Predicting Academic Achievement
Predicting Academic Achievement: The Effects of Multiple
Intelligences, Effort and Motivation
Stacie Bailey
Action Research Paper
California State University, Northridge
1
Predicting Academic Achievement
Abstract
This study’s objective was to determine whether multiple intelligences, effort or
motivation could be used to predict academic achievement in a College Prep
Biology class at Highland High School. Gardner’s multiple intelligences, the
School Achievement Motivation Rating Scale (SAMRS) and various classroom
measures were used to collect data. The relationships between intelligence
scores, SAMRS scores, assignment completion rates, and class and test grades
were analyzed using correlation statistics and regression analyses. The results
indicate that motivation is the strongest predictor of academic achievement,
followed by effort, and that intelligence is not related to academic achievement.
Further studies must be done to evaluate these relationships in other teacher’s
classrooms and other academic subjects.
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Predicting Academic Achievement
3
Predicting Academic Achievement: The Effects of Multiple
Intelligences, Effort and Motivation
Chapter 1
Introduction
“Genius is 1% inspiration and 99% perspiration” – Thomas Edison
“By natural ability, I mean those qualities of intellect and disposition, which urge
and qualify a man to perform acts that lead to reputation. I do not mean capacity
without zeal, nor zeal without capacity, nor even a combination of both of them,
without an adequate power of doing a great deal of very laborious work” –
Francis Galton, Hereditary Genius
As both of these quotes illustrate, popular belief holds that motivation and
hard work will lead to achievement and talent. A timeless question among
educators and parents is whether intelligence or hard work and motivation will
lead to academic success. In our current politically correct era, the idea that
genetic intelligence is the primary factor in academic success is frowned upon.
Our society likes to believe that no matter your genetic make-up, if you try hard
enough you can achieve anything. While this may be true to some extent, at
some point a certain level of intelligence is necessary for success. A brief survey
of the literature will illustrate that cognitive ability is the number one predictor of
academic achievement (Gagne & St. Pere, 2000). In my classroom, however, I
do not see this trend across the board.
In addition to the intelligent, highly motivated, high achieving students, I
see students who are intelligent, but put forth little to no effort and have low
academic achievement. I also have observed the opposite – students who do
not catch on right away, but who put in 110% effort and have high academic
achievement. I have rarely had a student for whom intelligence alone can lead to
Predicting Academic Achievement
4
high academic achievement. Ablard (2002) found that while it would seem
logical, intelligence does not necessarily equate to high motivation to achieve.
Before beginning my research for this paper, I conducted a brief survey of
my students’ performance in order to refine my study questions. I compared
each student’s overall grade with his or her
Figure 1.1 Relationship between missing
assignments and class grades in Biology
number of missing assignments. As would
110
100
90
relationship (see Figure 1.1). I found that as
the number of missing assignments
increased, the students’ grades decreased.
Grade (%)
be expected, I found an indirect linear
80
70
R2 = 0.8388
60
50
40
30
20
10
0
0
10
20
30
Number of Missing Assignments (out of 60)
While these results would appear to have a
logical and simple explanation, I found that the more I thought about this trend,
the less clear the explanation became. I could not tell from this small bit of data
what caused each student to miss assignments. I developed two possible
hypotheses; first, students with many missing assignments may just be lazy or
lack motivation, and second, students with many missing assignments may not
be doing their work because they lack an understanding of the concepts. I
wondered if low effort (hypothesis 1) or low intelligence (hypothesis 2) was to
blame for students not turning in their work and subsequently having low
academic achievement.
40
Predicting Academic Achievement
5
Purpose
The purpose of this paper was to examine the effects of intelligence and
effort/motivation on academic achievement. The questions I will be attempting to
answer are:

Can intelligence, effort or motivation be used to predict academic
achievement?

If so, which provides a better prediction of academic achievement, or is
academic achievement a result of some combination of the three?
I hypothesized that effort alone would be an effective predictor of academic
achievement. Without some degree of effort, I believe even the most intelligent
students will not do well academically in a subject as challenging as College Prep
Biology.
Importance of Study
The results of this study will be helpful to all teachers, but especially to
high school teachers. High school students seem to rapidly lose motivation and
many do not see any connection between effort and achievement. One of the
most common questions I hear from my low achieving students is “Why am I
getting a D (or F)?” Students do not seem to realize that their grade is a
reflection of their effort and hence their learning. Many students are under the
impression that seat time should be worth a passing grade. With the data from
this study, I hope that teachers can show students and parents that effort is the
key to academic achievement. This research will provide teachers with another
Predicting Academic Achievement
weapon in their arsenal of motivating techniques. I also hope to dispel the myth
that only smart kids do well in science.
Definition of Terms
The following terms are used frequently throughout this paper. The
definitions provided are my interpretation of each term and will illustrate how I
have used each term.

Intelligence is an innate ability to learn, process information, and see
connections between multiple sets of data.

Effort is the willingness to do work.

Motivation is an intrinsic desire to do work.

Achievement means performing at or above the academic average.
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Predicting Academic Achievement
7
Chapter 2
Literature Review
Intelligence
The concept of intelligence is a highly contentious topic. A review of the
literature on intelligence shows a divide between those who believe intelligence
is fixed and leads to achievement (Walberg, 1984; Necka, 1996; Snyderman,
1987) and those who feel intelligence is dynamic and does not necessarily lead
to achievement (Diseth, 2002; Williams, 2002; Maree & Ebersöhn, 2002; Reis &
McCoach, 2000; Pirozzo, 1992). In fact, Pirozzo (1992) found that among
students in the top 5% of intellectual ability (as measured by IQ) 50% do not
achieve at a commensurate level in school.
No single definition for intelligence exists, making a study into the effects
of intelligence difficult. The most commonly held definition of intelligence among
the public is the IQ score. IQ testing is still the most commonly used intelligence
or aptitude assessment in both psychology and education (Reis & McCoach,
2000; “Intelligence and Achievement”, n.d). In recent decades, IQ testing has
come under fire as being racially and culturally biased (Snyderman, 1987).
Additionally, most researchers feel that a single number cannot accurately
describe something as complex as intelligence (Gagne & St. Pere, 2000; Maree
& Ebersöhn, 2002).
The most prevalent alternate definitions of intelligence are emotional
intelligence, practical intelligence, general intelligence, and multiple intelligences.
Emotional intelligence includes the abilities to delay gratification, self-motivate
and be persistent (Maree & Ebersöhn, 2002). Practical intelligence is the ability
Predicting Academic Achievement
8
to manage oneself, others and the environment (Williams, 2002). Diseth (2002)
defines general intelligence as consisting of three parts: spatial intelligence, fluid
intelligence (physiological), and crystal intelligence (experiential, education).
Other research has included practical intelligence within the scope of general
intelligence (Williams, Blythe, White, Li, Gardner & Sternberg, 2002). In 1983,
Howard Gardner revolutionized the field of intelligence research with his book
Frames of Mind, in which he defined seven (he has since amended this number
to eight) areas of intelligence (Garnder, 2003; Brualdi, 1998; Shearer, 2004;
Greenhawk, 1997). Gardner coined his theory “multiple intelligences”. As cited
in Maree and Ebersöhn (2002), Van den Berg found the following four unifying
themes across all definitions of intelligence, “the ability to adjust to new
situations; the ability to learn; the ability to handle abstract relationships and
symbols; and the ability to solve new and diverging problems” (p264).
With the obvious internal disagreement in academia about the definition of
intelligence, the idea of measuring intelligence encroaches on a whole new array
of issues. The IQ test is the oldest means of measuring intelligence, having
originated during the nineteenth century (“Intelligence and Achievement”, n.d.).
Again the research is divided, with some studies asserting that an IQ test is
insufficient as a measure of true intelligence (Diseth, 2002; Williams, 2002;
Gardner, 2003) and others show that IQ provides an accurate measure of true
intelligence (Gagne & St. Pere, 2000; Gottfredson, 1997; Walberg, 1984;
Snyderman, 1987). For the purposes of my research, I tend to side with the
former group and contend that intelligence can be measured, but not through the
Predicting Academic Achievement
9
traditional IQ test. Therefore, I measured Gardner’s multiple intelligences (for
complete descriptions of Gardner’s multiple intelligences see Appendix A).
Gardner developed his theory of multiple intelligences in response to
perceived cultural and racial biases of traditional IQ tests (“Intelligence and
Achievement”, n.d.). Gardner (2003) asserted that people have a “set of
relatively autonomous intelligences” (p.4) rather than a single, general
intelligence (often referred to as g). The eight intelligences Gardner developed
were kinesthetic, logical, intrapersonal, visual/spatial, linguistic, interpersonal,
musical, and naturalistic. To use a metaphor, multiple intelligences are like a
tree with one branch being general intelligence (Shearer, 2004).
Effort and Motivation
Effort, or the time and energy a student is willing to put into their school
work, is dependent upon the student’s motivation. Effort requires motivation;
however, motivation does not necessarily lead to effort. Motivation can be
intrinsic or extrinsic. Intrinsic motivation results in students setting learning goals,
or learning for the pleasure of learning. Extrinsic motivation leads to
performance goals, or learning for the sake of achieving and being recognized
through grades or awards (Ablard, 2002; Deci, 1991; Vallerand & Bissonnette,
1992; McGregor & Elliot, 2002; Covington, 2000). Students who are intrinsically
motivated are less likely to experience a sense of failure for not achieving,
whereas those who are extrinsically motivated are more likely to give up after a
failure (Ablard, 2002). In a review of the literature, Covington (2000) found that
motivation, preferably intrinsic, is necessary for achievement. Vallerand and
Predicting Academic Achievement 10
Bissonnette (1992) studied over 1000 junior college students and found that
intrinsic motivation was correlated to successful course completion.
The source of motivation remains a complex issue. A conflict exists in the
literature with some studies showing a zero correlation between motivation and
IQ, while others show that the intellectually gifted have higher intrinsic motivation
or that their motivation is a part of their giftedness (Gagne & St. Pere, 2000). I
think it can be safely hypothesized that without motivation, intelligence alone will
not lead to achievement.
Chiu (1997) developed the School Achievement Motivation Rating Scale
(SAMRS) to “a) [identify] students’ levels of academic achievement motivation, b)
[predict] students’ academic achievement, c) [assess] how programs affect
students’ achievement motivation and d) [supplement] information from clinical
psychoeducational studies of children” (p.293). Chiu’s instrument consists of 15
statements regarding students’ behaviors which teachers evaluate and rate on a
type of Likert scale. Chui found that the SAMRS scores correlated with GPAs (r
= .73) and standardized test scores (r = .61). These moderate to high
correlations provide validity for the SAMRS instrument. Reliability was
established through testing and retesting (r = .91). For this study, I measured
motivation with the SAMRS.
Academic Achievement
While academic achievement may be easy to define, the causes of
achievement are not so obvious. An interesting dichotomy exists between
societal views of achievement in Japan, where student achievement is markedly
Predicting Academic Achievement 11
higher, and the United States. In Japan, effort is considered the primary cause of
academic achievement, while in the US, ability or intelligence is viewed as the
primary determinant for achievement (Holloway, 1987). Again the literature is
sharply divided between intelligence and motivation as the primary indicators of
academic achievement.
In a survey of almost 3000 studies, Walberg (1984) found correlations of
.71 between IQ and achievement (r2 = .5) and .34 between motivation and
achievement (r2 = .12). A study of 200 high school females also found that IQ
was the best predictor of achievement (Gagne & St. Pere, 2000). Other studies
have found that IQ does not predict achievement. Maree and Ebersöhn (2002)
assert that emotional intelligence has a much greater predictive power for
achievement than IQ. They propose that achievement is a result of positive
outlook, learning from failure and a belief that one can and will succeed. A study
of 89 Norwegian undergraduate students found a very small relationship between
IQ and achievement (Diseth, 2002). These results were obviously very
disconcerting to the author who spent a great deal of time offering possible
sources of error to explain why these unexpected results occurred.
It would be errant to assume that only IQ and motivation can affect
academic achievement. A plethora of factors such as socioeconomic status,
parental education level, past school success, peer groups, cultural background,
teachers’ expectations, and classroom atmosphere can all have an impact on
achievement (Myers, Nichols & White, 2003; Maree & Ebersöhn, 2002; Nichols &
White, 2001; Spera, 2005; Davis-Kean, 2005; Lubienski & Lubienski, 2005; Reis
Predicting Academic Achievement 12
& McCoach, 2000; Pirozzo, 1982). Due to the limitations of action research, I
narrowed the focus of my study to intelligence and effort, with the caveat that
these are not the only variables that affect achievement.
Measuring achievement tends to be rather straightforward. The two most
common achievement measures are standardized (state and federal) testing and
class grades (Reis & McCoach, 2000). I used a combination of class grades and
unit exams to measure achievement in my research.
Predicting Academic Achievement 13
Chapter 3
Methodology
The purpose of this study was to determine if intelligence, effort or
motivation better predicts academic achievement in a high school biology class.
Participants
As the researcher and teacher, I was a sixth year biology teacher with a
Bachelor’s of Science degree in biology and a teaching credential in the
biological sciences. My second period class participated in this study. This class
consisted of thirty-two students, 59% female and 41% male. They ranged in age
from fourteen to sixteen and none of them were classified as special needs or
gifted. Their ethnicities were varied, with 38% Hispanic, 34% Caucasian, 16%
Black, and 12% Asian. As a whole, the class tended to be quite attentive and
well behaved.
This study was conducted at Highland High School in Palmdale,
California. The surrounding community ranged from lower class to upper-middle
class with most students coming from middle to upper-middle class homes.
Highland High School had almost 4,000 students and was granted the California
Distinguished School award just prior to this study.
Materials and Procedures
All of the data collection was completed during the fall semester of 2005.
The units covered during this semester were Cells, Cellular Energy, Molecular
Genetics, and Mendelian Genetics.
Predicting Academic Achievement 14
Measuring intelligence. Students completed an online intelligence
assessment modeled after Gardner’s multiple intelligences (Multiple
Intelligences, n.d.). This assessment consisted of 40 items, each with six
possible responses ranging from “This is not like me at all” to “I am always like
this” (see Appendix A). At the end of the survey a score chart was produced
providing scores (25 was the maximum score) in each of Gardner’s eight areas
of intelligence (see Appendix B for a sample).
Measuring effort. To create a quantitative measure of a student’s effort,
the percentage of assignments each student turned in on time and complete was
calculated. A complete assignment was defined as a student receiving at least
50% credit. For example, if out of 60 total assignments, a student did not turn in
four and received less than half-credit on three more, their total on-time,
complete assignments would be 53 out of 60 or 88%.
Measuring motivation. Chiu’s (1997) School Achievement Motivation
Rating Scale (SAMRS) was completed by me for each student to gauge their
motivation (see Appendix D). Each survey consisted of 15 items with a Likert
type “never to always” rating system with five choices. The most favorable
response received a score of ‘5’ and the least favorable response a ‘1’ (see
Appendix E). The scores were summed and converted to a percentage.
Measuring academic achievement. The scores from the four unit exams
for the aforementioned units were averaged together to obtain a measure of
academic achievement. Each unit exam consisted of 50 multiple-choice
questions (see Appendix F for sample questions). The genetics exam also
Predicting Academic Achievement 15
included open-ended Punnett square problems (see Appendix G). The exams
are department exams created using the textbook publisher’s test bank software.
For the molecular genetics exam, an alternate version with free-response
questions, written by me, was offered to interested students (see Appendix H).
Students had to come after school to take the alternate exam. Students received
the higher of their two scores.
The overall class grades will also be used as a measure of academic
achievement. I will rely more heavily on the class grades in my data analysis
because I feel the tests are poorly written and measure more what a student has
memorized and how well a student can decipher the question than how much the
student understands the material. Because the class grade also incorporates lab
work, class work and homework, I believe it is a more accurate measure of
academic achievement.
Analysis
The scores from each of the eight areas of intelligence and the effort and
motivation scores were compared, using statistical analyses, to the academic
achievement scores. Correlation tests were completed as well as regression
analysis.
Predicting Academic Achievement 16
Chapter 4
Findings
The research questions guiding this study were: 1) Can intelligence, effort
or motivation be used to predict academic achievement? 2) If so, which provides
a better prediction of academic achievement, or is academic achievement a
result of some combination of the three? To answer these questions, students
took a multiple intelligence test measuring Gardner’s eight areas of intelligence, I
completed the School Achievement Motivation Rating Scale (SAMRS) for each
student and calculated their assignment completion rate, and students took
various unit exams.
Intelligence
The results of the multiple intelligences tests can be seen in Figure 4.1.
Interpersonal intelligence showed the highest average score ( X = 19.3, σ = 3.5),
while visual/spatial intelligence showed the lowest average score ( X = 14.5, σ =
4.3). The intelligence with the narrowest range of scores was logical intelligence
(range = 12), whereas visual/spatial intelligence showed the broadest range of
scores (range = 20).
Figure 4.1. Distribution of multiple intelligence scores
25
25
23
25
23
23
23
20
20
19.3
15.3
15
16.0
15.9
14.5
15.9
16.0
14.7
10
7
5
9
6
10
8
6
3
6
Linguistic
Maximum
Average
Minimum
Logical
Interpersonal
Intrapersonal
Musical
Visual/Spatial
Naturalistic
0
Kinesthetic
Intelligence Score (out of 25 max)
23
Predicting Academic Achievement 17
Effort
As shown in Figure 4.2 the assignment completion rate for this class is
relatively high ( X = 89.6%, σ = 7.2). The number of completed assignments
ranged from 43 out of 60 to 60 out of 60.
Figure 4.2. Distribution of assignment completion percentages
9
8
7
6
5
4
3
2
1
0
70-75
75.1-80
80.1-85
85.1-90
90.1-95
95.1-100
Assignment Completion (%)
Motivation
Student motivation, as measured by the SAMRS, showed a much wider
distribution than did the assignment completion rate (see Figure 4.3). The
SAMRS percentages ranged from 38.7% to 86.7% ( X = 64.1%, σ = 12.7)
Predicting Academic Achievement 18
Figure 4.3. Distribution of School Achievement Motivation Rating Scale Scores
7
6
5
4
3
2
1
0
35-40
40.1-45 45.1-50 50.1-55 55.1-60 60.1-65 65.1-70 70.1-75 75.1-80 80.1-85 85.1-90
SAMRS Score (%)
Academic Achievement
The distribution of test averages and class grades are shown in Figure
4.4. Tests equate to approximately 25% of the class grades which accounts for
the discrepancy between the test grades and class grades. The average test
grade for the class was a 62.3% (σ = 16.5) while the average class grade was a
75.2% (σ = 10.0). The correlation between test grades and class grades was
moderate (r = .66; see Figure 4.5).
Predicting Academic Achievement 19
Figure 4.4. Distribution of average test scores and class grades
14
14
12
Number of Students
11
10
10
8
7
6
7
6
4
2
2
2
3
0
Class Grade
2
< 60
60-69.9
Average Test Score
70-79.9
80-89.9
Average Scores (%)
90-100
Figure 4.5. Relationship between average test scores and class grades
100.0
90.0
R2 = 0.4379
80.0
Class Grade (%)
70.0
60.0
50.0
40.0
30.0
20.0
10.0
0.0
0.0
10.0
20.0
30.0
40.0
50.0
60.0
Average Test Score (%)
70.0
80.0
90.0
100.0
Predicting Academic Achievement 20
Relationship between intelligence and academic achievement
As previously mentioned, the multiple intelligence tests yielded eight
separate scores for each student. As a group each of these eight scores was
compared to the test averages and class grades (see Table 4.1). Logical and
visual/spatial intelligences showed positive correlations with average test scores,
albeit slight (r = .33 and .07 respectively). The other six intelligences showed
zero or negative correlations with
Table 4.1. Correlation (r) values between multiple
intelligences and academic achievement
Area of Intelligence
average test scores (Figure 4.6).
While none of the correlations
are of a significant value, an
interesting pattern does appear,
with logical and visual/spatial
Logical
Visual/Spatial
Intrapersonal
Kinesthetic
Naturalistic
Interpersonal
Linguistic
Musical
Correlation with
Test Average
0.33
0.07
-0.07
-0.16
-0.18
-0.28
-0.40
-0.40
Correlation with
Class Grade
0.25
0.01
-0.20
-0.24
-0.19
-0.25
-0.39
-0.38
intelligences topping the list and musical and linguistic intelligences at the bottom
of the list.
Figure 4.6. Trendlines showing relationship between mulitple intelligences
and average test scores
100.0
Kinesthetic
Naturalistic
Visual/Spatial
Musical
Intrapersonal
Interpersonal
Logical
Linguistic
Average Test Score (%)
90.0
80.0
70.0
60.0
50.0
40.0
0
5
10
15
20
Mulitple Intelligence Score
25
30
Predicting Academic Achievement 21
Relationship between motivation and academic achievement
The results of the SAMRS surveys correlated quite well with academic
achievement (r = .78 with class grades and .64 with test scores, see Figure 4.7).
The strongest relationship was seen between the SAMRS score and class
grades. A regression analysis indicated an R2 value of 0.614 indicating that the
SAMRS score is 61% effective at predicting class grades. The SAMRS score
was 40% effective at predicting test scores.
Figure 4.7. Relationship between SAMRS score and academic achievement
100.0
R2 = 0.614
90.0
80.0
R2 = 0.4055
Grade (%)
70.0
60.0
Class
Grade
Test Avg.
50.0
40.0
30.0
20.0
10.0
0.0
0.0
20.0
40.0
60.0
80.0
100.0
SAMRS Score (%)
Relationship between effort and academic achievement
The relationship between assignment completion and academic
achievement, while strong, is not as strong as the relationship between SAMRS
scores and academic achievement (see Figure 4.8). Assignment completion
correlates to test scores and class grades with r-values of 0.15 and 0.75,
Predicting Academic Achievement 22
respectively. Regression analysis shows that assignment completion is only 2%
effective at predicting test scores, but is 56% effective at predicting class grades.
In other words, students who completed their assignments were likely to do well
overall in the class, but were not necessarily likely to do well on the tests. The
opposite also held true in that a few students who had low assignment
completion rates did very well on the tests but tended to do poorly overall in the
class.
Figure 4.8. Relationship between assignment completion and academic achievement
100.0
90.0
2
R = 0.565
80.0
Grade (%)
70.0
R2 = 0.0214
60.0
50.0
40.0
Class
Grade
Test Avg.
30.0
20.0
10.0
0.0
40.0
50.0
60.0
70.0
80.0
90.0
100.0
110.0
Completed Assignments (%)
Synthesis of Findings
These results indicate that effort and motivation are better predictors of
academic achievement than is intelligence. In fact, most areas of Gardner’s
multiple intelligences were negative predictors of academic achievement or
showed no relationship at all to achievement. The only area of intelligence that
Predicting Academic Achievement 23
could be considered a very slight predictor of achievement was logical
intelligence. The strongest predictor of academic achievement in this study was
the student’s SAMRS score.
Predicting Academic Achievement 24
Chapter 5
Discussion
Overview of Study
The purpose of this study was two pronged. First, I wanted to determine if
effort, motivation or intelligence could predict academic achievement in my 2 nd
period biology class. Second, I attempted to establish which variable was a
better predictor of academic achievement. To answer these questions my
students completed a survey measuring Gardner’s multiple intelligences and I
completed the School Achievement Motivation Rating Scale for each student. In
addition, I recorded their test scores during the first semester and calculated their
assignment completion rates. Correlation scores and regression analyses were
used to interpret the results.
Summary of Findings
The results of this study showed that effort and motivation are better
predictors of academic achievement than Gardner’s multiple intelligences. In
fact, six of the eight areas of intelligences showed a negative correlation with
both test scores and class grades. The only two areas of intelligence to show
slight positive correlations were logical and visual/spatial. The best predictor of
both test grades and class grades was the SAMRS score. The assignment
completion rate was a good predictor of class grades, but not test grades.
Conclusions and Recommendations
This study indicates that the commonly held notion that intelligent people
will succeed in school may be flawed. I found that intelligence, in the form of
multiple intelligences, has very little to do with academic achievement. On the
Predicting Academic Achievement 25
other hand, effort and motivation lead to academic achievement regardless of
intelligence. This finding supports the results of Holloway (1987) who showed
that effort is prized by Japanese society where academic achievement is high,
whereas intelligence is valued in American society where academic achievement
is comparably low. These results also are comparable to those obtained in
numerous studies into motivation and achievement (Covington, 2000; Vallerand
and Bissonnette, 1992)
The only area of intelligence to show a slight correlation with academic
achievement was logical intelligence. The department tests I must give consist
of 35-50 multiple choice questions. Students who are logical thinkers will be
better able to sort through the choices presented and eliminate the illogical
answers, thus increasing their chances of choosing the correct answer. While it
is outside the scope of the questions asked in this study, I think these particular
results indicate the need to reexamine the tests my department is giving.
Since this study indicates that effort is the best predictor of academic
achievement, I will use these results to teach my students about the
characteristics of a successful student. Students who come to school prepared,
volunteer for extra credit, complete their assignments, and ask for help will
succeed in my class. I will emphasize these qualities to my students in the
hopes of motivating those who have always believed success in science is
reserved for the “smart” kids.
In classrooms, teachers must emphasize the importance of effort. At the
youngest ages, parents and teachers must reward our students for their efforts.
Predicting Academic Achievement 26
We cannot allow students to get away with the excuse that they are just not
smart in science, or math, or reading. This study shows that, in my class, the oft
heard phrase “I suck at science” is an excuse for laziness. With the proper
motivation and effort, any student can realize high academic achievement.
Students enter my classroom having grown up in our society which
encourages instant gratification and minimal effort for maximum results (Colvin,
2004; “Who wants”, 2003). From commercials for miracle diet pills to multimillion dollar “liability” lawsuit judgments, students are inundated with this
message from a young age. I must work against this emerging societal norm in
my classroom to raise student achievement. Most teachers face this battle with
their students. Often if students don’t see an immediate payback, they will not
put forth any effort. I believe this is the biggest problem currently facing our
schools.
Limitations of the Study
I believe my findings on intelligence were not as strong as they could have
been had I been able to give my students and IQ test. I have found no research
regarding a link between multiple intelligences and academic achievement. Most
research examining the relationship between intelligence and achievement uses
the IQ test to measure intelligence. I am not sure if my final conclusions would
have been different had I used an IQ test, but I think the effects of intelligence on
achievement certainly would have been clearer. My inability to use the IQ test
also prevented me from comparing my results to the plethora of research based
on IQ testing. There is certainly a need for more research examining the
Predicting Academic Achievement 27
relationships between IQ scores and multiple intelligence scores and between
multiple intelligences and academic achievement.
While my findings are limited to my classroom, anecdotal evidence from
my colleagues indicates that they see the same trends in their students. In the
future I would like to broaden my study to my entire department or school.
Another limitation to my study was the fact that, due to time and resource
constraints, I had to ignore the multitude of outside factors which can affect
academic achievement such as socioeconomic status, parental education level,
past school success, peer groups, and cultural background (Myers, Nichols &
White, 2003; Maree & Ebersöhn, 2002; Nichols & White, 2001; Spera, 2005;
Davis-Kean, 2005; Lubienski & Lubienski, 2005; Reis & McCoach, 2000; Pirozzo,
1982).
Some questions raised by this study that should be examined in future
research are:
1. Will the same results be seen department-wide or school-wide?
2. What, if any, relationship exists between multiple intelligences and IQ?
3. What is the relationship between multiple intelligences and academic
achievement in other subjects (such as English or math)?
4. What are some successful motivational techniques to raise student effort
and subsequently raise student achievement in my classroom?
Predicting Academic Achievement 28
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Predicting Academic Achievement 31
Appendix A
Multiple Intelligences Descriptions
Visual/Spatial (picture smart):
 Ability to picture and manipulate objects in your head
 Good at art, visual puzzles, graphing, map reading
Kinesthetic (body smart):
 Strong athletic ability
 Good coordination
 Good at reading body language
Naturalistic (nature smart):
 Notices patterns in the natural environment
 Good sensory perception
 Knows and remembers things about plants and animals
Musical (music smart):
 High ability to create music and interpret sounds
 Keen discrimination between musical notes and tones and voices
Logical (math smart):
 Ability to see patterns and connections, think inductively
 See geometric and numerical patterns
 Good with computers and puzzles
 Can process abstract concepts
Intrapersonal (myself smart):
 Strong sense of self
 Ability to resist peer pressure and take on leadership roles
Interpersonal (people smart):
 Ability to work well in groups
 Good sense of others’ feelings, empathetic
Linguistic (word smart):
 Ability to acquire and process language
 Can process abstract concepts
 Good at reading and writing
Predicting Academic Achievement 32
Appendix B
Multiple Intelligence Assessment
Now answer these questions by clicking on the box that you most agree with. There are 40 questions. You
will need to answer every question before you click the "Finish" button.
This is
not like
me at all
I can link things together and
pick out patterns easily.
I can recognize and name
different types of birds, trees
and plants.
I enjoy logic problems and
puzzles.
I learn best when I have to get
up and do it for myself.
I like to think through problems
while I walk or run.
I know myself well.
I am sensitive to the moods
and feelings of others.
I am interested in why people
do the things they do.
My mood changes when I listen
to music.
I have a good sense of balance
and like to move around a lot.
Pollution makes me angry.
I like to make lists.
I am good at mathematical
problems and using numbers.
I enjoy games involving other
people.
I like to work with my hands.
I can pick out different
instruments when I listen to a
piece of music.
I can use lots of different words
to express myself.
I remember things like
telephone numbers by
repeating them to a rhythm.
I enjoy social events like
parties.
I can take things apart and put
them back together easily.
I am very This is a
This is
I am like this
rarely like bit like sometimes like more often
this
me
me
than not
I am
always
like this
Predicting Academic Achievement 33
This is not I am very This is a
This is
I am like this
I am
like me at rarely like bit like sometimes like more often always like
all
this
me
me
than not
this
I have a good sense of
direction.
I learn well from listening to
others.
I like to use charts and
diagrams in my learning.
I like to work with a team.
I enjoy making music.
I can sort out arguments
between friends.
I enjoy being outdoors when I
learn.
I like to think out loud.
I like working and thinking on
my own and quietly.
I enjoy working on my own.
I always do things one-step at
a time.
I find it easy to explain to
others.
I enjoy writing things down.
I keep or like pets.
I am observant. I often see
things that others miss.
I am an independent thinker. I
know my own mind.
I need to see something in it
for me before I want to learn
something.
I can remember pieces of
music easily.
I get restless easily.
I can picture scenes in my
head when I remember
things.
Predicting Academic Achievement 34
Appendix C
Sample Multiple Intelligence Results
Predicting Academic Achievement 35
1. Chooses to work above and beyond what is expected (extra
credit, etc.)
2. Brings in materials (pictures, newspaper clippings, old
coins, etc.) related to classroom activities.
3. Is prepared for class daily (paper, pencil/pen, etc.)
4. Sticks with a task until it is completed.
5. Attempts to solve problems that others have difficulty with.
6. Chooses minimum over maximum assignment.
7. Asks questions to better understand materials being studied
or to aid in solving assignments.
8. Refuses to do assignments or homework.
9. Finds the answers to the assigned questions.
10. Participates in class discussions or activities.
11. Carelessly hurries through assignments.
12. Does something over again just to get it right.
13. Tries to avoid competitive situations.
14. Shows enthusiasm toward class studies.
15. Hesitates to start something that might lead to failing.
Always
Frequently
Occasionally
Seldom
Never
Appendix D
School Achievement Motivation Rating Scale
Predicting Academic Achievement 36
Always
Frequently
Occasionally
Seldom
Never
Appendix E
Key to SAMRS
1. Chooses to work above and beyond what is expected (extra
credit, etc.)
1
2
3
4
5
2. Brings in materials (pictures, newspaper clippings, old
coins, etc.) related to classroom activities.
1
2
3
4
5
3. Is prepared for class daily (paper, pencil/pen, etc.)
1
2
3
4
5
4. Sticks with a task until it is completed.
1
2
3
4
5
5. Attempts to solve problems that others have difficulty with.
1
2
3
4
5
6. Chooses minimum over maximum assignment.
5
4
3
2
1
7. Asks questions to better understand materials being studied
or to aid in solving assignments.
1
2
3
4
5
8. Refuses to do assignments or homework.
5
4
3
2
1
9. Finds the answers to the assigned questions.
1
2
3
4
5
10. Participates in class discussions or activities.
1
2
3
4
5
11. Carelessly hurries through assignments.
5
4
3
2
1
12. Does something over again just to get it right.
1
2
3
4
5
13. Tries to avoid competitive situations.
5
4
3
2
1
14. Shows enthusiasm toward class studies.
1
2
3
4
5
15. Hesitates to start something that might lead to failing.
5
4
3
2
1
Predicting Academic Achievement 37
Appendix F
Sample Unit Exam Questions
Cell Exam
1. Which means of particle transport requires input of energy from the cell?
a. Facilitated diffusion
b. Osmosis
c. Active transport
d. Diffusion
2. Which organelle makes proteins using coded instructions that come from the nucleus?
a. Golgi apparatus
b. Vacuole
c. Ribosome
d. Mitochondrion
3. You won’t find a cell wall in which of these kinds of organisms?
a. Plants
b. Animals
c. Fungi
d. None of the above
4. The cell theory applies to
a. Bacteria
b. Plants and animals
c. Multicellular organisms
d. All of the above
Cellular Energy Exam
1. Which of the following statements about enzymes is NOT true?
a. Enzymes work best at a specified pH.
b. All enzymes work inside cells.
c. Enzymes are proteins.
d. Enzymes are organic catalysts.
2. A student is collecting gas given off from a plant in bright sunlight at a temperature of
27°C. The gas being collected is probably
a. Oxygen
b. Carbon dioxide
c. ATP
d. Vaporized water
3. The stroma is the space that surrounds
a. Thylakoids
b. Chloroplasts
c. Plant cells
d. All of the above
4. Which of the following passes high energy electrons into the electron transport chain?
a. NADH and FADH2
b. ATP and ADP
c. Citric acid
d. Acetyl-CoA
Molecular Genetics Exam
1. Genes contain instructions for assembling
a. Purines
b. Nucleosomes
c. Proteins
d. Pyrimidines
Predicting Academic Achievement 38
2. Which of the following statements is false?
a. Some genes code for enzymes.
b. The instructions for making some proteins are not specified by genes.
c. An organism’s inherited traits depend on proteins.
d. An organism’s genes determine its inherited traits.
3. If a specific kind of protein is not continually used by a cell, the gene for that protein is
a. Always transcribed
b. Never expressed
c. Turned on or off at different times
d. Not regulated
4. Mutations may spread through a population if they
a. Occur in body cells
b. Occur in sex cells
c. Can be passed from one generation to the next
d. Both B and C
Mendelian Genetics Exam
1. When Gregor Mendel crossed true-breeding tall plants with true-breeding short plants, all
of the offspring were tall because
a. The allele for tall plants is recessive
b. The allele for short plants is dominant
c. The allele for tall plants is dominant
d. They were true breeding like their parents
2. Organisms that have two identical alleles for a particular trait are said to be
a. Hybrid
b. Homozygous
c. Heterozygous
d. Dominant
3. If an organism’s diploid number is 12, its haploid number is
a. 12
b. 6
c. 24
d. 3
4. Which of the following is caused by a dominant allele?
a. Huntington’s disease
b. PKU
c. Tay-Sachs disease
d. None of the above
Predicting Academic Achievement 39
Appendix G
Punnett Square Exam
Answer all questions on the provided answer sheet. Show all work in the spaces provided.
1. In the P generation, a tall plant (T) is crossed with a short plant (t). What is the
probability that an F2 plant will be tall?
2. If a man with blood type A and a woman with blood type B, both of whom are
heterozygous, produce an offspring, what are the offspring’s possible blood types? What
are the genotypes of the parents?
3. In watermelon plants, the allele for solid green fruit (G) is dominant over the allele for
striped fruit (g). Pollen from a flower of a homozygous striped watermelon plant is used
to pollinate a flower from a heterozygous green watermelon plant. What are the
genotypes of the parents? What percent of the offspring of this cross will have striped
watermelon?
4. In a population of pigs, curly tails (T) are dominant over straight tails (t). If two parents
are heterozygous for this trait, what percent of their offspring will have straight tails?
5. A colorblind woman marries a man who has normal color vision. What are the chances
of having a colorblind daughter?
6. In certain plants, the alleles for flower color, red (R) and white (W), show incomplete
dominance. In the P generation, a white flower is crossed with a red flower. Name the
phenotype ratios of the F1 and F2 generations.
7. A man with blood type A marries a woman with blood type AB. Name the one blood type
that their offspring could NOT be.
8. Black hair (B) is dominant over white hair (b). Two black mice are mated several times.
They produced 23 black and 8 white offspring. What are the parents’ genotypes?
9. Hemophilia is a recessive, sex-linked disease. If a carrier female marries a normal male,
what are the possible genotypes of the offspring? What are the genotypes of the
parents? What are the expected phenotypes of the offspring?
10. What allele combinations would be found in the gametes of a pea plant whose genotype
is RrTt?
11. If a pea plant that is heterozygous for round, yellow peas (RrTt) is crossed with a pea
plant that is homozygous for round peas but heterozygous for yellow peas (RRTt), how
many different phenotypes are their offspring expected to show?
12. Gerald bred his two pet aliens together. One was purebred for red eyes and little ears
and the other was purebred for green eyes and big ears. Red eyes (R) are dominant
over green eyes (r) and big ears (E) are dominant over little ears (e). Gerald’s aliens
follow Mendel’s laws of inheritance. What phenotypic ratio would Gerald expect to find in
the F2 generation?
Predicting Academic Achievement 40
Appendix H
Alternate Molecular Genetics Exam
Answer each question in complete sentences.
1. From Griffith’s experiment, explain why the mouse died when injected with a mixture of
heat-killed disease causing bacteria and live harmless bacteria.
2. Draw a DNA molecule. Label the sugar phosphate backbone. Show at least five base
pairs.
3. Replicate the following DNA strand:
ATCGTCAGT
4. Transcribe, then translate the following DNA strand:
TACGAATCGAGTGGC
5. What would be the tRNA anticodon for the mRNA codon AUG?
6. The process of making RNA from DNA is called
and it occurs in the
. The process of assembling a protein from RNA is called
and it occurs on the
.
7. Which type of mutation has a larger effect on the final protein sequence, a point mutation
or a frameshift mutation? Explain.
8. List two differences between DNA and RNA.