciences
Curriculum Study [BSCS], 1993). The typology describes the nature of
the question and the knowledge it generates along a gradually increasing
cognitive-level continuum. The lowest category consists of requests for
“factual” information which include terminological (What is www?),
historical (When was the computer invented?), descriptive (What does a
male mosquito eat?), and confirmatory (Is it true that the earth’s core is
liquid?) items. The second category consists of requests for “explanatory”
information, with basically “how” and “why
” questions. The third
category, consisting of questions asking for “methodological” informa-
tion, has to do with scientific ways of finding things out and with
scientific and technological procedures. The highest categories were
“predictions and contradictions”—cases in which the asker describes a
science-related situation and asks what its results will be or describes a
contradiction between two pieces of their scientific knowledge and
QUANTIFYING THE GENDER GAP IN SCIENCE INTERESTS
request “open-ended”-type information which deals with opinions,
controversial themes, and futuristic questions that science cannot answer
for the time being. For examples of the application of the categories in
this coding scheme, see Table 2.
Reason for Raising the Question: Motivation and Level of Autonomy. An
attempt was also made to identify and classify the questioners’ reasons for
raising their questions. Since it was not possible to ask the children why
they sent their questions, it was necessary to interpret their possible
motivation from the way in which their questions were worded and
phrased.
Motivation. Based on Baram-Tsabari & Yarden (2005), two categories
were chosen: “nonapplicative” and “applicative.” A question was labeled
TABLE 1
Examples of question classification according to subject and subsubject
Subject: subsubject
Example question
a
(gender, age group,
country of origin)
b
Technology: computers and the Internet I want to add a chat application to my Internet
site, what should I do? (m, junior high
school, IS)
Earth sciences: oceanography Why and how salt got in the sea and why is it
not in lakes? (m, high school, US)
Physics: mechanics Gravity pulls everything down … then how
come that fire goes up? (m, K–3, NA)
Chemistry: acids and bases My teacher is having us do an experiment
where we place an egg in vinegar … Are
the bubbles CO
2
? If so, what is the chemical
reaction that is occurring to generate CO
2
?
(f, junior high school, US)
Biology: evolution How can scientists even stand by evolutionary
theories of cells being formed out of a
“soup” and then evolving into the entire
animal and human races and species, and
yet, they can’t reproduce the effect
themselves? (f, high school, US)
Astrophysics: the solar system If there’s no oxygen in space, how can the sun
burn? (frequently asked question)
Nature of science If I want to conduct research and publish it,
what should I do? (m, junior high school, IS)
a
These are verbatim quotes or translations of verbatim quotes. In some cases, only part of the question
is shown
b
Where data are available: m male, f female, IS Israel, US United States, NA not available
AYELET BARAM-TSABARI AND ANAT YARDEN
as applicative if it was clear from the question that the answer was going
to be used in some way—e.g., to build something, to make a decision on
health and lifestyle issues, or to fulfill school assignments. For examples
of the application of the categories, see Table 2.
When one asks a question in order to fulfill a school assignment, it
may still be their own self-generated question—something the student
feels they have to know to complete a project. However, a question may
also be given to the student by their teacher. In order to differentiate
between the two scenarios, a scheme was added.
Level of Autonomy. Gross (2001) makes a distinction between questions
that are self-generated (internally motivated by personal context) and
those that are imposed (thought up by one person, such as a teacher, and
then given to someone else, such as a student, to resolve). Although all of
the questions in our sample were generated by students, not all of them
were the outcome of an intrinsic motivation to know. Some questions
TABLE 2
Examples of question classification according to cognitive level and reason for asking the
question
Cognitive level:
type of information
Reason for asking:
motivation, autonomy
Example
a
(gender, age group, country
of origin)
b
Factual Nonapplicative,
spontaneous
Do all animals see only black and
white? (m, K–3, IS)
Explanatory Nonapplicative,
spontaneous
How do cell and atoms relate? (f, 10–
12, US)
Methodological Applicative,
spontaneous
I received a microscope for my
birthday, and I don’t know how to use
it. My question is on how to use a
microscope. (m, K–3, IS)
Predictions and
contradictions
Nonapplicative,
spontaneous
How come a horse and a donkey can
reproduce if two different kinds of
animals (a dog and a cat) can’t
reproduce? (m, 7–9, IS)
Open-ended Applicative, imposed Is electricity necessary? What are the
merits & demerits of electricity?
Topic given by teacher in class to be
answered in 2 days’ time. (m, 10–12,
India)
a
These are verbatim quotes or translations of verbatim quotes. In some cases, only a part of the
question is shown
b
Where data are available: m male, f female, IS Israel, US United States, NA not available
QUANTIFYING THE GENDER GAP IN SCIENCE INTERESTS
were required for school assignments and were originally raised by
teachers or textbooks. To differentiate between the two types of
motivation for raising the question, we classified the questions as either
“spontaneous,” which can serve as an indication of intrinsic motivation to
know, or “imposed,” which can serve as an indication of an extrinsic
motivation to seek an answer. Questions were classified as imposed only
if it was explicitly stated in the question that the information was required
for a school assignment, such as a science fair project, report, or
homework. This classification was not applied to questions that were
collected in the school setting, since the students were prompted by an
external agent to ask questions. For examples of the application of the
categories in this coding scheme, see Table 2.
Data Analysis. Contingency tables analysis was performed using “R,”
7
a
free software environment for statistical computing and graphics.
Probabilities were calculated using chi-square test.
Quantification of the gap between females’ and males’ interests in seven
science subjects within each grade group was calculated using the
Minkowski distance (Weisstein, 2009). The distance between two points in
a two-dimensional space is usually given by the Euclidean distance. Since
we had seven dimensions in the form of science subjects, the Minkowski
distance was used in the following manner for each grade group:
Biology FðÞBiology MðÞ
jj
7
þ Chemistry FðÞChemistry MðÞ
jj
7
þ
n
Physics FðÞPhysics MðÞ
jj
7
þ Technology FðÞTechnology MðÞ
jj
7
þ
Astrophysics FðÞAstrophysics MðÞ
jj
7
þ Earth Sciences FðÞEarth
j
Sciences MðÞj
7
þ NOS FðÞNOS MðÞ
jj
7
g
1=7
¼ an arbitrary number that
describes the differences between females
0
and male
0
interests in a
particular grade group
. Th ese numbers were c ompared between the
different grade groups to highlight possible changes in the gender gap.
Sample Preparation and Characteristics
This study is part of a larger project in which nearly 6,000 science questions
collected from five different web-based, TV-based, and school-based sources
were rigorously analyzed in order to identify profiles of kindergarten to
grade 12 students’ interest in science and how these profiles change with age
(Baram-Tsabari & Yarden, 2009). The present study focuses on the
development of male and female students’ interest in different science fields
using the same sample.
Sample preparation included several stages. First, all of the data from
the various sources were collected and the questions were classified. Each
AYELET BARAM-TSABARI AND ANAT YARDEN
data source was studied and coded independently. To establish trustwor-
thiness and credibility for the data analysis, at least 10% of the data from
each data source was analyzed by two experienced coders. Agreement
between coders ranged from 84% to 98% for the different categories and
data sources.
Since the coding of the questions was not done simultaneously, there
were some differences in the coding scheme used for the different
sources. Therefore, our second step was to create a unified coding scheme
and to correct for any deviations from that scheme. These corrections
were first done semiautomatically using the “Access” software and then
manually during the iteration that followed. As part of the unification,
classifications that were missing from the original coding were added.
Our third step was to remove ambiguous data. Questions with no clear
science field were deleted (“indistinguishable”), as were questions that
dealt with mathematics. All of the cases in which the asker’s grade level
was not clear or was not kindergarten to grade 12 were removed at this
stage as well. This process resulted in a sample of 5,945 self-generated
science-related student questions. After removing questions that could not
be identified for gender, 4,989 questions remained. These questions
provided the sample for this analysis. The basic demographic character-
istics of the different data sources and the overall sample are detailed in
Table 3.
R
ESULTS
Self-generated, science-related kindergarten to grade 12 students’ ques-
tions (n = 4,989) collected from five different web-based, TV-based, and
school-based data sources were classified into one of seven science
subjects. The most popular science subject for students’ self-generated
questions was biology (45.9%), as previously found in formal (Osborne
& Collins, 2000) and informal settings (Baram-Tsabari et al., 2006, 2009;
Baram-Tsabari & Yarden, 2005, 2009). This was followed by chemistry
(15.7%), technology (11.4%), physics (10.3%), astrophysics (8.2%), earth
sciences (7.3%), and NOS (1.3%). The distribution of self-generated
questions with regard to science subject, gender, and grade group is
presented in Table 4.
Females’ and males’ interest in science developed along different paths
(p G 0.001; Figure 1) and resulted in a stereotypically gendered interest
pattern in the tenth to 12th grade group. This gap was not always
apparent: in the youngest kindergarten to third grade group, where there
QUANTIFYING THE GENDER GAP IN SCIENCE INTERESTS
TABLE 3
Basic demographic characteristics of the different data sources and the overall sample
Data source Type
Number of
questions
Number
of usable
questions
a
Grade level
Percent
females Origin Data collection period
Logi TV enrichment
show
b
1,486 1,025 97% K –ninth
graders
43 Israel August 2003–
January 2004
MadSci Network Ask-A-Scientist
site
1,525 1,143 Fourth–12th
graders
56 84% Americans August–October
2004
Bashaar Ask-A-Scientist
site
795 688 79% tenth–12th
graders
62 Israel October 2003–
January 2007
NEWTON Ask-A-Scientist
site
1,693 1,693 85% seventh–
12th graders
60 99.5% Americans June 22–August
20 and September 1–
November 5, 2006
17 classes from
five schools
Formal setting 446 440 Fourth–12th
graders
47 Israel 2006/2007
academic year
Overall sample Informal (Internet
and television)
and formal
settings
5,945 4,989 95% fourth–
12th graders
55 Israel
c
,US
d
,
Canada, UK,
India
e
2003–2007
a
After removing questions that could not be identified for gender
b
In this TV show, the answers were given on TV, but the questions were sent by e-mail
c
46% of the sample
d
93% of the questions in the international sites
e
A few questions originated from Australia, Mexico, and Korea
AYELET BARAM-TSABARI AND ANAT YARDEN
was no statistically significant difference among the science fields that the
two genders asked about (χ
2
= 0.9). The gap widened gradually and
rapidly: χ
2
= 8.5E − 06 among fourth to sixth graders; χ
2
= 3.2E − 09 among
seventh to ninth graders; and χ
2
= 2.3E − 20 among tenth to 12th graders.
In elementary school, girls and boys presented similar interest in
biology. It was the subject of 46.5% of the girls’ and 43.7% of the boys’
questions among kindergarten to third graders and 48.2% and 46.9%,
TABLE 4
Distribution of self-generated questions with regard to science subject, gender, and age
group (absolute numbers)
Subject
K–34–67–910–12
TotalMale Female Male Female Male Female Male Female
Biology 55 46 268 264 219 427 351 658 2,288
Physics 8 9 42 32 83 96 160 84 514
Chemistry 9 8 24 56 100 182 170 232 781
Earth sciences 9 6 33 54 75 74 56 56 363
Astrophysics 20 12 68 56 65 87 59 42 409
NOS 1 – 3 5 21 15 8 14 67
Technology 24 18 134 81 105 76 77 52 567
Total 126 99 572 548 668 957 881 1138 4,989
Figure 1. Students’ interests in science by grade group and gender. Shift in interest is
presented in seven science subjects along four grade levels, among female (F) and male
(M) students (n=4,989): NOS nature of science, Earth-S. earth sciences, Astro.
astrophysics, Tech. technology, Chem. chemistry
QUANTIFYING THE GENDER GAP IN SCIENCE INTERESTS
respectively, in fourth to sixth graders. However, the transition to junior
high school brought about a change in the boys’ interest, with only one
third of their questions dealing with biology. In senior high school, this
figure rose to 39.4%. Another change took place when the girls moved
from junior to senior high school: their share of biology questions rose to
57.8%.
During elementary and junior high school, there was no difference in
the percentage of physics questions asked by the two genders (9.1% of
the girls’ and 6.4% of the boys’ questions among kindergarten to third
graders; 5.8% and 7.3%, respectively, in fourth to sixth graders; and 10%
and 12.4%, respectively, among seventh to ninth graders). However, these
numbers changed in senior high school, where 18.2% of the boys’ questions
dealt with physics, compared with only 7.4% of the girls’ questions.
A different pattern was found with regard to interest in technology—
boys’ interest fluctuated (19%, 23.4%, 15.7%, and 8.7% in the increasing
grade groups, respectively), but girls’ interest steadily declined: 18.2%,
14.8%, 7.9%, and 4.6%, respectively, in those same grade groups.
A common pattern was found for both genders, however, with regard
to chemistry and astrophysics. Both girls and boys increasingly asked
more questions about the former and less about the latter as they matured.
To quantify the gap between boys’ and girls’ science interests in the
different grade groups, each distribution of interests was expressed as a
coordinate in a seven-dimensional space, with each of the dimensions
representing one of the science subjects. The Minkowski distance
between the girls’ and boys’ coordinate for a particular grade group was
calculated and expressed as a one-dimensional number with arbitrary
units. The difference between boys’ and girls’ interests was found to
increase over 20-fold as they grew older: from 0.0014 at the kindergarten
to third grade level to 0.0336 at the tenth to 12th grade level (Figure 2).
The stereotypical interest gap was also apparent when reviewing the
ten subsubjects that were more interesting to boys than to girls. These
were: computers and the Internet; modern physics; transportation; the
universe; electricity and magnetism; history of technology; meteorology;
robotics and electronics; optics, heat, and sound; and extinct animals.
Four of these topics are subsubjects of technology, and another three are
subsubjects of physics. The ten subsubjects that were more interesting to
girls than to boys were: botany and mycology;
8
what things are made of
and bonding and structure; ecology; nutrition; behavior; sickness and
medicine; man and animal relationship; microbiology and virology; cell
biology; and the environment. Nine of these topics are subsubjects of
biology. A similar trend was described among Italian users of an Ask-A-
AYELET BARAM-TSABARI AND ANAT YARDEN
Scientist service, where females posed fewer questions dealing with
mathematics, physics, and technology than males and more questions in
the fields of ethology, botany, general biology, and health care (Falchetti
et al., 2007). This finding was also mirrored by an analysis of questions
submitted to a Turkish Ask-A-Scientist site, which found that only 15%
of the physics questions were submitted by females versus 52% of the
biology questions (Yerdelen-Damar & Eryılmaz, 2009).
A difference was also found by chi-square test in the type of
information asked for by male and female students (p G 0.05). While
boys asked for more methodological information than girls, girls asked
more open-ended questions and for more explanatory information and
predictions than boys. The trend was stronger when testing only the
spontaneous questions (p G 0.001), and no such difference was found with
regard to the imposed questions. This may point to a genuine difference
in the types of knowledge and understanding expected by boys and girls.
Boys and girls had different reasons for asking their questions. More girls
than boys (34% and 29%, respectively) asked for applicative information
(p G 0.001) and more girls than boys (25% and 16%, respectively) asked
imposed questions (p G 0.001).
Figure 2. The gender gap in science interests along four grade levels. For each grade
level, female and male students' interests were expressed as a coordinate in a seven-
dimensional space, with each of the dimensions representing one of the science subjects.
The distance between the girls’ and boys’ coordinate for a particular grade group was
calculated and expressed as a one-dimensional number with arbitrary units
QUANTIFYING THE GENDER GAP IN SCIENCE INTERESTS
DISCUSSION
An analysis of almost 5,000 self-generated science questions demonstrat-
ed substantial differences in the gender gap in science interests between
different grade groups: the gap increased 20-fold between kindergarten to
third grade and tenth to 12th grade groups, with males increasingly more
interested in physics and females increasingly more interested in biology.
Intellectually talented males and females are both achieving high goals
by their mid-30s. They are, however, achieving in different areas and
appear to be on different developmental trajectories (Lubinski & Benbow,
2007). Despite relatively equal achievement in science education, girls
tend not to identify with science, a problem which grows in size the
further the girls progress along their potential s cience trajectory
(Calabrese Barton, Tan, & Rivet, 2008). The ensuing disengagement
with science not only li mits individuals’ future career paths, it is
damaging for science, as formal science education leaves students with
little understanding of why scientific knowledge matters or of the creative
and intellectual effort required for its achievement (Osborne, Simon, &
Tytler, 2009).
Because there are differences in males’ and females’ perceived
competence, enjoyment, and selection of science classes in college and
high school, researchers must consider students’ perceptions of science in
earlier grades (Patrick, Mantzicopoulos, & Samarapungavan, 2009). The
finding that the youngest grade group showed no gender difference in
science intere st is mirrored by that of Patrick et al. (2009)that
kindergarten children differ in perceived science competence depending
on their science instruction type, but not their gender, and by findings
from a widespread twin study by Haworth et al. (2008). Our results
support the view that the transition into gendered interest pattern occurs
during the junior high school years.
Osborne et al. (2009) suggest that efforts to engage school students
with science could be productively informed by (a) understanding the
formative influences on student career aspirations between the ages of 10
and 14 and (b) better understanding how to foster and maximize the
interest of this cohort of adolescents, particularly girls, in STEM-related
careers. It is the authors’ belief that relying on the spontaneous interest of
young students is a promising way to respond to these two points.
The unique contribution of this research is the utilization of the
Minkowski distance to compare gender gaps in science interests at
different grade levels. Using this method, the difference between boys’
and girls’ interests was found to increase to over 20-fold as they grew
AYELET BARAM-TSABARI AND ANAT YARDEN
older. This method may enable educational researchers to compare the
gender gaps in science interests found in different samples—a comparison
which today is for the most part qualitative.
Research Limitaions
The data source and data analysis procedure used in this study provided
us with a unique view of students’ informational needs, but not without
methodological costs. The following is a discussion of the research’s
limitations.
Coverage. Most of the data used in this study was collected online from
Ask-A-Scientist sites. The biggest threat to inferences from web research
is currently coverage error (Couper, 2000). However, most Western youth
do have access to the web. A Pew Internet Project survey found that 87%
of all American youth between the ages of 12 and 17 use the Internet
(Rainie & Hitlin, 2005), and in the fall of 2003, nearly 100% of the public
schools in the US already had access to the Internet (National Center for
Education Statistics, 2005). This coverage theoretically allows all
American students to send their questions via e-mail and to be part of
our sample. In Israel, which was the origin of approximately half of the
questions in this study, over 60% of households and over 80% of
businesses have Internet access (Ministry of Communication, 2006).
Interviews with 9- to 18-year-old Israeli children conducted in 2006
found that 93% of the children were connected to the Internet from home,
another 79% from school, and 60% have an Internet connection on their
cell phone (Lemish & Ribak, 2007).
Representativeness. The self-selecting sample used in this research does
not represent all children. It represents a group of children who are
probably more interested in science and have easier access to resources
than the child population as a whole. Students who are not motivated to
learn science are probably not represented in this self-selecting sample,
regardless of their access to the Internet. Therefore, the opportunistic
nature of the sample places some constraints on the validity of the results.
The validity of the study can be supported by the notion of using data that
originate from the researched population itself, not as a response to a
stimulus from a researcher, thus ensuring high ecological validity.
At least from a gender perspective, data mining may result in a more
representative sample than collecting questions in the classroom. Gender
has a direct effect on Internet use, with females using it more to
QUANTIFYING THE GENDER GAP IN SCIENCE INTERESTS
communicate, while males use it more to search for information (Jackson,
Ervin, Gardner, & Schmitt, 2001). Caspi, Chajut, & Saporta (2008)
examined gender differences in participation in face-to-face and web-
based classroom discussions by comparing the male–female actual
participation ratio to the male–female attendance (or login) ratio. They
found that males overproportionally spoke in the face-to-face classroom,
whereas females overproportionally posted messages in the web-based
conference. They suggested that females prefer written communication
more than males or that females prefer written communication over
spoken communication (Caspi et al., 2008).
Missing Data. In these free-choice science-learning environments, some
users do not bother to fill in all the required information, such as name
and age. Therefore, another assumption of this research is that the data we
were able to access are representative of the missing values as well.
Furthermore, we do not know how many different children actually
submitted the questions, since it is possible that some children submitted
more than one question. We assume that the number of repeated questions
by the same person is similar for different grade levels and genders. Using
the methodology applied in this study, it is not possible to determine
whether parents were involved in the process of submitting the questions.
Parental supervision and participation during Internet use seems likely in
the kindergarten to third grade group. Therefore, questions sent by this
small group may have involved parental input.
Web-based experiments of the kind used here are more difficult to
control in some respects than experiments conducted in a classroom
setting. However, they present an important methodological advantage for
studying students’ self-guided science learning, taking into consideration
that this kind and amount of data does not exist anywhere outside the
web.
C
ONCLUDING REMARKS
It is not within the scope of this research to determine why the gender gap
in science interest exists, nor do we argue that being interested in physics
is in any way superior to being interested in biology. We do, however,
provide a quantitative description of the way in which this gender gap in
interest forms and widens with age among school children. Since we used
cross-sectional rather than longitudinal data, we cannot describe changes
in students’ interest over time, only differences between grade groups.
AYELET BARAM-TSABARI AND ANAT YARDEN
On the down side, this method collapses all of the information about
interest in the various science subjects into a single arbitrary value.
However, in doing so, it enables a simple and visual comparison between
age groups, different sets of populations, and different data sources.
A
CKNOWLEDGMENTS
The authors would like to thank Nathan A. Unterman from the Division
of Educational Programs at Argonne National Laboratory in Illinois,
Ricky Sethi and Lynn Bry from MadSci Network, Bashaar and Logi
channel staff for giving us access to their archives of questions, Nir
Esterman, Tal Galili, and Eyal Nitzani for their superb expert statistical
advice, and Camille Vainstein for the language editing.
A
PPENDIX 1
Subsubjects in their order of popularity within the seven scientific
subjects.
Biology
Anatomy and physiology
Botany and mycology
Ecology
Sickness and medicine
Behavior
Genetics and reproduction
Nutrition
Cell biology
Man and animal relationship
Neurobiology and the mind
Microbiology and virology
Evolution
Other
Biotechnology
Biochemistry
Extinct animals
History of biology
QUANTIFYING THE GENDER GAP IN SCIENCE INTERESTS
Chemistry
What thing are made of and bonding and structure
Element and atom structure
Chemical reaction
Phases of matter
Thermodynamics
Acids and bases
Stoichiometry
Chemical energy
Chemical language
Technology
Computers and Internet
Other and low-tech technology
History of technology
Transportation
Robotics and electronics
Optics
Inventions and patenting
Media and communication
Physics
Mechanics
Optics, heat, and sound
Electricity and magnetism
Modern physics
History of physics
Astronomy and astrophysics
The solar system
The rest of the universe
Space missions
Big bang and star formation
Extraterrestrial life
Other
Earth sciences
Meteorology
Environment
AYELET BARAM-TSABARI AND ANAT YARDEN
Geology
Geography
Atmosphere
Oceanography
The end of the world
Other
Nature of science
Mathematics
Nondistinguishable and irrelevant
N
OTES
1
Questions automatically answered by the sites’ search engine are similar in a sense to
keywords entered into a general search engine (e.g., Google). This type of data has its own
limitations and advantages (Baram-Tsabari & Segev, 2009).
2
BBS stands for Bulletin Board Service. Such services are now extinct, but this was
part of the name at the site’s inception.
3
Our initial intent was to compare school-time questions with vacation-time questions.
During the latter period, students in some countries study and some do not. This is why no
data collection was performed during that time. Eventually, this comparison was
abandoned.
4
Data from NEWTON were not archived by the site, but collected in real time, as the
questions were submitted. Only questions with full background data were collected.
5
http://epublishing.nademoya.biz/japan/names_in_english.php.
6
http://www.gpeters.com/names/baby-names.php.
7
http://www.r-project.org.
8
Some of the “feminine” topics are also characterized by a large proportion of imposed
questions due to the greater tendency of female students to submit this type of question.
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Ayelet Baram-Tsabari
Department of Education in Technology and Science
Technion
Haifa 32000, Israel
E-mail: ayelet@technion.ac.il
Anat Yarden
Department of Science Teaching
Weizmann Institute of Science
Rehovot 76100, Israel
E-mail: anat.yarden@weizmann.ac.il
AYELET BARAM-TSABARI AND ANAT YARDEN
