Gender Gap and Gendered Education:
Myth or Reality?
Tatyana Sumner
Spring 2013
ED.7202.T
Brooklyn College
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Table of Contents
Abstract………………………………………………………………………………………….. 2
Introduction
Statement of the Problem………………………………………………...……………… 3
Literature Review……………………………………………………………………. 4-16
Statement of the Hypothesis…………………………………………………………… 16
Method
Participants……………………………………………………………………………... 16
Instruments……………………………………………………………………………... 17
Experimental Design……………………………………………………………………. 17
Threats to Internal Validity………………………………………………………...…... 17
Threats to External Validity………………………………………………………....…. 18
Procedure………………………………………………………………………………. 18
Results………………………………………………………………………………………….. 19
Discussion…………………………………………………………………………………….... 25
Implications…………………………………………………………………………………….. 26
References………………………………………………………………………………….. 27-30
Appendices
Appendix A: Parental Consent Form….…….…………………………………………. 31
Appendix B: Principal’s Consent Form………………………………………………... 32
Appendix C: Teacher’s Consent Form……..………………...………………………... 33
Appendix D: Teacher Consent Form……………………………………………….. 34-36
Appendix E: Excel Team Randomizer………………………………………………… 37
Appendix F: Results Tables………………………………………………………… 38-39
Appendix G: Results Graphs……………………………………………………….. 40-43
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Abstract
Prior research is locked in a debate whether mathematics is a gendered subject and is
embraced more by males than females. This action research examines the differences of
attitudes toward mathematics among male and female students by creating peer-assisted
environments and eliminating gender competition. In a quasi-experimental design, two groups
(9 male and 9 female students) are given a pre- and post-survey inquiring about their attitudes
toward math. Between surveys, both groups are exposed to a hypothetical treatment, involving
mathematics instructions 3 times a week, over 4 weeks in boy-girl pairs. The results show a
notable increase in positive attitude toward mathematics: 26.7% increase for female and 13.9%
for male students. This displays a fair positive correlation between working in gender-mixed
groups/pairs and positive attitudes toward math.
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Introduction
Statement of Problem
From the first moments of life each person enters a society that is structured on a binary
system of gender (West & Zimmerman, 1987). The first words uttered by a delivering doctor are
a declaration of an infant’s gender based on anatomic characteristics. In this type of gendered
society each child learns that males and females are different in many aspects from anatomical
and cognitive to cultural. These elements are often constructed into socially accepted gender
stereotypes (Ridgeway & Correll, 2004). Whether based on scientific facts or socially created
concepts, the socially imposed stereotypes can and often do impair our children’s education
(Gunderson, Ramirez, Levine, & Beilock, 2012; Ridgeway & Correll, 2004). The problem that
this research will be exploring is that socially created stereotypes, such as “math is for boys” or
“girls are not good at math,” are unconsciously and habitually reflected in educator’s instruction
methods and affect female students’ attitude and achievement in mathematics. In order to
attempt to create a change in the classroom, teachers should implement instructional strategies
that will benefit both boys and girls. There are a number of approaches that can be undertaken,
from differentiated instruction to single-gender education. This author’s proposal for
intervention is to change a learning environment in the classroom that eliminates competition
based on gender, boys against girls, and facilitates co-ed, cooperative, and peer-learning during
mathematics instructions.
In order to attempt to create a change in the classroom, teachers should adjust
instructional strategies to minimize bias treatment and reinforce a heterogeneous, peer-learning
environment. While this research focuses primarily on gender inequality in mathematics that
disadvantages girls, it is worthy of note that boys also find themselves victims in the gendered
imbalance in education, primarily in Literacy subjects and discipline (Buchmann, DiPrete, &
McDaniel, 2008).
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Review of Related Literature
Math gender gap or no gap: Is there a problem?
For the past four decades researchers from the fields of psychology, sociology, and
education have examined, compared, and quantified the academic achievement of male and
female students with a number of conflicting results (Ding, Song, & Richardson, 2006; Kane &
Mertz, 2012; You, 2010). Disagreements arose not only about the causes of possible gender gap
or at what age such gaps may occur, but also on the very existence of the differences in academic
achievement between gendered student population (Gool et al., 2006; Ding et al., 2006; Marks,
2008; You, 2010). As persistent and widespread as the belief in gender stereotype regarding
mathematics is in our society, such as “math is for boys” and “girls are not good at math,” one
can reasonably assume that it is reflected in our children’s academics success and achievement.
Ding et al. (2006) cite findings from the 1980s that show a strong male advantage in the
mathematics where boys scored above 600 on the math portion of Scholastic Aptitude Test
(SAT) 4 times more often than girls. Dr. Jean Atkin Gool (2006) and colleagues examined
works by a number of researchers and cite results based on 2004 Advanced Placement tests for
science and mathematics, showing boys leading anywhere from 0.27 to 0.47 points (on a 5.00
grading scale) in calculus, computer science, biology and physics. Nosek, Banaji, and
Greenwald (2002) provide the data from National Science Foundation (NSF) that displays an
existing gap of 41 points favoring male students among the SAT mathematics section. With
results like this and powerful gender stereotype, it appear that the popular cultural belief of
women (girls) being worse at math than men (boys) holds true and continues to function as a
self-fulfilling prophesy for future generations. The existence of gender gap is just one tool to
justify gender stratification in educational and workforce organizations. If the belief of mathgender stereotype is strong in the hiring manager, he or she is more likely to give a mathintensive job to a male applicant than an equally competent and qualified female.
On the other hand, in spite of popular belief that girls do not do as well as boy in
mathematics, a number of researchers (Ding et al., 2006; Gunderson et al., 2012; Nosek et al.,
2002) found the gender gap to be very minimal or not to exist all. Their studies and overviews
agree that gender gap in academic achievement has diminished over the past couple of decades.
Ding and colleagues (2006) report the findings of their own study, which display a very minimal
difference among male and female middle school students’ mathematic achievement, favoring
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male students. In their second study the high school student sample displayed results contrary to
the math gender stereotype where female students scored higher than male students. School of
Science and Mathematics posted a “Research in Brief” review that shows a number of studies
that produce similar results and identify the disparity in male and female student test results as
statistically insignificant (You, 2010).
To further display the conflicting research findings, Geist and King (2008) claim that data
from the National Assessment of Education Progress (NAEP) shows a gap of 2 points between
genders and that this gap has developed only in the last decade. They go further to support this
claim by citing prior research that displayed girls outperforming boys in 1970s. This prompts a
look at discrepancies about timing of these possible or nonexistent gender gaps. In other words,
determining at what stage in educational careers gender gaps are displayed. Ding et al. (2006)
present extensive literature overview that shows continued inconsistencies among researchers
from 1981 to 2002 on the time of development of gender differences in children’s academic
careers. In their review of scholarships, Ding and colleagues cite several studies that report
female students outperforming male students in earlier grades but by middle school and high
school the math achievement results favor males. They cite other researchers who provide polar
opposite conclusions where girls being their academics with lower math scores but then have
equal or advanced results over their male counterparts in higher grades. Another research by
Abigail Norfleet James (2007) uses data from NAEP to show a very small gender gap in
mathematics in 2004, however upon a more detailed review discovers that while on basic level
math boys and girls show the same levels of proficiency, in more advanced or complex areas
girls’ scores noticeably drop.
These disagreements among various studies lead to a question. Why do cultural beliefs
and stereotypes regarding gender and mathematics still persist today if there is no conclusive
data to support them being factually true? The answer lies partially in the gendered society and
how cultural beliefs structure social understandings of binary system of gender and established
stratification. Perception of women being as competent or more competent in mathematics as
men does not fit the cultural belief system that maintains patriarchal hierarchy. “Men are viewed
as more status worthy and competent overall and more competent at the things that “count most”
(e.g., instrumental rationality). Women are seen as less competent in general but “nicer” and
better at communal tasks…” (Ridgeway & Correll 2004, p.513). Since mathematics is a required
skill for many high-valued and demanding jobs, these positions in STEM fields are more readily
attributed with men. These hegemonic cultural believes lead to a conclusion that women are bad
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at math in opposition to men who are good at it, thus reinforcing social relational contexts theory
(Ridgway & Correll, 2004).
Gendered attitudes toward mathematics: Students’ implicit math-gender stereotypes.
While there is a debate regarding the existence of academic achievement inequality based
upon gender, another type of inequality is indisputable. Whether there is a gender gap favoring
male students or women generally perform as equally or better than men, one fact remains true:
women are underrepresented in the STEM field jobs. Although there have been many strides
toward improvement in gender equality in the job market over the past decades, “among the
recent doctoral degrees awared in the U.S., women accounted for 27% of women in
mathematics, 15% in physics, 20% in computer science, and 18% in engineering” (Leaper,
Farkas, & Brown, 2012, p.268). Fewer women than men pursue STEM careers and fewer female
students than male students enroll in advanced elective mathematics courses in college and high
schools (Brandell & Staberg 2008; Leaper et al., 2012; Steffens, Jelenec, & Noack, 2010).
Campbell, Verna and O’Connor-Petruso (2004) use data from year 2002 to demonstrate the
gender gap in representation at the Academic Olympiads. In the United States the ratio of male
to female Academic Olympians was 44:1 in mathematics.
The reason behind this lack of participation in mathematics is connected to socially
constructed stereotypes affecting girls’ and women’s attitudes, motivation and belief in their own
performances in mathematics. Several studies showed that children accept and employ the
socially constructed stereotype that portrays mathematics as male-oriented subject and skill
(Leaper et al., 2012; Nosek et al., 2002; Steffens et al., 2010; Stetsenko, Little, Gordeeva,
Grasshoff, & Oettingen 2000).
By associating math with ‘male’, and themselves with female, girls “appear to reflect a
free and individually determined choice when in fact they reflect group membership, the strength
of identity with the group and beliefs about the capability of the group” (Nosek et al., 2012,
p.44). Adhering to socially constructed math stereotypes females consciously or unconsciously
choose their behavior based on their gender identity and what is socially appropriate or accepted
for that group by comparing and positioning themselves in opposition to men and their behavior
(Risman, 2004).
Nosek and colleagues (2002) describe the process of gendered female logic perfectly in
their article title “Math = Male, Me = Female, Therefore Math ≠ Me”. This holds true and is
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supported by the results from their studies of college students expressing their opinions about
mathematics. In their research, both men and women have strong identifications with their own
gender. Both men and women displayed strong beliefs that math = male. Both groups were
asked to express their opinions about mathematics in comparison to other subjects such as art,
language, and science. As expected, women had increasingly higher levels of negative attitude
toward mathematics than men. Such math attitude differences were also true with men and
women with strong math-related majors. This displays that when measuring mathematics
against another subject, women lean more toward the socially acceptable “feminine” options.
Even women who are pursuing math-related fields express greater negative attitudes toward
mathematics than men and thus in part reinforcing the cultural belief system (Nosek et al., 2002).
Steffens and her colleagues (2010) examine similar research conducted with adults and
apply it to children to study when gender stereotypes begin to affect students’ competence and
attitudes. Using a sample group of children in Germany from 4th, 7th and 9th grades, the
researchers focused on detecting implicit stereotypes and attitudes toward mathematics vs.
German language. The results showed that children as young as 4th grade exhibit implicit mathgender stereotypes and have negative attitudes toward mathematics. An international research
examined students’ gendered differences in students’ beliefs about their own abilities in school
performance. The findings showed that boys and girls rated themselves realistically in
accordance with their performances. The most interesting data came from the girls who
outperformed boys in certain subjects, yet they still rated their performance equal to that of boys
and not higher. They also ascribed their high performances to luck or hard work rather than
talent (Steffens et al., 2010; Stetsenko et al., 2000).
Nature vs. nurture: causes of gender inequality in education.
Regardless of whether there is an actual quantitative gender gap in academic achievement
in mathematics or qualitative math attitude differences between boys and girls, there has to be a
reasonable explanation as to the cause or multiple causes of the gross underrepresentation of
women in STEM fields.
The social structure of gender dictates that men and women are different and oppositional
(Ridgeway & Correll, 2004; Risman, 2004; West & Zimmerman, 1987). Educational cultural
stereotypes and some research results adhere to this model with boys’ achievement being
associated with mathematics and girls’ with language (You, 2010). There are a number of
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theories and hypotheses as to what factors contribute to such differences ranging from biological
to social (Campbell et al., 2004; Ding et al., 2006; Kane & Mertz, 2012). This section will
outline a few of most prevalent hypothesis.
The first element of difference between a man and a woman has always been identified
with biological or physical markers. Differences in physique have been attributed to various
abilities or lack there of and have long settled into our social perception (e.g. “woman = weaker
sex”). When it comes to explaining why males supposedly tend to excel in mathematics over
women, researchers, mostly psychologists, proposed biological or cognitive differences as a
valid explanation. To generalize, biological hypotheses propose that men are better at
mathematics due to their genetic predisposition to various cognitive activities that assist in math
problem solving, and, by default, women lack or have lesser of those natural abilities. As an
example, Campbell, and colleagues (2004) cite a number of researchers claiming that the innate
predisposition to mathematic success lies in specific genes on the X and Y-chromosomes.
Zhixia You (2010) cites a number of researchers who propose that the differences were due to
biological differences in girls’ and boys’ brains as perceived by the observation that “girls are in
general better with language and writing, and boys are better at math due to better spatial
abilities” (p.115).
Geary, Saults, Liu, and Hoard (2000) propose that males hold an advantage in
arithmetical reasoning due to their innate special cognition ability and computational fluency.
They define arithmetical reasoning as the ability to solve complex world problems. Their
research findings concluded that there is a possible advantage that males have due to better
cognitive abilities.
James (2007) suggests that biological differences include hearing and vision differences
among a few others. Due to women’s high verbal skills they prefer to learn by hearing the
processes and explanations, where’s men prefer to read them. On the other side, men are
supposedly drawn to moving objects and different colors so the instructional strategies should
incorporate motion and displays. A research that encompassed a number of other countries
produced a wide variety of results in different countries rendering solely biological theory
invalid (Kane & Mertz, 2012). The hypotheses about biological factors benefiting mathematical
intelligence and achievement all have one underlining factor of unavoidability. If biological or
genetic factors affect our achievements that would mean that it could not be changed or altered.
Taking into the account that there have been changes in gender gap and mathematical
achievement between both genders over the past few decades, while no evolutionary changes to
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the human genetics have been reported, the biological hypotheses seem very unlikely (Ding et
al., 2006). If the biological theories were correct, the differences between male and female
students would be similar across all participating countries (Kane and Mertz, 2012).
On the other side of the spectrum are the socialization theories. The theories propose that
the differences between male and female achievement in mathematics is the different way the
two genders are socialized in our society (Cvencek, Meltzoff, & Greenwald, 2011; Ding et al.,
2006; Gool et al., 2006; Nosek et al., 2012; You 2010). The socializing element encompasses
the effect that society and cultural beliefs have on the child as well as ways that adult and peers
influence child’s decision and belief system. From birth, children are socialized differently; boys
and girls are taught to behave in a way that is socially acceptable for the appropriate gender.
“Boys are taught at a young age to be leaders, competitive, self-sufficient, independent and
willing to take risks” while “society asks our young girls to be affectionate, cheerful, loyal,
understanding, and sensitive to the needs of others” (Gool et al., 2006 p27). These are just a few
of cultural beliefs that define male and female natures within the complexities of gendered social
structure.
As children grow up and establish their gender identify they begin to function in the
social relational context, constructing their behaviors and, therefore, their gender in relation to
others, both male and female (Ridgeway and Correll, 2004). Society (media and social groups)
and individual adults in every child’s life affects their perception of what is acceptable in the
social gender arena. The way gender as social structure functions on the interactional level,
parents, teachers and peers play a key role in shaping a child’s beliefs about society and him or
herself (Risman, 2004). Eccles, Jacobs and Harold (1990) review a few longitudinal studies as
well as their own previous work to find supporting data that shows how parents’ support of
gendered bias and stereotypes influence their children’s perceptions of their own achievements.
“If parents hold gender-differentiated perceptions of, and expectations for, their children’s
competencies in various areas, then, through self-fulfilling prophecies, parents could play a
critical role in socializing gender differences in children’s self-perceptions, interests, and skill
acquisition” (Eccles, et al., 1990). Through exposure, games, and inexplicit remarks parents can
transmit to their child their high or low expectations and personal beliefs in their child’s
mathematic abilities.
In a more recent article, Gunderson et al. (2012) examine how teachers and parents affect
the development of children’s gender-related math attitudes. In their review, they “conceptualize
math attitudes as a cluster of beliefs and affective orientations related to mathematics, such as
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math anxiety, math-gender stereotypes, math self-concepts, and attributions and expectations for
success and failure in math” (Gunderson et al., 2012, p.152). They present various studies that
support their assessment of parents’ ability to explicitly and inexplicitly transfer their gender
math stereotypes and cognitive bias onto their children by showing more or less support and
encouragement, which then affects the child’s perception of their own math abilities. “Notably,
parents’ beliefs about their children’s math ability predict children’s self-perceptions in math
even more strongly than children’s own past math achievement; further, children’s selfperceptions about math affect their subsequent math achievement” (Gunderson et al., 2012,
p.155).
The review also examines how teachers’ gender bias and personal math anxiety can affect their
students’ self-perception of mathematics. The authors cite various studies that show teachers’
beliefs that boys are better and more interested in mathematics than equally competent girls.
They also outline how teachers can express their own math biases and anxieties through rushed
or biased instruction or unequal allocation of attention among different gender students
(Gunderson et al., 2012).
In an earlier study Beilock, Gunderson, Ramirez and Levine (2010) presented their
findings of how female teachers’ math anxiety affected girls but not boys in the classroom. The
study conducted involved several classrooms and female teachers with various levels of math
anxiety. Researchers examined the students’ math achievements in the beginning of the school
year and compared them to those at the end of school yet. The results showed a significant
relation between female teachers’ anxiety levels and girls’ math achievement at the end of the
year. This reflects their hypothesis that when it comes to gender, children are greatly affected by
adults of the same gender who serve as primary role models and shape girls’ attitudes toward
mathematics (Beilock et al., 2010).
To follow the socially imposed stereotypes, students can also underplay their
competencies to fit in with the gendered group. As a result, even though gifted children of both
genders would be encouraged to develop their abilities at earlier ages, “during early adolescence
and adulthood many gifted females learn to camouflage their talents in an effort to gain
acceptances by other females” (Campbell et al., 2004, para.10).
In between the polar opposite theories, biological and socialization lies a hypothesis
proposed by Robert Plomin that presents an even divide between nature and nurture elements
that effect children’s achievements in academics (Campbell et al., 2004). Plomin, a psychologist
and genetic behaviorist, conducted various studies with twins, which suggest that the cause of
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gender differences in academics stems from both genetic and diverse socialization (Pearce, 2003;
Spinath, Spinath, & Plomin, 2008). The studies showed that even similarly raised and cultured
twins have grown up with academic and motivational difference. The researchers thus
concluded that elements that account for those differences must be genetic inheritance of each
twin (Spinath et al., 2008).
Stereotype Threat Theory.
In addition to presumably being biological, sociological or both, gender inequities in
education have a strong psychological element. As people function with a binary gendered
society, they form their behavior based on situations they find themselves in as well as social
expectations (Ridgeway & Correll, 2004). Occasionally, these expectations are shaped by biased
stereotypes and have an effect on a person’s behavior and others’ perception or evaluation of
said person. According to research, stereotypes or hegemonic cultural beliefs play a strong role
in females’ lack of performance regardless of their true skills or abilities (Moe, 2012; Ridgeway
& Correll, 2004; Shapiro & Williams, 2012; Spencer, Steele, & Quinn, 1999; Tomasetto,
Alparone, & Cadinu, 2011).
Stereotype threat theory first emerged in 1995 through research that studied performances
of African Americans students on standardized tests compared to their white classmates. The
authors, Steele and Aronson, found that African American students who were made aware of the
test functioning as a comparison between students of different race, performed worse as opposed
to their classmates who were told it is a simple test to examine their mathematics skills.
(Schmader, Johns, & Forbes, 2008). Based on the developed theory “stereotype threat” refers to
the concern of being in a situation where one’s performance might lead to be judged based on
social stereotype. In other words, a person experiences heightened levels of pressure and anxiety
that his or her actions will confirm a negative stereotype as evaluated or judged by others
(Shapiro & Williams, 2012; Spencer et al., 1999).
Much like the aforementioned example, women suffer from the stereotype threat based
on the cultural beliefs that women are not good at math. “This experience begins with the fact
that most devaluing group stereotypes are widely known throughout a society (Spencer et al.,
1999). Several studies observed young children, adolescents and adult women confirming that
women underperform when introduced to stereotype threat situations (Moe, 2012; Shapiro &
Williams, 2012; Spencer et al., 1999; Tomasetto et al., 2011). When presented with a math skills
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test, male and female subjects showed very similar results until a group was introduced to
stereotype threat condition. Participants were informed that similar test produced gender
differentiated results and that men performed better than women or were proved to be genetically
better at math. Women who were made aware of such stereotype threat performed noticeably
worse on the same test, compared to women who’s gender was not made salient (Spencer et al.,
1999). While prior research was conducted with adult participants in the study with adolescents,
stereotype threat was introduced on a less direct level by making teenage students identify their
gender before or after the math test. Female students, who identified their gender before the test,
performed 33% worse than students who identified their gender after the exam (Shapiro &
Williams, 2012).
Tomasetto and colleagues (2011) took a step further and conducted a similar research
with younger students (5-7 years of age) and observed how parental gender stereotype attitude
affected children’s achievement in mathematics. In their results, these researchers observed
female students in the stereotype control group, for whom gender identity was made salient,
performed lower on math tasks. They further note that “performance disruption was found even
for girls in lower elementary grades who did not endorse – and probably did not even know – the
traditional gender stereotype about math, at least at an explicit level” (Tomasetto et al., 2011,
p.947). Therefore, their conclusion is that stereotypes are present in girls’ environment on the
implicit level and makes them vulnerable to stereotype threat. Further examination of parental
stereotype attitude concluded that mother’s (but not father’s) endorsement of the stereotype
threat increased the chances of the female student to be effected by the stereotype threat. The
authors finally conclude that the performances of those students whose mothers’ rejected mathgender stereotypes did not suffer as a result of a stereotype threat (Tomasetto et al., 2011).
Segregated or Together: Proposed Solutions for Gender Inequality in Education
Gender appears to be a simple binary system that everyone uses, but in reality it is a
complex system that affects people’s everyday life, their behavior, choices and performances
(Ridgeway & Correll, 2004). The gender system as a social structure creates inequalities in
various areas of life, such as jobs, education, private and social settings. It is so ingrained into
every individual as they function within the society that completely eliminating the gender-based
cultural belief system seems like an impossibility. However, though the past decades, the
changes to lessen gender inequality on the institutional and legal levels have been substantial
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(Ridgeway & Correll, 2004). Barbara Risman (2004) proposes that by institutionalizing children
differently a society can take first steps in the direction of recognizing sexism and figuring out a
way to battle it. When it comes to gender inequality in education, educators and researchers
alike have proposed various methods to provide students with equal education and opportunities
as well as lessen their gendered perception of mathematics. These solutions range from lowering
students’ anxiety and raising confidence to instructional and institutional changes.
As Risman (2004) has pointed out, the first step is recognizing the problem. Since
teachers are one of the stronger influences on children’s attitudes toward math and belief in their
achievement, educators need to become aware of any existing biased practices that occur in their
classrooms. Tracy and Lane (1999) propose and study the implementation of Gender-Equitable
Teaching Behaviors (GETB). By keeping a detailed record of her or his actions within the
classroom, such as praise, criticism, high level of questions, acceptance, wait time and physical
closeness, toward every student, a teacher can evaluate their behavior and spot any gender-biased
behavior. By recognizing an issue, a teacher will be able to adjust as needed to create a more
gender-balanced environment (Tracy & Lane, 1999).
While Tracy & Lane (1999) suggest a more equal treatment of students, Geist and King
(2008), as well as James (2007), propose a differentiated approach to teacher’s instruction. They
believe in cognitive differences between gendered and therefore offer solutions for boys and girls
who learn differently. James (2007) offers a number of strategies that will encompass the
differences between male and female students’ needs, such as auditory and visual learning. She
also implies that women gather information from body movement and facial expression of the
instructor, while men avoid looking or being looked at while they work. She further proposes,
and is corroborated by Geist and King (2008), that girls, who are highly verbal, will benefit from
hearing detailed explanations and comments on all steps and procedures. Boys, on the other
hand, are very visual and therefore will need colorful and moving imagery to accompany
instruction. When differentiating, the most difficult task is developing instruction that
encompasses a variety of learning levels, abilities and styles in the classrooms (Geist & King,
2008).
Taking a differentiation approach a step further, Herrelko, Jeffries and Robertson (2009)
present a study that illustrates a school’s attempt at improving mathematic achievement by
segregating its students into single gender classrooms. A failing school reformed its’ classrooms
to fully segregate its students by gender. The results of this change showed grade improvements
in younger grades but a decline in 6th grade. The authors’ subjects reported less disruptive
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behavior in classroom in earlier grades but higher level of misbehavior in 5th and 6th grades.
These results imply that single gender classrooms may benefit younger students who do not
specifically seek interactions with the opposite gender in a social setting while older students
who enter the social arena do not welcome segregation (Herrelko et al., 2009). The single-sex
classrooms approach is supported by Gurian, Stevens and Daniels (2009), who are strong
proponents of the theory of cognitive gender differences. They claim that such practices allow
instructress to create classrooms that are “more boy- and girl-friendly” (Gurian et al., 2009,
p.235). The article offers a number of success stories from schools in several central and
southwestern states, testimonials from teachers but none from students themselves.
As described earlier, not everyone shares this enthusiasm about single-sex education.
American Civil Liberties Union (ACLU) strongly opposes sex segregation; filing several law
suits against a number of states to investigate unlawful gender differentiation (“Sex-segregated
schools”, 2009). Based on ACLU complaints single-sex classrooms offer unequal education to
boys and girls. They claim that boys are being taught to be competitive while girls are treated in
passive, quiet environments. Boys and girls are explicitly taught gender stereotype “boys are
better than girls at math because boys’ bodies receive daily surges of testosterone, whereas girls
don’t understand mathematical theory very well except for a few days a month when their
estrogen is surging” (“Sex-segregated schools”, 2009, para. 2). The ACLU firmly believes that
single-sex education is unconstitutional, based on stereotypes, and does not prepare children for
coeducational real world (“Sex-segregated schools”, 2009).
The creation of single gender classrooms, while potentially beneficial, further reinforces
the implication of male – female differences. In the opposition to the above solution, several
researchers present benefits of co-educational approach. Cooperative learning, peer tutoring and
peer assisted learning strategies (PALS) have been researched in a variety of settings to help
students with disabilities, learners with behavioral problems, or just to enhance children’s
mathematical development (Kroeger & Kouche, 2006; Kuntz, McLaughlin, & Howard, 2001;
Tournaki & Criscitiello, 2003). While these studies do not specifically deal with gender
differences, they can be applied to gender-inequality situation and benefit students of both
genders in a variety of ways.
All of these strategies involve pairing a high achieving student with a low achieving
student, or a student with disabilities for a cooperative study sessions. During the session, the
students work together to learn and practice a new lesson based on the developed study guide.
The significance of these strategies lies in the detrimental co-teaching element. Instead of only
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having a high achieving student be the tutor and low achieving student being the learner, the
roles are frequently switched where each student acquires the responsibility of being the tutor
(Kuntz et al., 2001; Kroeger & Kouche, 2006; Tournaki & Criscitiello, 2003). There are several
benefits with this approach for both the teacher and the student. From the teacher’s side, “the
class-wide peer-tutoring approach permitted teachers to address a challenging mathematics
curriculum and simultaneously attend to a wide diversity of math skills in the classroom”
(Kroeger & Kouche, 2006, p.6). Kroeger & Kouche (2006) present evidence in their report that
students felt they were in a low-stress environment, were fully engaged during peer-assisted
learning sessions, and were urged to practice social skills. The ability for the low learners or
students with disabilities to be the tutor gives them confidence and support from their peers to
share their thoughts. “The role-reversal peer tutoring gives students with emotional and
behavioral disorders some responsibility – perhaps for the first time – and involves them in an
activity that makes them proud and thus eliminates the need for extrinsic motivators” (Tournaki
& Criscitello, 2003, p.23).
The success of these peer-assisted programs can be applied to gender differentiated
classrooms. If boys and girls do learn differently, they can both benefit from each other’s point
of view and support each other (Sparks, 2012). Instead of the competition, girls vs. boys, there
will be a cooperative learning atmosphere, where both genders can learn from each other and
gain knowledge equally. In addition, the co-educational activities have social benefits for young
children. Sarah Sparks (2012) explains that children of different genders tend to socialize
separately as they are being socialized into differentiating between genders. By working
together in mixed-gender groups or pairs, students learn social skills and tolerance. Sparks
points out, however, “classroom demographics and teacher practices can make a big differences
in how and whether students develop sex-based stereotypes and prejudices” (Sparks, 2012, p.15).
While the solution for gender inequality in the classrooms listed above present a good
stepping-stone in proving an equal education to both genders. To further address the issues of
why female students underperform in mathematics, Shapiro and Williams (2012) offer several
stereotype interventions. One of the interventions is a self-affirmation, which is a process of
creating positive thoughts about one’s importance or value that is different than the negative
threatening situation. Concentrating on a positive thought of value, unrelated to mathematical
tasks, elevates the stress of stereotype threat. Another intervention proposed in the article is
intended in reducing the differences between the two opposing sides. As an example, women
can examine how they are similar with men, instead of concentrating on differences, prior to the
15
test. This strategy has shown to improve women’s achievements significantly (Shapiro and
Williams, 2012).
Lastly, the importance of role models is indisputable. Gool et al. (propose that students
read about female scientists and mathematicians as well as other women that achieved success in
STEM fields. They also suggest that schools invite such influential women as guests to
encourage students to pursue these careers. “That is, seeing another individual who was similar
to themselves and who disconfirmed the stereotype about female math ability” may provide
female students confidence in themselves and serve as a buffer for stereotype threat (Shapiro &
Williams, 2012, p.178).
“Although inequality persists, recent decades have brought significant improvement in
women’s position in the public sphere, as reflected in indices such as the wage gap between men
and women and women’s representation in high-status occupations (Ridgeway & Correll, 2004,
p.527). Despite these strides, instructors and policy makers need to continue to ensure that girls
receive the same education as the boys. While policies are easier to put in place to enact change,
personal perceptions and decades-long cultural beliefs are the hardest to deconstruct. The
society needs to socialize and educate children about gender and inequality in order to raise a
new generation that can move in the direction of egalitarianism (Risman, 2004).
Statements of the Hypotheses
By establishing a dual-gender peer-assisted learning environment during regular math
instructions for 18 students (9 girls and 9 boys) in an urban setting in East New York and
Northern New Jersey, for a period of 4 weeks, 3 times a week, students’ attitudes toward
mathematics will improve.
Method
Participants
Participants of this study were a sample of convenience. The group is comprised of 18
close and distant acquaintances of the researcher, 9 male and 9 female students ranging in age
from 9 to 15 years old. All participants attend various public elementary, middle and high
16
schools in East New York and Northern New Jersey. All participating students are from lowermiddle and middle class families, 10 are of Eastern European descent, 2 African American and 1
of Hispanic background. In Group 1 (female) 7 out of 9 female are a “B” or higher students and
in Group 2 (male) 6 are a “B” or higher. Additionally, two of the participants are English
language learning (ELL) students and required some assistance with translation.
Instruments
The primary instrument to measure students’ attitudes toward mathematics is a survey
devised by the researcher. The survey was given to both groups as a pre- and post-test to gain an
initial understanding of their attitudes toward mathematics. The survey was intended to assess
each student’s personal opinion of how they view mathematics as a subject, their comfort during
class or home assignments, as well as their own math skill confidence. Because some direct
questions can elicit a less than honest answer, the questions are devised to elicit both explicit and
implicit answers.
Additionally, an excel formula for randomizing teams/pairs for the implementation of the
treatment was devised to avoid bias selection. This item was presented to the participants but not
put into use due to the hypothetical nature of the treatment.
Experimental Design
The research design implemented in this Action Research Project is a quasiexperimental, nonequivalent control group design. The sample of convenience consists of
two non-random groups of 9 male students and 9 female students. Both groups were pre-tested
in the form of a survey (O) before being exposed to a hypothetical treatment (X) and given a
post-test in a form of a repeat survey (O).
The symbolic design is:
O X1 O
O X2 O
The design and circumstances of this research project have revealed several possible and
evident threats to internal and external validity.
Threats to Internal Validity
The first primary threat to validity is differentiation selection of subject since the
participants are not chosen at random, are not from the same educational levels or cognitive
backgrounds. They are also not random enough to represent an accurate sample of general
17
population. In direct relation with aforementioned threat, statistical regression suggests that due
to a small sample number (n=18), the results may prove to be statistically insignificant especially
in comparing pretest and post-test results. Instrumentation is a significant threat to the validity
due to the fact that the treatment is not being implemented. Participants are being introduced to
the hypothetical treatment and basing their own opinion of possible changes on the information
provided. The results of the post-test cannot be claimed to be an effective change from the
treatment but only an opinion of each participant and their decision of possible change.
Other threats present are history where an occurrence of an event outside the scope of the
hypothetical treatment, such as inability to read the subjects, sicknesses and internet outages.
Maturation is a threat due to the longevity of the treatment and may cause participants to lose
interest. Similarly mortality is a threat because research is optional; therefore parents of
participants and students themselves can opt out and cease participating in the study.
Threats to External Validity
Four main threats to external validity have been identified in this study. Ecological
validity is a threat because treatment, even a hypothetical one, is intended to cause possible
changes to students’ attitudes toward mathematics, therefore the personal factor may prevent the
research to be replicated with the same result in another environment. Selection-treatment
interaction is one of the most prevalent threats to external validity. The small number of
selected participants may not be the representative of a greater population. Since the data from
pre- and post-test revolve around students’ personal opinions, it might be difficult to establish
and measure differences as a result of treatment, making specificity of variables a valid threat.
Lastly, experimenter effects is a valid threat because the hypothetical treatment will be described
and administered by the researcher, allowing researcher’s personal bias to affect students’ posttest results.
Procedure
The research began in February of 2013, with researcher identifying the gender disparity
issue within the attitude toward mathematics among students. After the review of the existing
literature the researcher concluded a possible intervention, which aims to minimize gender
differences and competition in the classroom. The researcher has made contact with 17 families
with children between 9 and 16 years of age. A total of 13 families agreed to have their children
18
participate in the study; one family had 3 children, 3 families had 2 children, and 9 families with
a single child within the age frame.
Prior to treatment all students were given the pre-survey consisting of questions designed
to measure their attitude toward mathematics as well as students’ own perception of their
mathematic ability. 15 surveys were returned by email while 3 participants submitted their
surveys in person.
Due to the varying age groups and diversified grade level of the participant sample as
well as the lack of one unified classroom, the implementation of the treatment as devised for this
study proved to be impossible. A hypothetical treatment was verbally and electronically
presented to the participants. The hypothetical treatment consisted of elaborating scenarios and
helping participants visualize their math classroom instruction, which would have been
redesigned to create boy-girl peer-assisted pairs to work together on classroom assignments. The
environment in the classroom would allow each student to work collaboratively with the
opposite gender classmate on all assignments. The hypothetical treatment was administered over
the period of 4 weeks in the forms of reminders, descriptions over the phone and in person.
After the hypothetical treatment was implemented, the students were given post-test over
the email. 16 students returned their post-test surveys over the email and 2 participants
submitted them on paper.
Results
After all the data was collected from the pre- and post-test the results were determined by
comparing female and male students’ attitudes toward mathematics before and after the
treatment. Based on the preliminary question (Figure 1) where students identified their favorite
subject, 55% of female students preferred literacy, 22% preferred math and the same amount,
22% preferred arts/music as their favorite subject. In group 2, 44% of male students preferred
math as their favorite subject, 33% enjoy literacy and 33% named science as their most enjoyed
subject. All participants gave the same answers to the favorite subject question on pre- and posttest (Figure 2 and 3).
19
Figure 1:
Favorite
subjects by
percentages.
Percentage of the group.
Favorite Subject
60.0%
50.0%
40.0%
Group 1
(Females)
30.0%
Group 2
(Males)
20.0%
10.0%
0.0%
Subjects
Female Students' Subject
Preferences
Figure 2:
Female
students’
subject
preference.
0%
22%
Literacy (1)
0%
56%
22%
Math (2)
Science (3)
Social Stu. (4)
Art/Music (5)
Male Students' Subject
Preferences
Figure 3:
Male
students’
subject
preference.
0%
33%
0%
22%
Literacy (1)
Math (2)
45%
Science (3)
Social Stu. (4)
Art/Music (5)
20
To measure the effectiveness of the hypothetical treatment and to analyze students’
attitude toward mathematic, questions 2, 3, and 9 of preferences section of the survey were
combined to calculate composite predictive variables. Students were asked whether they found
math interesting, whether they like solving math problems, and if they like math. The answers to
these questions were averaged for a single rating to analyze participants overall feelings toward
mathematics. The group results shown in figure 2 display a clear gender disparity in attitudes
toward mathematics. On pre-test, female attitude toward mathematics can be measured at 56%
while male students are at 67% on a positive rating scale. After the implementation of the
treatment both groups showed improvement of 14% and 9% for groups 1 and 2 respectively
(Figure 4).
Pre-Test Mean
Post-Test Mean
Change
Group 1 Mean
56%
70%
15%
Group 2 Mean
67%
76%
9%
Figure 4:
Average score of
questions 2, 3 & 9 by
group.
Positive Attitude Toward
Math
Math Attitudes - by group
80%
Group 1
(Females)
Group 2
(Males)
60%
40%
20%
0%
Pre-Test Mean Post-Test Mean
Average of Questions 2, 3 & 9
Using the same composite predictive variable mentioned, researcher examined each
group separately. Results for individual students from Group 1 show 6 out of 9 students rating
their attitudes toward math below or at 2.00 on a Likert scale. After the treatment, most
students’ attitudes have improved with all but one rating above 2.50 or above out of 4.00 (Figure
5). When analyzing Group 2 it becomes notable that majority of male students have initially
more positive attitude toward math, 6 out of 9 rate at 3.00 and higher. After the implemented
hypothetical treatment only 3 participants still rate mathematics at 2.67 or below (Figure 6).
21
Figure 5:
Group 1 – Individual attitudes toward mathematics, pre and post test results.
2.67
Student 4
3.00
3.33
Student 5
2.00
2.67
Student 6
2.67
3.00
Student 7
2.00
2.00
Student 8
3.67
3.67
Student 9
2.00
2.67
Figure 6:
2.00
Pre-Test Mean
1.00
Post-Test Mean
-
Student 9
2.00
Student 8
Student 3
3.00
Student 7
2.67
Student 6
1.00
Student 5
Student 2
Student 4
2.67
Student 3
1.67
(Based on Qestions 2, 3 and 9)
4.00
Student 2
Post-Test
Student 1
Pre-Test
Math Attitudes (Likert Scale)
Group 1: Attitude toward Math
Female
Students
Student 1
Female Students
Group 2 – Individual attitudes toward mathematics, pre and post test results.
Group 2: Attitudes Toward Math
Male
Students
Student 1
Pre-Test
3.00
3.33
4.00
Student 2
3.00
3.00
Student 3
3.00
3.33
3.67
Student 4
3.00
3.33
Student 5
3.00
3.00
Student 6
2.00
2.67
Student 7
1.67
2.33
Student 8
3.33
4.00
Student 9
1.67
2.00
(Based on Questions 2, 3 and 9)
Likert Scale
Post-Test
Pre-Test
2.00
1.00
Post-Test
-
Male Students
In addition to attitudes toward mathematics, this study aims to analyze students’ own
confidence in math skills. For this analysis a composite predictive variable was calculated using
questions 6 and 8, which ask the students how confident they are doing math homework by
themselves and their own assessment of their math skills. Figure 7 displays the difference
between the perception of self-confidence in mathematics between male and female students.
Female students define their confidence level on average at 53% while male students average at
64% confidence level. After the intervention both groups showed increase with Group 2 having
an 11% increase, just slightly higher than Group 1 (with a 10% increase).
22
Figure 7:
Average score
of questions 6
& 8.
Math confidence level
Math Confidence by Group
80%
60%
40%
Group 1
20%
Group 2
0%
Pre-Test
Post-Test
Average of Questions 6 & 8
Pre-Test
Group 1
Group 2
Post-Test
Change
63% 10%
75% 11%
53%
64%
In order to evaluate the effectiveness of the hypothetical treatment, the students were
asked to rate their preferences off working in groups/pairs or alone during math instructions and
exercises. Examining a correlation between math attitude and students’ preference for
cooperative work revealed a fair positive correlation (.324rxy) the more students prefer to work
in pairs/groups with their peers on math assignments, the better their attitudes toward
mathematics seems to be (Figure 8).
Post-Test Preferences: Working in Pairs
and Attitudes toward Math
Figure 8:
Attitude toward
Mathematics
Fair positive
correlation
between
math
attitude and
preference of
working in a
pairs or
groups
5.00
4.00
Post-Test
Preferences:
Working in
Pairs and
Attitudes
toward Math
3.00
2.00
1.00
-
0
2
4
6
Preferences of working on math
problems in pairs/groups
Correlation Coefficient = 0.326rxy
23
Since students’ attitudes and confidences are often affected by the factors outside of the
classrooms, the survey asked the students to rate how often they have family members helping
them with math homework (always, most of the time, less than half of the time, never). The
results were compared to students’ perception of own confidence while working on math
problems independently. The correlation calculations resulted in a fair negative correlation
(-.35rxy) displaying a clear relationship. The less time parents spend time helping children with
their homework, the more confident the child feels working independently (Figure 9).
Fair negative
correlation
between
parental
assistance
frequency
and math
confidence
Preferences: Student's confidence in
independent math work.
Figure 9:
Post-Test: Parental Assitance with Math and
Math Independance Confidence
4.5
Post-Test: Parental
Assitance with
Math and Math
Independance
Confidence
4
3.5
3
2.5
2
Linear (Post-Test:
Parental Assitance
with Math and
Math
Independance
Confidence)
1.5
1
0.5
0
0
2
4
Frequency: Parents helping with math homework
Correlation Coefficient = -0.35rxy
The Figure 8 depicts an examination of the study as it applies to the general population.
The bell curve represented is negatively skewed due to majority of scores on falling within and
beyond +1 and +2 standard deviation and no lower scores beyond -1SD. This study cannot be
applied to the general population primarily due to the sample of convenience and opinion based
scores.
24
Figure 10:
Negatively
skewed bell
curve. It
does not fit
general
population.
Discussion
The intent of this study was to examine possible gendered difference in attitudes and
confidences toward mathematics among school aged students. As expected and confirmed by
prior research (Nosek et al., 2002, Steffens et al., 2010) the initial data revealed that mathematics
is more often chosen by male students as their favorite subject while literacy is preferential to the
female students. Science is a close second choice for boys and arts/music is a second popular
choice for girls. These results fall in line with socially constructed stereotypes that boys are
expected to enjoy mathematics and science, while girls “must” enjoy reading and drawing due to
their different nature or abilities (Gunderson et al., 2012; Ridgeway & Correll, 2004).
Even though some research (Ding et al., 2006; Gunderson et al., 2012; Nosek et al.,
2002) may have concluded that there is little difference in math test scores between the two
genders, the results of this study show that personal attitudes toward mathematics are not equal
among male and female students. Data analysis showed that female students have more negative
attitudes toward mathematics and are not as confident in their own abilities as male students.
The treatment, although hypothetical, has shown to be effective in encouraging an
improvement in student’s opinions about mathematics. The idea of working with peers, being
25
able to discuss and help each other with mathematics problems, seems to cause students to
perceive the subject in better light and increase their own confidence.
The positive effect of the hypothetical data can be explained by calculated correlation
that shows students who prefer to work in groups or pairs have better attitudes toward
mathematics. While cooperative work in mathematics is not often encouraged, working with a
classmate of the opposite gender seems to inspire students and give them courage to ask
questions and work toward understanding of the material without the fear of competition or
embarrassment. Curiously enough, even though the correlation shows positive relationship
between peer-assisted learning and attitudes, frequent assistance from family members correlates
negatively with students’ confidence in one’s abilities to work independently.
It is worthy of note that even though both groups show improvement in math attitudes,
the imbalance of female scores being lower than those of male scores remains. This study,
however, is not claiming to achieve complete gender equality within the realm of personal
attitudes toward mathematics but move in that direction by improving female students’ attitudes
and compare them to those of the male students.
The data of the research shows this was
accomplished and the gender gap in attitudes closed by 6% within the 18 participants
(see figure 2).
Implications
Throughout the abundant research on gender disparity in mathematics, there is little
application of peer-assisted dual-gender groups or pairs to eliminate gender bias and competition
in the classroom. This teaching strategy is used for students with behavioral problems or special
needs. This researcher’s implication is that the strategy can not only be beneficial to all students
in reducing gender bias and gender competition but also teach children the skills needed for
collaborative environment of the job market in the future.
While there is no quick fix to bridging the gender gap that exists in mathematics
education, this study indicates that homogeneous peer-learning environment is conducive to
improving attitudes towards the subject in both male and female students and taking steps to
achieve parity. However, since this study was not conducted in one unified classroom, due to the
diversified age and location of the sample, the intervention was only hypothetical and further
research is highly recommended to implement an actual treatment with students of the same age.
Additional research is also necessary to include a diversified larger sample that would be a
representative of a larger population.
26
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30
Appendix A: Parental Consent Form
Dear Parent/Guardian,
I am currently a student at Brooklyn College in the process of completing a Childhood Education
Masters Program. As part of our curriculum, I am conducting an action research to determine
possible beneficial effects of peer-assisted and co-educational learning instructional strategies on
achievement in and attitude toward mathematics among boys and girls. Therefore, I am
requesting your permission to have your child participate in the implementation of
aforementioned instructional strategies and to use your child’s data that is relative to the
research.
All instruction will be administered during your child’s regular classroom time, following the
scheduled curriculum objectives. Students will be given a mathematics test and a survey before
and after implementation of the new instructional strategies. Data collected from these sources
will be reported as group findings; therefore, all participants’ names and other information will
remain anonymous. Additionally, at the end of the research I will gladly provide final results
upon request.
If you have any additional questions or concerns please feel free to contact me by email
vedmochka81@gmail.com
Thank you in advance for your support!
Sincerely,
Tatyana Sumner
I agree to my child, ________________________________________, participating in
(student’s name)
the action research described above.
I do NOT agree to my child, _________________________________, participating in
(student’s name)
the action research descried above.
Signature of Parent/Guardian ______________________________________ Date _________
31
Appendix B: Principal’s Consent Form
Dear Principal,
I am currently a student at Brooklyn College in the process of completing a Childhood Education
Masters Program. As part of our curriculum, I am conducting an action research to determine
possible beneficial effects of peer-assisted and co-educational learning instructional strategies on
achievement in and attitude toward mathematics among boys and girls. Therefore, I am
requesting your permission to use one fourth-grade classroom in your school to implement the
aforementioned instructional for the duration of the research.
All instruction will be administered during regular classroom time, following the scheduled
curriculum objectives. Modified instruction will take place 3 times a week for the period of 6
weeks. Students will be given a mathematics test and a survey before and after implementation
of the new instructional strategies. Data collected from these sources will be reported as group
findings; therefore, all participants’ names and other information will remain anonymous.
Additionally, at the end of the research I will gladly provide final results upon request.
If you have any additional questions or concerns please feel free to contact me by email
vedmochka81@gmail.com
Thank you in advance for your support!
Sincerely,
Tatyana Sumner
I, _______________________________________________, give permission to Tatyana
Sumner to use one (1) fourth-grade classroom for the action research as described above.
Signature of Principal ________________________________________ Date ______________
32
Appendix C: Teacher’s Consent form
Dear Teacher,
I am currently a student at Brooklyn College in the process of completing a Childhood Education
Masters Program. As part of our curriculum, I am conducting an action research to determine
possible beneficial effects of peer-assisted and co-educational learning instructional strategies on
achievement in and attitude toward mathematics among boys and girls. Therefore, I am
requesting your participation and cooperation to implement the aforementioned instructional
strategies in your classroom for the duration of the research.
All instruction will be administered during regular classroom time, following the scheduled
curriculum objectives. Modified instruction will take place 3 times a week for the period of 7
weeks. Students will be given a mathematics test and a survey before and after implementation
of the new instructional strategies. Data collected from these sources will be reported as group
findings; therefore, all participants’ names and other information will remain anonymous.
Additionally, at the end of the research I will gladly provide final results upon request.
If you have any additional questions or concerns please feel free to contact me by email
vedmochka81@gmail.com
Thank you in advance for your support!
Sincerely,
Tatyana Sumner
I, _______________________________________________, agree to participate in the action
research as described above.
Signature of Teacher _________________________________________ Date ______________
33
Appendix D: Initial and End-of-Study Questionnaire
Student Survey
Attitude Towards Math
INTRODUCTION: The aim of this survey is to find out how students feel about math. Please give us
your honest opinions.
Part I – Demographics
We want to learn more about you so please answer all of the questions below the best that
you can. (Please write the appropriate number answer for questions 3, 4 and 8)
Answers
1. Your Full Name
1. _____________________
2. How old are you?
2. _____________________
3 What is your gender? (Please choose one answer.)
3. _____________________
[ 1 ] Male
[ 2 ] Female
4. Which ethnicity best describes you? (Please choose one answer.)
4. ____________________
[ 1 ] American Indian or Alaskan Native
[ 2 ] Asian / Pacific Islander
[ 3 ] Black / African American
[ 4 ] Hispanic American
[ 5 ] White / Caucasian
[ 6 [ Other
6. What city and state do you live in?
6. ____________________
7 What grade are you in?
7.____________________
8. What is your favorite subject? (Please choose one answer.)
8. ___________________
[ 1 ] Literacy
[ 2 ] Math
[ 3 ] Science
[ 4 ] Social Studies
[ 5 ] Art/Music
34
Part II – SARS – Preferences
Attitude Towards Math (continued)
Think about each statement about yourself and tell us how you feel. (Write the number of your
answer in the space provided. (Please write appropriate answer number in the Answer space.)
1. I feel excited when math class begins.
Strongly Disagree
[1]
Disagree
[2]
Agree
[3]
Strongly Agree
[4]
Answer
Disagree
[2]
Agree
[3]
Strongly Agree
[4]
Answer
Agree
[3]
Strongly Agree
[4]
Answer
______________
2. I find math interesting.
Strongly Disagree
[1]
______________
3. I like solving math problems.
Strongly Disagree
[1]
Disagree
[2]
______________
4. I like to work on math problems by myself during class assignments.
Strongly Disagree
[1]
Disagree
[2]
Agree
[3]
Strongly Agree
[4]
Answer
______________
5. I prefer to work on math problems with a partner/group during class assignments.
Strongly Disagree
[1]
Disagree
[2]
Agree
[3]
Strongly Agree
[4]
Answer
Strongly Agree
[4]
Answer
______________
6. I am confident when I do math homework by myself.
Strongly Disagree
[1]
Disagree
[2]
Agree
[3]
______________
7. I feel comfortable asking questions about math concepts that I don’t understand.
Strongly Disagree
[1]
Disagree
[2]
Agree
[3]
Strongly Agree
[4]
Answer
Disagree
[2]
Agree
[3]
Strongly Agree
[4]
Answer
Disagree
[2]
Agree
[3]
Strongly Agree
[4]
Answer
______________
8. I am good at math.
Strongly Disagree
[1]
______________
9. I like math.
Strongly Disagree
[1]
______________
35
Attitude Towards Math (continued)
Part II – SARS – Frequencies
Tell us how often do you preform certain tasks (Write the number of your answer in the
space provided.
10. How often do you raise your hand and participate in math class?
Never
[1]
Less than half the time
[2]
Most of the time
[3]
Always
[4]
Answer
Never
[1]
Always
[4]
Answer
Never
[1]
Always
[4]
Answer
______________
11. How often do you ask questions in math class?
Less than half the
Most of the time
time
[3]
[2]
12. How often do you do math homework at home by yourself?
Less than half the
Most of the time
time
[3]
[2]
13. How often does your family help you with math homework?
______________
Never
[1]
Less than half the
Most of the time
Always
Answer
time
[3]
[4]
[2]
14. How often do you show your work, step by step, when working on math problems?
Never
[1]
Less than half the
time
[2]
15. How often do you read books?
More than half the
time
[3]
Less than once a
Once a week
Almost every day
week
[2]
[3]
[1]
16. How often do you work on extra math problems for practice?
Less than once a
week
[1]
Once a week
[2]
Almost every day
[3]
Always
[4]
Answer
Every day
[4]
Answer
Every day
[4]
Answer
______________
______________
______________
______________
______________
36
Appendix E: Excel Team Randomizer
37
Appendix F: Results Tables
Part 1 - Demographics
#
F1
F2
F3
F4
F5
F6
F7
F8
F9
M1
M2
M3
M4
M5
M6
M7
M8
M9
Gender
Race
City, State
Favorite
Subject
Students
Age
Grade
Alain
Jennifer
Rose
Eve
Karen
Alice
Inna
Natalia
Tasha
Derrek
John
David
Dan
Sam
Kyle
Alec
Benjamin
Isaac
12
14
9
2
2
2
5 Fair Lawn, NJ
5 Wayne, NJ
5 Brooklyn, NY
6
7
4
5
1
1
10
11
11
15
11
9
9
15
10
15
14
9
12
10
14
2
2
2
2
2
2
1
1
1
1
1
1
1
1
1
5
4
5
5
5
3
3
5
5
5
5
3
5
5
5
4
5
5
9
5
4
4
9
5
9
9
4
6
4
8
2
1
1
1
2
5
2
3
2
2
2
3
1
2
1
Brooklyn, NY
Fair Lawn, NJ
Brooklyn, NY
Wayne, NJ
Brooklyn, NY
Brooklyn, NY
Brooklyn, NY
Wayne, NJ
Brooklyn, NY
Fair Lawn, NJ
Fair Lawn, NJ
Brooklyn, NY
Brooklyn, NY
Brooklyn, NY
Brooklyn, NY
Part 2 – SARS – Preferences - Group 1 (Females)
Female
Students
Q1
Pre
Q1
Post
Q2
Pre
Q2
Post
Q3
Pre
Q3
Post
Q4
Pre
Q4
Post
Q5
Pre
Q5
Post
Q6
Pre
Q6
Post
Q7
Pre
Q7
Post
Q8
Pre
Q8
Post
Q9
Pre
Q9
Post
F1
1
2
2
3
2
3
1
1
2
2
1
2
1
2
1
2
1
2
F2
1
3
1
2
1
3
2
2
2
2
1
1
3
3
1
3
1
3
F3
2
2
2
2
2
3
2
2
2
4
2
2
2
3
2
2
2
3
F4
3
3
3
3
3
4
3
2
2
3
3
3
3
3
3
3
3
3
F5
2
3
2
3
2
2
2
2
3
3
2
3
1
2
2
3
2
3
F6
2
2
3
3
2
3
2
2
2
3
3
3
2
3
2
3
3
3
F7
2
3
2
2
2
2
2
2
3
3
2
2
3
3
2
2
2
2
F8
3
4
4
4
3
3
3
3
2
2
3
3
3
3
4
4
4
4
F9
2
3
2
3
2
3
1
2
3
2
2
2
1
1
2
2
2
2
38
Part 2 – SARS – Preferences - Group 2 (Males
Male
Students
Q1
Pre
Q1
Post
Q2
Pre
Q2
Post
Q3
Pre
Q3
Post
Q4
Pre
Q4
Post
Q5
Pre
Q5
Post
Q6
Pre
Q6
Post
Q7
Pre
Q7
Post
Q8
Pre
Q8
Post
Q9
Pre
Q9
Post
M1
3
3
3
3
3
4
3
3
2
2
2
2
3
3
3
3
3
3
M2
2
3
3
3
3
3
3
3
2
3
3
3
1
3
3
3
3
3
M3
3
3
3
3
4
4
4
4
2
3
3
3
2
3
3
4
3
4
M4
2
3
3
4
3
3
2
2
3
3
3
3
3
3
3
3
3
3
M5
4
4
3
3
3
3
3
2
2
2
4
4
3
4
3
4
3
3
M6
2
3
2
3
2
3
2
2
2
2
2
3
2
2
2
2
2
2
M7
2
2
2
2
2
2
2
2
2
2
2
3
1
2
1
2
1
3
M8
4
4
3
4
3
4
3
3
3
3
3
3
1
3
4
4
4
4
M9
1
2
2
2
2
2
1
1
3
2
1
3
3
3
1
2
1
2
Part 2 – SARS – Frequencies - Group 1 (Females)
Male
Students
Q10
Pre
Q10
Post
Q11
Pre
Q11
Post
Q12
Pre
Q12
Post
Q13
Pre
Q13
Post
Q14
Pre
Q14
Post
Q15
Pre
Q15
Post
Q16
Pre
Q16
Post
F1
2
3
2
2
3
3
2
2
2
2
3
3
1
1
F2
2
3
3
3
3
2
2
2
2
2
3
3
2
2
F3
2
3
2
3
2
3
3
3
3
3
3
3
2
3
F4
3
3
2
3
4
4
1
1
3
3
2
2
2
2
F5
2
3
1
2
4
4
2
2
2
3
4
4
2
2
F6
2
2
2
2
2
2
3
3
2
2
4
4
1
1
F7
1
1
3
3
3
3
2
2
2
3
3
3
1
1
F8
4
4
2
3
3
3
2
2
3
3
3
3
1
1
F9
3
3
3
3
2
2
3
3
2
2
2
2
1
1
Part 2 – SARS – Frequencies - Group 2 (Males)
Male
Students
Q10
Pre
Q10
Post
Q11
Pre
Q11
Post
Q12
Pre
Q12
Post
Q13
Pre
Q13
Post
Q14
Pre
Q14
Post
Q15
Pre
Q15
Post
Q16
Pre
Q16
Post
M1
3
3
3
3
3
4
3
3
2
2
2
2
3
3
M2
2
3
3
3
3
3
3
3
2
3
3
3
1
3
M3
3
3
3
3
4
4
4
4
2
3
3
3
2
3
M4
2
3
3
4
3
3
2
2
3
3
3
3
3
3
M5
4
4
3
3
3
3
3
2
2
2
4
4
3
4
M6
2
3
2
3
2
3
2
2
2
2
2
3
2
2
M7
2
2
2
2
2
2
2
2
2
2
2
3
1
2
M8
4
4
3
4
3
4
3
3
3
3
3
3
1
3
M9
1
2
2
2
2
2
1
1
3
2
1
3
3
3
39
Appendix G: Results Charts
Figure 1:
Favorite
subjects by
percentages.
Percentage of the group.
Favorite Subject
60.0%
50.0%
40.0%
Group 1
(Females)
30.0%
Group 2
(Males)
20.0%
10.0%
0.0%
Literacy Math (2) Science
(1)
(3)
Social
Stu. (4)
Subjects
Female Students' Subject
Preferences
Figure 2:
Female
students’
subject
preference.
0%
22%
Literacy (1)
0%
56%
22%
Math (2)
Science (3)
Social Stu. (4)
Art/Music (5)
Male Students' Subject
Preferences
Figure 3:
Male
students’
subject
preference.
0%
33%
0%
22%
Literacy (1)
Math (2)
45%
Science (3)
Social Stu. (4)
Art/Music (5)
40
Pre-Test Mean
Change
Group 1 Mean
56%
70%
15%
Group 2 Mean
67%
76%
9%
Math Attitudes - by group
Positive Attitude Toward
Math
Figure 4:
Average score of
questions 2, 3 & 9 by
group.
Figure 5:
Post-Test Mean
80%
Group 1
(Females)
Group 2
(Males)
60%
40%
20%
0%
Pre-Test Mean Post-Test Mean
Average of Questions 2, 3 & 9
Group 1 – Individual attitudes toward mathematics, pre and post test results.
2.67
Student 4
3.00
3.33
Student 5
2.00
2.67
Student 6
2.67
3.00
Student 7
2.00
2.00
Student 8
3.67
3.67
Student 9
2.00
2.67
Figure 6:
2.00
Pre-Test Mean
1.00
Post-Test Mean
-
Student 9
2.00
Student 8
Student 3
3.00
Student 7
2.67
Student 6
1.00
Student 5
Student 2
Student 4
2.67
Student 3
1.67
(Based on Qestions 2, 3 and 9)
4.00
Student 2
Post-Test
Student 1
Pre-Test
Math Attitudes (Likert Scale)
Group 1: Attitude toward Math
Female
Students
Student 1
Female Students
Group 2 – Individual attitudes toward mathematics, pre and post test results.
Group 2: Attitudes Toward Math
Male
Students
Student 1
Pre-Test
3.00
3.33
4.00
Student 2
3.00
3.00
Student 3
3.00
3.33
3.67
Student 4
3.00
3.33
Student 5
3.00
3.00
Student 6
2.00
2.67
Student 7
1.67
2.33
Student 8
3.33
4.00
Student 9
1.67
2.00
(Based on Questions 2, 3 and 9)
Likert Scale
Post-Test
Pre-Test
2.00
1.00
Post-Test
-
Male Students
41
Average score
of questions 6
& 8.
80%
60%
40%
Group 1
20%
Group 2
0%
Pre-Test
Post-Test
Average of Questions 6 & 8
Pre-Test
Group 1
Group 2
Post-Test
Change
63% 10%
75% 11%
53%
64%
Post-Test Preferences: Working in Pairs
and Attitudes toward Math
5.00
Attitude toward
Mathematics
Figure 7:
Math confidence level
Math Confidence by Group
4.00
Post-Test
Preferences:
Working in
Pairs and
Attitudes
toward Math
3.00
2.00
1.00
-
0
2
4
6
Preferences of working on math
problems in pairs/groups
Correlation Coefficient = 0.326rxy
42
Figure 9:
Fair negative
correlation
between
parental
assistance
frequency
and math
confidence
Preferences: Student's confidence in
independent math work.
Post-Test: Parental Assitance with Math and
Math Independance Confidence
4.5
Post-Test: Parental
Assitance with
Math and Math
Independance
Confidence
4
3.5
3
2.5
2
Linear (Post-Test:
Parental Assitance
with Math and
Math
Independance
Confidence)
1.5
1
0.5
0
0
2
4
Frequency: Parents helping with math homework
Correlation Coefficient = -0.35rxy
Figure 10:
Negatively
skewed bell
curve. It
does not fit
general
population.
43