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The relationship between motivational factors and engagement in an urban high school setting

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The relationship between motivational factors and engagement in an urban high school setting

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Content Running head: MOTIVATIONAL FACTORS AND ENGAGEMENT i

THE RELATIONSHIP BETWEEN MOTIVATIONAL FACTORS AND
ENGAGEMENT IN AN URBAN HIGH SCHOOL SETTING

by

Glenn Kunitoshi Seki

_______________________________________________________

A Dissertation Presented to the
FACULTY OF THE USC ROSSIER SCHOOL OF EDUCATION
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF EDUCATION

August 2014

Copyright 2014 Glenn Kunitoshi Seki
MOTIVATIONAL FACTORS AND ENGAGEMENT

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Dedication
This dissertation is dedicated to my wife Pamela, without whose love and support
this would not have been possible.
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Acknowledgements
This dissertation would not have been possible without the help, support, advise,
understanding, and expertise of my dissertation chair, Dr. Robert Rueda. To Dr. Dennis
Hocevar, thank you for all your help and expertise with the statistics section, I never
could have completed it without your help. To Dr. Gustavo Loera, thank you for
investing your time, energy, and commitment to my dissertation. To my wife Pamela, I
never would have finished without your love and support.
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Abstract
The purpose of this study was to examine the relationship between ethnicity and
grade level, motivational factors (instrumental motivation, reading, math, self-
efficacy/self-beliefs, learning strategies, and academic self-concept) and learning
strategies, and engagement (effort and perseverance) in a large urban high school setting.
A regression analysis was conducted to determine the relationships between the variables
to determine two path models, one for ethnicity as the predictor variable and one for
grade level as the other predictor variable.
Both ethnicity and grade level proved to be small, but significant, predictors for
the motivational factors examined (instrumental motivation, reading, math, self-
efficacy/self-beliefs, and academic self-concept), and learning strategies. The mediating
variables (instrumental motivation, reading, math, self-efficacy/self-beliefs, learning
strategies, and academic self-concept), proved to be significant predictors for effort and
perseverance while controlling for ethnicity and grade level.

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Table of Contents
Dedication ........................................................................................................................... ii
Acknowledgements ............................................................................................................ iii
Abstract .............................................................................................................................. iv
Chapter 1: Introduction ....................................................................................................... 1
Background of the Problem ................................................................................................ 1
Statement of the Problem .................................................................................................... 3
Purpose of the Study ........................................................................................................... 3
Research Questions ............................................................................................................. 4
Significance of the Problem ................................................................................................ 4
Definition of Terms ............................................................................................................. 4
Delimitations ....................................................................................................................... 9
Assumptions ........................................................................................................................ 9
Limitations .......................................................................................................................... 9
Chapter 2: Review Of The Literature ................................................................................ 10
Motivation ......................................................................................................................... 11
Competence and Expectancy ............................................................................................ 12
Expectancy and Value Constructs ..................................................................................... 18
Reasons for Engagement ................................................................................................... 22
Connections Between Motivational and Cognitive Processes .......................................... 27
Learning Strategies ............................................................................................................ 31
Research with Diverse Learners ........................................................................................ 32
Summary of Research with Diverse Learners ................................................................... 36
Summary of Research with Grade Level .......................................................................... 38
Conclusion ......................................................................................................................... 39
Chapter 3: Research Methodology .................................................................................... 41
Research Questions ........................................................................................................... 41
Methodology ..................................................................................................................... 41
Research Design ................................................................................................................ 42
Chapter 4: Findings ........................................................................................................... 49
Overview of Findings ........................................................................................................ 62
Chapter 5: Discussion ....................................................................................................... 65
Implications for the District .............................................................................................. 72
Future Research ................................................................................................................. 73
References ......................................................................................................................... 75
Appendix: Survey Questions ............................................................................................. 93

List of Tables

Table 1: Demographic and Background Profile of Sample (N = 3180) .......................... 42
Table 2: Summary of Survey Items Used in Study ............................................................ 44
Table 3: Coefficient Alphas of Survey Items Used in Study ............................................. 49
Table 4: Means and Standard Deviations for Ethnicity and Grade Level ....................... 50
Table 5: Correlations Among the Variables, Means, and Standard Deviations .............. 51
MOTIVATIONAL FACTORS AND ENGAGEMENT

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Table 6: Summary of Multiple Regression Analysis for Ethnicity and Instrumental
Motivation, Reading, Math, Self-Efficacy/Self-Beliefs, Learning Strategies,
Academic Self-Concept, and Effort and Perseverance ...................................... 54
Table 7: Summary of Multiple Regression Analysis for the Mediating Variables,
Instrumental Motivation, Reading, Math, Self-Efficacy/Self-Beliefs,
Learning Strategies, and Academic Self-Concept, and the Dependent
Variables Effort and Perseverance While Controlling for Ethnicity ................. 55
Table 8: Summary of Multiple Regression Analysis for Grade Level and Instrumental
Motivation, Reading, Math, Self-Efficacy/Self-Beliefs, Learning Strategies,
Academic Self-Concept, and Effort and Perseverance ...................................... 60
Table 9: Summary of Multiple Regression Analysis for the Mediating Variables,
Instrumental Motivation, Reading, Math, Self-Efficacy/Self-Beliefs,
Learning Strategies, and Academic Self-Concept, and the Dependent
Variables Effort and Perseverance While Controlling for Grade Level ............ 61

List of Figures

Figure 1. The proposed path model predicting the relationship between effort and
perseverance and ethnicity with instrumental motivation, reading, math,
self-efficacy/self-beliefs, learning strategies, and academic self-concept
serving as mediating variables. .......................................................................... 47
Figure 2. The proposed path model predicting the relationship between effort and
perseverance and grade level with instrumental motivation, reading, math,
self-efficacy/self-beliefs, learning strategies, and academic self-concept
serving as mediating variables. .......................................................................... 48
Figure 3. The path model predicting the relationship between effort and perseverance
and ethnicity with instrumental motivation, reading, math, self-efficacy/self-
beliefs, learning strategies, and academic self-concept serving as mediating
variables. ............................................................................................................ 57
Figure 4. The path model predicting the relationship between effort and perseverance
and grade level with instrumental motivation, reading, math, self-
efficacy/self-beliefs, learning strategies, and academic self-concept serving
as mediating variables. ....................................................................................... 62

MOTIVATIONAL FACTORS AND ENGAGEMENT

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Chapter 1: Introduction
Background of the Problem
Robert J. Samuelson wrote in his Newsweek article, “Why School ‘Reform’ Fails:
Student motivation is the problem;”
If the students aren’t motivated, even capable teachers may fail. Motivation
comes from many sources: curiosity and ambition; parental expectations; the
desire to get into a “good” college; inspiring or intimidating teachers; peer
pressure. The unstated assumption of much school “reform” is that if students
aren’t motivated, it’s mainly the fault of schools and teachers. The reality is that,
as high schools have become more inclusive (in 1950, 40 percent of 17-year-olds
had dropped out) and adolescent culture has strengthened, the authority of
teachers and schools has eroded. That applies more to high schools than to
elementary schools, which helps explain why early achievement gains evaporate
(Samuelson, 2010).

Samuelson’s (2010) article has “hit the nail on the head,” according to
administrators of California Unified School District (CUSD), a large urban K-12 school
district. They go on to say, “our test scores are only a little better than the state’s
(California’s) average test scores… Standardized test scores have become the measure of
academic success for the district.”
To illustrate the poor performance of California’s high school students reading
and math proficiency, the California Department of Education’s 2010 Standardized
Testing and Reporting (STAR) Program results showed that far too many high school
students struggle to meet grade level expectations in Math and Language Arts. Across
California, only 31% of all students scored Proficient or above in Algebra I and only 43%
scored Proficient or above in 11th-grade English Language Arts. In Algebra I, grades 8-
11, 61% of African American students are Below/Far Below Basic and 54% of Latino
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students are Below/Far Below Basic. While only 33% of White students are Below/Far
Below Basic and 17% of Asian students are Below/Far Below Basic. In the 11th-grade
English Language Arts CST scores, 72% of African American students were Basic or
below and 70% of Latino students were Basic or below (The Education Trust–West,
2010).
In 2003 the Algebra I Grades 8-11 CST scores showed, 68% of African American
students, 63% of Latino students, 37% of White students, and 24% of Asian students
failed to reach Basic (The Education Trust–West, 2008). In seven years (2003 – 2010)
there was only an improvement of 7% for African American students, 5% for Latino
students, 4% for White students, and 7% for Asian students in their Algebra I Grades 8-
11 CST scores. Growing are vast gaps between low-income, Latino and African-
American students, and their White, Asian and more affluent peers (The Education
Trust–West, 2010).
The California Unified School District (CUSD) CST scores for high school
African-American, Latino, Asian, White students are similar to the state averages. In
Algebra I, grades 8-11, 57% of African American students are Below/Far Below Basic
and 51% of Latino students are Below/Far Below Basic. While only 30% of White
students are Below/Far Below Basic and 31% of Asian students are Below/Far Below
Basic. In the 11th-grade English Language Arts CST scores, 45% of African American
students, 40% of Latino students, 19% of White students, and 24% of Asian students
failed to reach Basic (California Department of Education, 2010). The administrators of
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CUSD agree with Samuelson’s (2010) article and believe that motivation is a problem
with high school students and contribute to their low CST scores.
Statement of the Problem
Low motivation is a problem with high school students and leads to low academic
achievement. The administrators of CUSD would like a model of the interactions
between motivational factors and engagement for their students.
Purpose of the Study
The purpose of this study is to examine the relationship between motivational
factors, learning strategies, and engagement in a CSUD high school setting. CUSD will
use this information as an initial step in developing a research approach to developing
interventions and professional development curriculum for the teachers and
administrators assigned to their high schools. They will also use this information to better
target specific groups of students based on their motivational beliefs.
This study was a secondary analysis of an existing data set. The data was
collected by the CUSD. The data set contains information on a self-reported survey of
motivational factors and learning strategies for all students in a single high school.
Motivational beliefs (self-efficacy, self-concept, effort and perseverance, and interest),
and learning strategies (elaboration, memorization, and control strategies), will be
addressed for this study. A thorough description of this data set will be provided in the
methods section.

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Research Questions
Research Question 1
What is the relationship between ethnicity and motivational factors and learning
strategies, and engagement with urban high school students?
Research Question 2
What is the relationship between grade level and motivational factors and learning
strategies, and engagement with urban high school students?
Significance of the Problem
The study will provide a model of motivational factors and engagement of
students in one CSUD high school. The results will then help CUSD develop
intervention, curriculum, and professional development programs to meet the needs of
their students. The study will also add to the body of literature by providing a study,
incorporating a large number of students in an urban high school, and by developing a
motivational factors and engagement model.
Definition of Terms
CST – “The California Standards Tests in English-language arts, mathematics,
science, and history-social science are administered only to students in California public
schools. Except for a writing component that is administered as part of the grade four and
seven English-language arts tests, all questions are multiple choice. These tests were
developed specifically to assess students’ knowledge of the California content standards.
The State Board of Education adopted these standards that specify what all California
children are expected to know and be able to do in each grade or course. The 2004 CSTs
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were required for students who were enrolled in the following grades/courses at the time
of testing or who had completed a course during the 2003-04 school year, including 2003
summer school” (California Department of Education, 2004).
Standardized Testing and Reporting (STAR) Program - The 2004 STAR Program
included four components:
1. California Standards Tests (CSTs)
2. California Alternate Performance Assessment (CAPA)
3. California Achievement Tests, Sixth Edition Survey (CAT/6 Survey)
4. Spanish Assessment of Basic Education, Second Edition (SABE/2)
The CSTs are a major component of California’s accountability system for
schools and districts. CST and CAPA results are the major components used for
calculating each school’s Academic Performance Index (API). These results are also used
for determining if elementary and middle schools are making adequate yearly progress in
helping all students become Proficient on the state’s academic content standards as
required by the federal No Child Left Behind Act of 2001 (California Department of
Education, 2004).
“CSTs measure the degree to which students are achieving the academically
rigorous content standards adopted by the State Board of Education. California uses five
performance levels to report student achievement on the CSTs: Advanced, Proficient,
Basic, Below Basic, and Far Below Basic.
The performance levels for each grade and subject area are based on scale scores
that range between 150 and 600. The score dividing the basic scores from below basic is
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300 for every grade and subject area. The score dividing basic scores from proficient
scores is 350 for every grade and subject area. Tables that include the score range for
each grade level, subject area, and performance level are available on the California
Department of Education Web site. The target is for all California students to score at
Proficient or above” (California Department of Education, 2004).
Extrinsic motivation - “a construct that pertains whenever an activity is done in
order to attain some separable outcome” (Ryan & Deci, 2000b, p. 60). Ryan and Deci
(2000a) explain that in the Self-Determination Theory (SDT), extrinsic motivation can
vary greatly in the degree to which it is autonomous versus the perspectives that view
extrinsically motivated behavior as being invariantly non-autonomous.
Instrumental motivation – “Instrumental rewards are a motivating source when
individuals believe that the behaviors they engage in will lead to certain outcomes such as
pay, praise, etc.” (Schmidt, 2013, p. 2).
Intrinsic motivation - performing an activity for its inherent satisfaction (Ryan
and Deci, 2000a). Ryan and Deci’s (2000a) approach focuses primarily on the innate
psychological need for competence, autonomy, and relatedness. According to the authors,
quality of individual motivation and mental health are determined by the extent to which
these needs are considered. They also address the importance of intrinsically interesting
activities that emphasize the classic developmental appeal of novelty, spontaneity, and
challenge to individual motivation.
Mediator variables – “In general, a given variable may be said to function as a
mediator to the extent that it accounts for the relation between the predictor and the
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criterion. Mediators explain how external physical events take on internal psychological
significance… mediators speak to how or why such effects occur” (Baron & Kenny 1986,
p. 1176). A mediator variable is one that explains the relationship between the predictor
and the criterion.
Mental effort – amount of mental energy/effort one commits to a task (Pintrich &
Schunk, 2002)
Moderator variables – “In general terms, a moderator is a qualitative (e.g., sex,
race, class) or quantitative (e.g., level of reward) variable that affects the direction and/or
strength of the relation between an independent or predictor variable and a dependent or
criterion variable. Specifically within a correlational analysis framework, a moderator is a
third variable that affects the zero-order correlation between two other variables... In the
more familiar analysis of variance (ANOVA) terms, a basic moderator effect can be
represented as an interaction between a focal independent variable and a factor that
specifies the appropriate conditions for its operation” (Baron & Kenny 1986, p. 1174). A
moderator variable is one that influences the strength (plus or minus) of a relationship
between two other variables.
Motivation - Motivation is among one of the most studied topics in educational
psychology. The construct itself is so wide-ranging that sub-categories are justified to
provide for a more thorough understanding of just what the word “motivation” means.
Rooted in the Latin word “to move,” motivation generally describes the relationship
between the internal processes of beliefs, values, and goals with the external expression
of action, such as choice, perseverance, and performance (Eccles & Wigfield, 2002;
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Wigfield & Eccles, 2000). According to Ryan and Deci (2000b), to be motivated means
to be moved to do something. Someone who is energized toward an end is considered
motivated, while someone who feels no inspiration to act is considered unmotivated.
People have different amounts (different levels of motivation) and different kinds of
motivation (different orientation or type of motivation) (Ryan & Deci, 2000b).
Perseverance – continuing to work at a task, even in the face of obstacles, to
completion (Pintrich & Schunk, 2002).
Personal interest – “a personality trait or a personal characteristic of the
individual that is relatively stable, enduring disposition of the individual” (Pintrich &
Schunk, 2002, p. 301). Personal interest has been measured as a general liking for a
subject area (e.g., I like math.) (Pintrich & Schunk, 2002).
Reciprocal determinism – “is the term coined by Bandura (1978) to describe the
foundation of his theory of social cognition – psychological functioning involves a
continuous reciprocal interaction among behavior, cognitive, and environmental,
influences” (Williams & Williams, 2010, p. 453).
Self-concept - generally refers to “the composite of ideas, feelings, and attitudes
people have about themselves” (Hilgard, Atkinson, & Atkinson, 1979, p. 605).
Self-efficacy – “is defined as people's beliefs about their capabilities to produce
designated levels of performance that exercise influence over events that affect their
lives. Self-efficacy beliefs determine how people feel, think, motivate themselves and
behave. Such beliefs produce these diverse effects through four major processes. They
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include cognitive, motivational, affective and selection processes” (Bandura, 1994, p.
71).
Learning strategies – “the plans that students draw up and carry out to achieve
their learning goals.” (Marsh, Hau, Artelt, Baumert, & Peschar, 2006, p. 317)
Delimitations
Since the study only involves high school students from one large high school in
an urban school district, generalizations can only be drawn to like subjects with similar
environments. Generalizations cannot be drawn to middle school and elementary students
or from non-urban high schools or from urban high school with different environments.
Assumptions
It is assumed that there was no systematic bias in the missing data and that the
students answered the questions truthfully.
Limitations
The district determined which high school was chosen for the survey and when it
was administered. The survey was entirely based on self-reported data. (Students that
tend to rate one question highly, may also rate other questions highly even in the absence
of associations between the questions.) The district also determined what additional
information was included in the data set given to the researcher above and beyond the
survey. The study will not be able to determine underlying causal mechanisms.
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Chapter 2: Review Of The Literature
The purpose of this chapter is to provide a review of the literature related to the
motivational factors and learning strategies sections of the survey (used by CUSD) and
how they relate to engagement factors of urban high school students. The motivational
factors and learning strategies questions used in the CUSD survey were adapted from a
survey that was developed by Marsh, Hau, Artelt, Baumert, and Peschar, (2006). The
motivational factors questions survey self-efficacy, self-concept, effort and perseverance,
and intrinsic motivation as represented by domain-specific interest (interest in reading
and interest in mathematics) while the learning strategies questions cover elaboration,
memorization, and control strategies.
The literature review focuses on the specific studies that include one or more of
the variables used in the survey (self-efficacy, self-concept, effort and perseverance,
intrinsic motivation, elaboration, memorization, and control strategies).
The review is divided into seven sections; motivation, competence and
expectancy (self-efficacy, self-concept and self-efficacy, and control theories),
expectancy and value constructs (attribution theory, expectancy-value model), reasons for
engagement (intrinsic and extrinsic motivation, interest, and goals), connections between
motivational and cognitive process (social cognitive theories of self-regulation and
motivation, linking motivation and cognition, and integrating expectancy-value and self-
regulation models), learning strategies, research with diverse learners, and research with
grade level.
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Motivation
“Motivation is the process whereby goal-directed activity is instigated and
sustained” (Schunk, Pintrich, & Meece, 2008, p. 4). Schunk et al. (2008) describe
motivation as a process rather than a product and that as a process, one does not observe
motivation directly. One can infer motivation from actions such as choice of tasks, effort,
perseverance, and verbalizations (e.g., “I really like math”). Motivation includes goals
that provide incentive for and direction to action. Individuals may not have their goals
well formulated and may change over time, but they are conscious of something that they
are trying to attain or avoid. Motivation requires mental and/or physical activity. Mental
activity includes making decisions, planning, rehearsing, organizing, monitoring,
assessing progress, and solving problems, while physical activity involves effort,
perseverance, and other overt actions. Motivational activity is also instigated and
sustained. Many major goals are long term (e.g., earning a college degree) and starting
towards a goal can often be difficult because it involves making a commitment and these
motivational processes are critically important to sustain continued action (Schunk et al.,
2008).
Schunk et al. (2008) explain how motivation can influence what, when, and how
one learns. Students that are motivated to learn about a particular topic are more likely to
engage in activities they believe will help them learn about the topic they are interested
in. These motivated students would more likely listen carefully to instruction, organize
and rehearse the information to be learned, take good notes, check their level of
understanding, and ask for help if they need it. Students unmotivated to learn would not
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likely be systematic in their efforts to learning.
“Motivation bears a reciprocal relation to learning and performance… motivation
influences learning and performance and what students do and learn influences their
motivation” (Schunk et al., 2008, p. 6). When a student attains their learning goals, this
goal attainment conveys to them that they have the capability to succeed in learning.
These beliefs then motivate them to set higher goals for themselves. Students that are
often motivated to learn find that over time they are intrinsically motivated to continue
learning (Schunk et al., 2008).
Competence and Expectancy
There are several theories that focus on an individual’s beliefs about their
competence and efficacy, these are their expectancies for success or failure, their sense of
control of the outcomes. The question, “Can I do this task?” is directly related to their
beliefs. When individuals answer this question affirmatively they generally perform
better and are motivated to select more challenging tasks (Eccles & Wigfield, 2002).
Self-Efficacy
Researchers have defined self-efficacy as an individual’s confidence in their
ability to organize and carry out a given course of action to accomplish a task or solve a
problem. Therefore, some individuals have a strong sense of self-efficacy while others do
not. Some people have efficacy beliefs that include several situations, while others have
very narrow efficacy beliefs. Some believe they are effective even on the more difficult
tasks, while others believe they can only be successful on easier tasks (Bandura, 2006;
Bong, 2004; Baumeister, Campbell, Krueger, & Vohs, 2003).
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Researchers differentiate between two kinds of expectancy beliefs, outcome
expectations and efficacy expectations. Outcome expectations refer to beliefs that certain
behaviors will lead to certain outcomes, whether one expects to succeed or fail at a
certain task, (e.g., the belief that studying will improve one’s test scores). Efficacy
expectations refer to the beliefs about whether one can effectively perform the behaviors
necessary to produce the outcome, (e.g., one can study sufficiently hard enough to
perform well on the test). These two expectancy beliefs are different because people can
believe that a certain behavior will produce a certain outcome (outcome expectation), but
may not believe they can sufficiently perform the behavior (efficacy expectation). An
individual’s efficacy expectations are the major determinant of goal setting, activity
choice, effort, and perseverance (Bandura, 2006; Baumeister et al., 2003; Bong, 2004).
Usher and Pajares (2009) conducted a study that developed and validated items to
assess Bandura’s (2006, 1997) theorized sources of self-efficacy among middle school
mathematics students, grades six, seven, and eight. The study revealed that each of the
four sources of self-efficacy (mastery experience, vicarious experience, social
persuasions, and physiological state) correlated significantly with the four mathematics
self-efficacy measures (grade self-efficacy, mathematics skills self-efficacy, courses self-
efficacy, and self-efficacy for self-regulated learning) with motivational constructs (such
as mathematics self-concept, task goals, and optimism). Their final model was invariant
across gender and ethnicity (White and African American) and subscales correlated with
self-efficacy, self-concept, mastery goals, and optimism. Their results suggested that the
source scales is psychometrically sound and could be adapted for use in other domains.
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Williams and Williams (2010) stated the reciprocal determinism (according to
Bandura (1978) is the reciprocal interaction among behavioral, cognitive, and
environmental influences) of self-efficacy and performance seems to be without direct
empirical support, probably because the longitudinal, repeated measures data often
considered necessary for this purpose are not available. They went on to say it is possible
to model reciprocal effects with cross-sectional data. They achieved this using a
structural equation model in which the mutual influence of self-efficacy and performance
in mathematics is represented as a feedback loop. This model was estimated in each of 33
nations on the basis of data on the mathematics self-efficacy and mathematics
achievement of 15-year olds. The model was a good fit to the data in 30 nations and was
supportive of reciprocal determinism in 24 of these, suggesting a fundamental
psychological process that transcends national and cultural boundaries.
Self-Concept and Self-Efficacy
Self-concept is not the same as self-efficacy or self-esteem. Self-efficacy is
oriented towards the future, “a context-specific assessment of competence to perform a
specific task” (Pajares, 1997, p. 15). The emphasis of self-efficacy is on one’s ability to
successfully accomplish a particular task with no need of comparison. Self-concept
generally refers to “the composite of ideas, feelings, and attitudes people have about
themselves” (Hilgard, Atkinson, & Atkinson, 1979, p. 605). Self-concept uses other
aspects of one’s self or other people as frames of reference; it is developed as a result of
external references. Self-concept beliefs are weaker predictors of behavior than self-
efficacy (Bandura, 2006, 1997; Bong, 2004). Self-concept contains many perceptions
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about one’s self, including self-esteem, and is a more global construct. Self-esteem and
self-concept are often incorrectly used interchangeably. Self-concept is a cognitive
structure, a belief about who you are, while self-esteem is an affective reaction, a
judgment about who you are. Self-esteem is concerned with judgments of self-worth,
while self-efficacy is concerned with judgments of personal capabilities. There is no
direct relationship between self-efficacy and self-esteem (Woolfolk, 2004).
Feelings about the self are important to understanding how students perceive their
own learning strategies and processes that direct their learning. Self-concept describes an
individual’s perceptions of him or herself that are a result of experiences and interaction
with the environment (Marshall & O’Mara, 2008; Marsh, Shavelson & Byrne, 1992).
Additionally, evaluations by others and one’s own attributions contribute to the
development of one’s self-concept. Like the constructs interest and control expectations,
self-concept is not an individual trait within the individual alone, but instead reflects the
prediction of how a person acts and will act in the future (Marshall & O’Mara, 2008).
Self-esteem is an important component of self-concept, in that it enables an evaluative
aspect to the construct. For instance, a student with high self-esteem may have the self-
concept that they are good at a particular task. In regards to academics, students’ self-
concepts differ among certain subjects. Self-worth theory (Covington, 1992) adds a
motivational component, in that it describes how an individual strives to maintain a
positive sense of the self. Eccles & Wigfield (2002) has argued that students’ competence
and achievement are linked to self-worth, and that they will strive to achieve in class to
sustain a good sense of worth (Eccles & Wigfield, 2002; Covington, 1992). In other
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words, how a student perceives him or herself is influenced by math and verbal
achievement, but the effects are isolated in each domain. For instance, math achievement
positively influence math self-concept, but not verbal self-concept, and vice versa. More
importantly, achievement in both areas are correlated with one another, but self-concept
in both areas is not (Marsh et al., 2006). This supports the importance of looking at each
domain separately to evaluate how we might improve performance in specific subjects.
Control Theories
Another type of expectancy-based theory are the locus of control theories
(Perkrun, 2006; Crandall, Katkovsky, & Crandall, 1965). According to these theories, an
individual should expect to succeed to the degree that one feels in control of one’s
successes and failures. Connell and Wellborn (1991) integrated control beliefs into a
broader framework and proposed competence, autonomy, and relatedness as three basic
psychological needs. They connected competence needs to control beliefs (i.e., A student
who believes they control their achievement outcomes should feel more competent.).
They also proposed that the ways in which these needs are fulfilled determine
engagement in different activities. When the needs of the individual are fulfilled, the
individual will be fully engaged. When the needs are not fulfilled, the individual becomes
unmotivated (Perkrun, 2009, 2006; Connell, Spencer, & Aber, 1994).
Skinner (1995) proposed a model of perceived control focusing on understanding
goal directed activity and described three critical beliefs: means-end, control, and agency
beliefs. Means-end beliefs are the expectations that certain causes can produce certain
outcomes such as attributions (Graham & Williams, 2009; Weiner, 1985) and unknown
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control. Agency beliefs are the expectations that an individual has access to the means
needed to produce different kinds of outcomes. Control beliefs are the expectations an
individual has that they can produce a desired outcome. All three beliefs influence
performance on achievement tasks (Perkrun, 2009, 2006; Skinner, Zimmer-Gembeck, &
Connell, 1998).
Anderson, Hattie, and Hamilton (2005) found that the significant school-level
differences in environment, and the significant school-level effects in terms of locus of
control, motivated behavior, and achievement meant that the effects of interventions, or
other classroom-level research across several schools, needed to be interpreted with
caution. School-level effects could interfere with the efficacy of interventions, or
compromise change at the classroom level. Their results suggested that environmental
interventions could override locus of control attributes that might disadvantage some
students. They go on to state that, on the whole, environmental variables had a more
powerful effect on academic achievement and related behavior than locus of control did.
Furthermore, some school environments eliminate between-group differences on those
measures by locus of control type. Their findings suggested that it is better to direct
energy and effort at environmental interventions, to create school environments that
foster achievement and motivation for all, and minimize between-group differences in
achievement and related behaviors than attempt to change students’ control orientations.

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Expectancy and Value Constructs
Attribution Theory
In 1985 Weiner’s attribution theory emphasizes an individual’s interpretations of
their achievement outcomes (their causal attributions or explanations) determines
subsequent achievement strivings and are key motivational beliefs (Graham & Williams,
2009). Weiner determined ability, effort, task difficulty, and luck as the principal
achievement attributions. He categorized these attributions into three causal aspects:
locus of control, stability, and controllability. The locus of control has two poles: internal
versus external locus of control; while stability captures whether causes change over time
or not, and controllability contrasts causes one can control from causes one cannot
control. For example, ability is classified as a stable, internal cause, that one cannot
control. These causal aspects influence various aspects of achievement behavior. The
locus of control is associated strongly with affective reactions, while stability influences
an individual’s expectancies for success (Graham & Williams, 2009; Forsterling, 2001;
Weiner, 1992).
Expectancy-Value Model
Eccles and her colleagues (Eccles et al., 1983) developed an expectancy-value
model of achievement performance, perseverance, and choice, that is linked to an
individual’s expectancy-related and task-value beliefs. This differs from Atkinson’s
(1964) expectancy-value theory in that both expectancy and value components are more
elaborate and are linked to a wider array of determinants, and expectancies and values are
assumed to be positively related to each other as opposed to inversely related as in
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Atkinson’s (1964) expectancy-value theory (Wigfield, Tonks, & Klauda, 2009; Higgins,
2007; Eccles & Wigfield, 2002).
The expectancy-value model developed by Eccles et al. (1983) is a social
cognitive model that focuses on the role of a student’s expectancies for academic success
and their perceived value for academic tasks. The two most important predictors of
achievement behavior are expectancy and task value. Expectancy and task value are
internal cognitive beliefs of the student, while achievement behaviors are observable and
can sometimes be overt. Expectancy pertains to the beliefs of students about their future
expectancy for success, and is usually measured by querying students to predict how well
they will do in the future on some task or in some domain (Pintrich & Schunk, 2002).
Research by Eccles and her colleagues (Wigfield et al., 2009, 2008; Higgins, 2007;
Eccles & Wigfield, 2002; Wigfield & Eccles, 2000; Eccles et al., 1983) has shown that
higher expectancies for success are positively related to all types of achievement behavior
including achievement, choice, and perseverance. Task value consists of four
components: attainment value, intrinsic value, utility value, and cost (Wigfield et al.,
2009; Higgins, 2007; Eccles et al., 1983). Attainment value is described as the personal
importance of doing well on the task. Intrinsic value is the pleasure the individual
receives from performing the activity or the subjective interest the individual has in the
subject. Utility value is defined by how well a task relates to current and future goals.
Cost is described in terms of the negative aspects of participating in the task, as well as
the amount of effort needed to succeed and the lost opportunities that result from making
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one choice over another choice (Wigfield et al., 2009; Higgins, 2007; Eccles & Wigfield,
2002).
A less empirically explored aspect of the expectancy-value model is affective
memories. These affective memories can be set in motion by anticipation of engagement
in the task and can lead to different positive or negative values for the task through
classical conditioning mechanisms or direct association (Pintrich & Schunk, 2002).
Goals and self-schemas are other motivational components of the expectancy-
value model, which includes an individual’s short-term and long-term goals as well as
their general self-schemas. Self-schemas reflect an individual’s beliefs and self-concepts
about themselves (Wigfield et al., 2009; Higgins, 2007; Eccles et al., 1983). Individuals
have beliefs about their personality and identity, including beliefs about what kind of
person they are and what kind of person they could become, as well as more domain-
specific self-concepts about their academic ability and social competence. Goals (short-
term and long-term) are cognitive statements of what an individual is striving for, or
trying to attain, which are shaped by self-schemas and self-concepts. An individual who
has as a part of their self-schema that they are someone who is good with computers may
set a long-term goal of becoming an engineer. Perceptions of task demands, the last
aspect of goals and schemas, concern an individual’s judgments of the difficulty of the
task as well as other features of the task, such as how interesting the task appears. It is
assumed that these task difficulty perceptions are relatively domain specific (Pintrich &
Schunk, 2002).
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Another cognitive and internal process concerns how an individual perceives and
interprets different events that happen to them. This interpretive process is determined by
the types of attributions an individual makes for past events and actual performances
(Pintrich & Schunk, 2002).
In addition to the individual processes associated with motivation are those
external and environmental influences that equally impact student outcomes. An
individual’s social knowledge is informed by the events they have engaged in and been
exposed to in the past, and predicts how they will act in the future. Individuals are
influenced by the explicit instruction from important others such as parents and peers
(Burks, Dodge, Price, & Laird, 1999). Bandura (1986) supported the notion of situated
learning, and his social cognitive theory asserts that learning is mediated between an
individual and their environment, where the context, or situation, plays an important role
(Bandura, 1986; Schunk, 2004). This theory emphasizes that learning occurs in a social
environment through the use of models and observation. Additionally, individuals learn
about potential consequences and outcomes of their behaviors by observing models, and
base their beliefs about their abilities on these models.
Two important contexts to consider are those at school, and in the student’s
culture and family. Researchers have found that school-related forces, such as the
instructional practices employed by teachers and the campus climate, have considerable
effects on students’ motivation and performance (Wigfield, et al., 2009; Eccles &
Midgley, 1989). Academic support from the opposite-sex parent explained the most
variation in academic motivation for 216 ninth grade Mexican-origin students living in
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intact families (Plunkett, Henry, Houltberg, Sands, & Abarca-Mortensen, 2008). Having
high attitudinal familism predicts fewer classes missed and greater academic effort for
143 Latino high school students (Esparza & Sanchez, 2008). Findings indicated that
fathers’ and teachers’ academic support were positively related to 154 Latino adolescent
boys’ academic motivation, while mothers’ and teachers’ academic support were
positively related to 156 Latina adolescent girls’ academic motivation (Alfaro, Umana-
Taylor, & Bamaca, 2006). Cultural and family-related values play an integral role in
modeling student perceptions and motivation as well, especially in populations of
students from minority backgrounds (Plunkett, Henry, Houltberg, Sands, & Abarca-
Mortensen, 2008; Esparza & Sanchez, 2008; Alfaro, Umana-Taylor, & Bamaca, 2006).
Reasons for Engagement
An individual may be certain that they can perform a task, but they may have no
compelling reason to do so. This section will address itself to the theories that focus on
the question why.
Intrinsic and Extrinsic Motivation
A source of motivation for individuals can come from internal or external reward
systems. People who are motivated to engage in a task for personal gains, such as
enjoyment or interest, are said to be intrinsically motivated, while those who engage in
the task purely for external rewards, such as money, are extrinsically motivated (Ryan &
Deci, 2009; Eccles & Wigfield, 2002; Deci & Ryan, 1985). The self-determination theory
of Deci and Ryan (1985) combines aspects of the level of stimulation and need for
competence that leads to intrinsically motivated individuals. For instance, they argued
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that people would seek out tasks that are optimally challenging because they demonstrate
competence and are intrinsically motivating. A self-determined person is one whose
intrinsic motivation is maintained by competence and personal causation (Ryan & Deci,
2009; Eccles & Wigfield, 2002). Some recent theorists have argued that intrinsic
motivation is not only situational, but also enduring as a trait within the individual. These
researchers describe this trait-like construct as consisting of a preference for challenging
tasks, learning that is curiosity-driven, and the need for competence and mastery
(Gottfriend, 1990; Eccles & Wigfield, 2002). In general, students who are intrinsically
motivated have higher rates of achievement, namely in regards to the quality of their
learning and the level of their creativity (Ryan & Deci, 2009; Ryan & Deci, 2000a).
Deci and Ryan (1985) believe that the basic needs of competence and self-
determination play a role in more intrinsically motivated behavior. For example, a student
chooses a class because they believe this will increase their job opportunities and they
will make more money. The student is led by his or her basic need for competence and
self-determination, but the choice of class is based on reasons extrinsic to the class itself.
Deci and Ryan (1985) suggest that the basic need for interpersonal relatedness explains
why individuals turn external goals into internal goals through internalization (Ryan &
Deci, 2009).
Ryan and Deci (2000a) have expanded the extrinsic-intrinsic motivation
dichotomy in their deliberation of internalization. Internalization is the process of
transferring the regulation of behavior from outside to inside the individual. They
describe four levels in the process of going from external to internal regulation. The first,
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external, is when regulation is coming from outside the individual. The second,
introjected, is the internal regulation based on feelings that has to do with ones behavior.
The third, identified, is the internal regulation based on the utility of the behavior. The
last level, integrated, is the regulation based on what the individual thinks is valuable and
important to one’s self. However, the last level is not fully internalized and self-
determined (Ryan & Deci, 2009; Eccles & Wigfield, 2002).
Otis and Pelletier (2005) examined changes in intrinsic and extrinsic motivation
during the transition from junior to senior high school. To obtain a more complete
analysis of change, they examined motivation according to Deci and Ryan’s (1985) self-
determination theory. A total of 646 students completed a questionnaire in eighth, ninth,
and 10th-grade. Results revealed that students’ intrinsic and extrinsic motivation
decreased gradually from eighth to 10th-grade.
Interest
Interest refers to a student’s attraction to, or liking of, a certain subject, such as
mathematics or reading (Schiefele, 2009; Pintrich & Schunk, 2002) and serves as a
function of informing the utility value of a subject, which leads to motivation (Higgins,
2007; Eccles et al., 1998). Interest is unique in its status as a motivational variable in its
reference to affective and cognitive processes, its biological roots in human behavior, and
its condition as an outcome of the interaction between a student and subject or task (Hidi
& Ainley, 2008). Cognitive and affect components of interest are among those often
observed in the literature. Many researchers consider interest to be an initial emotion,
which gradually becomes more cognitive in nature, thus reflecting a motivational belief.
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This cognitive state creates a predisposition to engage in a particular task (Renninger,
2000). Neuroscientists posit that interest is an evolutionary trait that has guided adaptive
behavior through generations of humankind (Panksepp, 2000). Finally, some researchers
believe that interest is something that is initiated within a person, but is not complete as a
construct until the resultant contextual behavior is considered (Krapp, 2000). For
instance, the classroom environment of a student guides the direction that the expressed
interest will take, and thus shapes the development of that interest.
Goals
Several different approaches have emerged in researching students’ achievement
goals and their relation to achievement behavior (Wigfield et al., 2009, 2008; Eccles &
Wigfield, 2002). For example, specific, proximal, and somewhat challenging goals
promote self-efficacy and improved performance (Maehr & Zusho, 2009; Bandura, 1997;
Schunk, 1990).
Wentzel (2000, 1993, 1991) has examined the goals of adolescents in
achievement settings and her view on goals focuses on the content of the goals, rather
than on the mastery versus performance criteria of success. She has determined that both
social and academic goals relate to a student’s school performance and behavior. Wentzel
(2000, 1993, 1991) has demonstrated that the goals related to school achievement include
visualizing oneself as successful, dependable, wanting to learn new things, and to get
things done. She also found that higher achieving students have higher levels of social
responsibility and achievement goals than lower achieving students.

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In another example, ego-involved goals and task-involved goals (learning goals)
are defined as two major motivationally relevant goal patterns or orientations (Maehr &
Zusho, 2009; Nicholls, Cobb, Yackel, Wood, & Wheatley, 1990). Individuals with ego-
involved goals (i.e. performance goals) work towards maximizing favorable evaluations
of their competence and minimizing negative evaluations of their competence.
Individuals with task-involved goals (i.e. learning goals) focus on mastering tasks and
increasing their competence (Maehr & Zusho, 2009; Dweck, 1999; Nicholls et al., 1990).
Ames (1992) differentiated between the relationship of performance goals (ego-involved
goals) and mastery goals (task-focused goals) with both performance and task choices. In
the case of ego-involved goals (performance goals), individuals try to outperform others
and are more likely to perform tasks they know they can do. With task-involved goals
(mastery goals), individuals select more challenging tasks and are more concerned with
their own improvement than with outperforming others (Ames, 1992). Research by
Sideridis (2005) with fifth and sixth grade students found that performance-approach has
positive correlations with achievement, effort and perseverance; whereas avoidance goals
were associated with low achievement, negative affect, and depression, supporting the
need for further defining performance goals (Maehr & Zusho, 2009).
Long, Monoi, Harper, Knoblauch, and Murphy (2007) discussed goal orientations
in a study of eighth and ninth grade students in the urban Midwest and stated that how an
individual constructs goals leads to that individual’s goal orientation and that goal
orientations affect academic behavior and achievement.
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Connections Between Motivational and Cognitive Processes
Drawing connections between motivational and cognitive processes has
increasingly interested motivational theorists. One group is concerned with how
individuals regulate their behavior through competence and control beliefs. Competence
beliefs will not motivate students unless they also perceive themselves in control of the
learning (Schunk & Zimmerman, 2006; 1994). Another has studied the connections
between motivation and the construct of interest. Interest is defined as both situational
and individual. Situational interest is the transient attention or emotional response to
elements within the text and individual interest refers to a more long-term interest in the
domain (Alexander et al., 1994). Alexander et al. (1994) studies have shown overall that
the interaction between knowledge, recall, and interest is significant. Kuhl (1987) and
Corno (1993) have contended for the differentiation between motivation and volition,
with motivation directing decisions about engaging in particular activities, and volition
directing the behaviors used to attain the goal. These groups of theorists concentrate on
two areas: how motivation gets converted into regulated behavior, and how motivation
and cognition are connected (Wigfield et al., 2009, 2008; Eccles & Wigfield, 2002).
Social Cognitive Theories of Self-Regulation and Motivation
Schunk & Zimmerman (2006), and their colleagues’ work focus on the direct link
between motivation and self-regulation. Zimmerman (2008; 1989) characterized self-
regulated students as being metacognitively, motivationally, and behaviorally engaged in
their own learning processes and in achieving their own goals (Wigfield, Hoa, & Klauda,
2008; Eccles & Wigfield, 2002). Self-regulated learners have three attributes; (1) they use
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an assortment of self-regulated strategies, (2) they believe they can perform efficaciously,
and (3) they set numerous and varied goals for themselves (Zimmerman, 2008; 2000).
According to Zimmerman (2008; 2000) self-regulated learners engage in three important
processes, (1) self-observation, (2) self-judgment, and (3) self-reactions to performance
outcomes. When the self-reactions are favorable, students are more likely to continue
even if the reactions are in response to a failure.
Schunk (1990) highlighted the reciprocal roles of goal setting, self-evaluation, and
self-efficacy. He determined that when goals are proximal, specific, and challenging they
are effective in motivating a child’s behavior, which increases their self-efficacy
(Zimmerman & Cleary, 2009: Schunk et al., 2008).
The social cognitive view of self-regulation highlights the significance of self-
efficacy beliefs, causal attributions, and goal setting in regulating behavior aimed at
accomplishing a task or activity. Once an individual engages in a task, they must monitor
their behavior, evaluate its outcomes, and respond to the outcomes in order to regulate
their behavior (Wigfield et al., 2009, 2008; Eccles & Wigfield, 2002).
Cleary and Chen (2009) examined grade level, achievement group, and math-
course-type differences in student self-regulation and motivation in a sample of 880
suburban middle school students, grades six and seven. The majority of the sample was
White (80%). The other participants were Asian (12.2%), Hispanic (5.6%), Black (2.2%),
and Native American (0.1%). Students in the seventh grade reported less frequent use of
regulatory strategies and more frequent displays of maladaptive behaviors than the sixth
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graders. The seventh graders were also less interested in math activities than their
younger peers.
Linking Motivation and Cognition
Winne & Marx (1989), Borkowski & Muthukrisna, (1995), and Pintrich (2003,
2000) are a few of the motivation researchers that are interested in how motivation and
cognition interact to influence self-regulated learning (Wigfield et al., 2009, 2008; Eccles
& Wigfield, 2002). Winne & Marx (1989) put forward that motivation should be thought
of in cognitive processing terms. They discussed that motivational thoughts and beliefs
are governed by the basic principles of cognitive psychology, differing only in their
content. Winne & Marx (1989) also explored the conditions under which tasks are
performed, the operations needed to complete the task, the product the student produces
when the task is completed, and the evaluation of the task and how motivation can
influence each aspect.
Pintrich (2003) developed a model of the relations between motivation and
cognition that incorporated student entry characteristics, social aspects of the learning
setting, motivational constructs derived from expectancy-value and goal theories,
cognitive constructs, self-regulatory strategies, and metacognitive strategies. In this
model it is postulated that cognitive and motivational constructs influence each other, and
that these cognitive and motivational constructs are also influenced by the social context.
Both the cognitive and motivational constructs are assumed to influence a student’s
involvement with their learning, and therefore, their achievement outcomes (Pintrich,
2003).
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Integrating Expectancy-Value and Self-Regulation Models
Wigfield and Eccles (2002) pointed out that a variety of models of self-regulation
include competence or efficacy beliefs as crucial influences on self-regulation in their
discussion of the integrations of self-regulatory and expectancy-value models (Wigfield
et al., 2009, 2008). In Rheinberg, Vollmeyer, and Rollett’s (2000) model of self-
regulation they included achievement values. They specified different questions
individuals pose to themselves concerning potential links of their actions to desired
outcomes. One of the “values” questions an individual can ask himself or herself: Are the
results of the action beneficial to me? If the answer is no, then they are less likely to act,
if the answer is yes, then they are more likely to engage in the action (Rheinberg et al.,
2002).
Carver and Scheier (2002), Pintrich (2000, 2003), and Zimmerman (2000)
proposed models of self-regulation that stressed goals rather than values. In their models,
goals play a prominent role in leading individuals to action. Carver and Scheier (2002)
postulated that some goals are organized into hierarchies. Goals at the higher levels of the
hierarchy are more important to the individual while goals at the lower levels of the
hierarchy are less important. From the point of view of the expectancy-value theory, goal
hierarchies also could be organized around task values (Wigfield et al., 2009, 2008;
Wigfield & Eccles, 2002). Wigfield et al. (2009) have predicted that the relative value
connected to the goal should effect its position in a goal hierarchy, as well as the
probability that the individual will try to achieve the goal.
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Learning Strategies
Marsh et al. (2006) define learning strategies as the “plans that students draw up
and carry out to achieve their learning goals.” These strategies include elaboration,
memorization, and control strategies. Cognitive and metacognitive describe the two main
learning strategies used by students. Memorization strategies (learning terms, reading
material aloud several times, etc.) and elaboration strategies (integrating new material
into prior knowledge) are cognitive learning strategies and control strategies (used to self-
regulate learning process to ensure that goals are achieved) are metacognitive.
Memorization strategies typically lead to information being stored verbatim in the
memory with little or no further processing. This can be an effective method for learning
basic information and processes, but typically does not lead to a deep understanding of
the new knowledge. It would be better for the student to integrate the new information
into their prior knowledge base and existing competences. The use of elaborative
strategies such as exploring how the new information relates to things they have learned
in other contexts or determining how the new knowledge might be useful to the student,
would be a good ways to achieve their goals (Weinstein, Acee, & Jung, 2011, Marsh et
al., 2006).
Students gain control over the new information by choosing strategies that are
appropriate to the task at hand by actively integrating the new knowledge into their
previously stored knowledge in their long-term memory. Students that are not equipped
with such strategies are unlikely to succeed in their learning activities (Weinstein, Acee,
& Jung, 2011, Marsh et al., 2006). Learning strategies are crucial for retention and deep
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understanding, and students can be taught these strategies (McNamara, 2007; Glogger,
Schwonke, Holzäpfel, Nückles, & Renkl, 2012).
Research with Diverse Learners
The literature on motivation for ethnic minorities in an urban setting is small
compared to the number of studies on motivation. Though they may be few, there are
important studies that examine different approaches used by different groups. For
example, Garcia, Yu, and Coppola (1993) studied gender and ethnic differences in
science achievement in a sample of 557 college students (297 male and 260 female).
They performed a pre-post test at two different times to examine changes in patterns of
motivation and learning strategies. Latino and African-American students’ success was
correlated with motivation and prior achievement. Minority students, African American
and Latino, reported a higher extrinsic motivation orientation over their White
counterparts. Garcia et al. (1993) concluded that gender and ethnicity do not affect
achievement outcomes, but preparedness, motivation and use of learning strategies do.
Two studies indicated that there were significant relationships between racial and
ethnic identification and self-efficacy (Smith, Walker, Fields, Brookins, & Seay, 1999;
Arroyo & Ziegler, 1995;). Arroyo and Ziegler (1995) examined the development of the
Racelessness Scale and the correlates of the construct of racelessness in 389 African
American and Caucasian students ages 13 to 20. They found significant relationships
between racial identity and self-efficacy for African Americans. Further, they found that
membership and self-efficacy were significantly correlated for Caucasian individuals. A
study by Smith et al. (1999) conducted with 100 sixth grade students (67 African
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American) indicated positive relationships between self-esteem and efficacy, and
ethnicity and efficacy.
Stevens, Olivarez, Lan, and Tallent-Runnels (2004) evaluated self-efficacy and
motivational orientation across Hispanic and Caucasian students (358 students in grades
9 and 10 who attended a West Texas high school) to predict variables related to
mathematics achievement. Their findings supported that self-efficacy predicts
motivational orientation and mathematics performance and suggested that the
relationship between prior mathematics achievement and self-efficacy was stronger for
Hispanic students. Caucasian students do not place as much emphasis on prior mastery
experiences as do Hispanic students, suggesting that other factors are active in
influencing their self-efficacy.
Jonson-Reid, Davis, Saunders, Williams and Williams (2005) reviewed research
on the effects of self-esteem and self-efficacy on achievement and examined the
relationships of factors that affect academic self-efficacy (169 African American high
school students). They found that improving African American students’ cognitions
about the importance of education in order to affect their academic self-efficacy would
have greater effects than solely increasing self-esteem.
Malka and Covington (2005) examined the effects of perceived school
performance on goal setting and graded performance in an ethnically diverse population
of college undergraduates (study one n = 133, ethnically diverse population; study two n
= 195, 48.7% Asian, 35.9% Caucasian, 4.1% Latino, 2.1% African American, and 9.2%
other or unreported; study three n = 91, 46.2% Asian, 27.5% Caucasian, 3.3% Latino,
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1.1% African American, and 22% other or unreported) at a west coast university. Results
indicated that perceived self-efficacy is a predictor of academic performance.
Zimmerman and Kitsantas (2005) made similar findings with high school students.
Zimmerman and Kinsantas (2005) examined female (n = 179, 44% White, 14%
Black, 27% Hispanic, and 15% Asian/others) high school students’ self-beliefs and
performance on homework tasks in relation to grade point average. They found that self-
efficacy had direct and indirect causal relationships with grade point average and
homework quality.
Unrau and Schlackman (2006) investigated the effects of intrinsic and extrinsic
motivation on reading achievement for approximately 2,000 urban middle school
children, which consisted of grades 6, 7, and 8. The population included about 75%
Hispanic and 20% Asian, the other 5% was a mix of African American, American Indian,
and White students. They found that the relation between intrinsic motivation, extrinsic
motivation and reading achievement was stronger for Asian students than for Hispanic
students. For Asian students intrinsic motivation was positively related to reading
achievement and extrinsic motivation was negatively related, while for Hispanic students
neither intrinsic motivation nor extrinsic motivation had a direct effect on reading
achievement. For all of the participants, intrinsic motivation and extrinsic motivation
declined significantly as students moved from grade 6 to grade 7 to grade 8.
Researchers have reported that motivational variables, such as interest and self-
efficacy, positively relate to forms of achievement (e.g., standardized test scores and
grades); other studies indicate that motivation’s contribution to achievement is
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35
inconsistent. Fewer studies have studied these connections within African American
students (Long et al., 2007).
Long et al. (2007) investigated eighth (n = 255) and ninth-grade (n = 159)
students, focusing on motivation and GPA in a large, urban, predominantly African
American, school district in the Midwest. Regression analyses of self-report levels of
three motivational variables (i.e., self-efficacy beliefs, goal orientations, and domain
interest) revealed that significant gender differences existed in goal orientation and
achievement scores in both grades. Self-efficacy and learning goals contributed to
domain interests but the predictive value of these three motivational variables on
achievement differed at each grade level.
Rueda et al. (2008) studied 200 students ninth through 12th-grade, with 10th-
grade making up the largest portion of the sample (41%). Hispanics made up 95% of the
student sample at this central city high school in Los Angeles. These students were
administered a survey which assessed learning, motivation, and academic engagement. It
was found that the learning and motivational variables were predictive of academic
engagement for this sample.
Wilkins and Kuperminc (2010) used Elliot and McGregor’s (2001) 2 x 2 model of
achievement motivation (mastery approach, mastery-avoidance, performance-approach
and performance-avoidance) among 143 Latino middle school students to examine how
achievement motivation changes over time, and whether perception of academic climate
influences eventual academic outcomes. Their findings suggested that perception of a
task-performance focused academic climate plays an important role in Latino
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36
adolescents’ academic achievement, and that Latino adolescents’ achievement motivation
and perception of academic climate may be influenced by their transition to high school.
Summary of Research with Diverse Learners
African American and Latino college students reported a higher extrinsic
motivation orientation over their White counterparts (Garcia et al., 1993).
Arroyo and Ziegler (1995) found significant relationships between racial identity
and self-efficacy for African American students ages 13 to 20.
A study by Smith et al. (1999) conducted with sixth grade African American
students indicated positive relationships between self-esteem and efficacy, and ethnicity
and efficacy.
Stevens et al. (2004) findings (grades 9 and 10) supported that self-efficacy
predicts motivational orientation and mathematics performance and suggested that the
relationship between prior mathematics achievement and self-efficacy was stronger for
Hispanic students and that Caucasian students do not place as much emphasis on prior
mastery experiences as do Hispanic students.
Jonson-Reid et al. (2005) found that improving African American high school
students’ cognitions about the importance of education in order to affect their academic
self-efficacy would have greater effects than solely increasing self-esteem.
Malka and Covington (2005) results indicated that perceived self-efficacy is a
predictor of academic performance with an ethnically diverse population of college
undergraduates.
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37
Zimmerman and Kinsantas (2005) found that self-efficacy had direct and indirect
causal relationships with grade point average and homework quality female high school
students.
Long et al. (2007) found that self-efficacy and learning goals contributed to
domain interests but the predictive value of these motivational variables on achievement
differed with eighth and ninth-graders (predominately African American).
Rueda et al. (2008) found that learning and motivational variables were predictive
of academic engagement for high school students (95% Hispanic).
Wilkins and Kuperminc (2010) findings suggested that perception of a task-
performance focused academic climate plays an important role in middle school Latino
adolescents’ academic achievement.
Unfortunately there was very little research involving high school minority
students, in particular African American, Latino, and Asian students in a large urban
district. The sample sizes used in the studies ranged from 91 to 557, none incorporated a
sample size numbered in the thousands (a typical large urban California high school
numbers in the thousands). Self-efficacy was examined in five of the studies above and
three studied goal theories. Mathematics, domain interest, and learning strategies were
each investigated in two studies each. Instrumental motivation, self-concept reading,
interest in reading, academic self-concept, and effort and perseverance were examined in
one of the studies above.

MOTIVATIONAL FACTORS AND ENGAGEMENT

38
Summary of Research with Grade Level
Research by Sideridis (2005) with fifth and sixth grade students found that
performance-approach had positive correlations with achievement and effort and
perseverance.
Otis and Pelletier (2005) examined changes in intrinsic and extrinsic motivation
during the transition from junior to senior high school. Results revealed that students’
intrinsic and extrinsic motivation decreased gradually from eighth to 10th-grade.
Unrau and Schlackman (2006) investigated the effects of intrinsic and extrinsic
motivation on reading achievement for approximately 2,000 urban middle school
children, which consisted of grades 6, 7, and 8. For all of the participants, intrinsic
motivation and extrinsic motivation declined significantly as students moved from grade
6 to grade 7 to grade 8.
Long et al. (2007) investigated eighth and ninth-grade students, focusing on
motivation and GPA in a large, urban, predominantly African American, school district
in the Midwest. Regression analyses of self-report levels of three motivational variables
(i.e., self-efficacy beliefs, goal orientations, and domain interest) revealed that significant
gender differences existed in goal orientation and achievement scores in both grades.
Self-efficacy and learning goals contributed to domain interests but the predictive value
of these three motivational variables on achievement differed at each grade level.
Cleary and Chen (2009) examined grade level, achievement group, and math-
course-type differences in student self-regulation and motivation in a sample of 880
suburban middle school students, grades six and seven. Students in the seventh grade
MOTIVATIONAL FACTORS AND ENGAGEMENT

39
reported less frequent use of regulatory strategies and more frequent displays of
maladaptive behaviors than the sixth graders. The seventh graders were also less
interested in math activities than their younger peers.
When researchers looked at multiple grade levels together (Sideridis, 2005) they
were able to find positive correlations. When the grade levels were broken down to their
individual grades, motivation decreased gradually as the grades went higher (Otis &
Pelletier, 2005, Unrau & Schlackman, 2006, Cleary & Chen, 2009) or they differed at
each grade level (Long et al., 2007).
Unfortunately there was very little research involving high school grade level
studies, in particular, students in a large urban district. Self-efficacy was examined in two
of the studies above, two studied intrinsic and extrinsic motivation, one studied effort and
perseverance, and one study focused on math related motivational factors.
Conclusion
Motivational researchers have learned much about the reasons why individuals
choose to engage or disengage in different activities by focusing on an individual’s
beliefs, values, and goals, and how these beliefs, values, and goals relate to an
individual’s achievement behaviors (Wigfield et al., 2009, 2008; Eccles & Wigfield,
2002). However, an important gap is that few studies have examined these associations
with adolescents in urban high school settings (e.g., Long, Monoi, Harper, Knoblauch, &
Murphy, 2007; Oyserman, Bybee, & Terry, 2006; Sanchez, Colon, & Esparza, 2005;
Urdan, 2004; Yowell, 2000). A study could be performed to provide data on the
motivational beliefs (self-efficacy/self-beliefs, self-concept reading, self-concept math,
MOTIVATIONAL FACTORS AND ENGAGEMENT

40
academic self-concept, instrumental motivation, interest in reading, interest in math,
effort & perseverance) and learning strategies (elaboration, memorization, control
strategies), and engagement of urban high school students, and in particular, minority
students, which have not been studied using large numbers of students.
The study will add to the body of literature by providing a path analysis model
that examines the relationship between motivational factors and learning strategies, and
engagement in a large urban high school setting.

MOTIVATIONAL FACTORS AND ENGAGEMENT

41
Chapter 3: Research Methodology
Research Questions
Research Question 1
What is the relationship between ethnicity and motivational factors and learning
strategies, and engagement with urban high school students?
Research Question 2
What is the relationship between grade level and motivational factors and learning
strategies, and engagement with urban high school students?
Methodology
The data for the study was obtained from a questionnaire that was administered
by CUSD and distributed to all students in one high school in CUSD during 2010.
Only a subset of the total data collected by CUSD will be used for this study. The
subset includes motivational beliefs (self-efficacy, self-concept, effort and perseverance,
and interest), learning strategies (elaboration, memorization, and control strategies),
along with the student’s ethnicity and grade level.
The motivational belief and learning strategies questions used in the CUSD
survey were adapted from a survey that was developed by Marsh et al. (2006).
CUSD personnel collected the data, aggregate the collected data with existing
district data on each student, and gave the researcher an Excel file of the data, minus any
individual student identifying information.
MOTIVATIONAL FACTORS AND ENGAGEMENT

42
Research Design
The present study examines the relationship between ethnicity and grade level,
motivational factors and learning strategies, and engagement in a large urban high school
setting. This study was a descriptive study of the population of an entire high school.
Population and Sample
CUSD educates nearly 90,000 students in 93 public schools in a multitude of
cities. It is one of the largest school districts in California; it serves the most diverse large
city in the United States, with dozens of different languages spoken by local students
(CUSD, 2009). One high school was chosen by CUSD and all of the students in
attendance the day of the survey were surveyed. CUSD personnel administered the
survey. Table 1 shows the demographic and background profile of the sample.
Table 1
Demographic and Background Profile of Sample (N = 3180)
Characteristic % of Sample

Gender (N = 3180)

Female
Male
49.8
50.2

Ethnicity/Race (N = 3180)
African American/Black
Asian
Hispanic
White
16.0
13.6
41.9
28.4

Grade Level (N = 3180)
9
10
11
12
24.2
25.3
26.6
24.0

MOTIVATIONAL FACTORS AND ENGAGEMENT

43
Low SES (N = 3180)
No
Yes
56.2
43.8

Fluency (N = 3180)
ELL
EO
FEP
3.6
67.5
28.8

Special Ed (N = 3180)
No
Yes
89.9
10.1

CST 2009 ELA PS (N = 3180)
No Score
1
2
3
4
5
5.0
5.8
10.4
27.3
30.1
21.4

CST 2009 Math PS (N = 3180)
No score
1
2
3
4
5
10.0
5.5
19.8
29.2
27.9
7.7

Data Collection
This study examined the data from ethnicity and grade level, and the questions
used in the motivational beliefs and learning strategies sections. The survey and data was
administered, collected, and aggregated by CUSD. The district collected background
information on each student consisting of, ethnicity, gender, grade level, English
Language Learner (ELL) level, SES, and CST scores (ELA and Math). These additional
data was aggregated with the survey data and CUSD gave the researcher the results of the
MOTIVATIONAL FACTORS AND ENGAGEMENT

44
survey without the names of the students, in an Excel file, making the data completely
anonymous to the researcher.
Instrumentation and Reliability
The questionnaire used by the district consisted of 67 items, of which 40 items
were used for this study (see Table 2). CUSD adapted the survey from one that was
developed by Marsh et al. (2006). This study will only be examining the questions used
in the motivational beliefs section that involve self-efficacy/beliefs, self-concept, effort
and perseverance, and intrinsic motivation as represented by domain-specific interest
(interest in reading and interest in mathematics) and learning strategies (elaboration,
memorization, and control strategies). A summary of the questions are presented in Table
2, with a more detailed explanation of the questions following the table. The survey
questions are presented in appendix A.
Table 2
Summary of Survey Items Used in Study

Domain

Subscale
No.
Items

Source
Available
Psychometrics
Motivational
Beliefs

Self-Efficacy/Self-Beliefs
Self-Concept Reading
Self-Concept Math
Academic Self-Concept
Instrumental Motivation
Interest in Reading
Interest in Math
Effort & Perseverance
5
3
3
3
3
3
3
4
Marsh et al. (2006)
Marsh et al. (2006)
Marsh et al. (2006)
Marsh et al. (2006)
Marsh et al. (2006)
Marsh et al. (2006)
Marsh et al. (2006)
Marsh et al. (2006)
.79
.77
.86
.79
.83
.82
.75
.83

Learning
Strategies

Elaboration
Memorization
Control Strategies
4
4
5
Marsh et al. (2006)
Marsh et al. (2006)
Marsh et al. (2006)
.81
.78
.83
Total = 40

MOTIVATIONAL FACTORS AND ENGAGEMENT

45
Motivational beliefs. Eight subscales from Marsh et al.’s (2006) Motivational
Beliefs questionnaire were utilized. The subscales included control expectations (e.g.,
“When I sit myself down to learn something really difficult, I can learn it.”), self-concept
reading (e.g., “I get good grades in English.”), self-concept math (e.g., “I have always
done well in mathematics.”), academic self-concept (e.g., “I learn things quickly in most
school subjects.”), interest in reading (e.g., “I read in my spare time.”), interest in math
(e.g., “When I do mathematics, I sometimes get totally absorbed.”), and effort and
perseverance (e.g., “When studying, I work as hard as possible.”). The reliabilities of
these eight subscales have ranged from α = .75 to α = .86 in past research (Marsh et al.,
2006).
Learning strategies. The elaboration (e.g., “When I study, I try to relate new
material to things I have learned in other subjects.”), memorization (e.g., “When I study, I
memorize all new material so that I can recite it.”), and control strategies (e.g., “When I
study, I try to figure out which concepts I still haven’t really understood.”) subscales
from Marsh et al.’s (2006) learning strategies survey were administered. The elaboration
(α = .81), memorization (α = .78), and control strategies (α = .83) subscales have shown
acceptable reliability in past research (Marsh et al., 2006).
Data Analysis
All data was analyzed using IBM SPSS version 21 for Macintosh.
An internal consistency reliabilities, Chronbach’s alpha, test was performed to
determine the reliability of the subscales; (1) self-efficacy/self-belief, (2) self-concept
reading, (3) self-concept math, (4) academic self-concept, (5) instrumental motivation,
MOTIVATIONAL FACTORS AND ENGAGEMENT

46
(6) interest in reading, (7) interest in math, (8) effort & perseverance, (9) elaboration, (10)
memorization, and (11) control strategies, with the current student sample. Tests of
normality was performed employing the use of histograms, Skewness, and Kurtosis, for
the eleven subscales.
A factor analysis was performed and the following mediating variables were
determined and two proposed path models were ascertained, Figures 1 and 2. The
independent variables were grade level and ethnicity, the mediating variables were
instrumental motivation, reading (composed of self-concept reading and interest reading),
math (composed of self-concept math and interest math), self-efficacy/self-beliefs,
learning strategies (composed of elaboration, memorization, and control strategies), and
academic self-concept, and the dependent variables were effort and perseverance. R
square values will be used to show comparisons for ethnicity and grade level, and beta
values will be used to show relationships for mediating variables.
Dummy coding was used for both ethnicity and grade level. For ethnicity, African
American/Black, Asian, and Hispanic were converted into the dummy variables with
White as the reference category. For grade level, ninth, 10th, and 11th- grades were
converted into the dummy variables with 12th-grade as the reference category.
To examine the associations among the variables, bivariate correlations were
computed and means and standard deviations were generated. Multiple regressions were
conducted to determine the descriptive relationship between the independent variables,
grade level and ethnicity, and the mediating variables, instrumental motivation, reading,
MOTIVATIONAL FACTORS AND ENGAGEMENT

47
math, self-efficacy/self-beliefs, learning strategies, and academic self-concept, and the
dependent variables effort and perseverance.

Figure 1. The proposed path model predicting the relationship between effort and
perseverance and ethnicity with instrumental motivation, reading, math, self-
efficacy/self-beliefs, learning strategies, and academic self-concept serving as mediating
variables.
Effort & Perseverance
Dependent Variable
Academic Self-Concept
Instrumental Motivation
Reading
Self-Concept Reading
Interest Reading
Math
Self-Concept Math
Interest Math
Self-Efficacy/Self-Beliefs
Learning Strategies
Elaboration
Memorization
Control Strategies
Mediating Variables
Ethnicities
African American/Black,
Asian,
and Hispanic,
vs. non-Hispanic White
MOTIVATIONAL FACTORS AND ENGAGEMENT

48

Figure 2. The proposed path model predicting the relationship between effort and
perseverance and grade level with instrumental motivation, reading, math, self-
efficacy/self-beliefs, learning strategies, and academic self-concept serving as mediating
variables.

Effort & Perseverance
Dependent Variable
Academic Self-Concept
Instrumental Motivation
Reading
Self-Concept Reading
Interest Reading
Math
Self-Concept Math
Interest Math
Self-Efficacy/Self-Beliefs
Learning Strategies
Elaboration
Memorization
Control Strategies
Mediating Variables
Grade Level
9th grade
10th grade
11th grade
vs. 12th grade
MOTIVATIONAL FACTORS AND ENGAGEMENT

49
Chapter 4: Findings
The purpose of the study was to examine the relationship between ethnicity and
grade level, motivational factors and learning strategies, and engagement in a large urban
high school setting. This study was a descriptive study of the population of an entire high
school. The independent variables were grade level and ethnicity, the mediating variables
were instrumental motivation, reading (composed of self-concept reading and interest
reading), math (composed of self-concept math and interest math), self-efficacy/self-
beliefs, learning strategies (composed of elaboration, memorization, and control
strategies) and academic self-concept, and the dependent variables were effort and
perseverance.
Item Reliability
The level of internal consistency was examined using Cronbach’s alpha. Table 3
presents the coefficient alpha (α) for all questions used in the study and shows that while
the coefficient alphas are slightly lower than the available psychometrics from Marsh et
al. (2006) the reliability data still suggests high consistency in the student’s responses.
Table 3
Coefficient Alphas of Survey Items Used in Study

Domain

Subscale
No.
Items
Coefficient
Alpha
Motivational
Beliefs

Self-Efficacy/Self-Beliefs
Self-Concept Reading
Self-Concept Math
Academic Self-Concept
Instrumental Motivation
Interest in Reading
Interest in Math
Effort & Perseverance
5
3
3
3
3
3
3
4
.68
.71
.82
.65
.72
.74
.69
.76

Learning Elaboration 4 .74
MOTIVATIONAL FACTORS AND ENGAGEMENT

50
Strategies

Memorization
Control Strategies
4
5
.70
.77
Total = 40

Means and Standard Deviations for Ethnicity and Grade Level
Table 4 displays the means and standard deviations for African American/Black,
Asian, Hispanic, White, ninth, 10th, 11th, and 12th-grade for instrumental motivation,
reading, math, self-efficacy/self-beliefs, learning strategies, academic self-concept, and
effort and perseverance.
Table 4
Means and Standard Deviations for Ethnicity and Grade Level
1 2 3 4 5 6 7
M SD M SD M SD M SD M SD M SD M SD
African
American/Black
2.95 .76 2.88 .66 2.27 .79 3.03 .54 2.90 .60 2.97 .61 2.87 .69
Asian 3.16 .71 2.85 .68 2.52 .81 3.08 .53 2.93 .57 3.01 .65 2.97 .65
Hispanic 3.07 .75 2.77 .66 2.39 .78 3.02 .53 2.89 .59 2.99 .62 2.88 .67
White 2.94 .80 2.88 .71 2.30 .85 2.98 .58 2.78 .59 3.05 .66 2.79 .70

9
th
grade 2.96 .77 2.78 .68 2.46 .81 3.01 .56 2.89 .60 2.99 .68 2.91 .66
10
th
grade 3.00 .78 2.79 .67 2.42 .80 2.97 .55 2.82 .60 2.95 .67 2.80 .68
11
th
grade 3.03 .77 2.86 .68 2.30 .79 3.02 .54 2.86 .58 3.03 .61 2.86 .69
12
th
grade 3.11 .74 2.88 .68 2.29 .83 3.08 .52 2.90 .62 3.06 .58 2.87 .71
Note: 1 = instrumental motivation; 2 = reading; 3 = math; 4 = self-efficacy/self-beliefs; 5 = learning
strategies; 6 = academic self-concept; 7 = effort and perseverance; scale, minimum 1 (disagree), maximum
4 (agree); N = 3180.

Correlations Among the Variables, Means, and Standard Deviations
To examine the associations among the variables, bivariate correlations were
computed. Relations between variables were examined with Pearson product-moment.
Table 5 presents the correlations between all variables in the study. Instrumental
motivation (r = .504), reading (r = .361), math (r = .280), self-efficacy/self-beliefs (r =
.457), learning strategies(r = .797), and academic self-concept (r = .379) all were
MOTIVATIONAL FACTORS AND ENGAGEMENT

51
significantly correlated (p < .01) with effort and perseverance. Their means and standard
deviations are also presented in Table 4.
Table 5
Correlations Among the Variables, Means, and Standard Deviations
1 2 3 4 5 6 7
1. Instrumental Motivation -
2. Reading .339** -
3. Math .270** -.048** -
4. Self-Efficacy/Self-Beliefs .398** .315** .427** -
5. Learning Strategies .505** .371** .269** .480** -
6. Academic Self-Concept .382** .397** .352** .591** .375** -
7. Effort & Perseverance .504** .361** .280** .457** .797** .379** -
M 3.01 2.82 2.35 3.01 2.86 2.98 2.86
SD .771 .679 .804 .549 .598 .645 .687
Note: **p < .01, two-tailed, N = 3180.
Regression Analysis
A regression analysis was conducted to determine the relationships between the
variables to determine path models (see Figures 3 and 4). Multiple regressions were
conducted to determine the descriptive relationship between the independent variables
(X
1
) grade level and ethnicity, and the mediating variables (X
2
) instrumental motivation,
reading, math, self-efficacy/self-beliefs, learning strategies, and academic self-concept,
and the dependent variables (Y
3
) effort and perseverance. Separate regressions were
conducted to determine the relationships between X
1
and X
2
, and a separate regression
was conducted to determine the direct relationship between X
1
and Y
3
. Separate
regressions were conducted between X
1
and X
2
and Y
3
to determine the relationship
between X
2
and Y
3
. In other words, two regressions were performed for each variable
MOTIVATIONAL FACTORS AND ENGAGEMENT

52
(for each independent variable, ethnicity and grade level), the first to determine the R
2

values and the coefficients for the group ethnicity and grade level, and the second to
determine the coefficients for effort and perseverance (for each mediating variable) while
controlling for ethnicity and grade level.
Research Question 1
What is the relationship between ethnicity and motivational factors and learning
strategies, and engagement with urban high school students?
Table 6 shows the summary of multiple regression analysis for ethnicity and
instrumental motivation, reading, math, self-efficacy/self-beliefs, learning strategies,
academic self-concept, and effort and perseverance.
The R
2
values indicate the amount of variance in instrumental motivation (.010, p
= .001), reading (.007, p = .001), math (.010, p = .001), self-efficacy/self-beliefs (.003, p
< .05), learning strategies (.009, p = .001), and academic self-concept (.003, p < .05),
effort and perseverance (.007, p = .001), by ethnicity.
R square values indicate the amount of variance in the dependent variable by the
predictor variables. In the case of instrumental motivation (dependent variable), ethnicity
(the predictor variable), accounts for 1% of the variance. In the case of reading .7%, math
1%, self-efficacy/self-beliefs .3%, learning strategies .9%, academic self-concept .3% and
for effort and perseverance it is .7%.
The beta coefficient (β) or standardized coefficient refer to how many standard
deviations a dependent variable (in this case, for example, instrumental motivation) will
change, per standard deviation increase (or decrease) in the predictor variable (in this
MOTIVATIONAL FACTORS AND ENGAGEMENT

53
case, for example, Asian, African American/Black, and Hispanic). When using dummy
variables (such as Asian, African American/Black, Hispanic), the beta coefficient is how
much more the dependent variable increases (or decreases if the beta value is negative)
when the dummy variable increases in relation to the omitted reference category (White
in this case).
For instrumental motivation, Asian β = .096, F(3, 3116) = 10.86, and Hispanic β
= .082, F(3, 3116) = 10.86, were both significant (p = .001), while African
American/Black was not significant. For reading, Hispanic β = -.077, F(3, 3067) = 7.12,
was significant (p = .001), while Asian and African American/Black were not significant.
For math, Asian β = .094, F(3, 3074) = 9.87, was significant (p = .001), while African
American/Black and Hispanic were not significant. For self-efficacy/self-beliefs, Asian β
= .064, F(3, 3088) = 3.55, was significant (p < .05), while African American/Black and
Hispanic were not significant. For learning strategies, Asian β = .157, F(3, 2932) = 3.55,
African American/Black β = .115, F(3, 2932) = 3.55, and Hispanic β = .112, F(3, 2932) =
3.55, were significant (p =.001). For academic self-concept, African American/Black β =
-.048, F(3, 3130) = 3.54, was significant (p < .05), while Asian and Hispanic were not
significant. For effort and perseverance Asian β = .088, F(3, 3046) = 4.28, p = .001,
African American/Black β = .047, F(3, 3046) = 2.27, p < .05, and Hispanic β = .065, F(3,
3046) = 3.01, p < .05, were all significant.
For instrumental motivation, Asian (β = .096) would be .096 plus the average of
White and Hispanic (β = .082) would be .082 plus the average of White, both were
associated with an increase, for reading, Hispanic (β = -.077) was associated with a
MOTIVATIONAL FACTORS AND ENGAGEMENT

54
decrease, for math, Asian (β = .094) was associated with an increase, for self-
efficacy/self-beliefs, Asian (β = .064) was associated with an increase, or learning
strategies, Asian (β = .157), African American/Black (β = .115), and Hispanic (β = .112),
all were associated with an increase for academic self-concept, African American/Black
(β = -.048) was associated with a decrease, and for effort and perseverance Asian (β =
.088), African American/Black (β = .047), and Hispanic (β = .065), all was associated
with an increase.
Table 6
Summary of Multiple Regression Analysis for Ethnicity and Instrumental Motivation,
Reading, Math, Self-Efficacy/Self-Beliefs, Learning Strategies, Academic Self-
Concept, and Effort and Perseverance
R R
2
F B SE Beta t p
instrumental motivation
Asian
African American/Black
Hispanic

.102 .010*** 10.86
.217
.009
.129

.045
.043
.033

.096***
.004
.082***

4.79
.21
3.86

.000
.832
.000
reading
Asian
African American/Black
Hispanic

.083 .007*** 7.12
-.016
.031
-.107

.040
.038
.030

-.008
.017
-.077***

-.41
.82
-3.60

.684
.415
.000
math
Asian
African American/Black
Hispanic

.098 .010*** 9.87
.218
-.045
.068

.047
.045
.035

.094***
-.020
.042

4.62
-.99
1.96

.000
.325
.051
self-efficacy/self-beliefs
Asian
African American/Black
Hispanic

.059 .003* 3.55
.102
.057
.041

.032
.031
.024

.064*
.038
.037

3.16
1.83
1.73

.002
.068
.083
learning strategies
Asian
African American/Black
Hispanic

.095 .009*** 8.90
.157
.115
.112

.036
.035
.027

.091***
.070***
.092***

4.36
3.33
4.20

.000
.001
.000
MOTIVATIONAL FACTORS AND ENGAGEMENT

55
academic self-concept
Asian
African American/Black
Hispanic

.058 .003* 3.54
-.036
-.085
-.084

.038
.036
.028

-.019
-.048*
-.064

-.95
-2.36
-3.01

.342
.018
.003
effort & perseverance
Asian
African American/Black
Hispanic
.081 .007*** 6.68
.174
.088
.091

.041
.039
.030

.088***
.047*
.065*

4.28
2.27
3.01

.000
.023
.003
Note: *p < .05, ***p = .001, N = 3180.

Table 7 shows the summary of multiple regression analysis for the mediating
variables, instrumental motivation, reading, math, self-efficacy/self-beliefs, learning
strategies, and academic self-concept, and the dependent variables effort and
perseverance while controlling for ethnicity. All mediating variables, instrumental
motivation β = .448, F(4, 3011) = 260.16, reading β = .370, F(4, 2967) = 120.72, math β
= .236, F(4, 2973) = 67.02, self-efficacy/self-beliefs β = .572, F(4, 2987) = 200.61,
learning strategies β = .914, F(4, 2887) = 1261.93, and academic self-concept β = .410,
F(4, 3023) = 125.84, were all significantly correlated (p = .001) with effort and
perseverance.
All mediating variables, instrumental motivation (β = .448), reading (β = .370),
math (β = .236), self-efficacy/self-beliefs (β = .572), learning strategies (β = .914), and
academic self-concept (β = .410), were all associated with an increase in relation to effort
and perseverance when controlling for ethnicity. For example, as instrumental motivation
increased (β = .448), effort and perseverance increased, when controlling for ethnicity.
Table 7
Summary of Multiple Regression Analysis for the Mediating Variables, Instrumental
Motivation, Reading, Math, Self-Efficacy/Self-Beliefs, Learning Strategies, and
MOTIVATIONAL FACTORS AND ENGAGEMENT

56
Academic Self-Concept, and the Dependent Variables Effort and Perseverance While
Controlling for Ethnicity
R
2
F B SE Beta t p
1 .257 260.16 .448 .014 .502 31.80 .000
2 .140 120.72 .370 .017 .366 21.41 .000
3 .083 67.02 .236 .015 .276 15.67 .000
4 .212 200.61 .572 .020 .454 27.90 .000
5 .636 1261.93 .914 .013 .797 70.68 .000
6 .152 125.84 .410 .018 .383 22.83 .000
Note: 1 = instrumental motivation; 2 = reading; 3 = math; 4 = self-efficacy/self-beliefs; 5 = learning
strategies; and 6 = academic self-concept. p = .001, N = 3180.

Figure 3 shows the path model for the relationship between effort and
perseverance and ethnicity with instrumental motivation, reading, math, self-
efficacy/self-beliefs, learning strategies, and academic self-concept serving as mediating
variables.
MOTIVATIONAL FACTORS AND ENGAGEMENT

57

Figure 3. The path model predicting the relationship between effort and perseverance and
ethnicity with instrumental motivation, reading, math, self-efficacy/self-beliefs, learning
strategies, and academic self-concept serving as mediating variables.
Research Question 2
What is the relationship between grade level and motivational factors and learning
strategies, and engagement with urban high school students?
Table 8 shows the summary of multiple regression analysis for grade level and
instrumental motivation, reading, math, self-efficacy/self-beliefs, learning strategies,
academic self-concept, and effort and perseverance.
Effort & Perseverance
Dependent Variable
Academic Self-Concept
Instrumental Motivation
Reading
Self-Concept Reading
Interest Reading
Math
Self-Concept Math
Interest Math
Self-Efficacy/Self-Beliefs
Learning Strategies
Elaboration
Memorization
Control Strategies
Mediating Variables
Ethnicities
African American/Black,
Asian,
and Hispanic,
vs. non-Hispanic White
R2 = .010*** .502***
R2 = .007***
.366***
R2 = .010***
R2 = .003*
R2 = .009***
R2 = .003*
.276***
.454***
.797***
.383***
* p < .05
*** p = .001
R2 = .007***
MOTIVATIONAL FACTORS AND ENGAGEMENT

58
The R
2
values indicate the amount of variance in instrumental motivation (.004, p
< .005), reading (.005, p = .001), math (.006, p = .001), self-efficacy/self-beliefs (.067, p
< .05), learning strategies (.003, p < .05), and academic self-concept (.004, p < .05), effort
and perseverance was not significant, by grade level.
R square values indicate the amount of variance in the dependent variable by the
predictor variables. In the case of instrumental motivation (dependent variable), grade
level (the predictor variable), accounts for .4% of the variance. In the case of reading
.5%, math .6%, self-efficacy/self-beliefs 6.7%, learning strategies .3%, and for academic
self-concept it is .4%.
The beta coefficient (β) or standardized coefficient refers to how many standard
deviations a dependent variable (in this case, for example, instrumental motivation) will
change, per standard deviation increase (or decrease) in the predictor variable (in this
case, for example, ninth, 10th, and 11th-grade). When using dummy variables (such as
ninth, 10th, and 11th-grade), the beta coefficient is how much more the dependent
variable increases (or decreases if the beta value is negative) when the dummy variable
increases in relation to the omitted reference category (12th-grade in this case).
For instrumental motivation, ninth grade β = -.069, F(3, 3166) = 3.76, and 10th-
grade β = -.059, F(3, 3166) = 3.76, were both significant (p < .05), while 11th-grade was
not significant. For reading, ninth grade β = -.070, F(3, 3067) = 5.23 and 10th-grade β = -
.065, F(3, 3067) = 5.23 were significant (p < .05), while 11th-grade was not significant.
For math, ninth grade β = .080, F(3, 3074) = 6.49, p = .001, and 10th-grade β = .061, F(3,
3074) = 6.49, p < .05, were significant, while 11th-grade was not significant. For self-
MOTIVATIONAL FACTORS AND ENGAGEMENT

59
efficacy/self-beliefs, ninth grade β = -.060, F(3, 3088) = 4.68, p < .05, and 10th-grade β =
-.080, F(3, 3088) = 4.68, p =.001, were significant, while 11th-grade was not significant.
For learning strategies, 10th-grade β = -.053, F(3, 2932) = 2.74, p < .05, was significant,
while ninth and 10th-grades were not significant. For academic self-concept, ninth grade
β = -.048, F(3, 3130) = 4.12, and 10
th
grade β = -.068, F(3, 3130) = 4.12 were significant
(p < .05), while 11th-grade was not significant. For effort and perseverance ninth, 10th,
and 11th-grades were all not significant. In the proposed path model (Figure 2) a path
from grade level to effort and perseverance was proposed. This was removed in the final
path model (Figure 4).
For instrumental motivation ninth grade (β = -.069) would be .069 less than the
average for White, and 10th-grade (β = -.059) would be .058 less than the average for
White, they both were associated with a decrease, for reading, ninth grade (β = -.070),
and 10th-grade (β = -.065) both were associated with a decrease, for math, ninth grade (β
= .080), and 10th-grade (β = .061) both were associated with an increase, for self-
efficacy/self-beliefs, ninth grade (β = -.060), and 10th-grade (β = -.080) both were
associated with a decrease, for learning strategies, 10th-grade (β = -.053) was associated
with a decrease, and for academic self-concept, ninth grade (β = -.048), and 10th-grade (β
= -.068) both were associated with a decrease. Eleventh grade was not significant for any
mediating variable.

MOTIVATIONAL FACTORS AND ENGAGEMENT

60
Table 8
Summary of Multiple Regression Analysis for Grade Level and Instrumental
Motivation, Reading, Math, Self-Efficacy/Self-Beliefs, Learning Strategies, Academic
Self-Concept, and Effort and Perseverance
R R
2
F B SE Beta t p
instrumental motivation
ninth grade
10th-grade
11th-grade

.060 .004* 3.76
-.123
-.104
-.088

.040
.039
.039

-.069*
-.059*
-.050*

-3.10
-2.64
-2.26

.002
.008
.024
reading
ninth grade
10th-grade
11th-grade

.071 .005*** 5.23
-.112
-.102
-.022

.035
.035
.034

-.070*
-.065*
-.014

-3.17
-2.92
-.63

.002
.003
.531
math
ninth grade
10th-grade
11th-grade

.079 .006*** 6.49
.152
.112
.013

.042
.041
.041

.080***
.061*
.007

3.63
2.73
.331

.000
.006
.740
self-efficacy/self-beliefs
ninth grade
10th-grade
11th-grade

.067 .005* 4.68
-.077
-.100
-.048

.029
.028
.028

-.060*
-.080***
-.038

-2.69
-3.56
-1.71

.007
.000
.087
learning strategies
ninth grade
10th-grade
11th-grade

.053 .003* 2.74
-.003
-.073
-.054

.032
.031
.031

-.002
-.053*
-.040

-.09
-2.32
-1.72

.926
.020
.085
academic self-concept
ninth grade
10th-grade
11th-grade

.063 .004* 4.12
-.072
-.100
-.018

.033
.033
.032

-.048*
-.068*
-.012

-2.18
-2.07
-.55

.030
.002
.581
effort & perseverance
ninth grade
10th-grade
11th-grade
.049 .002† 2.44
.044
-.050
-.015

.036
.035
.035

.027
-.032
-.010

1.23
-1.41
-.42

.220
.159
.672
Note: *p < .05, ***p = .001, † sig. = .063, N = 3180.

Table 9 shows the summary of multiple regression analysis for the mediating
variables, instrumental motivation, reading, math, self-efficacy/self-beliefs, learning
MOTIVATIONAL FACTORS AND ENGAGEMENT

61
strategies, and academic self-concept, and the dependent variables effort and
perseverance while controlling for grade level. All mediating variables, instrumental
motivation β = .502, F(4, 3011) = 261.74 reading β = .363, F(4, 2967) = 114.42, math β =
.281, F(4, 2973) = 65.46, self-efficacy/self-beliefs β = .458, F(4, 2987) = 199.89, learning
strategies β = .797, F(4, 2887) = 1260.94, and academic self-concept β = .380, F(4, 3023)
= 129.64, were all significantly correlated (p = .001) with effort and perseverance.
All mediating variables, instrumental motivation (β = .502), reading (β = .363),
math (β = .281), self-efficacy/self-beliefs (β = .458), learning strategies (β = .797), and
academic self-concept (β = .380), were all associated with an increase in relation to effort
and perseverance when controlling for grade level. For example, as instrumental
motivation increased (β = .502), effort and perseverance increased, when controlling for
grade level.
Table 9
Summary of Multiple Regression Analysis for the Mediating Variables, Instrumental
Motivation, Reading, Math, Self-Efficacy/Self-Beliefs, Learning Strategies, and
Academic Self-Concept, and the Dependent Variables Effort and Perseverance While
Controlling for Grade Level
R
2
F B SE Beta t p
1 .258 261.74 .452 .014 .502 32.18 .000
2 .134 114.42 .368 .017 .363 21.20 .000
3 .081 65.46 .240 .015 .281 15.94 .000
4 .211 199.89 .577 .021 .458 28.12 .000
5 .636 1260.94 .915 .013 .797 70.89 .000
6 .146 129.64 .407 .018 .380 22.57 .000
Note: 1 = instrumental motivation; 2 = reading; 3 = math; 4 = self-efficacy/self-beliefs; 5 = learning
strategies; and 6 = academic self-concept. p = .001, N = 3180.

MOTIVATIONAL FACTORS AND ENGAGEMENT

62
Figure 4 shows the path model for the relationship between effort and
perseverance and grade level with instrumental motivation, reading, math, self-
efficacy/self-beliefs, learning strategies, and academic self-concept serving as mediating
variables.

Figure 4. The path model predicting the relationship between effort and perseverance and
grade level with instrumental motivation, reading, math, self-efficacy/self-beliefs,
learning strategies, and academic self-concept serving as mediating variables.
Overview of Findings
Both ethnicity and grade level proved to be small, but significant, predictors for
the motivational factors examined (instrumental motivation, reading, math, self-
Effort & Perseverance
Dependent Variable
Academic Self-Concept
Instrumental Motivation
Reading
Self-Concept Reading
Interest Reading
Math
Self-Concept Math
Interest Math
Self-Efficacy/Self-Beliefs
Learning Strategies
Elaboration
Memorization
Control Strategies
Mediating Variables
Grade Level
9th grade
10th grade
11th grade
vs. 12th grade
.506***
.363***
.281***
.458***
.797***
.380***
* p < .05
*** p = .001
R2 = .004*
R2 = .005*
R2 = .006***
R2 = .005*
R2 = .003*
R2 = .004*
MOTIVATIONAL FACTORS AND ENGAGEMENT

63
efficacy/self-beliefs, and academic self-concept), and learning strategies. The mediating
variables (instrumental motivation, reading, math, self-efficacy/self-beliefs, learning
strategies, and academic self-concept), proved to be significant, predictors for effort and
perseverance while controlling for ethnicity and grade level.
Ethnicity
Looking at the path model for ethnicity (Figure 3), the R squared values ranged
from .010 (1%) to .003 (.3%) for the various mediating variables. While small, they were
all significant at the .05 or .001 level. Thus indicating the predictive importance of
ethnicity as a group.
Within ethnicity (Table 6), Asians had a small but positive and significant Beta
values for five of the six mediating variables, Hispanics had two positive and one
negative significant Beta values, and African American/Blacks had one positive and one
negative significant Beta values. Asians, African American/Blacks, and Hispanic all had
positive and significant Beta values for effort and perseverance.
Grade Level
Looking at the path model for grade level (Figure 4), the R squared values ranged
from .006 (.6%) to .003 (.3%) for the various mediating variables. While small, they were
all significant at the .05 or .001 level. Thus indicating the predictive importance of grade
level as a group.
Within grade level (Table 8), ninth grade had a small but positive and significant
Beta value for one of the six mediating variables and a small but negative and significant
Beta value for three of the six mediating variables, 10th-grade had a small but positive
MOTIVATIONAL FACTORS AND ENGAGEMENT

64
and significant Beta value for one of the six mediating variables and a small but negative
and significant Beta value for four of the six mediating variables, and the 11th-grade had
a small but negative and significant Beta value for one of the six mediating variables. For
effort and perseverance, all three grades had no significant results. Of the twelve
significant results, two were positive and ten were negative Beta values.

MOTIVATIONAL FACTORS AND ENGAGEMENT

65
Chapter 5: Discussion
The purpose of this study was to examine the relationship between ethnicity and
grade level, motivational factors (instrumental motivation, reading, math, self-
efficacy/self-beliefs, and academic self-concept) and learning strategies, and engagement
(effort and perseverance) in a large urban high school setting. A regression analysis was
conducted to determine the relationships between the variables to determine two path
models, one for ethnicity as the predictor variable and one for grade level as the other
predictor variable.
Both ethnicity and grade level proved to be small, but significant, predictors for
the motivational factors examined (instrumental motivation, reading, math, self-
efficacy/self-beliefs, and academic self-concept), and learning strategies. The mediating
variables (instrumental motivation, reading, math, self-efficacy/self-beliefs, and academic
self-concept, and learning strategies), proved to be significant predictors for effort and
perseverance while controlling for ethnicity and grade level.
The findings of the current study extend previous research on the relationship
between ethnicity and motivational factors, learning strategies, and engagement (Garcia
et al., 1993; Smith et al., 1999; Stevens et al., 2004; Jonson-Reid et al., 2005; Malka &
Covington, 2005; Zimmerman & Kitsantas, 2005; Rueda et al., 2008; Wilkins &
Kuperminc, 2010), and grade level and motivational factors, learning strategies, and
engagement (Otis & Pelletier, 2005; Unrau & Schlackman, 2006; Long et al., 2007;
Cleary and Chen, 2009), by examining a large sample (N = 3180), at an urban high
school. The study also adds to our understanding of the relationship between motivational
MOTIVATIONAL FACTORS AND ENGAGEMENT

66
factors (instrumental motivation, reading, math, self-efficacy/self-beliefs, and academic
self-concept), learning strategies, and effort and perseverance in a large urban high school
with a large minority population (71.5% of the sample).
Ethnicity
Garcia et al. (1993) studied ethnic differences with college students and reported
African American and Latino students had a higher extrinsic motivation orientation over
their White counterparts. They also concluded that gender and ethnicity do not affect
achievement outcomes, but preparedness, motivation and use of learning strategies do.
This study supports their findings for Hispanic students, but for African American
students the results were not significant. This study also supports their findings regarding
motivation and use of learning strategies. While the sample populations were very
different, some of the findings of this study supports Garcia et al.’s (1993) findings. The
differences are probably due to the different sample populations and that high school age
African Americans respond differently concerning extrinsic motivation than college age
African American students studied by Garcia et al. (1993).
A study by Smith et al. (1999) conducted with 100 sixth grade students (67
African American) indicated positive relationships between self-esteem and efficacy, and
ethnicity and efficacy. This study also found a positive relationship between ethnicity and
self-efficacy, but did not find a significant relationship for African Americans and self-
efficacy. Again, the sample populations were very different, and the differences in the
two studies are probably due to the different sample populations, in particular the age
MOTIVATIONAL FACTORS AND ENGAGEMENT

67
difference. This study did find a positive relationship between ethnicity and self-efficacy
and does support Smith et al. (1999) findings in that regard.
Stevens et al. (2004) evaluated self-efficacy and motivational orientation across
Hispanic and Caucasian students (358 students in grades 9 and 10 who attended a West
Texas high school) to predict variables related to mathematics achievement. Their
findings supported that self-efficacy predicts motivational orientation and mathematics
performance and suggested that the relationship between prior mathematics achievement
and self-efficacy was stronger for Hispanic students. This study only found a significant
correlation between Asians and self-efficacy, and Asians and math. Again, the
differences are probably due to the differences in sample populations, in particular the
difference between a West Texas high school and a large urban high school.
Unrau and Schlackman (2006) investigated the effects of intrinsic and extrinsic
motivation on reading achievement for approximately 2,000 urban middle school
children, which consisted of grades 6, 7, and 8. The population included about 75%
Hispanic and 20% Asian, the other 5% was a mix of African American, American Indian,
and White students. They found that the relation between intrinsic motivation, extrinsic
motivation and reading achievement was stronger for Asian students than for Hispanic
students. For Asian students intrinsic motivation was positively related to reading
achievement and extrinsic motivation was negatively related, while for Hispanic students
neither intrinsic motivation nor extrinsic motivation had a direct effect on reading
achievement. This study found a significant relationship between Asians and extrinsic
motivation and Hispanics and extrinsic motivation, supporting Unrau and Schlackman
MOTIVATIONAL FACTORS AND ENGAGEMENT

68
(2006) findings, while for reading, Hispanics had a negative relationship and Asians were
not significant. Again, population sample differences probably account for the
differences, in particular the age differences between the two samples.
Rueda et al. (2008) studied 200 students ninth through 12th-grade, with 10th-
grade making up the largest portion of the sample (41%). Hispanics made up 95% of the
student sample at this central city high school in Los Angeles. It was found that the
learning and motivational variables were predictive of academic engagement for this
sample. This study also found that the learning and motivational variables were
predictive of academic engagement. Hispanics were found to have significant positive
relationships with instrumental motivation, learning strategies, and effort and
perseverance, and a significant negative relationship with reading. This study duplicated
some of Rueda et al. (2008) study, but with a much larger population and supports their
findings and expands on others where they did not have a large enough sample size.
Wilkins and Kuperminc (2010) findings suggested that perception of a task-
performance focused academic climate plays an important role in Latino middle school
students’ academic achievement. While this study did not examine academic climate, it
did examine effort and perseverance, and Hispanics displayed a significant positive
relationship with effort and perseverance, adding some support to their study.
In regards to the means displayed for the ethnicity in Table 4, Means and
Standard Deviations for Ethnicity and Grade Level, African American/Black and
Hispanic have slightly higher means for instrumental motivation, self-efficacy/self-
beliefs, learning strategies, and effort and perseverance, than Whites. While this is not a
MOTIVATIONAL FACTORS AND ENGAGEMENT

69
common pattern, which is different than the usual pattern in studies where White students
typically score higher on these types of measures (Stevens et al., 2004: Raineri, 2010), it
is not unheard of (Garcia et al., 1993). For example, Garcia et al. (1993) reported African
American and Latino college students displayed a higher extrinsic motivation orientation
over their White counterparts. The higher means in this study by African American/Black
and Hispanic students may be due to a special class started by the district that targets
underserved African American/Black and Hispanic students. The goals of the program
are to empower students through academics, culture, and history; integrate extracurricular
opportunities within the school program; provide opportunities for post-secondary
options and advancement; improve self awareness and respect, while encouraging
positive relationships with adults and peers; and instill qualities that lead to success.
Another program that targets African American/Blacks and Hispanics at the high school
is the AVID (Advancement Via Individual Determination) program. AVID’s mission is
to close the achievement gap by preparing all students for college readiness and success
in a global society. Avid is an elective class and program for grades 6-12 designed to
target and enroll students from the academic middle into the school’s most rigorous
courses. Students receive support in the AVID elective class through tutoring and a
proven curriculum based on writing, inquiry, collaboration, and reading. The combination
of these two programs might explain the unexpected higher means of the African
American/Black and Hispanic students.

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70
Grade Level
Otis and Pelletier (2005) examined changes in intrinsic and extrinsic motivation
during the transition from junior to senior high school. Results revealed that students’
intrinsic and extrinsic motivation decreased gradually from eighth to 10th-grade. This
study found a significant negative relationship between ninth, 10th, and 11th-grades and
extrinsic motivation. The negative values decrease gradually from ninth to 11th-grade.
Again, population sample differences probably account for the differences, in particular
the age differences between the two samples, implying the differences between middle
school and high school students.
Unrau and Schlackman (2006) investigated the effects of intrinsic and extrinsic
motivation on reading achievement for approximately 2,000 urban middle school
children, which consisted of grades 6, 7, and 8. For all of the participants, intrinsic
motivation and extrinsic motivation declined significantly as students moved from grade
6 to grade 7 to grade 8. Again, this study found a significant negative relationship
between ninth, 10th, and 11th-grades and extrinsic motivation. The negative values
decrease gradually from ninth to 11th-grade. Again, population sample differences
probably account for the differences, in particular the age differences between the two
samples, implying the differences between middle school and high school students.
Long et al. (2007) investigated eighth and ninth-grade students, focusing on
motivation and GPA in a large, urban, predominantly African American, school district
in the Midwest. Self-efficacy and learning goals contributed to domain interests but the
predictive value of these three motivational variables on achievement differed at each
MOTIVATIONAL FACTORS AND ENGAGEMENT

71
grade level. This study found an overall significant positive relationship between self-
efficacy, and effort and perseverance while correcting for grade level, but found a
significant negative relationship between ninth and 10th-grades and self-efficacy. Again,
population sample differences probably account for the differences, in particular the age
differences and the differences between a Midwest and urban West coast schools.
Cleary and Chen (2009) examined grade level, achievement group, and math-
course-type differences in student self-regulation and motivation in a sample of 880
suburban middle school students, grades six and seven. The seventh graders were less
interested in math activities than their younger peers. This study found a significant
positive relationship between ninth and 10th-grades and math (interest in math and self-
concept math), with ninth grade being higher than 10th-grade. Again, population sample
differences probably account for the differences, in particular the age differences between
the two samples, implying the differences between middle school and high school
students, and suburban and urban students.
Summary
This study supported some of the above research (Garcia et al., 1993; Smith et al.,
1999; Unrau & Schlackman, 2006; Rueda et al., 2008; Wilkins & Kuperminc, 2010;
Long et al., 2007) and added to their findings, but for the great majority of research, this
study found different findings most likely due to the difference in sample populations, for
example, middle school verse high school, rural verse urban, etc. Due to the large,
comprehensive data set that was given to this researcher by the district, of a large urban
high school, a study was performed using a sample size many times larger than previous
MOTIVATIONAL FACTORS AND ENGAGEMENT

72
research in this area of study. These finding may very well be unique, in that a data set
this large, of this particular type of population, has never been made available for study.
This study just scratched the surface of the available data that was included in the data
set. Many additional studies could be performed utilizing this data.
Implications for the District
All the mediating variables (instrumental motivation, reading, math, self-
efficacy/self-beliefs, learning strategies, and academic self-concept), proved to be
significant predictors for effort and perseverance. It is recommended to the district that
they incorporate the motivational factors, (instrumental motivation, reading, math, self-
efficacy/self-beliefs, and academic self-concept, and learning strategies), into future
teacher in-services.
Concerning Hispanics and the motivational factor reading (self-concept reading
and interest in reading), they displayed a significant negative relationship. This is a
specific area and population that should be targeted for intervention.
Concerning African American/Blacks and the motivational factor academic self-
concept, they were found to have a significant negative relationship. This is a specific
area and population that should also be targeted for intervention.
Concerning ninth and 10th-graders and the motivational factors, instrumental
motivation, reading (self-concept reading and interest in reading), self-efficacy/self-
beliefs, and academic self-concept, both displayed significant negative relationships with
the motivational factors above. Ninth and 10th-graders should be targeted in these areas
for intervention.
MOTIVATIONAL FACTORS AND ENGAGEMENT

73
Tenth grade also had a significant negative relationship with learning strategies.
They should be targeted in this area for intervention.
Future Research
The results only show descriptive relationships, while valuable, it does not show
any causal relationships or answer the question, why? These questions could be answered
by future studies in these areas.
While the results of the study can only be applied to the specific sample studied,
the results could be inferred to apply to like populations in similar high schools within the
district and to other like populations in similar high schools in other urban high schools.
Future studies could be conducted with the remaining high schools and/or all the students
in the district.
The small, but significant results for ethnicity and grade level, could be due to the
hundreds of other possible predictor variables (for example, socioeconomic standing, first
language, parental education levels, culture, gender, etc.) of which ethnicity and grade
level are just a part of the bigger picture. Again, future studies could include other
possible predictor variables.
The 11th-grade had only one small but negative and significant Beta value of the
six mediating variables. This study did not reveal much usable data for eleventh graders,
thus indicating the need for further research with 11th-graders.
As stated before, this study just scratched the surface of the available data that
was included in the data set. Many additional studies could be performed utilizing this
data.
MOTIVATIONAL FACTORS AND ENGAGEMENT

74
The big question still remains unanswered, how do you motivate students to be
academically successful? If they put in the effort and they persevere and they keep trying
to improve, they will become successful, but how does one do that for all of the students?
This study at least helped provide a little more data to help answer the “big” question.

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75
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Appendix: Survey Questions
Directions: Please fill in one response for each question.

1. If you were free to go as far as you wanted to go in school, what level of
education would you like to complete?

Less than high school graduation
High school diploma
Vocational or technical program (less than two years)
Two-year college degree
Bachelor’s degree (four years of college)
Master’s degree
Professional level degree (Ph.D., M.D., or J.D. lawyer)

2. If you were completely free to choose any job, what would you like to do most in
the future?

Arts/Media/Entertainment
Business/Finance
Education
Health/Medical and Human Services
Law
Public Service
Science/Engineering
Other/Undecided

3. Sometimes what we like to do is not the same as what we expect to do. What
level of education do you expect to complete?

Less than high school graduation
High school diploma
Vocational or technical program (less than two years)
Two-year college degree
Bachelor’s degree (four years of college)
Master’s degree
Professional level degree (Ph.D., M.D., or J.D. lawyer)

4. What job do you actually expect to end up with in the future?

Arts/Media/Entertainment
Business/Finance
Education
Health/Medical and Human Services
Law
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94
Public Service
Science/Engineering
Other/Undecided

Directions: Please indicate how true each statement is for you on a scale of 1 (Not at all
True) to 5 (Very True) by circling a number on the rating scale.

Not
at all
True

Very
True
5. An important reason that I try to do well in school is to
please my parents.

1 2 3 4 5
6. I want to do well in school so that I can be better
prepared to take care of my family.

1 2 3 4 5
7. The main reason I try to do well in school is to bring
honor to my family.

1 2 3 4 5
8. It is important to me that my parents-guardians are
proud of my achievement in school.

1 2 3 4 5

Directions: Please rate each statement below from 1 (Disagree) to 4 (Agree) by circling
a number on the rating scale.
Disagree
Somewhat
Disagree
Somewhat
Agree Agree
9. When I sit myself down to learn something really difficult, I
can learn it.
1 2 3 4
10. I’m hopeless in English classes.

1 2 3 4
11. I get good grades in mathematics.

1 2 3 4
12. I learn things quickly in most school subjects.

1 2 3 4
13. I study to increase my job opportunities.

1 2 3 4
14. Because reading is fun, I wouldn’t want to give it up.

1 2 3 4
15. When I do mathematics, I sometimes get totally absorbed. 1 2 3 4
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95
Disagree
Somewhat
Disagree
Somewhat
Agree Agree

16. I’m confident I can understand the most complex material
presented by the teacher.
1 2 3 4
17. I learn things quickly in English class.

1 2 3 4
18. Mathematics is one of my best subjects.

1 2 3 4
19. I’m good at most school subjects.

1 2 3 4
20. I study to ensure that my future will be financially secure.

1 2 3 4
21. I read in my spare time.

1 2 3 4
22. Because doing mathematics is fun, I wouldn’t want to give it
up.

1 2 3 4
23. If I decide not to get any bad grades, I can really do it.

1 2 3 4
24. I get good grades in English.

1 2 3 4
25. I have always done well in mathematics.

1 2 3 4
26. I do well in tests in most school subjects.

1 2 3 4
27. If I decide not to get any problems wrong, I can really do it.

1 2 3 4
28. I study to get a good job.

1 2 3 4
29. When I read, I sometimes get totally absorbed.

1 2 3 4
30. Mathematics is important to me personally.

1 2 3 4
31. If I want to learn something well, I can.

1 2 3 4

Directions: Please rate each statement below from 1 (Disagree) to 4 (Agree) by circling
a number on the rating scale.

Disagree
Somewhat
Disagree
Somewhat
Agree Agree
32. When I study, I try to relate new material to things I have
1 2 3 4
MOTIVATIONAL FACTORS AND ENGAGEMENT

96
Disagree
Somewhat
Disagree
Somewhat
Agree Agree
learned in other subjects.
33. When I study, I try to memorize everything that might be
covered.

1 2 3 4
34. When I study, I start by figuring out exactly what I need
to learn.

1 2 3 4
35. When studying, I work as hard as possible.

1 2 3 4
36. When I study, I figure out how the information might be
useful in the real world.
1 2 3 4
37. When I study, I memorize as much as possible.

1 2 3 4
38. When I study, I force myself to check to see if I remember
what I have learned.
1 2 3 4
39. When studying, I keep working even if the material is
difficult.

1 2 3 4
40. When I study, I try to understand the material better by
relating it to things I already know.
1 2 3 4
41. When I study, I memorize all new material so that I can
recite it.

1 2 3 4
42. When I study, I try to figure out which concepts I still
haven’t really understood.
1 2 3 4
43. When studying, I try to do my best to acquire the
knowledge and skills taught.
1 2 3 4
44. When I study, I figure out how the material fits in with
what I have learned.
1 2 3 4
45. When I study, I practice by saying the material to myself
over and over.
1 2 3 4
46. When I study, I make sure that I remember the most
important things.
1 2 3 4
47. When studying, I put forth my best effort.

1 2 3 4
MOTIVATIONAL FACTORS AND ENGAGEMENT

97
Disagree
Somewhat
Disagree
Somewhat
Agree Agree
48. When I study, and I don’t understand something I look for
additional information to clarify this.
1 2 3 4

Abstract (if available)

Abstract The purpose of this study was to examine the relationship between ethnicity and grade level, motivational factors (instrumental motivation, reading, math, self‐efficacy/self-beliefs, learning strategies, and academic self‐concept) and learning strategies, and engagement (effort and perseverance) in a large urban high school setting. A regression analysis was conducted to determine the relationships between the variables to determine two path models, one for ethnicity as the predictor variable and one for grade level as the other predictor variable. ❧ Both ethnicity and grade level proved to be small, but significant, predictors for the motivational factors examined (instrumental motivation, reading, math, self‐efficacy/self-beliefs, and academic self‐concept), and learning strategies. The mediating variables (instrumental motivation, reading, math, self‐efficacy/self‐beliefs, learning strategies, and academic self‐concept), proved to be significant predictors for effort and perseverance while controlling for ethnicity and grade level.

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Asset Metadata

Creator Seki, Glenn Kunitoshi (author)

Core Title The relationship between motivational factors and engagement in an urban high school setting

School Rossier School of Education

Degree Doctor of Education

Degree Program Education

Publication Date 06/23/2014

Defense Date 05/15/2014

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag academic self‐concept,African American,Asian,Black,Education,educational psychology,effort,engagement,Ethnicity,grade level,High School,Hispanic,instrumental motivation,K12,Latino,learning strategies,math,minorities,Motivation,OAI-PMH Harvest,path model,Reading,secondary education,self‐efficacy/self‐beliefs,survey,urban high school

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Rueda, Robert (committee chair), Hocevar, Dennis (committee member), Loera, Gustavo (committee member)

Creator Email glenn.seki@gmail.com,gseki@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-424859

Unique identifier UC11286667

Identifier etd-SekiGlennK-2580.pdf (filename),usctheses-c3-424859 (legacy record id)

Legacy Identifier etd-SekiGlennK-2580.pdf

Dmrecord 424859

Document Type Dissertation

Format application/pdf (imt)

Rights Seki, Glenn Kunitoshi

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA