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CHAPTER I
INTRODUCTION
Overview
Why do students and adults alike seem to dislike mathematics?
Some will roll their eyes or let out a sigh. They give many reasons,
such as “It's too hard,” “I'm not good at math,” or “why do I even need
math?” Where does this attitude come from? The National Council of
Teachers of Mathematics (1991) made this statement: “One of the
curious aspects of our society is that it is socially acceptable to take
pride in not being good in mathematics.” Is it something that can be
changed? First we need to know why.
This study will explore the reasons why students dislike
mathematics. There are many possible reasons for these attitudes.
There have been several studies on math anxiety (Ho, H., Senturk, D.,
Lam, A., Zimmer, J., Hong, S., Okamoto, S., Nakazawa, Y., Wang, C.,
2000; Ma 1999; Cates & Rymer, 2003) that have attempted to describe
the effect math anxiety has on math achievement. It may have
something to do with the classroom experience (Schefele &
Csikszentmihalyi, 1995). Some students may believe erroneously that
they are not “math people” (Anderson, 2007). Maybe the student fell
behind and was unable to catch up because of the sequential nature of
mathematics. It’s also possible that has to do with the difficulty of a
particular grade level (Cates & Rymer, 2003). Maybe the students
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don’t understand why they will need mathematics and don’t see the
real world connections. This study will focus why some students have
such a negative attitude about mathematics.
Problem of the Study
Having a negative attitude toward mathematics may be related to
achievement of math students. If students don't like math, they may
be able to struggle through the classes and make good grades, but the
long-term effect will probably be that they will not pursue the subject
any more than they have to. They will certainly not pursue a career in
a math related field. If the reasons for this dislike can be determined,
then teachers can take steps to change the student’s attitude.
Purpose of the Study
This study will seek to determine what student attitudes are
about mathematics and in particular, if they dislike math, what are the
reasons.
Significance of the Study
Educators and government officials have been seeking to find ways
to increase student achievement in mathematics and science. With
many students having a dislike of mathematics, it will be difficult to
have meaningful change in achievement. If a particular reason can be
found for this dislike, then it may be possible to intervene in a timely
manner.
Definition of Terminology
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Dislike of math in this study is defined as a negative attitude
toward math causing a desire to avoid mathematics classes.
Limitations
The survey used for this study was created by the researcher, and
was not validated. The students will take the survey during their math
class and may feel some pressure to respond positively. This may be
from their parents, the teacher or from their peers. Since the sample is
eighth grade students their maturity level may prevent them from
taking the survey seriously, or they may find it boring. In an attempt to
minimize this, the researcher will be present and administer the survey
and attempt to explain its importance and emphasize its
confidentiality.
Summary
This study consists of five chapters. Chapter I introduces the study
and defines the problem to be investigated. It also includes the
purpose, significance, limitations, and defines terms used in the study.
Chapter II will review related research. Chapter III describes the
methodology and procedures, which includes the population, sample,
data collection instruments, procedures, and research questions and
related hypothesis. Chapter IV will analyze and discuss the data
collected and chapter V will summarize the findings and give any
conclusions, recommendations, or implications of the study.
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CHAPTER II
LITERATURE REVIEW
Introduction
“I’m not good at math”, “I hate math” or “math is too hard” are
common phrases heard by teachers and parents. “One of the curious
aspects of our society is that it is socially acceptable to take pride in
not being good in mathematics” (National Council of Teachers of
Mathematics [NCTM], 1991, ¶16). Where do these attitudes and beliefs
come from? Can they be changed? Through reviewing literature, three
main ideas surfaced as possible reasons students dislike math: math
anxiety, lack of motivation in mathematics, and a negative attitude
toward mathematics.
Math Anxiety
Math anxiety is a condition in which students experience negative
reactions to mathematical concepts and evaluation methods (Cates &
Rhymer, 2003). Math anxiety can lead to several consequences. For
example, Suinn and Richardson (1972) found that mathematics anxiety
may prevent students from pursuing higher-level math courses and
HO, Senturk, Lam, Zimmer, Hong, Okamoto, Chui, Nakazawa, & Wang
(2000) stated, “math anxiety has been found to have a negative
relationship with mathematics performance and achievement” (p.362).
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Anxious individuals may avoid mathematics classes, may be more
likely to have negative attitudes toward mathematic related activities,
or if they become elementary teachers, may not spend as much time
teaching mathematics as their less anxious colleagues (Ho et al.,
2000). Several studies have proposed that math anxiety has two
dimensions: affective (nervousness, tension, dread, fear) and cognitive
(worry) (Meece, Wigfield, & Eccles, 1990; Wigfield & Meece, 1988; Ho
et al., 2000).
Ho et al. conducted a study across three nations consisting of
671 sixth grade students from China (211, 92 girls and 119 boys),
Taiwan (214, 106 girls and 108 boys), and the United States (246, 111
girls and 135 boys). The focus in this study was to address the
differential predictions of the affective and cognitive factors of math
anxiety for mathematics achievement. For the anxiety measure the
MAQ (Math Anxiety Questionnaire) was used. It contained 11 items
using a Likert scale and contained items in the cognitive and affective
dimensions. For the math achievement dimension, two similar tests
were given 4 to 6 weeks apart with reliability coefficient of .82. One
third of the items were from textbooks, one-third from another cross-
national study, and the other third developed by the researchers. The
relationship between the affective math anxiety factor and
achievement showed a strong negative effect (p<.05). Cognitive
anxiety was inconsistent across the samples. China and U.S. samples
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were not significant, whereas, Taiwan had significant and positive
effects (p<.05) from cognitive anxiety. Analysis of the gender
interaction showed only Taiwan had significant effect with girls having
higher affective anxiety (p<.05). Taiwanese and U.S. girls had higher
cognitive anxiety (p<.05) than Taiwanese and U.S. boys. Gender
differences in China were not significant. In mathematics achievement
only the main effect for nation was significant (p<.05). Gender and
interaction of gender by nation were not significant. The results
suggest that the affective factors of math anxiety are consistently
related to mathematics achievement, while the cognitive factors yield
inconsistent results. Ho et al. (2000) conclusion is that the affective
dimension of math anxiety correlates more strongly with negative
performance than does the cognitive dimension.
Meece, Wigfield, & Eccles (1990) conducted a 2-year long
longitudinal study that focuses on the influence of math anxiety on
students' course enrollment plans and performance in math. The study
had two goals; to identify important predictors of math anxiety and
assess the predictive influence math anxiety has on enrollment plans.
The sample included 250 students in 7th through 9th grade at
predominantly white middle-class suburban communities. The 7th and
8th grade students were enrolled in classes of approximately equal
difficulty. Ninth grade students were enrolled in regular algebra or
advanced algebra. Seven students were enrolled in a slow-paced
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algebra class. Questionnaires were administered in the spring of year
one and two. The Student Attitude Survey (SAQ) was used which
contains items to assess students expectancies for success, perceived
values, perceived ability, perceived effort, perceived task difficulty in
both math and English, and several other items. Most items were
assessed using two or more 7 point Likert scale items. Predictor
variables were divided into three factors. The perceived math ability
measure consists of three items tapping students' sense of their math
ability and how well they were doing in math. The expectancies
measure consists of two items asking students how well they expected
to do in their current math class. The importance measure consists of
items asking students to rate how important it is for them to do well at
math and to get good grades. The SAQ also includes an item asking
students to indicate whether they would take more math classes in the
future if they were not required. A measure of math anxiety was
included in the second year of the study. It contained 11 items to
assess cognitive (concern about doing well in math) and negative
affective dimensions of math anxiety. Math achievement information
was collected on each student for both years from school records. The
final grade for each year was used. The study suggests those students'
current performance expectancies in mathematics (highly significant at
p<.01) and to a lesser extent perceived importance of mathematics
have the strongest direct effect on their anxiety and are stronger
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predictors of performance and course enrollment than math anxiety.
Their findings also support the idea that it is the students’
interpretations of their achievement outcomes and not the outcomes
themselves that have the strongest effects on students' affective
reactions to achievement.
Other studies have focused on the effect anxiety has on
achievement. In one such study, Ma (1999) conducted a meta-analysis
consisting of 26 individual studies that investigated the relationship
between math anxiety and achievement in math. The population
correlation for the relationship between math anxiety and math
achievement between the studies was significant (p<.01). The U3
statistic corresponding to the population correlation is .71. This
indicates that “the measures (or treatments) that resulted in
movement of a typical student in the group of high anxiety into the
group of low anxiety would be associated with improvement of the
typical students level of achievement from the 50th percentile to the
71st percentile” (Ma, 1999, p. 528). This study suggests that there is a
significant relationship between anxiety and achievement. It also
quantified the potential improvement when anxiety is reduced. Most
studies have emphasized addressing affective factors, but the
significance of the relationship indicates the value of addressing
cognitive based treatments such as skill development (Ma, 1999).
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Cates & Rymer (2003) conducted a study that builds on MA’s
(1999) meta-analysis study, by connecting it to the learning hierarchy.
The learning hierarchy suggests that there are four stages of learning:
acquisition, fluency, generalization, and adaptation. Their purpose was
to investigate the extent to which level of math anxiety may be related
to a more advanced stage of the learning Hierarchy than to the initial
acquisition stage by assessing fluency as opposed to overall accuracy.
The study involved fifty-two college students taking an introductory
psychology. They were given the FSMAS (a mathematics anxiety test)
and divided into a low anxiety group and a high anxiety group. These
groups were then given a timed math probe with multiple operations
including addition, subtraction, multiplication, division, and linear
equations. The results showed a significant difference (p<.05) on
fluency between high and low anxiety groups. “Students with lower
anxiety completed more digits correct per minute an all probes. There
was no significant difference in error rates between high and low
anxiety groups. Both groups were equally accurate on basic
mathematics operations” (Cates & Rymer, 2003, p 30). These results
suggest that fluency in math may be more related to math anxiety
than overall performance. In other words, math anxiety may increase
with problem complexity. One implication is that as students progress
through high school and classes become more complex their anxiety
level will increase.
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Motivation
Motivation can be divided into two categories: extrinsic and
intrinsic. Extrinsic motivation is desire to obtain rewards for academic
tasks, such as grades, or avoid punishments. "Academic intrinsic
motivation is the drive or desire of the student to engage in learning
‘for its own sake’” (Middleton & Spanias, 1999, p. 66).
Schiefele & Csikszentmihalyi (1995) conducted a study to
answer questions related to motivation. First, is quality of experience
when doing mathematics more dependent on ability or motivational
characteristics? Second, are subject-matter-specific measures of
motivation more predictive of quality of experience and achievement
than general measures of motivation? Third, do motivational
characteristics and quality of experience when doing mathematics
predict achievement in mathematics independently of ability? The
study included 108 freshman and sophomores from two suburban high
schools. From the 108 students, teachers nominated students they
thought were talented in one or more subject matters. Students were
given a questionnaire to gauge interest in mathematics and
achievement motivation. Ability was measured by scores on the PSAT
(Preliminary Scholastic Aptitude Test). Quality of experience was
measured using the Experience Sampling Method (ESM). This method
provides the subject a pager and throughout the day whenever the
subject is signaled they fill out the questionnaire. Semester grades
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were used as an indicator of mathematics achievement. Students who
were talented in mathematics had significantly higher (p<.001) values
for mathematic ability, better grades for the first four years, and a
higher course level than those talented in other subjects. The results
clearly indicate that interest was the strongest predictor of quality of
experience in the mathematics class (Schiefele & Csikszentmihalyi,
1995). Specifically, interest showed significant relations to potency
(p<.01), intrinsic motivation (p<.05), self-esteem (p<.01), and
perception of skill (p<.001). “Surprisingly, level of mathematic ability
was not related to experience at all” (Schiefele & Csikszentmihalyi,
1995, p. 173). This study suggests that teachers should create more
interest in order to improve motivation. Wiess (cited in Schiefele &
Csikszentmihalyi, 1995), for example, found that teachers tend to
emphasize learning facts and principles and to develop a systematic
approach to problem solving. Their methods were lecture, discussion,
and seatwork. These approaches however, may not create much
interest in mathematics.
Anderson (2007) conducted a qualitative study to address “the
notion of identity, drawn from the social theories of learning as a way
to view students as they develop as mathematics learners” (p. 7). The
students in this study were participants in a larger study of students’
enrollment in advanced mathematics classes. Fourteen students were
selected from one high school for semi-structured interviews. Two
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groups were formed: students enrolled in Precalculus or Calculus and
students not taking a mathematics course that year. All of the students
had taken the two required and any elective high school mathematics
in the same high school. “One teacher taught most of these courses.
When interviewed, this teacher indicated the ‘traditional’ nature of the
curriculum and pedagogy: ‘We’ve always stayed pretty traditional…
We haven’t really changed it to the really ‘out there’ hands-on type of
programs.’” (Anderson, 2007, p. 7-8) Anderson (2007) describes the
four faces of mathematics identity (how we define ourselves and how
others define us as mathematics learners) as engagement (direct
experiences in the classroom), imagination (envisioning how activities
fit into the big picture), alignment (how the curriculum fits with future
plans), and nature (abilities we’re born with). From the interviews, the
social learning theory, and previous studies conclusions are drawn
about how the four faces impact a students’ mathematic identity.
“Some students may not identify themselves as being a ‘math person’.
Students may mistakenly believe that they are unable to learn
mathematics or they weren’t born with the genetics needed to be good
at math, but scientific evidence does not support these ideas”
(Anderson, 2007, p. 8).
While all four faces contribute to the formation of students’
identities as mathematics learners, the nature face provides the
most unsound and unfounded explanations for students’
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participation in the mathematics community. To allow for the
development of all students to identify as mathematics learners,
students and teachers must discount the nature face and build
on the other three faces of identity (Anderson, 2007, p. 11).
“Mathematical tasks that engage students in doing mathematics,
making meaning of mathematics, and generating their own solutions
to complex mathematical problems can be beneficial in engaging
students and supporting their identity as mathematics learners” (NCTM
as cited in Anderson, 2007, p.12). Anderson (2007) suggests that to
increase interest, instruction should involve more active and student-
centered activities. “Teachers can reinforce the idea that mathematics
is an interesting subject, used in other disciplines, and is an admission
ticket for colleges and careers. Teachers could have working
professionals to visit the classes and share how they use mathematics
in their profession” (Anderson, 2007 p. 12).
Stipek, Salmon, Givvin, & Kazemi (1998) ask the question: What
are the associations between teaching practices, student motivation
and mathematics learning? In their study, twenty-four 4th through 6th
grade teachers were selected from schools in a large urban ethnically
diverse area. Three groups were formed. Two groups had expressed a
commitment to implementing reforms and agreed to teach using a
reform-oriented unit on fractions. One of those groups was given
training on implementing reforms. The third group taught using
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standard methods and textbooks and expressed no interest in reforms.
Six hundred ninety four (694) students of diverse ethnic backgrounds
participated. Each teacher was videotaped for at least two periods and
evaluated for teaching practices and a questionnaire was given asking
teachers about their assessment practices. Students completed a
questionnaire twice: once before the intervention and once after the
unit on fractions related to motivational dimensions. Students were
also evaluated from the videotapes of the classroom. Students were
assessed on fractions from routine to conceptually challenging. Tests
were given at the beginning of the year and after the fractions unit.
The effects on student motivation based on teacher practices were
significant between help seeking (p<.001) and enjoyment (p<.05) with
the positive affective practices of the teacher. The effects were also
significant for positive emotions (p<.05), enjoyment (p<.05) and
learning conceptual items (p<.05) with the learning orientation
practices of the teacher. The learning orientation of the teacher refers
to the teacher giving timely and substantive feedback and focuses on
improvement and mastery over grades. The study suggests that the
affective climate is a strong predictor of students’ motivation and
fosters mastery orientation in students.
“Students’ feeling of relatedness to their teachers was strong
predictors of their cognitive, behavioral, and emotional engagement in
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classroom activities” (Stipek et al., 1998, p. 483). Davis, Maher, and
Noddings (1990) gave this example:
Jaime Escalante, the real-life hero of the film Stand and Deliver,
insists that he must teach his students for three years if they are
to succeed in AP calculus. He conscientiously builds relations of
care and trust with each student. He shows steady concern for
the integral development of his students – how they are doing in
English, how their home lives are going, what jobs and sports
they participate in. This attitude and effort that accompanies it
are part of teaching mathematics. As we build such relations, our
students learn to trust us. When the work is not as exciting as
we’d like it to be or when they have low moments (as we all do),
students will often persist in mathematical endeavors for their
teacher. “Okay, if you say so. I’ll do it - just for you” (p. 191).
Middleton & Spanias (1999) conducted a review of literature to
“describe theoretical orientations guiding research in mathematics
motivation and to discuss findings in terms of how they facilitate or
inhibit achievement" (Middleton & Spanias, 1999, p. 65). The
conclusions are as follows: “students' perception of success in
mathematics are highly influential in forming their motivational
attitudes” (Middleton & Spanias, 1999, p. 79); “motivations towards
mathematics are developed early, are highly stable over time, and are
influenced greatly by teacher actions and attitudes" (Middleton &
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Spanias, 1999, p. 80); “providing opportunities for students to develop
intrinsic motivation in mathematics is generally superior to providing
extrinsic incentives for achievement” (Middleton & Spanias, 1999, p.
81); and “Last, and most important, achievement motivation in
mathematics, though stable, can be affected through careful
instructional design” (Middleton & Spanias, 1999, p. 82).
Attitude
“Attitude toward mathematics is defined as a general emotional
disposition toward the school subject of mathematics” (Haladnya et al.,
1983, p. 20). Maple and Stage (as cited in Schiefele &
Csikszentmihalyi, 1995) found that “attitude toward mathematics
significantly influenced choice of mathematics major” (p. 177). “One of
the most important reasons for nurturing a positive attitude in
mathematics is that it may increase one’s tendency to elect
mathematics courses in high school and college and possibly to elect
careers in a math related field” (Schiefele & Csikszentmihalyi, 1995, p.
177). One important factor in students’ attitude toward mathematics is
the teacher and classroom environment.
Haladnya et al. (1983) conducted a study designed to examine
teacher and learning environment variables that were believed to be
the most powerful causal determinants of attitude toward
mathematics. Over 2,000 students in grades 4, 7, and 9 participated in
the study. The students were given the Inventory of Affective Aspects
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of Schooling (IAAS) that addressed student motivation, teacher quality,
social-psychological class climate, management-organization class
climate and attitude toward math. The correlations of each
independent variable with attitude and motivation were all significant
(p<.05) using a one-tailed test. A path analysis was also conducted to
determine causal relationships. The findings suggest that teacher
quality (enthusiasm, respect, commitment to help students learn,
fairness, praise and reinforcement) seems to be consistently related to
attitude toward mathematics.
Wilkins & Ma (2003) conducted a study to answer questions
about how student attitudes changed from middle school to high
school. Data came from Longitudinal Study of American Youth (LSAY),
a national study, which tracked over 3,000 seventh-grade students for
six years. Information about student affect was collected (via
questionnaires) and three measures created: attitude toward
mathematics, social importance of mathematics (usefulness of math in
daily lives and on the job), and nature of mathematics (whether
changes in science theory over time cause more good than harm). The
findings show that mathematical beliefs and attitudes change
gradually. “However, the important trend highlighted in this study is
that students in secondary school become increasingly less positive
with regard to their attitude toward mathematics and their beliefs in
the social importance on mathematics” (Wilkins & Ma, 2003 p. 58).
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Students’ notions of the nature of science showed little change. In
regard to middle school changes, attitude and social importance of
mathematics declined at a significantly slower rate (p<.001) for
students with positive teacher push and positive peer influence.
Parental push was also a significant (p<.05) influence. In high school,
positive peer influence (p<.001), positive teacher push (p<.05), and
curriculum (students taking higher math) (p<.001) were related to
slower rates of decline in attitude and social importance. Wilkins and
Ma (2003) make several observations and suggestions such as: “If
teachers hold high expectations and present students with challenging
mathematics, then students may be more likely to enjoy mathematics
and recognize it usefulness” (p. 59) and “teachers’ choice of activities
and mathematics problems can have a strong impact on the values
that are portrayed in the classroom and on how students view
mathematics and its usefulness” (Wilkins and Ma, 2003, p. 59).
Supporting positive peer networks and involving parents in school
activities involving mathematics can help slow decline of students’
negative attitude toward mathematics (Wilkins & Ma, 2003).
Ma & Kishor (1997) conducted a meta-analysis of 113 studies to
examine the relationship between attitude toward math and
achievement in math. Although the study produced no significant
results, there was an indication that junior high may be the most
important period for students to understand and shape their attitude
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as it relates to their achievement in math. Therefore, the junior high
years may provide teachers an opportunity to treat negative attitudes
toward math and foster high achievement.
Summary
It is clear from the research reviewed that math anxiety, motivation,
and attitude all play important roles in whether or not students will
pursue advanced mathematics courses and careers in math related
fields. As the National Council of Teachers of Mathematics (1991)
suggests, it has not only become acceptable to not be good at
mathematics, but acceptable to be proud of not being good in
mathematics. Many suggestions have been offered to address the
problem, for example: change teaching methods, get students actively
involved in learning mathematics, show students the relevance of
mathematics in their lives, build relationships with the students,
promote a positive affective environment, and create interest in the
mathematics field are just a few. In any case, the affective
environment can play a large role in reversing the trend of negative
attitudes about mathematics, lack of motivation, and the adverse
effect of math anxiety on our students.
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CHAPTER III
METHOD
Introduction
This chapter describes the methods and procedures of the study
and how data will be collected and analyzed. It will state the purpose
of the study and the research question. Also discussed will be any
limitations of the procedure that may affect the outcome and how the
subjects will be debriefed.
Purpose
There seems to be a widespread dislike of mathematics. Research
suggests that students who dislike math will avoid taking higher level
math classes and may not seek careers in a math related field or any
field that will require math. The purpose of this study is to determine,
from the students’ perspective, why students dislike mathematics.
Research Question
If students dislike math, what are the reasons?
Procedures
Subjects
The sample consisted of 49 eighth grade math students at a rural
northeast Tennessee school. The school has about 800 students and is
K-8. The school consists of mostly white (98%) low to middle income
students from a farming community.
Tests/surveys
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A confidential survey was given to the students during their normal
math class time. The survey did not ask for names or any other
identifying information. Parents were given an informed consent
request to sign. Once consent was obtained the survey was given. It
consisted of fourteen Likert scale questions and three open ended
questions that asked questions about math anxiety, interest,
motivation, their expected grade, their suggestions to make math
more enjoyable, and their experiences in math. A copy of the survey is
in the appendix.
Data Collection
The researcher collected data on the same day the survey is given.
The data was then be analyzed for results.
Data Analysis
Statistical analysis was performed using Pearson r correlation
analysis and descriptive statistics.
Debriefing
A copy of the results of the survey and conclusions will be made
available to the school and school board office.
Limitations
The survey will be conducted during the normal math class time
and therefore the students may feel rushed or affected by peer
pressure. The survey was constructed by the researcher and not
validated.
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Summary
This chapter focuses on the procedure through which data will be
collected. Students will be asked to fill out a survey to determine if
they dislike math and why. The survey will allow the reasons to be
analyzed to determine if there is a particular reason students dislike
math.
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CHAPTER IV
RESULTS AND CONCLUSIONS
Introduction
This chapter presents the results and conclusions of the data.
The data are analyzed using various statistical methods and presented
in written and graph form.
Results
Of the approximately 90 eighth grade students, 49 returned
consent forms and were given the survey. The survey consisted of 14
Likert scale questions and three open-ended questions. The Likert
scale range was from 1 (strongly agree) to 6 (strongly disagree). A
score of 1 also indicates a strong negative attitude toward math for
that specific question. One of the open-ended questions was “what
grade do you think you will make in this class?” which indicates the
students perception of their achievement in that math class. The other
two open-ended questions were related to classroom affective
environment. (see appendix 1)
The students mean score for the 14 Likert scale questions was
compared to their expected grade. The results showed a highly
significant correlation (df=47, r=.704, p<.001) indicating that students
with a negative attitude toward math expect worse grades than those
with positive attitudes. This was an expected result and emphasizes
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the need to change negative attitudes toward math. The results are
plotted in Graph 1 showing the strong positive correlation.
Scatter Graph
50
60
70
80
90
100
0 1 2 3 4 5 6
Mean of Scores (df=47, r=.704, p<.001)
Expected Grade
Graph 1
The questions covered many areas that could possibly be related
the negative attitudes. Of the 49 students 18 (37 percent) indicated a
dislike for math (see question 1). While there is no comparison to
students who dislike other subject matter areas, 37 percent is a large
number of students who dislike math.
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Like vs Dislike
37%
63%
Graph 2
Graph 3 compares the answers of students who like and dislike
math using the mean for each question.
Answer Comparison
14131211109876543210
1
2
3
4
5
6
Question
Answer
Agree = 1, Disagree = 6
Dislike
Like
Graph 3
This study’s focus was on students who dislike math, therefore
those students’ answers are now considered. For the students who
dislike math, there were many answers that were correlated. Some of
the interesting and important correlations are discussed. Table 1
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shows the correlations between answers of the students who indicated
they dislike math. The correlations are for each question compared to
every other question. For example, the first column shows the
correlation of answers on question 1 with questions 2 through 15.
Question 1 2 3 4 5 6 7 8 9 10 11 12 131 1
2 0.129 1
3 0.487 0.233 1
4-
0.042 0.508-
0.018 1
5 0.025 0.230 0.250 0.104 1
6 0.315 0.414 0.485 0.057 0.263 1
7 0.176 0.534 0.468 0.282 0.455 0.253 1
8 0.267 0.551 0.286 0.220 0.456 0.218 0.447 1
9 0.439 0.020 0.431 0.385 0.219 0.164 0.189 0.006 1
10 0.179-
0.301 0.174-
0.409 0.076-
0.110 0.130 0.036 0.371 1
11 0.153 0.387 0.052 0.322 0.536 0.135 0.453 0.600 0.189 0.098 1
12 0.346 0.170 0.215 0.589 0.348 0.107 0.194 0.422 0.697 0.273 0.595 1
13 0.166-
0.005 0.234-
0.285 0.484 0.355 0.223 0.338 0.223 0.343 0.365 0.183 1
14 0.025 0.159 0.280 0.354 0.261 0.068 0.311 0.111 0.342 0.196 0.282 0.401-
0.125
15-
0.170 0.197 0.261 0.311 0.257 0.213 0.523 0.410 0.061 0.154 0.408 0.395 0.352
Table 1(df=34, p<.05=.331, p<.01=.428. p<.001=.527)
Although there are many correlations, three important
correlations related to the research question of this study and previous
research are discussed. First, “I don’t like math” had highly significant
correlations with: “I’m not good at math” and “I dread having to do
math”, and a significant correlation with: “I’m afraid to answer
questions in math class”. Second, “The personality of the math teacher
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is not very important” had significant correlations with “Math is too
hard”, “When taking a math test, I usually feel nervous and uneasy”
(highly significant), “I’m afraid to ask questions in math class” (highly
significant), “I’m afraid to answer questions in math class”, and “I will
only take math courses that are required” (highly significant). Lastly, “I
will only take math courses that are required” had significant
correlations with “The personality of the math teacher is not very
important”, “Math is boring”, “When taking a math test, I usually feel
nervous and uneasy”, “It scares me to think I will be taking harder
more advanced math”, and “I’m afraid to ask questions in math class.”
Of the 18 students who indicated they dislike math, a tally was
taken of the questions for which the students strongly agreed (1 on the
Likert scale) and is shown in table 2 below.
“It scares me to think I will be taking harder or more advanced
math
6
“I’ve had at least one year I fell behind in math” 5
“I’m not good at math” 4
“I will only take math courses that are required” 4
“When taking a math test, I usually feel nervous and uneasy” 3
“Math is too hard” 3
“Math is boring” 2
“I dread having to do math” 2
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Dislike of Math
“I’m afraid to answer questions in math class” 1
Table 2
Further insight into students’ thoughts about math and the
classrooms affective environment can be obtained in reviewing
answers to the last two open-ended questions. For this analysis, all
students’ responses were used (like and dislike).
The first question was “What can teachers do to make math
more enjoyable?” Following is a list of the most popular responses
along with the number of students making that suggestion:
Play more math games – 19
Fun activities/Make it more fun/interesting – 12
Have group assignments - 5
Less homework – 4
Use real life examples – 4
The second question was “Describe your best and worst
experience in math.” The overwhelming responses for both best and
worst experiences were related to achievement (grades,
understanding particular topic, test scores, etc.). Some examples of
the students’ answers are:
“My best experience was when I could answer all the
questions and worst was when I really make a bad grade
on an important test”
“Ratio was my best and integers was my worst”
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Dislike of Math
“Best – when I actually started to understand, worst – when
we do algebra”
“best – making a B, worst – making a C”
“Well, the best would have to be when I pass a math test. I
have my worst experience when I do poorly on a math
exam”
Summary
This chapter analyzed the data from the surveys. These results
suggest a significant number of students dislike math and there were
several significant correlations between answers that may indicate the
cause of their dislike.
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Chapter V
IMPLICATIONS, RECOMMENDATIONS, CONCLUSIONS
Introduction
This chapter will discuss the findings of the study, implications
for educators, and recommendations for future research.
Summary of Findings
The finding of this study was that 37 percent of the eighth grade
students who took the survey disliked math. This is a large percentage
and indicates how widespread the problem may be. Wilkins and Ma
(2003) found that students’ attitudes toward math became less
positive as they entered high school. Since 37 percent already dislike
math when they leave middle school and they get even less positive in
high school, its no wonder that there is such a widespread dislike of
math.
There was a highly significant correlation (p<.01) of those
students who did not like math and those that indicated they weren’t
good at math. This agrees with the highly significant correlation
(P<.01) between the students answers on the survey and their
expected grade in their current math class. One possible reason
students say they’re not good at math is may be that they had at least
one year they fell behind. Table 2 shows that 5 of the 18 students who
dislike math agreed strongly with that statement. Also, answers on the
open-ended question asking about their best and worst experiences in
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math were overwhelmingly related to achievement (test scores,
grades, understanding). This indicates that students’ achievement in
math is an important factor in whether or not they like math.
Therefore, this suggests students place a lot of emphasis on extrinsic
motivation. In this case, extrinsic motivation can lead to a negative
attitude toward math.
Affective anxiety also had significant correlations with the dislike
of math. Affective anxiety can be described as nervousness, fear,
dread, or tension (Ho et al., 2000). The two questions: “I dread having
to do math” and “I’m afraid to answer questions in math class” had
significant correlations (p<.01 and p<.05 respectively) with “I don’t
like math.”
There were some other interesting correlations that may shed
some light on why students dislike math. The question: “The
personality of the math teacher is not important,” relates to the
affective environment of the classroom and had significant correlations
to “Math is too hard”, “When taking a math test, I usually feel nervous
and uneasy” (highly significant), “I’m afraid to ask questions in math
class” (highly significant), “I’m afraid to answer questions in math
class”, and “I will only take math courses that are required” (highly
significant). These results suggest that the affective environment in
the classroom (in this case the teachers personality) plays an
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Dislike of Math
important role in the students’ affective anxiety, future plans of the
students, and their perception of the difficulty of math.
Previous research has indicated that students with negative
attitudes will avoid taking math classes that are not required or
choosing a career in a math related field. For example, Schiefele &
Csikszentmihalyi (1995) stated “One of the most important reasons for
nurturing a positive attitude in mathematics is that it may increase
one’s tendency to elect mathematics courses in high school and
college and possibly to elect careers in a math related field” (p. 177).
The definition of dislike for this study is the desire to avoid math
classes. The last correlation discussed relates to that definition. The
question: “I will only take math courses that are required” had
significant correlations with the teacher personality, lack of interest
(boring), anxiety, and perceived ability.
Implications for Educators
The results of this study suggests that educators should focus on
improving the classroom affective environment, addressing affective
anxiety, and reducing the effects of negative extrinsic motivation in
order to foster positive attitudes in math. The students gave some
interesting suggestions on how to make math more enjoyable. They
were play more math games, have some fun activities, make it
interesting/relevant, have some group activities, use real life
examples, and less homework. All of these suggestions address the
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results of this study. In discussions with the classes, the idea of
bringing in professionals in various fields (not just math/science) was
offered. The students seemed to like the idea and it would help them
envision where math fits in the “big picture.” The students were asked
whether they would like and/or use a homework “chat room” where
the teacher would be available during certain times to answer
questions. The students seemed very receptive and excited about the
idea. Since most students had Internet access and since “instant
messaging” is so popular with the students this may be an innovative
way to get them interested in doing homework. One eighth teacher
indicated that in eighth grade the requirements and amount of
curriculum to cover increased greatly over sixth or seventh grades.
Thus, she felt she didn’t have time to engage in most of the students’
suggestions such as playing math games. This is a conundrum a lot of
teachers face.
Educators should look for ways to foster positive attitudes in
students at all grade levels, but since a negative attitude toward math
is evident by eighth grade, educators at earlier grade levels should
help students build positive identities as math learners. For those
students who dislike math, focusing on the students understanding
instead of grades may help counter the negative effects of extrinsic
motivation. The results of this study agree with the findings of Stipek
et al. (2000) which found students enjoyment and positive emotions
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Dislike of Math
toward math were higher when there was a focus on improvement and
mastery over grades. To address the problem of students’ affective
anxiety (e.g. being afraid to answer questions in math class), teachers
should focus on creating an emotionally safe environment where
students feel comfortable and know they won’t be looked down upon
by the teacher or other students if they get the answer wrong or don’t
understand. Since falling behind may be reason students say they
aren’t good at math and therefore don’t like math, teachers should
strive to make sure as many students as possible obtain mastery of the
subject. Tutoring is now available in most schools (including the middle
school surveyed) and should be encouraged and expanded. Peer
tutoring during class time or group work may help those students who
are falling behind. Math skills build on earlier skills and understanding
and become more complex.
Limitations
The survey was conducted at only one school and only 49 of the
approximately 90 eighth graders participated. The school is ethnically
(98% white) and economically (low to middle income) homogeneous
providing no diversity.
Summary
The results suggest that the reasons students dislike math are
related to the negative effects of extrinsic motivation, affective anxiety
and the affective environment of the classroom.
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Recommendations for Future Research
Since the dislike of math is evident in eighth grade, future
research should focus on when students begin to dislike math.
Research should also study the effects of interventions suggested for
effectiveness.
Conclusion
This chapter discussed the results and findings of the survey,
implications for educators, and recommendations for future research.
With the widespread dislike of math and focus of government and the
society on improving math and science achievement, educators should
focus on changing these negative attitudes.
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