Ethnic differences on students’ approaches to learning: Self-regulatory cognitive and
motivational predictors of academic achievement for Latino/a and White college students.
Robert Rueda
Hyo Jin Lim,
Harry O’Neil
University of Southern California
Noelle Griffin
University of California, Los Angeles
Shel Bockman
Barbara Sirotnik
California State University, San Bernardino
NOTE: The work report herein was supported at the University of Southern California by the
Office of Naval Research, under Award No. N00014-06-1-0711 at UCLA. The work was
supported by a subcontract from UCLA. The opinions, findings and conclusions or
recommendations expressed in this material are those of the author(s) and do not reflect the
views of the Office of Naval Research.
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Abstract
We examined (a) the construct validity across White and Latino college students in terms
of motivational and the use of learning strategies constructs; and (b) the differences in the
relationships among sociocultural variables and motivation/learning related factors and
academic achievement. Results of multi-group confirmatory factor analyses revealed that
the scales of motivation and learning-related factors proved to be equivalent across the
groups. We tested a theoretical model of relationships among sociocultural backgrounds
and motivational factors which affected achievement using SEM methods. In general,
the patterns of relationships were in the expected direction. There were significant ethnic
differences in terms of specific effects of background characteristics on motivational
and/or learning related factors. Ethnic differences were also found in the relationships
between the use of adaptive and maladaptive learning strategies and academic
achievement. The research is discussed with respect to relatively sparse literature on
sociocultural aspects of motivation and their effects on academic achievement with
college-level Latino students.
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Table of Contents
Introduction..................................................................................................................
Achievement Differences Among Subgroups in Higher Education: Social and Cultural
Factors...........................................................................................................................
Ethnicity............................................................................................................
SES.....................................................................................................................
Secondary effects: Opportunity to learn and prior knowledge.........................................
Gender.............................................................................................................
Cognitive and Motivational Factors in Learning and Achievement..............................
Self-Efficacy......................................................................................................
Goal Orientation.................................................................................................
Learning Strategies and Self-regulated Learning...............................................
Help-seeking......................................................................................................
Text Anxiety and
Worry.............................................................................................
Summary and Purpose of the Study................................................................................
Method............................................................................................................................
Research Context................................................................................................
Subjects...............................................................................................................
Measures.............................................................................................................
Background variables.................................................................................
Motivation and attitudes............................................................................
Personal motivational beliefs......................................................................
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Strategy use......................................................................................................
Academic achievement....................................................................................
Procedure............................................................................................................
Telephone interview...................................................................................
Online survey..............................................................................................
Data Analysis......................................................................................................
Results.............................................................................................................................
Reliabilities, descriptive statistics and intercorrelations...........................................
Reliabilities.................................................................................................
Correlational analyses based on theoretical expectations..........................................
Correlations among motivation/ learning strategies variables....................
Correlations between learning strategies and achievement........................
Multi-group Confirmatory Factor Analysis (MG-CFA)............................................
Preliminary CFA for overall model............................................................
Multiple-group CFA...................................................................................
Structural Equation Model (SEM) Analysis.............................................................
SEM MIMIC model for all students..........................................................
Two single-group SEMs (Latino vs. White).............................................
Summary Findings....................................................................................................
Discussion and Conclusion.................................................................................................
References...........................................................................................................................
Tables.....................................................................................................................................
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Figures...................................................................................................................
Appendix
A.............................................................................................................................
B..............................................................................................................................
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The national move toward accountability, primarily focused on K-12 schools, has
begun to focus on postsecondary education as well. Accountability demands for student
academic performance and persistence rates is growing (Carey, 2004, 2005; Hearn &
Holsworth, 2002). In addition, postsecondary education is widely seen as only a
minimum goal in education, as a high school diploma is no longer seen as sufficient
preparation for contemporary workforce demands. However, there are wide disparities in
the success rates among groups in the country. In 2007-08, for example, there were 2,757
four year postsecondary institutions in the United States, and one third of these (33.1%)
were public institutions. Within these institutions, 67% of the degrees conferred were
awarded to White, non-Hispanic students. In contrast, 8.5% and 7.1% of the degrees were
awarded to Black, non-Hispanic and Hispanic students respectively (Knapp, Kelly-Reid,
& Ginder, 2008). These data belie significant differences in other college-related
outcomes between Latino and other students in the U.S.
Latinos have been found to lag every other population group in attaining college
degrees, especially bachelor’s degrees. To better understand that problem and help
identify policy responses, the Pew Hispanic Center conducted a new analysis of the
educational performance of Latino high school graduates. This analysis is based on
Current Population Survey data collected by the U.S. Census Bureau from 1997 to 2000.
The data was combined and averaged to create a solid statistical basis for assessing
different forms of college attendance for Latinos as compared to other groups and for
making important distinctions among sub-groups of the Latino population. The report
(Fry, 2002) revealed the following enrollment patterns:
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 Latino high school graduates are enrolled in some form of college at a surprisingly
high rate (10 %, compared to 7% of the total population). Moreover, after the age of
24, the proportion of Latino high school graduates enrolled in college courses is
higher than that of whites (5% vs. 7%) ,although those students who finish at an
earlier age tend to see greater economic rewards
 There is a substantial enrollment gap between Latinos and all other groups among
18-24 year olds – only 35% of Latino high school graduates in that age group are
enrolled in college compared to 46% of whites
 Latinos are far more likely to be enrolled in two year colleges than any other group
– about 40% of Latinos in the 18 to 24 year old range) compared to about 25% of
white and black students in that age group
 Latinos are more likely to be part time students (about 85% of Whites are enrolled
full time compared to about 75% of Latinos)
 Latinos lag behind in the pursuit of graduate and professional degrees in the 25-34
year old age group compared to Whites (1.9 % compared to 3.8%)
There are within-group differences that have been reported as well. For example,
first-generation students (those whose parents did not attend college and who are most
often African American or Latino) also have a lower rate of postsecondary attainment
than White students. Based on the National Education Longitudinal Study of 1988
(National Center for Education Statistics [NCES], 2005), 43% of the first-generation
students who enrolled in postsecondary education between 1992 and 2000 “left without a
degree” and 25% had attained an undergraduate degree by 2000. In contrast, 20% of the
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students whose parents had a bachelor’s degree or higher “left without a degree” from a
postsecondary institution, while almost 70% attained an undergraduate degree by 2000.
These inequities in outcomes are troubling for a number of reasons, yet they only
document the current situation rather than suggesting a solution. Attempts to examine the
underlying causes of these achievement differences have focused on a number of areas
including correlates resulting in hypothesized or possible causes of the achievement
patterns noted above. These are described below.
Achievement Differences Among Subgroups in Higher Education: Social and Cultural
Factors
College enrollment has been found to be influenced by a variety of academic,
personal, social and economic factors. 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 (Alfaro, Umana-Taylor, & Bamaca, 2006; Esparza
& Sanchez, 2008; Fuligni, Tseng, & Lam, 1999; Plunkett & Bamaca-Gomez, 2003;
Plunkett, Henry, Houltberg, Sands, & Abarca-Mortensen, 2008). For instance, Eccles,
Vida, and Barber (2004) found that high school achievement, family expectations,
mother’s education level and family income were predictors of college enrollment.
Abrego (2006) took a qualitative approach to examining the problem of undocumented
youth in high school, and her findings revealed how these students faced the difficult and
often impossible task of entering college or finding a job due to their undocumented
status, thus resulting in decreased motivation and increased tendency to dropout.
In regards to families, Goldenberg, Gallimore, Reese, and Garnier (2001) looked at
how the aspirations and expectations of immigrant Latino families impacts student
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performance. This study was found that low performing students do not come from
families with low expectations. In fact, the contrary was found, in that immigrant families
hold high expectations and aspirations for their children’s current and future performance
in school. Additionally, these beliefs are influenced by their children’s’ performance in
school, rather than the beliefs influencing performance (Goldenberg et al., 2001), and
these beliefs later affect students’ enrolment in college (Davis-Kean, Vida, & Eccles,
2001).
Other commonly examined sociocultural factors include ethnic/racial background,
socioeconomic status, and gender. Ethnicity, SES, and gender have been found to either
predict or moderate the prediction of college retention and GPA (Nettles, Thoeny, &
Gosman, 1986; Nguyen, Allen, & Fraccastoro, 2005; Robbins, Lauver, Le, Davis,
Langley, & Carlstrom, 2004; Robbins, Allen, Casillas, Peterson, & Le, 2006). We
consider each of these in the following paragraphs.
Ethnicity. Almost all discussions about the achievement gap in U.S. education
include ethnic, racial, and cultural aspects. As noted earlier, achievement based on ethnic
and cultural differences are prominent features of the educational system. Surveys
conducted by the National Center for Education Statistics (NCES) indicated that, on
average, minority students (primarily Latino and African American) lagged behind their
White peers in terms of academic achievement (U.S. Department of Education, 2000).
While early work tended to focus on presumed cultural deficits as a primary cause of
ethnic/racial/cultural achievement gaps, this view has been widely discredited (Graham &
Hudley, 2006; Gutierrez & Rogoff, 2003). More recently, other factors have been
suggested, including the effects of living in low-income households or in single parent
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families; low parental education levels, and schools with lower resources. All of these
factors are components of SES and linked to academic achievement (National
Commission on Children, 1991). We consider some of these factors below.
SES. Socioeconomic status (SES) is one of the most widely used contextual
variables in education research, and is often looked at in relation to academic
achievement (Bornstein & Bradley, 2003; Brooks-Gunn & Duncan, 1997; Coleman,
1988). There seems to be relatively widespread agreement that SES is comprised of three
components: parental income, parental education, and parental occupation (Gottfried,
1985; Hauser, 1994). While these variables are often moderately correlated, there is
evidence that the components are unique and that each one measures a substantially
different aspect of SES that should be considered to be separate from the others (Bollen,
Glanville, & Stecklov, 2001; Hauser & Huang, 1997). In the current study, we used
combination of mother’s education and parental income.
As these findings suggest, it is only a convenience to consider SES as a stand-alone
variable, since it is linked directly as well as indirectly to academic achievement. These
include, for example, students’ racial and ethnic background, grade level, and
school/neighborhood location (Bronfenbrenner & Morris, 1998; Brooks-Gunn & Duncan,
1997;; Eccles, Lord, & Midgley, 1991; Lerner, 1991). Social capital appears to be
necessary to success in school. That is, the supportive relationships among structural
forces and individuals (e.g. parent–school collaborations) that promote the sharing of
societal norms and values(Coleman, 1988; Dika & Singh, 2002) is one of the indirect
factors linked to family SES, in addition to such directly linked factors as home
resources.
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An early, comprehensive meta-analysis of SES and academic achievement (White,
1982) showed that the relation varies significantly with a number of factors such as the
types of SES and academic achievement measures, and later studies have reported
inconsistent findings. Correlations have ranged from strong (e.g., Lamdin, 1996; Sutton
& Soderstrom, 1999) to no significant correlation at all (e.g., Ripple & Luthar, 2000;
Seyfried, 1998). A more recent analysis (Sirin, 2005) examined journal articles
published between 1990 and 2000, and included 101,157 students, 6,871 schools, and
128 school districts gathered from 74 independent samples. The results showed a medium
to strong SES–achievement relation. However, the relation was moderated by the unit
(e.g., the individual student vs. an aggregated unit), the source (e.g. secondary sources vs.
self-report), the range of SES variable, and the type of SES–achievement measure. The
relation was also contingent upon school level, minority status, and school location. In
terms of grade level, the mean effect size (ES) was .19 for the kindergarten students, .27
for the elementary school students, .31 for middle school students, and .26 for high
school students. In addition, the mean ES for White student samples (.27) was
significantly larger than the mean ES for minority student samples (.17). Unfortunately,
the review did not consider information on college level effects.
Overall, the average correlation in Sirin’s (2005) review was .30, as compared with
White’s (1982) average correlation of .34. In sum, the best evidence indicates that using
Cohen’s (1977) effect size guidelines, the overall ES of Sirin’s analysis suggests a
medium level of association between SES and academic achievement at the student level
and a larger degree of association at the school level. Importantly, the review also
suggests that parents’ location in the overall SES hierarchy has a strong impact on
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students’ academic achievement. As Coleman (1988) has suggested, family SES sets the
stage for students’ academic performance both by directly providing resources at home
and by indirectly providing the social capital that is necessary to succeed in school. This
same factor also helps to determine the kind of school and classroom environment to
which the student has access (Reynolds & Walberg, 1992a). These indirect influences of
SES are often subsumed under the conceptually larger label of opportunity to learn,
which is considered next.
Secondary effects: Opportunity to learn and prior knowledge
Opportunity to learn (OTL) is a way of measuring and reporting whether students
have access to the different ingredients that make up quality schools, for example access
to challenging curriculum and to qualified teachers. The more OTL ingredients that are
present in an individual students’ educational history, the more opportunities students
have to benefit from a high quality education. OTL standards provide a benchmark
against which the opportunities that a school provides can be measured. The notion of
OTL is important since it opens the possibility that student outcomes are not solely a
function of inherent student or family characteristics.
Using OTL standards as a guide, students can estimate the quality of their
educational experience and whether they have a realistic possibility of competing for
college. These learning opportunities can either enhance or limit students’ equal access to
a high quality education and future educational achievement. Examples of OTL
indicators include students’ access to qualified teachers, clean and safe facilities, up-todate books and quality learning materials, high quality coursework, and related school
conditions that provide students a fair and equal opportunity to learn and achieve
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knowledge and skills. For example, taking a high-level math course is the one consistent
course predictor of college preparedness. Adelman (1999) found that the single greatest
predictor of successful college completion was the taking of high level mathematics
courses during high school. If students do not successfully complete algebra, they are
unlikely to succeed in institutions of postsecondary education (Checkley, 2001). Thus, if
students find themselves in a school that does not offer high level courses or teachers
qualified to teach such content, a negative relationship to academic outcomes would be
expected.
Several research studies (Adelman, 1999; Alexander, Pallas & Holupka, 1987;
Cabrera & La Nasa, 2000a, 2000b; Horn & Kojaku, 2001; Kane & Spizman, 1994;
McDonough, 1997; Stage & Rushin, 1993) [NOTE: HON to add CRESST study here]
have looked at other factors that fall under the general umbrella of OTL. These studies
have shown the following to be the strongest predictors of college attendance and
completion, particularly for minority and low income students: academic preparation,
social support, access to information, and parental involvement and knowledge about
college, and financial aid.
As might be expected, there is evidence that there are important differences in OTL
between low-SES schools and higher-SES schools. Differences have been found in the
following areas: instructional arrangements, materials, teacher experience, and teacherstudent ratio (Wenglinsky, 1998). Reinforcing the complex interrelationships among
sociocultural factors, family SES has been found to influence the quality of the
relationship between school personnel and parents (Watkins, 1997).
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Gender. There is a significant amount of work on the issue of gender differences in
academic achievement in general, and math achievement specifically, spanning several
decades (Caplan & Caplan, 1997; Chipman, 1996; Favreau, 1997). Historically, males
generally outperformed girls on measures of math and science achievement, with the gap
increasing in middle and high school (Byrnes, 2001, 2005; Halpern, 2000). Although
some more recent large-scale studies have documented girls performing lower on math
and science assessments than their math peers, (Braswell, Lutkus, Grigg, Santapau, TayLim, & Johnson, 2001; NCES, 2002; OECD, 2000) other researchers have found this
trend diminished or even reversed (see Baker, Griffin, & Choi, 2008 for a review).
Women in particular tend to outperform men in terms of many post-secondary
outcomes, with differences particularly evident for African American and Hispanic
students (Freeman, 2004). For example, females currently have greater success than
males in attaining post-secondary education, higher aspirations than males while in high
school, are more likely to enroll in college immediately after graduating from high
school, and persist and complete degrees at higher rates than males. Measures of postsecondary academic achievement (e.g., GPA) also tend to be higher for women than men.
More than half of all bachelor’s and master’s degrees are awarded to females.
Nevertheless, gender differences in majors still exist, with female bachelor’s degree
recipients much less likely than their male peers to major in computer science,
engineering, and physical sciences. Females also still lag behind males in enrolment in
first–professional and doctoral programs, and these gaps are particularly evident in fields
such as science, engineering, and mathematics (Snyder, Dillow, & Hoffman, 2007.).
These differences persist into the world of work as well, with women continuing to be
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under-represented in math, science, and technology professions (Commission on
Professionals in Science and Technology, 2006).Thus, while gaps have narrowed in some
areas, gender differences in some aspects of math achievement and attainment continue.
Many factors have been suggested as explanations for the patterns just reported
(Ben-Zeev, Fein, & Inzlicht, 2005; Geary, Saults, Lui, & Hood, 2000; Halpern & Lemay,
2000). For example, Byrnes (2005) posited an approach to the relationship between
gender and math outcomes that integrates several types of underlying factors:
exposure/opportunity to learn, motivation, and aptitude/ability. Byrnes suggests that this
constellation of conditions, influenced by a range of social, psychological, and
physiological variables, creates the gender differences often found in math achievement.
The role of socialization and stereotyped gender expectations, particularly in the
classroom setting, has also been highlighted in considering the origins and maintenance
of gender differences in math outcomes (Huguet & Requer, 2007)
One important line of investigation in this area has targeted metacognitive and
motivational variables as contributors to differences in outcomes. For example, as part of
the large scale PISA study, Marsh, Hau, Artelt, Baumert, & Peschar (2006) found gender
differences in math-related metacognitive, motivational, and affective factors. Hong,
O’Neil, and Feldon (2005) found no significant gender effects on math achievement,
however they did identify gender differences in state-based anxiety and self-regulation,
which themselves predicted math outcomes. Given this background, it is clear that
gender is an important variable in examining group differences in achievement, and it
was included in the present study.
Cognitive and Motivational Factors in Learning and Achievement
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It is clear that social and cultural factors are critical in considering achievement
differences in college and other academic settings. In addition, cognitive and
motivational factors have been shown to be related to a wide variety of academic
outcomes, and here we describe some of the relationships that have been found in the
literature. We begin by examining some of the key motivational variables.
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, persistence and performance (Eccles & Wigfield, 2002; Schunck,
Pintrich, & Meece, 2008; Wigfield & Eccles, 2000). We consider two of the motivational
variables that have been heavily studied and which have been shown to be important
influences on learning, self-efficacy and goal orientation.
Self-efficacy. Self efficacy is defined as “[P]eople’s judgments of their capabilities
to organize and execute courses of action required to attain designated types of
performances” (Bandura, 1986, p. 391). Although there are many motivational
constructs, self-efficacy is central to promoting students' engagement and learning. These
context-specific and domain-specific beliefs affect behavior by influencing the choices
that people make and the courses of action they follow (Linnenbrink & Pintrich, 2004).
Pajares (1996) points out that people take on tasks in which they feel knowledgeable,
experienced and confident and avoid those in which they do not. Self-efficacy motivates
students because when people expect to do well, they tend to try hard, persist, and
perform better (Schunk, Pintrich & Meece, 2008). By believing that they are able and
have the ability to do well, students are lead to be more motivated in terms of effort,
persistence, and behavior than students who do not (Bandura, 1997).
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One reason that self-efficacy is important is that it has been associated with a
variety of other motivational and learning-related variables. For example, Ross, Shannon,
Salisbury- Glennon, and Guarino (2002) point out that a large number of studies have
demonstrated a positive relationship between mastery goal orientation and self-efficacy.
There is also evidence that when students feel confident they can learn, they tend to use
more self-regulatory strategies (Pintrich & De Groot, 1990; Zimmerman, 2000b).
Much of the work on self-efficacy has been carried out with young students. One
important study with college age students was reported by Chemers, Hu, and Garcia
(2001). The study involved the entire freshman class at a major university in California.
Academic self-efficacy (among other variables) was measured along with high school
GPA and first year college GPA. While high school grades were significantly related to
college grades, self-efficacy at the time of college entrance was related to grades at the
end of the first year of college, even when prior GPA was controlled. Other recent studies
(Pietsch, Walker, & Chapman, 2003; Pintrich, 2003) as well as a recent review of selfefficacy studies (Valentine, DuBois, & Cooper, 2004) have confirmed these patterns.
Consistent with this body of research, it was expected that self-efficacy would be related
to learning strategies and achievement.
Goal orientation. Students’ perceptions about achievement behaviors and the
meanings they assign to such behaviors is the root of goal orientation-theory. The
classroom environment influences the goals students adopt. Mastery and performance
goals are two major constructs of goal orientation. Students with a mastery goal
orientation have the desire to learn for understanding, knowledge or insight, whereas
students with a performance goal orientation want to be perceived as competent in
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comparison to their peers, and may seek to outperform their classmates (Ames, 1992;
Dweck, 1986; Dweck & Leggett, 1988; Elliot, 1999). Goal theorists have made an
important distinction between those achievement goals that are performance-approach in
which students want to look good in comparison to their peers, and those that are
performance-avoidance in which students want to not look bad in comparison to their
peers. In general, the research suggests that mastery and performance-approach goals
tend to be positively related to academic achievement, and performance-avoidance goals
sometimes related negatively to academic achievement (Harackiewicz, Barron, Pintrich,
Elliot, & Thrash, 2002).
In spite of these generalizations, there is some controversy about the benefits of
mastery versus performance goals (Elliot, 2007). There is little disagreement about the
negative consequences of performance avoidance goals, but more disagreement about the
role of performance approach goals. For example, Midgley, Kaplan, and Middleton
(2001) argued that performance approach goals may lead to low level strategy use such as
memorizing to do well on tests, may be differentially beneficial to some groups or in
some situations more than others (e.g. males, older students, and students in classrooms
that are competitive in nature). In contrast, in a pair of related studies, Harackiewicz,
Barron, Tauer, and Elliot (2002) and Harackiewicz, Barron, Tauer, Carter, and Elliot
(2000) found that in studies with college students, mastery goals predicted increased
interest, enjoyment, and course-taking (in psychology courses), but not to grades in the
course. These researchers and others (Pintrich, 2003) have argued for a multiple goal
perspective in which mastery and performance approach goals play a positive role in
academic performance.
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One consistent finding in the achievement goal literature is that students who
support mastery goals are more likely to use deep-level strategies such as elaboration and
organization than students who support performance goals (Fredricks, Blumenfeld, and
Paris, 2004; Ross, Shannon, Salisbury-Glennon & Guarino, 2002;Wolters, Yu & Pintrich,
1996). A great deal of research, as Patrick, Kaplan, and Ryan (2007) point out, has
documented that mastery goals are connected with students’ use of cognitive and selfregulatory strategies and interacting with others about their ideas and understanding.
Multiple studies found that performance goals, especially performance avoidance, were
positively correlated with low levels of cognitive engagement (Patrick et al, 2007).
Mastery goals are usually associated with many positive cognitive, motivational, and
behavioral outcomes (Pintrich, 2003). Improvements in student achievement are found
when students go beyond the classroom requirements and do more work than is required
(Fredrick et al., 2004). Given the preceding research, it was expected that mastery goals
would be positively related to the use of learning strategies, and negatively related to
maladaptive help-seeking and worry.
Learning strategies and self-regulated learning.
Research has suggested that self-
regulated learning incorporates cognitive, motivational and metacognitive dimensions
(Zimmerman & Schunk, 2001) and suggests the importance of self-regulatory skills in
academic achievement (Zimmerman, 1990; Zimmerman & Martinez-Ponz, 1990).
Research also indicates that students who self-monitor their learning progress and
engagement with tasks generate internal feedback. This internal feedback works to assess
possible gaps in performance, and the result may lead to improved learning outcomes
(Azevedo & Cromley, 2004; Butler & Winne, 1995; Ley &Young, 2001).
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Students who are capable of effective learning tend to choose appropriate learning
goals, use adaptive knowledge and skills to direct their learning. These students are also
able to select effective learning strategies appropriate to the task at hand. In other words,
they are able to regulate their learning and take responsibility for the acquisition and
maintenance of new skills. Boekaert (1997, 1999)’s model suggests that self-regulated
learning is a complex, interactive process involving motivational as well as cognitive
self-regulation. According to the theory of self-regulated learning, there is a relationship
between different types of knowledge, strategy use, and goals.
One of the central aspects of self-regulated learning is students’ ability to select,
combine, and coordinate learning activities (Zimmerman & Martinez-Ponz, 1990). The
cognitive aspects of effective self-regulated learning are elaboration (e.g. exploring how
the information might be useful in the real world) and control strategies. Additionally,
effective learning requires adaptive motivational beliefs related to factors such as selfefficacy, interest, goal orientation, attributions, and other variables which in turn affect
effort and persistence in learning [Note to HON to add study here]. There is a significant
body of research findings that support the connection between motivational variables,
learning strategies, self-regulatory behaviour, and academic achievement (Bandura, 1997;
Deci & Ryan, 1985; Zimmerman, 1999, 2000a).
With respect to performance goals in particular, some work has found that they are
negatively related to self-regulation of learning, decreasing the use of deep cognitive
processing strategies that lead to better understanding (Graham & Golan, 1991; Meece,
Blumenfeld, & Hoyle, 1988; Nolen, 1988). In contrast, mastery goal orientations held by
individuals have been found to influence self-regulatory strategies (e.g., surface
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processing, deep processing, and help-seeking behaviors) and academic achievement
(e.g., Elliot, McGregor, & Gable, 1999; Middleton & Midgley, 1997). However, other
research has found no relation between classroom performance goals and self-regulated
learning (Ames & Archer, 1988), and one study (Ryan & Pintrich, 1997) found no
relation between performance goals and adaptive help seeking, one form of selfregulated learning. In line with this research, it was expected that students’ selfregulatory and learning strategies would be related to achievement because of the
centrality of these factors to a wide range of variables associated with improved academic
outcomes.
Help-seeking. Adaptive help-seeking has been described as asking for help needed
in order to learn independently, not simply to obtain the correct answer. It is manifested
when students monitor their academic performance, show awareness of difficulty they
cannot overcome on their own, and exhibit the wherewithal and self-determination to
remedy that difficulty by requesting assistance from a more knowledgeable individual,
they are exhibiting mature, strategic behaviour (Newman, 2002, p. 132). While help
seeking was once viewed as an indication that students were dependent, or immature,
learning researchers have come to view help seeking as an adaptive strategy, especially
when it is used to overcome difficulties in order to achieve mastery and autonomy in
learning (Schunk & Zimmerman, 1994).
Research indicates that students who tend to seek more help are also more likely to
seek instrumental help (Karabenick, 2004; Tobias, 2006). Research further indicates that
students who engage in instrumental help-seeking become more competent and
independent learners, and they are more self-regulated and metacognitively aware, which
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influences their awareness of a need for help (Tobias, 2006; Karabenick, 2004).
Karabenick (2004) found that mastery oriented students not only tend to seek more help,
but also perform better. In contrast, performance oriented students tend to have lower
achievement due to their threat of help-seeking and resistance to obtaining the help they
need, thereby decreasing their chances of success (Karabenick, 2004). Studies have also
shown that help-seeking behaviors can lead to increased academic achievement
(Karabenick, 2003; Wigfield & Eccles, 2002).Help-seeking is related to motivational
variables as well. For example, students who have higher efficacy are more likely to seek
help (Ryan, Gheen, & Midgley, 1998). Students may modify their engagement by
adjusting or setting new goals. They may further reexamine their learning tactics and
strategies and select more productive approaches, adapt available skills, or even generate
new learning or monitoring procedures (Butler & Winne, 1995).
Unfortunately, students who need help the most are often those least likely to seek it
out (e.g. Ryan & Pintrich, 1997; 2001). In addition, while help-seeking can be an
important self-regulatory strategy, it is not necessarily always positive. Students who are
motivated by work-avoidance goals are reluctant to seek help or seek help for the wrong
reasons. Students may ask for assistance when it is not necessary, for example, or when
they have not attempted the work on their own. They may also be motivated to seek help
in order to complete a task and move on to other activities, or they may seek help for
social rather than academic reasons.
There are a variety of reasons that students can engage in maladaptive helpseeking. These include factors such as lack of awareness of the need for help, concern
about one’s competence, concern about negative evaluations by teachers or peers,
1.
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classroom structures that are judged to sanction help-seeking, concern about the amount
of effort needed to seek help, negative judgments abouthe competence or utility of
available help, etc.(Ryan, Pintrich, & Midgley, 2001). There is evidence that there is a
social dimension to help-seeking as well. The goal to form and maintain positive peer
relationships has been found to be negatively related to avoidance of help seeking (Ryan
et al., 1997).
Group differences. Though the literature on learning strategies that has examined
ethnic differences is small, there are important studies that examine different approaches
used by different groups. For instance, Garcia, Yu, and Coppola’s (1993) study unpacked
gender and ethnic differences in science achievement. The design was 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, and not with learning strategies. Also, minority students (African
American, Latino and Asian) reported a higher extrinsic motivation orientation over their
White counterparts. These strategies included metacognitive awareness (e.g. planning,
monitoring, regulating) and effort management. The authors conclude that gender and
ethnicity do not affect achievement outcomes, but preparedness, motivation and use of
learning strategies do. [Note for HON to add study here]
Stough and Songeroth (1994) interviewed 28 college students to examine the role of
teacher instruction and learning strategies for successful students (identified as persisting
in school and having a GPA of over 2.0). Learning strategies defined in this study were
reading books, working supplemental problems, asking for help and working with peers
on homework. Additionally, successful students, over non-successful students, were more
1.
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articulate about their use of learning strategies and more metacognitively aware of their
application. In one of the first studies to look at cross-cultural differences in selfregulated learning and learning strategies, Purdie, Hattie, and Douglas (1996) looked at
conceptions of learning and use of strategies, suggesting that conceptions inform
strategies used. They found that Japanese students use strategies of rote memorization,
the use of textbooks and do not seek help from others. Both groups used learning
strategies (e.g. “environmental structuring”, “self-evaluating”) at similar frequencies and
the only difference in strategies was which ones were used more by both groups
(“reviewing previous work” was the least used strategy of both groups). In other words,
there was no between-group difference in types of strategies used.
Test anxiety and worry. There is a significant amount of work in the area of
motivation that has examined to role or affect or emotion on academic performance
(Hembree, 1988; Zeidner & Matthews, 2005). Because of the variety of related terms that
are often used in this work, Forgas (2000) proposed a distinction among affect (a broad
and inclusive term which encompasses specific emotions as well as general moods),
mood (relatively low-intensity, diffuse, but enduring affective states that have no salient
antecedents and that have little cognitive content), and emotions (short-lived, intense
phenomena that usually have a salient cause in the immediate environment of which the
person is aware, and which have clear cognitive content – such as pride at doing well on
an exam). In addition, Pekrun (1992) and Pekrun and Frese(1992) proposed a taxonomy
which divides emotions in to task-related and social, and which further subdivides taskrelated emotions according to whether they are experienced during a task (process-
1.
P a g e | 24
related), in anticipation of a task (prospective), or after a task (retrospective). In this
framework, anxiety falls under the category of task-related, prospective emotions.
One important emotion is test anxiety because of the obvious connections to
achievement. Test anxiety is often seen as a specific form of generalized anxiety, in
reference to test or evaluative situations. It is recognized as having phenomenological,
physiological, and behavioural responses that occur in conjunction with concern about
possible negative consequences of low performance (Zeidner, 1998). In addition,
researchers often distinguish trait anxiety, which is more stable and enduring, from state
anxiety, which is more context-specific (Covington, 1992; Spielberger, 1972). Test
anxiety is also commonly seen as having a cognitive component (worry) and an
emotional component (emotionality such as fear and uneasiness) (Zeidner & Matthews,
2005).
There are two general patterns in this literature. First, the negative effects of test
anxiety on performance are strong and consistent (Hembree, 1988; Hill & Wigfield,
1988). One review found a correlation coefficient of up to -.60 between test anxiety and
achievement (Hill & Wigfield, 1988). Second, the worry component of test anxiety is
more closely linked to performance and achievement than the emotionality component
(Covington, 1992). The effects on performance have been suggested as resulting from
various sources including interference with attentional processes and thus increased
cognitive load, as well as to a more generalized lack of access to efficient learning
strategies which impacts encoding and retrieval (Schutz & Davis, 2000; Zeidner, 1998).
Given these findings, it was hypothesized that worry would be negatively related to
achievement.
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Summary and Purpose of the Study
Pintrich (2003) provided a comprehensive review and summary of recent work and
unanswered questions and issues in research and theory that connects motivation, selfregulation, learning strategies, and achievement. In the review, Pintrich provided
summary generalizations about the role of motivational beliefs in learning and
achievement, but a raised a key question about whether they hold for all ethnic groups
(e.g. African American, Asian American, Latino, Native American, etc.) in western
cultures as well as in other nonwestern cultures. There is some evidence from large scale
research studies that there is invariance among a broad range of cultural groups on selfregulatory, motivational, and learning strategy constructs (Marsh, Hau, Artlet, Baumert,
& Peschar, 2005). However, there is also some evidence for cultural specificity in these
domains (McInerney & Van Etten, 2001; Urdan, 2004; Urdan & Giancarlo, 2001;
McInerney, Watkins, Akande, & Lee, 2003; Rueda & Chen, 2005; Vansteenviste, Zhou,
Lens, & Soenens, 2005). While the learning and motivation literature has increasingly
recognized sociocultural factors as important, there is only limited work exploring the
robustness of the relationships among these factors for students from different ethnic,
linguistic, and cultural groups, especially in the U.S. context with specific groups such as
the increasingly large Latino population.
To address this gap, this study included constructs tapping students’ approaches to
learning, motivation related factors (e.g. academic efficacy and goal orientation),
opportunity to learn (e.g. advanced level of math and science education in high school),
and demographic/background variables (e.g. gender, ethnicity, SES and prior
knowledge). The first specific purpose of this study was to examine cross-ethnic
1.
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invariance across White and Latino students in terms of self-regulatory beliefs, learning
strategies, and motivational constructs in a four year college setting. In addition, a
second purpose of the study was to develop and test a structural model of relationships
between, student background characteristics, opportunity to learn, learning strategies,
motivational variables and academic achievement.
Method
Research Context
The study examined the relationship between learning strategies and academic
achievement and the differences between 1Hispanic and non-Hispanic students’ use of
strategies, educational attitudes, and motivation to learn in a four year state college
setting in the Southwest. This college selects from among the top 30% of high school
students in California. At the time of the study, the college had a total of 16,479 enrolled
students. The majority of students attending were of Mexican American, Hispanic
descent. The breakdown for the campus by ethnicity was 36.9% Hispanic, 13.0% African
American, .9% Native American, 6.3% Asians, 0.5% Pacific Islander, 36.4% White,
3.0% Filipino, and 3.1% other ethnicities. The percentage of new freshmen was 30.41%
and 44.2% of the new transfers attended local community colleges. [Noelle, is there a
CSUSB reference we can cite here?]
1
There are a variety of terms found in the literature including the generic terms Latino and Hispanic as well
as more population-specific terms such as Mexican-American or Cuban. There is not widespread
agreement about the use of a single term, and arguments for different choices based on political and other
considerations are numerous. In this report we use the term Hispanic because it is the designation used by
the research site.,
1.
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Subjects
There were two components to the data collection, a phone survey and an online
survey. The final number of students contacted for the phone survey was 2232 nonHispanics (63.4% of the total sample) of which the majority were White (1,402, 39.8%)
and the remainder of the sample were Hispanic/Latino (1,295, 38.7%). The final number
of students responding to the online survey was 2,012 (1,307 65% non-Hispanics and 705
35% Hispanics).
Measures
The measures included questions about students’ demographic characteristics,
educational attitudes and beliefs, academic efficacy, and learning strategies such as
elaboration and control.
Background variables. For the students’ background variables, Socioeconomic
Status (SES) was measured by using a combination of mother’s highest degree of
education (1 = didn’t finish high school, 4 = graduated from college) and income (1 =
less than $25,000, 4 = more than $75,000). Two questions assessed students’ Prior
achievement level (Prior knowledge) including high school grade point average (GPA, M
= 3.13, SD = 0.47), SAT1 verbal score (M = 455.33, SD = 91.27) and SAT1 math score
(M = 462.18, SD = 86.93).
Opportunity to learn. This construct measured whether students had received
different educational or instructional ingredients that make up quality school experience.
Our questions included “how many semesters of Advanced Placement (AP) math did you
take in high school?” and “how many semesters of AP science did you take in high
school?” The questions ranged from 1 to 8.
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Motivation and attitudes. The questions about educational attitudes and motivation
were revised from Marsh et al. (2006)’s Student Approaches to Learning (SAL)
instrument. Academic efficacy consisted of four items asking students’ beliefs in their
capabilities to master the skills being taught and to overcome barriers through their own
efforts. The Worry scale was derived from Spielberger, Gonzlez, and Taylor (1980)’s
Text Anxiety Inventory (α = .92) and is conceptualized as cognitive concern over failure.
Eight items were used to indicate how they felt while taking the exam and how they felt
anxiety on failing the exam. Students were instructed to select a number ranging from
1(almost never) to 4 (almost always) for each item of these scales.
Personal motivational beliefs. The items from this scale were from Anderman,
Urdan and Roeser (2003)’s Patterns of Adaptive Learning Scale. Three personal goal
orientations were assessed. Mastery goal orientation included five items reflecting
students’ emphasis on learning and their concern with understanding, developing
competence and improvement. (α = .85). Performance approach goal orientation
consisted of five items that tapped into students’ desire to demonstrate competence, for
example by outperforming others in their class work (α = .89). Four items assessed
performance avoidance goal orientation, reflecting students’ concern with not appearing
incompetent or less competent than others in their class work (α = .74). Students
indicated their response to each item on a 5-point scale (1 = not at all true, 5 = very much
true).
Learning strategies. Students’ use of different learning strategies was assessed with
items derived from Marsh et al. (2006)’s Student Approaches to Learning (SAL)
instrument. Elaboration consisted of four items measuring how often students engage in
1.
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construction, integration, and transfer strategies when completing class work. Control
included five items and measured the extent to which students adopt a self-regulating
perspective during the learning process. This scale asked questions about whether
students engage in activities such as checking that they remember what they have already
learned or what they do not yet understand (α = .83). Effort included four items tapping
into students’ levels of effort expended, as well as their willingness to continue to try
even when presented with difficult material. Students responded on a 1 (almost never) to
4 (almost always) in those scales.
Finally, six questions were adapted from Meuschke, Dembo, and Gribbons’s (2006)
Adaptive Help-Seeking Scale to assess students’ use of maladaptive help-seeking
strategies (e.g. guessing, simply avoiding the work, or giving up the task without asking
for help). The items were scored on a 5-point scale ranging from 1 (not at all true) to 5
(very much true) (α = .72).
Each of the scales above was averaged to form 9 distinct constructs (Elaboration,
Control, Maladaptive help-seeking, Effort, Worry, Academic efficacy, Mastery goal
orientation, Performance approach goal orientation, and Performance avoidance goal
orientation).
Academic achievement. The outcome variable of this study was measured by
students’ total grade point average (GPA) (M = 2.92, SD = 0.52).
Procedure
Data were obtained from the Office of Institutional Research in the form of a list of
all 16,488 students registered at the college for the 2006 Fall quarter. Graduate students
were deleted from the list, as were students who were not American citizens. Further,
1.
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students with no contact information were deleted. The final sampling frame contained
contact information for 11,569 undergraduate students. Our data were collected in the fall
of 2006 using two methods of data collection—a phone survey that included 3,523
students (2,232 non-Hispanics and 1,295 Hispanics) and an online survey that included
2,012 students (1,306 non-Hispanics and 705 Hispanics).
Telephone interview. Students were initially contacted by phone in order to explain
the purpose of the research. Interviewers asked the student respondents a few questions to
elicit some basic demographic data as well as some initial questions regarding their
learning strategies. In addition, interviewers confirmed the student’s e-mail address so
that a link to a web survey could be sent. The link to the web survey was then sent to
each student, with at least two follow-up reminders sent to all non-respondents. In
addition, a lottery system was used by which students could win money/coupons to be
used to purchase items at the University’s bookstore.
In the telephone survey part of the data collection, telephone surveys on
demographics and students’ background were conducted by trained staff from a research
institute of the college, using computer assisted telephone interviewing (CATI)
equipment and software. In order to maximize the chances of finding a student at home,
telephone surveys were conducted Monday through Friday, from 9:00 AM – 9:00 PM
and weekends (Saturday 10am – 5pm and Sunday 1pm – 7pm). In an effort to ensure the
quality and reliability of the interviews, institute research staff supervised all interviews
conducted. To further ensure quality control, supervisory personnel randomly selected
completed interviews (at least one completion per interviewer) and made call-backs for
verification. A pairwise method was used for missing data.
1.
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Online survey. For the second part of the data collection, on-line surveys related to
academic motivation and the use of learning strategies were completed from the students’
own computer or in computer labs in the college. [Note from HON to Noelle: cite PISA?]
Data Analysis
The first part of the analysis focused on evaluating the psychometric characteristics
of the measures. We examined internal consistency of scales by calculating Cronbach’s
alpha coefficients. Also, in order to evaluate responses to the same instrument by
different groups (in this case Latino and white students) separately, the second part of
analysis examine whether the constructs were same across White and Latino/a groups.
Therefore, multiple group confirmatory factor analysis (MG-CFA) was employed to test
factorial invariance between those groups. MG-CFA test traditionally posits a series of
nested models in which the end-point is the least restrictive model with no invariance
constraints and the most restrictive (total invariance) model with all parameters
constrained to be equal across all groups. Thus, we first examined the extent to which the
theoretical structure of the instrument was invariant to ensure the structural equivalence
of underlying constructs between White and Latino/a groups. We also investigated the
extent to which the factor patterns and weighing of loadings were invariant, in order to
ensure the equivalent interpretation of item content across two different groups.
Finally, we aimed to determine how demographic and background information related to
motivation/learning strategies and academic achievement for White and Latino/a college
students. We initially developed a model for the entire sample (n = 3,257) to test the
theoretical structural equation model (SEM) and then we estimated two single-group
1.
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SEMs using White students and Latino students to compare the significant path
coefficients between two groups.
Results
Reliabilities, Descriptive Statistics and Intercorrelations
Reliabilities. Cronbach’s alpha reliability coefficients were computed for 9 scales
on motivation/learning strategies items. Scale reliabilities for all scales were generally
satisfactory (see Table 1).
Table 1. Reliability coefficients for all students (N = 3527)
N of items
Cronbach’s α
Elaboration
4
.82
Control
5
.71
Maladaptive Help6
.86
seeking
Academic Efficacy
4
.82
Effort
4
.80
Worry
8
.89
Mastery Goal
5
.87
orientation
Performance Approach 5
.93
Performance Avoidance 4
.86
All
45
.84
For the total sample of 3,527 participants, reliability coefficients varied from .71 to .93
(M = .84). This average reliability and the reliabilities of each of the 9 scales are higher
than at least .70, suggesting that the measures used in this study appears to meet basic
psychometric criteria. Table 2 presents the reliability estimates for Latino/a and White
students. Generally, the coefficients of White students were higher than those of Latino/a
students.
1.
P a g e | 33
Table 2. Reliability coefficients for Latino/a and White students
N of
items
Elaboration
Control
Maladaptive Helpseeking
Academic Efficacy
Effort
Worry
Mastery Goal orientation
Performance Approach
Performance Avoidance
All
4
5
6
α
.80
.71
.86
Latino/a
(n = 1,125)
Mean
SD
2.81
.65
3.07
.53
2.25
.83
4
4
8
5
5
4
45
.81
.81
.88
.85
.91
.85
.86
2.72
3.05
2.09
4.08
1.98
2.26
2.65
.62
.62
.70
.73
.96
.99
.35
α
.83
.72
.87
White
(n = 1,156)
Mean
2.90
3.06
2.05
SD
.66
.54
.77
.83
.82
.90
.88
.93
.87
.84
2.84
3.06
1.83
3.90
2.17
2.41
2.61
.64
.62
.67
.76
1.00
.99
.32
Descriptive analyses were conducted to provide overall information of the study
variables. Table 3 presents means and standard deviations of primary variables, and
Tables 4 and 5 present the data by group. Table 6 provides a comparison of mean
differences between groups, and Figure 1 provides a graphical representation.
Table 3. Descriptive statistics for study variables (N=3,527)
Variable
High School GPA
SAT1 verbal
SAT1 math
Elaboration
Control
Maladaptive Help-seeking
Academic Efficacy
Effort
Worry
Mastery Goal orientation
Performance Approach
Performance Avoidance
Total GPA
M
3.10
455.6
462.4
2.88
3.08
2.12
2.78
3.05
1.96
3.99
2.12
2.32
2.84
SD
.47
90.7
86.3
.66
.54
.79
.64
.62
.69
.77
.99
.99
.53
Observed Range
1.1-4.3
200-730
200-790
1-4
1-4
1-5
1-4
1-4
1-4
1-5
1-5
1-5
.67-4.25
1.
P a g e | 34
Table 4. Descriptive statistics for study variables for White students (N=1,156)
Variable
High School GPA
SAT1 verbal
SAT1 math
Elaboration
Control
Maladaptive Help-seeking
Academic Efficacy
Effort
Worry
Mastery Goal orientation
Performance Approach
Performance Avoidance
Total GPA
M
3.21
486.3
495.9
2.90
3.06
2.05
2.84
3.06
1.83
3.90
2.17
2.41
3.04
SD
.49
90.7
86.3
.66
.54
.79
.64
.62
.67
.77
1.0
.99
.50
Observed Range
1.6-4.3
200-730
200-790
1-4
1-4
1-5
1-4
1-4
1-4
1-5
1-5
1-5
1.26-4.25
Table 5. Descriptive statistics for study variables for Latino students (N=1,125)
Variable
High School GPA
SAT1 verbal
SAT1 math
Elaboration
Control
Maladaptive Help-seeking
Academic Efficacy
Effort
Worry
Mastery Goal orientation
Performance Approach
Performance Avoidance
Total GPA
M
3.10
436.6
444.5
2.81
3.07
2.25
2.72
3.03
2.09
4.08
1.98
2.26
2.78
SD
.43
90.7
86.8
.65
.53
.82
.62
.63
.70
.73
.96
.99
.52
Observed Range
1.6-4.3
200-670
200-680
1-4
1-4
1-5
1-4
1-4
1-4
1-5
1-5
1-5
1.15-4.00
1.
P a g e | 35
Table 6. Mean differences between White and Latino students
Elaboration
Control
Maladaptive Helpseeking
Academic Efficacy
Effort
Worry
Mastery Goal orientation
Performance Approach
Performance Avoidance
High School GPA
Total GPA
White
(n = 1,156)
2.90
3.06
2.05
Latino
(n = 1,125)
2.81
3.07
2.25
F (1, 2,218)
6.54**
.03
22.37***
p
.011
.857
.000
2.84
3.06
1.83
3.90
2.17
2.41
3.05
3.05
2.72
3.03
2.09
4.08
1.98
2.26
2.78
2.78
11.30***
1.03
49.85***
19.70***
12.46***
7.42**
19.10***
129.1***
.001
.310
.000
.000
.000
.007
.000
.000
1.
Figure 1. White and Latino comparison on study variables
P a g e | 36
1.
P a g e | 37
Correlational Analyses Based on Theoretical Expectations
Correlations among motivation/ learning strategies variables. In evaluating the
nature of relations among scales, we began by examining the patterns of correlations
consistent with theoretical expectations. The relationships among scales revealed that all
of the correlations (see Table 7 for the entire sample and Tables 8 and 9 for each group)
among the adaptive learning strategies (elaboration, control, and effort) measures were
positive and statistically significant (rs = .38 to .59, p < .01). Elaboration, control, and
effort scales were positively correlated with the efficacy scale (rs =.40, .39, .39,
respectively, p < .01) but negatively correlated with worry (rs = −.07, −.09, −.07, p <
.01). Maladaptive help-seeking had a negative correlation with efficacy (r = −.33, p <
.01) but it was positively correlated with worry (r = .38, p < .01). With regard to
personal motivational beliefs, two of the performance goal orientation scales
(performance approach and performance avoidance) were found to be highly correlated
with each other (r = .74, p < .01). However, those performance goal orientation scales
showed no statistically significant correlations with mastery goal orientation. Mastery
goal orientation scale was positively correlated with all of the learning strategies
(elaboration; r = .39, p < .01, control; r = .48, p < .01, effort; r = .54, p < .01). Both of
the performance goal orientation scales were correlated only with elaboration (rs = .07 to
.03, p < .01) and effort (r = −.09, p < .01). Finally, performance goal orientation scales
showed positive correlations with worry (rs = .13 to .18, p < .01) whereas mastery goal
orientation showed no statistically significant correlation with worry.
1.
P a g e | 38
Table 7. Bivariate correlations among primary indicators of motivation and learning strategies (White and Latino students N=2,281)
1
-
2
1. Gender (Female=0)
2. Ethnicity (White=0)
.06**
-
3. AP Math
.01
.03
-
4. AP Science
.01
.00
.61**
-
5. High school GPA
-.12**
.12**
.09**
.05
-
6. SAT1 verbal
.09**
.27**
.10**
.16**
.25**
-
7. SAT1 math
.20**
.29**
.16**
.16**
.30**
.62**
-
8. Efficacy
.18**
.09**
.10**
.11**
.11**
.25**
.17**
-
9. Worry
-.12**
-.19**
-.07*
-.04
-.19**
-.34**
-.34**
-.38**
-
10. Elaboration
.05
.07*
.05
.06*
.07
.14**
.11**
.40**
-.07**
-
11. Control
-.07*
-.01
.05
.03
.12**
.02
-.02
.39**
-.09**
.45**
-
12. Effort
-.12**
.03
.05
.01
.05
-.11**
-.11**
.39**
-.07**
.38**
.59**
-
13. Mastery goal
.00
-.12**
.05
.05
-.01
-.07
-.12**
.35**
-.03
.39**
.48**
.54**
-
.04
.10**
.05
.07*
.05
.04
.04
.03
.13**
.07**
-.03
-.09**
-.02
-
.01
.08**
.02
.02
.08
.06
.07
-.03
.18**
.03
-.01
-.09**
-.02
.74**
-
.03
-.13**
-.05
-.01
-.09*
-.05
.00
-.33**
.38**
-.25**
-.33**
-.42**
-.28**
.22**
.30**
-
-.03
.25**
.01
.01
.38**
.39**
.32**
.24**
-.34**
.12**
.17**
.20**
.05
.04
.02
-.26**
14. Performance
approach
15. Performance
avoidance
16. Maladaptive
helpseek
17. Total GPA
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
-
1.
P a g e | 39
Table 8. Bivariate correlations among primary indicators of motivation and learning strategies (White students N=1,156)
1. Gender (Female=0)
1
-
2
3
4
5
6
7
8
9
10
11
12
13
14
15
2. AP Math
.01
-
3. AP Science
.01
.68**
-
5. SAT1 verbal
6. SAT1 math
-.15**
.09**
.18**
.06
.06
.13**
.06
.13**
.17**
.24**
.31**
.62**
-
7. Efficacy
.15**
.10**
.10**
.11
.31**
.18**
-
8. Worry
-.13**
-.06
-.05
-.14**
-.42**
-.34**
-.44**
-
.01
.03
.04
.07
.12*
.08
.40**
-.12**
-
10. Control
-.10*
.05
.01
.14*
.02
-.01
.38**
-.14**
.46**
-
11. Effort
-.18**
.02
-.03
.04
-.18**
-.11*
.33**
-.10**
.40**
.61**
-
-.04
.04
.01
.02
-.06
-.04
.30**
-.07
.44**
.51**
.54**
-
.02
.05
.04
.10
.03
.00
.05
.12**
.05
-.05
-.09*
.02
-
-.02
.00
-.03
.14*
.07
.04
-.02
.18*
.02
.03
-.09*
.00
.75**
-
.04
.00
-.02
-.06
.00
.05
-.32**
.35**
-.28**
-.39**
-.46**
-.34**
.19**
.26**
-
-.07*
-.01
.00
.41**
.38**
.25**
.26**
-.33**
.13**
.20**
.26**
.15**
.05
.04
-.25**
4. High school GPA
9. Elaboration
12. Mastery goal
13. Performance
approach
14. Performance
avoidance
15. Maladaptive
helpseek
16. Total GPA
16
-
1.
P a g e | 40
Table 9. Bivariate correlations among primary indicators of motivation and learning strategies (Latino students N=1,125)
1. Gender (Female=0)
2. AP Math
3. AP Science
1
-
2
.00
-
3
4
5
6
.19**
.24**
.57**
-
7
8
9
10
11
12
13
14
15
.01
.53**
-
5. SAT1 verbal
6. SAT1 math
-.11**
.06
.19**
.10**
.11**
.17**
.04
.19**
.17**
7. Efficacy
.21**
.10**
.12**
.09
.17**
.13*
-
8. Worry
-.10*
-.07
-.03
-.19**
-.21**
-.27*
-.29**
-
9. Elaboration
.08*
.07
.08
.05
.13*
.11*
.39**
.01
-
10. Control
-.04
.05
.06
.10
.03
-.02
.41**
-.05
.44**
-
11. Effort
-.04
.09*
.05
.04
-.07
-.14*
.46**
-.04
.36**
.57**
-
12. Mastery goal
.06
.06
.10*
.00
-.02
-.13*
.44**
-.04
.36**
.45**
.56**
-
.06
.04
.11**
-.02
.00
.03
.00
.19**
.09*
.00
-10*
-.04
-
.05
.04
.08
.00
.01
.07
-.05
.21**
.03
.02
-.10*
-.01
.73**
-
.03
-.11**
.00
-.08
-.03
.05
-.32**
.38**
-.21**
-.28**
-.39**
-.25**
.29**
.37*
-
-.01
.02
.01
.34**
.36**
.31**
.18**
-.27**
.09*
.16**
.13**
-01
-.01
-06
-.22**
4. High school GPA
13. Performance
approach
14. Performance
avoidance
15. Maladaptive
helpseek
16. Total GPA
16
-
1.
P a g e | 41
With regard to personal motivational beliefs, two of the performance goal
orientation scales (performance approach and performance avoidance) were found to be
highly correlated with each other (r = .74, p < .01). However, those performance goal
orientation scales showed no statistically significant correlations with mastery goal
orientation. Mastery goal orientation scale was positively correlated with all of the
learning strategies (elaboration; r = .39, p < .01, control; r = .48, p < .01, effort; r = .54,
p < .01). Both of the performance goal orientation scales were correlated only with
elaboration (rs = .07 to .03, p < .01) and effort (r = −.09, p < .01). Finally, performance
goal orientation scales showed positive correlations with worry (rs = .13 to .18, p < .01)
whereas mastery goal orientation showed no statistically significant correlation with
worry.
Correlations between learning strategies and achievement. Consistent with the
findings from prior research, learning strategies were positively correlated with the
achievement variable (elaboration; r = .12, p < .01, control; r = .17, p < .01, effort; r =
.20, p < .01). However, maladaptive help-seeking had a negative correlation with
achievement (r = −.26, p < .01), Academic efficacy was also positively correlated with
achievement (r = .24, p < .01), whereas worry was negatively correlated (r = −.34, p <
.01). Research on worry supports the negative effects of worry and academic outcome
(Elliot & McGregor, 1999; Vagg & Papsdorf, 1995). The intercorrelations among key
variables are presented in Tables 7-9.
Multi-group Confirmatory Factor Analysis (MG-CFA)
Preliminary CFA for overall model. Preliminary confirmatory factor analyses were
conducted with M plus 3.13 (Muthén & Muthén, 2004) using maximum likelihood
1.
P a g e | 42
estimation. Usually, a researcher posits a priori structure of hypothesized constructs and
tests the fit of a solution based on the structure of the data. Thus, we hypothesized a 9factor model in which the items for each factor load on their respective latent variable.
When using a chi-square test of fit, although nonsignificant p values are preferred, large
samples increase the likelihood of obtaining significant p values (Hu & Bentler, 1995).
Thus, we used additional indices to evaluate model fit: (a) Comparative Fit Index (CFI),
(b) Tucker-Lewis Index (TLI), and (c) root-mean-square error of approximation
(RMSEA). The results supported the hypothesized theoretical model, indicating that all
the factor loadings were significant and our 9-factor solution had a good fit to the data
with regard to acceptable levels of fit (Comparative Fit Index [CFA] = .96; Tucker-Lewis
Index [TLI] = .96; root-mean-square error of approximation [RMSEA] = .03), although
the chi-square value was statistically significant (χ2= 2429.14, df = 878, p = .000). It
should be noted that chi-square is sensitive to sample size, and the larger the sample size,
the more likely the rejection of the model and the more likely a Type II error. Thus, as the
case here, with large samples, even small differences between the observed model and
the perfect-fit model may be found significant. One fit index that is less sensitive to
sample size is the chi-square/df statistic (Bryant & Yarnold, 2001). In this case, the value
of χ2/df was 2.77, within acceptable limits. Table 10 presents the factor structure and
standardized factor loadings for this model.
Table 10. Standardized factor loadings and 9 factor solutions for all students.
Factor
Item
Standardized
factor loading
Elaboration
Q1
.64
Q2
.34
Q3
.69
Q4
.86
Control
Q9
.69
1.
Maladaptive
Help-seeking
Efficacy
Effort
Worry
Mastery goal
Performance
approach
Performance
avoidance
Q5
Q6
Q7
Q8
Q19
Q16
Q17
Q18
Q20
Q21
Q25
Q22
Q23
Q24
Q29
Q26
Q27
Q28
Q30
Q31
Q32
Q33
Q34
Q35
Q36
Q37
Q46
Q44
Q45
Q47
Q48
Q53
Q49
Q50
Q51
Q52
Q56
Q54
Q55
Q57
P a g e | 43
.39
.55
.63
.56
.75
.71
.60
.66
.76
.72
.80
.53
.69
.79
.68
.63
.67
.75
.62
.79
.60
.66
.81
.76
.69
.74
.79
.64
.79
.76
.68
.94
.70
.84
.78
.90
.81
.67
.79
.84
Multiple-group CFA. For this aspect of the analysis, we selected the two ethnic
groups focused on in the study, including White (n = 1,156, 32.8%) and Latino (n =
1,125, 31.9%). Our confirmatory factor analysis next dealt with the question of whether
1.
P a g e | 44
the 9 measures were equivalent across the White and Latino/a subgroups. The
assumption of structural equivalence was tested using a multi-group CFA that allows
factorial invariance across groups. Testing for factorial invariance involves comparing a
set of models in which factor structure and factor loadings are held equal across groups,
and assessing fit indices when elements of these structures are constrained (Marsh et al.,
2006). Thus, we developed an initial baseline model (Model 1), which is totally noninvariant in that there were no assumptions being made about group invariance across
groups. Then we imposed invariance constraints for factor loadings and factor means
(Model 2), and factor variances (Model 3). Table 11 presents the fit indices for the
model comparison for 9 factors.
1.
P a g e | 45
Table 11. Goodness of fit statistics for the multiple-group comparison for the 9 factors.
Model
1
2
3
Invariance
Constraints
None (Totally
non-invariant)
Invariance of
factor loadings,
factor means
Invariance of
factor loadings ,
factor means, and
factor variances
*** p < .000, ** p < .01
χ2
df
CFI
TLI
RMSEA
Δ χ2 (Δdf)
AIC
BIC
3199.21
χ2 /
df
1.79
1792
.951
.946
.038
--
106571.46
108417.91
3360.47
1.82
1837
.947
.943
.039
161.26(45) ***
106642.72
108263.37
3397.08
1.83
1855
.946
.943
.039
36.61(18)**
106643.33
108173.67
P a g e | 46
Model 1 examined whether the items in each of the 9 factors grouped in accordance with
theoretical expectations (χ2= 3199.21, df = 1,792, p = .000, χ2/df = 2.14, CFI=.95, TLI=.95,
RMSEA=.04). Model 2 tested whether factor loadings and factor means were the same
across groups. This model is important because a minimal criterion for structural equivalence
is that the factor loadings relating each indicator to its hypothesized factor are the same
across all groups. (χ2= 3360.47, df = 1,837, p = .000, χ2/df = 1.83, CFI=.947, TLI=.943,
RMSEA=.039). The difference in chi-square between the constrained and unconstrained
models was significant (Δχ2 = 161.26, Δdf = 45, p <.000), however, the differences in
RMSEA and other fit indices were very small.
We then tested the assumption that factor variances as well as factor loadings and factor
means were equal across groups (Model 3). The most restrictive model provided a good fit
to the data (χ2 = 3397.08, df = 1,855, p = .000, χ2/df = 1.83, CFI=.946, TLI=.943,
RMSEA=.039), and the Bayesian Information Criteria (BIC) showed that this model best fit
to the data. Although all three models showed good fit, Model 3 is most parsimonious in
terms of number of parameters to be estimated. In summary, there was good support for the
invariance of factor loadings, factor means and factor variances across White and Latino/a
groups.
Structural Equation Model (SEM) Analysis
SEM MIMIC model for all students. An approach based on a multiple-indicatormultiple-indicator cause model (MIMIC; Kaplan, 2000, Jöreskog & Sörbom, 1993) was used
for evaluating relations between motivational and learning strategy factors and background
variables. This extended SEM MIMIC model was seen as appropriate here given that it is
based on latent constructs having measurement error and that it allows for a test of the
P a g e | 47
underlying structural model (Marsh et al., 2006). We hypothesized a structural model (see
Figure 2) among cognitive and motivational factors and other criterion variables.
P a g e | 48
Figure 2. A hypothesized structural equation model for cognitive and motivational factors
Learning
strategies
self
efficacy
Gender
SES
academic
achievement
us
min
OTL
min
u
s
performance
approach
worry
Ethnicity
performance
avoidance
maladaptive
help-seeking
Prior
Knowledge
mi
mastery
goal
s
nu
P a g e | 49
Figure 3 shows the results of SEM estimating for all students. Learning strategies factor was
indicated by three 1st order factors (control, effort, and elaboration). −−For the overall
sample, the results indicated that the model produced a good fit to the data (χ2 = 3694.90, df
= 1,371, p = .000, χ2/df = 2.70, CFI=.94, TLI=.93, RMSEA=.03).
Figure 3. Estimation of cognitive and motivational factors of academic achievement (all
groups) (N= 2,281)
control
.97
.32
0.1
self
efficacy
Gender
effort
0.38
elaboration
learning
strategies
2
0. 1
.98
.98
18
0.
OTL
0.07
5
0. 1
performance
approach
-0
.0
7
academic
achievement
-0.3
SES
0.5
-0.1
8
7
4
0.
1
7
0.0
0 .2
performance
avoidance
0.08
7
0 .55
0.14
.59
4
.98
3
.1
-0
0.3
worry
.83
9
0 .2
Ethnicity
-0.27
prior knowledge
-0.24
-0.1 8
maladaptive
help-seeking
.78
mastery
goal
.70
.94
Most of the path coefficients were statistically significant. In particular, SES had positive
effects on opportunity to learn (measured by number of semesters taking AP math and
science) (β = .15, p < .001) and prior knowledge (measured by high school GPA, SAT
verbal and math scores) (β = .25, p < .001), suggesting that students who had higher SES had
higher opportunities to learn and a higher level of achievement in high school. Ethnicity had
a direct effect on prior knowledge (β = −.27, p < .001), showing that White students
P a g e | 50
achieved better in high schools than Latino/a peers. In terms of the relationships between the
motivational variables and background variables, we found significant effects of opportunity
to learn on efficacy (β = .12, p < .001), performance approach goal orientation (β = .07, p <
.001), and mastery goal orientation (β = .07, p < .001). Students who experienced more
learning opportunities in high school showed higher academic efficacy and higher
performance approach and mastery goal orientations in college.
The use of adaptive learning strategies (a latent factor indicated by effort, control and
elaboration) was strongly affected by motivational factors including academic efficacy (β =
.38, p < .001) and mastery goal orientation (β = .57, p < .001). As expected, students with
high mastery goal orientation reported greater use of learning strategies, which in turn,
positively predicted academic achievement (measured by GPA, β = .18, p < .001). Also,
efficacy was found to have a positive influence on learning strategies (β = .38, p < .001).
However, students who reported higher use of maladaptive help-seeking strategies were
found to have a stronger performance avoidance goal orientation (β = .29, p < .001). There
was a relatively high positive influence of maladaptive help-seeking (β = .29, p < .001) and
performance avoidance (β = .07, p < .001) on worry. Students were found to have lower
GPA when they felt more worry (β = −.18, p < .001) and when they employed more
maladaptive help-seeking (β = −.13, p < .001).
Single-group SEMs (Latino vs. White). Next, we removed ethnicity from the overall
model and performed two single-group SEM analyses for White and Latino/a students in
order to compare the differences in the relationships among motivation, learning strategies
and academic achievement (See Figures 4 and 5). The SEM results produced good fits to data
(Latino/a group - χ2 = 2498.05, df = 1,322, p = .000, χ2/df = 1.90, CFI=.92, TLI=.92,
P a g e | 51
RMSEA=.03; White - χ2 = 2540.59, df = 1,322, p = .000, χ2/df = 1.92, CFI=.94, TLI=.93,
RMSEA=.03).
Figure 4. Estimation of cognitive and motivational factors of academic achievement for
White students. (N = 1,156)
.99
.29
self
efficacy
Gender
.99
0.33
learning
strategies
9
0.0
0.
OTL
4
-0.1
academic
achievement
25
performance
approach
5
-0
.
0.53
0.6
SES
21
.98
.97
prior knowledge
.97
worry
0.11
0.18
performance
avoidance
4
0.2
0.25
.86
maladaptive
help-seeking
-0.3
mastery
goal
.99
4
.68
.56
P a g e | 52
Figure 5. Estimation of cognitive and motivational factors of academic achievement for
Latino students. (N = 1,125)
.95
.31
self
efficacy
0.39
0.1 6
OTL
6
0.2
0.1 3
.98
academic
achievement
35
-0.
performance
approach
.94
SES
learning
strategies
-0
.1 6
0.14
Gender
0.1
worry
0.11
prior knowledge
.79
0. 5
3
5
.99
-0
.15
0.46
0.
46
performance
avoidance
-0
.2
0.3
0.39
.83
maladaptive
help-seeking
8
.69
mastery
goal
.91
For Latino/a students, the overall relationships among opportunities to learn, prior level
of achievement and motivation factors were similar to those of all students (e.g. positive
effects of motivation factors (efficacy, mastery goal) on learning strategies). However,
unlike the overall SEM, the Latino subgroup did not show any significant effect of prior
knowledge on performance avoidance, although they showed a positive effect of opportunity
to learn on performance approach (β = .12, p < .001). We also found that male Latino
students reported more academic efficacy than female Latino students (β = .14, p < .001), as
expected in findings from the studies on gender and self-efficacy. Efficacy and mastery
influenced learning strategies positively (β = .39 and β = .53, respectively, p < .001) but
efficacy had negatively influence help-seeking (β = −.34, p = .001). Maladaptive helpseeking strategies were positively associated with worry (β = .30, p < .001). The pattern of
.71
P a g e | 53
effects on GPA (positive effects of prior knowledge, negative effects of worry and helpseeking) was similar to those of the overall model.
For White students, the results showed different patterns in relations of SES to prior
knowledge and opportunity to learn. White female students achieved more in high school
than their male peers (β = −.14, p < .001) and students who had more OTL reported higher
self-efficacy (β = .09, p < .001). Also, there was no effect of opportunity to learn on mastery
goal orientation, whereas there was a positive effect of prior knowledge on performance
avoidance (β = .19, p < .001). Self-efficacy and mastery goal orientation positively affected
learning strategies (β = .33 and β = .65, respectively, p < .001). Like Latino students, both
of maladaptive help-seeking and performance avoidance positively affected worry (β = .24
and β = .11, respectively, p < .001) and worry had a negative effect on achievement (β =
−.25, p < .001). Prior knowledge and learning strategies were found to be two prominent
predictors of academic achievement (β = .53 and β = .21, respectively, p < .001).
Multiple-group SEM: White and Latino groups. Finally, we analyzed a multi-group
SEM for White and Latino groups to examine the extent to which parameter estimates in our
theoretical model differ between White and Latino groups. (See Figure 6).
We first estimated the constrained model, which fixed all common parameters between
groups. Our constrained model resulted in a CFI of .91 and TLI of .90 with χ2 = 6083.70 (df
= 2,773, χ2/df = 2.19, p < .000). We then performed chi-square difference tests several
times, in order to identify which pairs of path coefficients were significantly different in
multiple groups. For example, we freed the parameter of the path from gender to prior
knowledge to test whether there was a significant chi-square decrease. The model improved
P a g e | 54
significantly (Δχ2 = 82.11, Δdf = 1, p <.001) which means that the there was a significant
difference in parameter estimates between two groups.
Figure 6. Multiple-group (White/Latino) estimation of cognitive and motivational factors of
academic achievement (White=1,156, Latino=1,125)
learning
strategies
self
efficacy
.55
/.2
1
Gender
0/
.0 9
OTL
-.12/ns
performance
approach
.0
1/ -.0
SES
academic
achievement
1.0
.9
2/0
- .2
6/
- .4
9
4/0
-.2
4
.11
/ns
4
worry
prior knowledge
.25/.17
performance
avoidance
maladaptive
help-seeking
8/-.0
.49
mastery
goal
In other words, although both of the standardized path coefficients were negative, the
effect of gender on prior knowledge was slightly greater for Latino students (β = −.04, p
<.001) than for White students (β = −.01, p <.001). Figure 4 shows different effects for
White and Latino students for our theoretical model.2
2
All of the unconstrained model results in this section produced good fit indices (CFI and TLIs = .90 to .91 and
RMSEAs < .04). All of the chi-square difference tests reported in this section showed significant model
improvement, indicated by significant chi-square decrease.
P a g e | 55
Latino students had a positive effect of OTL on self-efficacy (β = .09, p <.001) but
White students showed no effect of OTL. Prior knowledge had a negative effect on mastery
goal orientation but had a stronger relation for Latino students (β = −.08 for White and β =
−.49 for Latino, p < .001). Prior knowledge was also one of the strong predictor of academic
achievement (β = 1.02 for White and β = .99 for Latino, p < .001). Interestingly, it revealed
that there was a weaker effect of prior knowledge on performance avoidance for Latino
students (β = .17, p <.001) than for White students (β = .25, p <.001). Performance
approach goal orientation showed a different pattern of relationship between groups. It had a
negative impact on GPA for White students (β = −.12, p <.001) but no effect for Latinos.
The effects of adaptive/maladaptive learning strategies also showed some interesting
differences in White and Latino groups. First, while both groups had positive relations of
learning strategies to GPA, the latent factor had a greater impact on GPA among White (β =
.55, p <.001) than Latino (β = .21, p <.001). Second, maladaptive help-seeking had negative
effects for both White and Latino groups, but the relation was stronger for White (β = −.46,
p <.001) than for Latino (β = −.24, p <.001). Lastly, worry had a negative effect on
achievement for White students (β = −.24, p <.001) but no effect for Latinos.
Summary Findings
Scale reliabilities were generally satisfactory: For the total sample of 3,523 participants,
reliability estimates of the measures ranged from .71 to .93. Confirmatory factor analysis
results for the total combined sample showed that the cognitive and motivational factors of
effective learning strategies were well defined in a 9-factor solution (elaboration, control,
maladaptive help-seeking, efficacy, effort, worry, mastery goal orientation, performanceapproach goal orientation, performance- avoidance goal orientation) and the results of the
P a g e | 56
multiple-group CFA indicated that there was good support for a totally invariant model with
a 9-factor solution across White and Latino groups. For the overall sample, SEM results
indicated that the model fit the data, suggesting that prior knowledge and learning strategies
were positively related to students’ achievement measured by GPAs, while maladaptive helpseeking and worry were negatively related. In general, sociocultural factors were related to
factors predictive of achievement and were consistent with what would be expected given
previous research and theory. For example, SES was positively related to OTL and prior
knowledge. Male students have higher self-efficacy than female students. In addition,
students who had higher self-efficacy endorsed a performance-approach goal orientation
more than student who had lower efficacy. In addition, the positive impact of prior
knowledge on performance goals but negative effects on mastery goal orientation is
consistent with motivational theories regarding students who may have weak academic
backgrounds.
The results of goal orientation variables were also largely consistent with prior
research. A mastery goal orientation was strongly related to learning strategies and
negatively to maladaptive help-seeking, while a performance avoidance goal approach
related positively to maladaptive help seeking and worry. As expected, worry and
maladaptive help-seeking negatively related to achievement.
In terms of ethnic differences, gender was related negatively to prior knowledge for
both of groups, but Latino groups had slightly greater relations among those. SES was
positively related to prior knowledge for White students but not Latino students. For White
students, having a family with higher income and more educated mothers significantly
affected students’ prior levels of achievement, but this relation was not statistically
P a g e | 57
significant for Latino students. Latino students showed a stronger association between OTL
and academic efficacy than White students. For White students, the effect of prior
knowledge on the achievement was greater than for Latino students. White group also
appeared to be more influenced by maladaptive help-seeking and worry, compared to Latino
group. Although both groups had positive effects of learning strategies on GPA, the relation
of White students was greater than that of Latino students.
Discussion and Conclusion
The results of this study provide support for factors that previous literature and theory has
shown to be important for self-regulated learning and the relationships to motivational and
learning strategy use. From an educational perspective, this is encouraging news, since the
motivational and learning strategies of the type examined in this investigation are amenable
to instruction and modifiable. This is important because of a tendency of postsecondary
institutions, as well as the field of education in general, to consider low achievement a
function of unmodifiable student or family deficits (Bensimon, 2005, Valencia, 1997). While
there is some concern in psychological research about the tendency to focus on a limited
range of sociocultural contexts and populations (Arnett, 2008), there is good evidence that
there are similarities across sociocultural groups with respect to the motivational and
learning-related constructs examined here, as evidenced by the PISA (Program in
International Student Assessment) international data (Marsh et al, 2006). This is consistent
with the significant body of research findings that support the connection between
motivational variables, learning strategies, self-regulatory behaviour, and academic
achievement (Bandura, 1997; Deci & Ryan, 1985; Zimmerman, 1999, 2000a). This does not
negate the importance of sociocultural factors, however.
P a g e | 58
In spite of the invariance of the constructs investigated, there were some differences
between the two groups examined. Latinos were somewhat lower on elaboration, academic
efficacy, performance approach, and performance avoidance for example, and somewhat
higher on maladaptive help-seeking, worry, and mastery goal orientation. There is no simple
explanation for these differences, as the literature focusing on comparisons between the two
groups in this study is too small. One factor that may be important is the select nature of the
Latino students in this sample, Given that they were in a four year college, they may not be
comparable to the general population of Latino students. While their prior grades and SAT
scores were lower than those of White students, the fact that they were admitted to a
relatively selective institution may have been a factor in higher mastery goal orientation, and
lower levels of prior knowledge (high school GPA and SAT verbal and math) may have been
related to lower efficacy and increased maladaptive help-seeking. The findings here were
somewhat consistent with Garcia, Yu, and Coppola’s (1993) study with Latino and AfricanAmerican students which found that success was correlated with motivation and prior
achievement, and not with learning strategies. Given the extensive literature that suggests the
impact of learning strategies on academic outcomes (Azevedo & Cromley, 2004; Butler &
Winne, 1995; Ley &Young, 200; Zimmerman, 1990; Zimmerman & Martinez-Ponz, 1990:
Zimmerman & Schunk, 2001), this deserves more exploration.
In addition, SES was related to OTL and prior knowledge for Latino but not for White
students. This may be a function of other prior work on differences in OTL between low-SES
schools and higher-SES schools. As noted earlier, differences have been found in
instructional arrangements, materials, teacher experience, and teacher-student ratio
(Wenglinsky, 1998). Interestingly, while OTL was not found to have significant direct effects
P a g e | 59
on achievement, it was related to other motivational variables self-efficacy, performance
approach, and mastery goal orientation) in the study relative to White students (academic
self-efficacy only).
One of the most interesting findings was the very strong effect of prior knowledge on
GPA in comparison to the relatively smaller effects of background variables (and other
variables as well) such as OTL and SES compared to what we expected. For example, prior
knowledge was found to be strongly and positively related to achievement for both White
and Latino students. This is a different pattern that what other studies have found, in
particular the relatively stronger impact of self-efficacy compared to prior knowledge.
Chemers, Hu, and Garcia (2001), for example, found that while high school grades were
significantly related to college grades, self-efficacy at the time of college entrance was
related to grades at the end of the first year of college, even when prior GPA was controlled.
Other recent studies (Pietsch, Walker, & Chapman, 2003; Pintrich, 2003) as well as a recent
review of self-efficacy studies (Valentine, DuBois, & Cooper, 2004) have confirmed these
patterns. Because the focus of the current study was on math in particular, this may have had
an impact on the present findings.
An additional interesting finding was the lack of direct effects of the motivational
variables on achievement. As the previous literature has suggested, arguments have been
made for a multiple-goal approach in which mastery and performance approach goals play a
positive role in academic performance (Pintrich, 2003). Previous studies have found, similar
to the present results (Harackiewicz, Barron, Tauer, & Elliot, 2002: Harackiewicz, Barron,
Tauer, Carter, & Elliot, 2000) that in studies with college students, mastery goals did not
predict course grades, but rather predicted increased interest, enjoyment, and course-taking
P a g e | 60
(in psychology courses). While these variables were not measured in the present study, future
studies should consider adding them as important mediators.
The present study was also consistent with findings in the achievement goal literature
that students who support mastery goals are more likely to use deep-level strategies such as
elaboration and organization than students who support performance goals (Fredricks,
Blumenfeld, & Paris, 2004; Patrick, Kaplan, & Ryan, 2007; Ross, Shannon, SalisburyGlennon & Guarino, 2002; Wolters, Yu & Pintrich, 1996). A great deal of research, as point
out, has documented that mastery goals are connected with students’ use of cognitive and
self-regulatory strategies. Yet it is puzzling why these factors did not exhibit stronger effects
on achievement for either group. A more significant and stronger (negative) relationship to
achievement was found for worry, the cognitive component of stress and anxiety, consistent
with the work in this area (Zeidner & Matthews, 2005).
This study is one of the few studies which have investigated sociocultural, learning, and
motivation related factors with college-aged Latino students. Because relatively little work
has been done with ethnic differences among college-level students’ approach to learning,
this study contributes to the knowledge that aims to describe the relationships among those
aspects of learning students have already obtained in a given domain, those aspects that are
still being developed, and those where students need further support. Rather than exclusively
focusing on programs which rely on social and academic integration or financial aid, colleges
should continue investigating the sociocultural, motivational, and learning aspects of
academic achievement, addressing issues of students’ different approaches to learning and
how these can be bolstered both prior to and after students arrive on campus. Few faculty
members in college teaching positions receive educational experiences which provide the
P a g e | 61
knowledge to consider these factors in their classroom instruction. This is increasingly
important, however, as college campuses experience both increasing diversity as well as
continued pressure for accountability and student learning outcomes. Drawing upon studies
such as this, institutions of higher education should be able to establish professional
development programs in order to train faculty at all levels in motivational principles and
learning strategies methods which are appropriate to the needs of at-risk students, informed
by possible between-group as well as individual differences.
P a g e | 62
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APPENDIX A
Phone Survey Questionnaire
PHONE SURVEY QUESTIONNAIRE
HELLO Hello, I am calling from XXXX.
HEA
Have I reached [INSERT STUDENT’S NAME]?
1. Yes
[SKIP TO INTRO]
2. No
[CONTINUE]
9.
REFUSED
[TERMINATE SURVEY]
HEAD
HEAD
Is [INSERT STUDENT’S NAME] at home?
1. Yes
[SKIP TO INTRO]
2. 2. Not here right now
[SKIP TO APPT]
3. 3. No longer live here…new phone # (record on next screen)
4. 4. No one here by that name
[TERMINATE]
5. 5. No longer lives here…don’t have a new phone # [TERMINATE]
6. REFUSED
[TERMINATE – CALL BACK LATER]
What time can we call back to talk with [INSERT STUDENT’S NAME]?
__________________ TERMINATE AFTER GETTING TIME
INTRO We’re conducting a study of XXX students regarding their learning styles. I have
just a few questions to ask you right now, and then the main part of the study is a 10 minute
on-line survey. If you agree to participate in both parts you will be entered into a drawing
for a chance to win up to $100 in Coyote bookstore gift certificates. Is it alright to ask you
these questions now?
YES [SKIP TO BEGIN]
NO
APPT
Is it possible to make an appointment to ask you the survey questions at a
more convenient time?
7. Yes (SPECIFY)________________
8. No
BEGIN Before we start, let me assure you that your identity and your responses will remain
completely confidential, and of course, you are free to decline to answer any particular
survey question. I should also mention that this call may be monitored by my supervisor for
quality control purposes only.
First, since part of the survey is on-line, I need to verify your e-mail address. According to
our database, your e-mail address is [INSERT E-MAIL FROM DATABASE]. Is that
correct?
Yes [SKIP TO QUESTION4]
No [SKIP TO 2C]
8.
DON’T KNOW [SKIP TO TRANSJB]
9.
REFUSED [SKIP TO TRANSJB]
2C.
Do you have an e-mail address?
1. Yes
2. No [SKIP TO TRANSJB]
8. DON’T KNOW [SKIP TO TRANSJB]
9. REFUSED [SKIP TO TRANSJB]
3.
What is your e-mail address? [INTERVIEWER: TYPE CAREFULLY AND
READ BACK TO RESPONDENT]
TRANSJB
Students who do not have an e-mail address can come to JB 277 to complete
the second part of the survey. Please come to JB 277 after next Wednesday, November 8, to
complete the on-line portion. Your name will be entered into the bookstore drawing after
you complete both the phone and on-line survey.
4.
What was the highest grade of school your father completed?
Didn’t finish high school
Graduated from high school
Had some education from college
Graduated from college
DON’T KNOW
REFUSED
5.
How about your mother…how far did she go in school?
Didn’t finish high school
Graduated from high school
Had some education from college
Graduated from college
DON’T KNOW
REFUSED
6.
What does your father do for a living?______________________ IF RETIRED
OR DECEASED, ASK WHAT HE USED TO DO
7.
What does your mother do for a living? ______________________ IF RETIRED
OR DECEASED, ASK WHAT SHE USED TO DO
8.
Yes
No
Are you receiving financial aid to help pay for school?
9.
9.
REFUSED
Please tell me whether you Strongly Agree, Agree, Disagree, or Strongly
Disagree with the following statements:
My family emphasized the importance of reading when I was growing up
1.
STRONGLY AGREE
2.
AGREE
3.
NEUTRAL / NO OPINION
4.
DISAGREE
5.
STRONGLY DISAGREE
8.
DON'T KNOW
9.
REFUSED
9a.
My family emphasizes the need for a college education.
1.
2.
3.
4.
5.
8.
9.
STRONGLY AGREE
AGREE
NEUTRAL / NO OPINION
DISAGREE
STRONGLY DISAGREE
DON'T KNOW
REFUSED
10.
When you were growing up, how often did people in your home talk to each other
in a language other than English? Would you say it was….
1.
Never,
2.
Once in a while,
3.
About half of the time, or
4.
All or most of the time
8.
DON’T KNOW
9.
REFUSED
11.
Is English your first language?
1. Yes
2. No
9. REFUSED
12.
Were you born in the United States?
1. Yes [SKIP TO TRANS]
2. No
9. REFUSED [SKIP TO TRANS]
13.
How long have you been living in the United States? ____ years
TRANS Now I’d like to ask you about the math and science courses you took in high
school. Specifically,
14.
How many semesters of AP math did you take in high school?______ [ENTER 98
FOR DON’T KNOW/FORGOT, 99 FOR REFUSED]
15.
How about AP science?_______ [ENTER 98 FOR DON’T KNOW/FORGOT, 99
FOR REFUSED]
16.
And now, focusing on your courses at CSUSB, about how many pages of reading
do you do for your courses each day?
1. 5 or fewer,
2.
6-10,
3.
11-15,
4.
16-20,
5.
21-40,
6.
More than 40
8.
DON’T KNOW
9.
REFUSED
TRANS Now I have just a few questions about your preferred learning style. Please rate
each of the following statements on a scale from 1 to 7, with 1 meaning strongly agree and 7
meaning strongly disagree. First statement:
17.
I prefer to learn visually…how strongly do you agree on the 1 to 7 scale?
18.
I am good at learning from labeled pictures, illustrations, graphs, maps, and
animations
19.
I prefer to learn verbally
20.
I am good at learning from printed text
21.
Yes
No
8.
9.
Finally, a question about your background. Are you of Hispanic or Latino origin?
DON’T KNOW
REFUSED
22.
How would you describe your race or ethnicity? [MULTIPLE RESPONSE
ALLOWED]
White
Black or African American
Asian
American Indian or Alaska Native
Native Hawaiian or other Pacific Islander
Hispanic
[SAY IF THEY HAVE E-MAIL] Thanks, that’s it. In a day or so you will find in your email account our on-line questionnaire. We’ll need you to return it as soon as possible, and
then you will be entered into the bookstore drawing. Have a great day.
OR
[SAY IF THEY DON’T HAVE E-MAIL] Thanks, that’s it. Please come to JB-277 in a
few days to do the brief on-line questionnaire. Once you do that, you will be entered into the
bookstore drawing.
INTERVIEWER QUESTIONS
IQ1.
How cooperative was the respondent?
1. Cooperative
2. Uncooperative
3. Very Uncooperative
IQ2.
How well did the respondent understand the questions?
1. Very easily
2. Easily
3. Some difficulty
4. Great deal of difficulty
APPENDIX B
Written Survey Questionnaire
WRITTEN SURVEY
Hello:
Thank you for agreeing to participate in our survey about XXX students' learning styles and
strategies. It will take approximately 10 - 15 minutes to complete the questionnaire.
Your participation in this study is completely voluntary. If you feel uncomfortable answering
a particular question, feel free to continue to the next question without answering. Answer as
much as you can. It is very important for us to learn your opinions.
Your survey responses will be strictly confidential and data from this research will be
reported only in the aggregate. If you have questions at any time about the survey or the
procedures, you may contact XXX or by email at the email address specified below.
Thank you very much for your time and support. Please start with the survey now by clicking
on the Continue button below.
When I study, I try to relate new material to things I have learned in other subjects.
When I study, I figure out how the information might be useful in the real world.
When I study, I try to understand the material better by relating it to things I already
know.
When I study, I figure out how the material fits in with what I have already learned.
When I study, I start by figuring out exactly what I need to learn.
When I study, I force myself to check to see if I remember what I have learned.
When I study, I try to figure out which concepts I still haven’t really understood.
When I study, I make sure that I remember the most important things.
When I study, and I don’t understand something I look for additional information to
clarify this.
1. Almost never
2. Sometimes
3. Often
4. Almost always
10)
help.
11)
12)
13)
14)
15)
When I don't understand my work, I often guess instead of asking someone for
I don't ask questions in my classes, even when I don't understand the work.
If my course work is too hard for me, I just don't do it rather than ask for help.
I usually don't ask for help with my work, even if the work is too hard to do on my
own.
When I don't understand my work, I often put down any answer rather than ask for
help.
If I need help to do part of my coursework, I skip it.
1.
2.
3.
4.
5.
Not at all true
Somewhat true
Very true
16)
I’m certain I can understand the most difficult material presented in texts.
17)
I’m confident I can understand the most complex material presented by the
teacher.
18)
I’m confident I can do an excellent job on assignments and tests.
19)
I’m certain I can master the skills being taught.
1. Almost never
2. Sometimes
3. Often
4. Almost always
20)
21)
22)
23)
1.
2.
3.
4.
When studying, I work as hard as possible.
When studying, I keep working even if the material is difficult.
When studying, I try to do my best to acquire the knowledge and skills taught.
When studying, I put forth my best effort.
Almost never
Sometimes
Often
Almost always
24)
25)
26)
27)
28)
29)
30)
31)
Thinking about my grade in a course interferes with my work on tests
I freeze up on important exams
During exams I find myself thinking about whether I'll ever get through school
The harder I work at taking a test, the more confused I get
Thoughts of doing poorly interfere with my concentration on tests
I seem to defeat myself while working on important tests
During tests I find myself thinking about the consequences of failing
During examinations I get so nervous that I forget facts I really know
1. Almost never
2. Sometimes
3. Often
4. Almost always
32)
33)
34)
35)
My work in math was frequently encouraged by my instructors
College and high school math instructors often praised me
When I did well in math, my instructors confirmed that
I feel useless in math classes
36)
1.
2.
3.
4.
5.
Math makes me feel bad about myself
Strongly agree
Agree
Neutral
Disagree
Strongly disagree
37)
38)
39)
40)
41)
It is important to me that I learn a lot of new concepts this year
One of my goals in class is to learn as much as I can
One of my goals is to master a lot of new skills this year
It’s important to me that I thoroughly understand my class work
It’s important to me that I improve my skills this year
1. Not at all true
2.
3. Somewhat true
4.
5. Very true
42)
work
43)
44)
45)
46)
It’s important to me that other students in my class think I am good at my class
47)
48)
48)
class
50)
It is important to me that I don’t look stupid in class
One of my goals is to keep others from thinking that I’m not smart in class
It is important to me that my teacher doesn’t think that I know less than others in
One of my goals is to show others that I’m good at my class work
One of my goals is to show others that class work is easy for me
One of my goals is to look smart in comparison to the other students in my class
It is important to me that I look smart in comparison to others in my class
1. Not at all true
2.
3. Somewhat true
4.
5. Very true
One of my goals in class is to avoid looking like I have trouble doing the work
1. Not at all true
2.
3. Somewhat true
4.
5. Very true
51)
I am more comfortable working in a group than working alone
52)
I feel that high quality learning can take place without having face-to-face
interaction
Strongly agree
Agree
Neutral
Disagree
Strongly disagree
53)
54)
55)
56)
1.
2.
3.
4.
5.
I am comfortable using computers
If an assignment requires a computer, I will need help getting started
I am among the first of my peers to play with new computer technologies
I like experimenting with computers
Strongly agree
Agree
Neutral
Disagree
Strongly disagree
57)
Are you Hispanic or Latino? Select one or more
No, I am not Hispanic or Latino
Yes, I am Mexican, Mexican American, or Chicano
Yes, I am Puerto Rican or Puerto Rican American
Yes, I am Cuban or Cuban American
Yes, I am from some other Hispanic or Latino background
58)
Which of the following best describes you? Select one or more:
White
Black or African American
Asian
American Indian or Alaska Native
Native Hawaiian or other Pacific Islander
Hispanic
Other
59)
My career plans after graduation are the following: Select one or more
Enter the world of work
Go to graduate school
Enter the U.S. Military
Take some time off
Give back to the community through volunteer work
Other
I don’t know
60)
Which of the following categories best describes your parents’ total household or
family income before taxes, from all sources? If you are self-supporting, list your total
household income.
3.
4.
8.
9.
61)
take
1. Less than $25,000
2. $25,000 to $49,999
$50,000 to $74,999
$75,000 or more
DON'T KNOW
REFUSED
True or false: My ELM score determines how many college math classes I must
1.
2.
8.
62)
True
False
DON’T KNOW
True or false: Remedial courses don’t count toward graduation
1. True
2. False
8. DON’T KNOW
Thank you! Your response has been saved and recorded with ID# ________. You will be
notified within a month if you will be receiving one of the bookstore coupons. Thank you for
your help!