belonging and academic engagement among...
TRANSCRIPT
Belonging and Academic Engagement AmongUndergraduate STEM Students: A Multi-institutionalStudy
Denise Wilson • Diane Jones • Fraser Bocell • Joy Crawford •
Mee Joo Kim • Nanette Veilleux • Tamara Floyd-Smith •
Rebecca Bates • Melani Plett
Received: 26 December 2013 / Published online: 6 March 2015� Springer Science+Business Media New York 2015
Abstract This study examined the links between multiple levels of belonging and forms
of behavioral and emotional engagement among STEM undergraduates in five geo-
graphically and culturally distinct institutions in the United States. Data were gathered
from a survey specifically designed to capture the links between these key elements of the
undergraduate experience. Results from over 1500 student participants in the survey
clearly supported the importance of belonging for behavioral and emotional engagement in
STEM courses when measured in the context of the classroom. The most consistent and
significant links among models for the five participating institutions occurred between
belonging at the class level and positive emotional engagement, while the least frequent
and least consistent occurred between belonging to the university and all forms of en-
gagement. Patterns of association to engagement were also similar for belonging and self-
efficacy. The results of this study confirm the importance of belonging in the STEM
classroom context and provide additional insights into the concurrent importance of self-
efficacy in supporting student engagement. These results also demonstrate that belonging is
D. Wilson (&)Department of Electrical Engineering, University of Washington, Seattle, WA 98195, USAe-mail: [email protected]
D. Jones � F. Bocell � J. Crawford � M. J. KimCollege of Education, University of Washington, Seattle, WA 98195, USA
N. VeilleuxDepartment of Mathematics, Statistics & Computer Science, Boston, MA 02115, USA
T. Floyd-SmithDepartment of Chemical Engineering, Tuskegee University, Tuskegee, AL 36088, USA
R. BatesDepartment of Computer Science, Minnesota State University, Mankato, Mankato, MN 56001, USA
M. PlettSchool of Engineering, Seattle Pacific University, Seattle, WA 98119-1957, USA
123
Res High Educ (2015) 56:750–776DOI 10.1007/s11162-015-9367-x
a distinct attribute related to engagement and is not simply reducible to feelings of
self-efficacy.
Keywords STEM � Belonging � Engagement � Self-efficacy
Introduction
The need for new scientists and engineers in the U.S. labor market has been expected to
grow significantly in the coming years, making academic engagement and persistence of
undergraduates in these fields a national priority (Chief Information Officers Council 2010,
Chapter 3). The continuing concern with issues of academic engagement and persistence
among undergraduate students in the STEM fields has resulted in a large body of scientific
research on STEM students and disciplines in higher education. This research has sup-
ported the fact that what happens during college can be just as important as what happens
before college in determining the achievement and persistence decisions of STEM
undergraduates.
A vast body of research has found that both social and academic connections have been
essential to demonstrating achievement and persistence outcomes for students. Tinto
(1975) proposed the classic model of academic persistence by creating a space for both the
educational expectations and commitments that an individual brings to college and also the
academic and social systems within a college or university. According to Tinto’s model, it
is the integration into the academic and social systems, that is, accepting and achieving a
sense of fit with the cultural practices and norms of the institution, that most directly
impacts individual learning and continued enrollment in college. Other characteristics of
the academic and social systems have also been of interest to scholars for several decades.
For example, involvement in academic and social experiences (Astin 1984), student en-
gagement and effort (Kuh et al. 2008), and behavioral and emotional classroom engage-
ment (Fredricks et al. 2004; Gasiewski et al. 2012) have figured prominently in the
attempts to understand differences in student learning and educational outcomes. A more
recent area of research inquiry has been to explore the sense of belonging as a combined
indicator of both academic and social integration, which has implications for both aca-
demic and educational outcomes (Bollen and Hoyle 1990; Hausmann et al. 2007; Hurtado
et al. 2007).
The current research built on the extant research by using multiple levels of belonging
to reflect how well students were integrated both socially and academically into college
life. The term integration refers to how much students have attitudes and beliefs in
common with their peers and faculty and also how well they adopt the culture of their
home institution (Wolf-Wendel et al. 2009). Influenced in part by the original Tinto model
on integration in the university setting, much of the literature on belonging has been
limited to the examination of belonging at campus level (Bollen and Hoyle 1990; Haus-
mann et al. 2007; Hurtado et al. 2007; Johnson 2011; Museus and Maramba 2010).
However, both social and academic integration can occur at a number of levels, including
the highly contextualized settings of classrooms or the academic unit of major as well as
the larger institutional context. Thus, this study expands on the existing research by
spanning the classroom, major, and university settings and examining belonging in each of
these settings as an affective and motivational indicator of overall academic and social
Res High Educ (2015) 56:750–776 751
123
integration. In particular, our study seeks to identify how sense of belonging at multiple
levels is linked to behavioral and emotional components of action that students experience
in the process of engaging in the classroom. These forms of engagement are both proximal
outcomes of motivation and major components of the link between student motivation and
academic outcomes.
Background
Engagement
Engagement has been an associated with both positive changes in skills and abilities and
greater psychological adjustment during the college years. For example, engagement in
academic activities has been positively and strongly linked to critical thinking (Carini et al.
2006; Gellin 2003), integration of information (Pike and Killian 2001), grades (Carini et al.
2006; Hughes and Pace 2003; Handelsman et al. 2005), and persistence (Hughes and Pace
2003; Nelson Laird et al. 2008). Better academic performance has, in turn, been linked to
stronger persistence whether through greater engagement or other pathways (Astin 1993a,
Pascarella and Terenzini 2005). Kinzie et al. (2008) demonstrated that engagement has a
positive effect on persistence even after accounting for a broad range of demographic,
academic, and socioeconomic characteristics. Academic engagement has a compensatory
effect for certain groups of students in their first year, especially those of color or those
with lower abilities (Kuh et al. 2008).
The study of student engagement has been notable for the variety of approaches and
terminology in the literature. This ‘‘tangled web of terms’’ (Wolf-Wendel et al. 2009) has
made for challenges in understanding similarities and differences across research programs
that use different approaches. The confusions have been particularly evident in the research
on engagement among college students that has been inspired by the work of Astin (1984)
on involvement and Kuh et al. (2006) on engagement. Although Astin’s involvement has
encompassed both the energy expended on and time invested in tasks, most of the research
that has used involvement theory including that of the Cooperative Institutional Research
Program (CRIP) has emphasized time on task (Astin 1993b). Like Astin’s involvement,
Kuh’s engagement constructs also have tended toward assessing time spent in certain
activities. In Kuh’s framework of engagement, however, these activities have been
benchmarks of effective educational practice. For example, in the National Survey of
Student Engagement (NSSE), engagement has been measured in terms of five such
benchmarks: ‘‘academic challenge, active and collaborative learning, student-faculty in-
teraction, enriching educational experiences, and supportive campus environment.’’ (Wolf-
Wendel et al. 2009, p. 414). Thus, Kuh’s engagement measures were intended to connect
student behavior to effective educational practice while Astin’s involvement focused more
broadly on student investment in activities associated with college life. Although there are
distinctions in the approaches of Kuh and Astin and despite the fact that the terms are used
interchangeably by some researchers (Pascarella and Terenzini 2005), the common theme
in these approaches has been that learning and persistence were related to how students
spend their time and energy in academic, extracurricular, and social experiences (Kuh et al.
2006; Wolf-Wendel et al. 2009). In the current study, our measures of academic en-
gagement have differed from Astin’s involvement in that they focused on how students
perceived what they have intended to do and have felt rather than how much they actually
did. Our measures have also differed from Kuh’s engagement in that our measures of
752 Res High Educ (2015) 56:750–776
123
academic engagement were intended to connect to motivations by measuring what students
generally think or feel about their engagement rather than time on task in specific types of
educational activity.
This approach to engagement comes from motivational research (Fredricks et al. 2004;
Gasiewski et al. 2012). In the motivational approach, academic engagement has been
considered a multidimensional construct, which has included perceived behavioral and
affective dimensions (Fredricks et al. 2004). Behavioral engagement has been defined as
student involvement in academic activities and has included measures of effort and par-
ticipation in class discussions (Gasiewski et al. 2012). Emotional engagement has been
defined in terms of both perceived positive and negative emotional responses to academic
experiences. These emotional responses can range from feelings of interest and enjoyment
to anxiety/worry and discouragement (Connell and Wellborn 1991; Miserandino 1996).
Research has demonstrated that both behavioral and emotional engagement have been
fundamental to the learning process (Rocca 2010; Sansone and Thoman 2005). Taken
together, behavioral and emotional engagement have provided a broader foundation for
understanding the dynamics of learning in the college classroom.
There are obvious similarities/parallels among the approaches outlined above. In this
study, we choose to focus on behavioral and emotional measures of engagement in the
academic context that were consistent with motivational approaches to engagement. We
call these measures academic engagement in order to distinguish them from the more
frequency-oriented behavioral measures across multiple arenas of college life used by Kuh
and Astin in studying engagement and involvement. Our measures of academic engage-
ment emphasize how students perceive their behavior and feelings within the context of
classrooms and academic majors.
Belonging
An extensive review by psychologists Baumeister and Leary (1995) helped to give
prominence to belonging as a construct and to provide evidence on its linkages to cognitive
and emotional well-being. Belonging has been considered a basic human need (Maslow
and Lowry 1968; Baumeister and Leary 1995), which is dependent upon personal and
frequent social connections for fulfillment. In the higher education literature, measures of
belonging have significant conceptual overlap with school membership (Goodenow 1993),
fit, and psychological sense of community (Lounsbury and DeNeui 1995, 1996; DeNeui
2003). In order to provide some unity to these related concepts and avoid confusion, we use
the term belonging in this study to describe contexts ranging from the classroom to the
whole university.
Scholars who have examined the need to belong in the context of schooling have
assumed that academic classrooms and/or whole schools are important contexts where
individuals experience interpersonal connections that inform a sense of belonging
(Goodenow 1993). A sense of belonging in college settings has been defined primarily as
perceptions of acceptance, fit, and inclusion in the campus milieu (Bollen and Hoyle 1990;
O’Brien et al. 2011; Locks et al. 2008; Museus and Maramba 2010). This body of research
established that a positive sense of belonging was related to academic and social adjust-
ment (Hurtado et al. 2007; Ostrove and Long 2007), involvement and intention to persist in
college (Hausmann et al. 2007), and decreased burnout in college (McCarthy et al. 1990).
There has also been interest in examining the sense of belonging within academic
majors and the college classroom, in part, because the traditional college academic ex-
perience has been situated in the classroom and academic major. Although fewer in
Res High Educ (2015) 56:750–776 753
123
number, these studies have reported linkages between classroom or major belonging and
greater academic confidence, engagement, and achievement (Freeman et al. 2007; Zum-
brunn et al. 2014). Furthermore, a sense of belonging has been shown to play a key role in
persistence and interest within STEM majors. For example, Good et al. (2012) found that
sense of belonging was diminished for women by stereotypes that women have less math
ability than men. This diminished sense of belonging mediated a woman’s intent to pursue
math in the future. Similarly, in engineering, a lack of belonging was found to be a
significant contributor to students’ decision to leave engineering regardless of gender
(Marra et al. 2012). A sense of isolation or lack of belonging has also played a key role in
women leaving engineering, early in their undergraduate careers (Brainard and Carlin
1998). For computer science students, Cheryan et al. (2009) found that an ambient sense of
belonging based on the physical structure and environment of a particular context was
associated with women’s interest in computer science, regardless of whether women were
placed in an environment consisting of mostly women or mostly men. Thus, understanding
the role of belonging in the academic experience at the major and classroom levels has
relevance for all STEM students. Therefore in the current study, we distinguished among
connection to the classroom, major, and whole university communities in order to more
clearly understand the relationships between different levels of belonging and academic
engagement.
Belonging and Engagement
There are several reasons to expect relationships between engagement and belonging.
Individuals have been shown to acquire interests, goals, and achievement motivation when
connected to others (Freeman et al. 2007; Walton and Cohen 2011; Walton et al. 2012).
The need to belong in a particular group or domain has served as an important source of
motivation within that group. Walton et al. (2012) highlighted this connection between
belonging and achievement motivation by conducting experiments where other sources of
motivation were held constant and opportunities for belonging were manipulated via social
links within groups. The motivation to persist on domain-related tasks was shown to
improve even with minor social connections with other people, termed a ‘‘mere’’ sense of
belonging by Walton et al. (2012).
If the results of these artificially controlled experiments were to translate successfully to
the less controlled environment of the college classroom setting, we would expect that a
student’s sense of belonging in a particular course would result in increased motivation to
achieve and persist in the course. Likewise, a student who felt a strong sense of belonging
in his or her major would also tend to be more motivated to achieve within that major. It is
important to remember that interest in particular academic majors has also been diminished
by characteristics that undermine a sense of belonging (Cheryan et al. 2009; Good et al.
2012). For these reasons, we expected significant relationships among the levels of be-
longing and multiple types of academic engagement.
Research Model and Questions
Our research model (Fig. 1) posited that belonging at multiple levels (class, academic
major, and university) would be positively related to behavioral engagement (effort, par-
ticipation) and emotional engagement, but negatively (inversely) related to negative
emotional engagement.
754 Res High Educ (2015) 56:750–776
123
In evaluating the contribution of belonging indices to behavioral and emotional en-
gagement, our model also controlled for other factors that have demonstrated relationships
to academic engagement. In particular, we looked at year-in-school and self-efficacy be-
cause of their importance in STEM fields as correlates of persistence (Pajares 1996, 2002)
and as predictors of performance (Bandura 1997; Fantz et al. 2011). Self-efficacy in
particular has been thought to be a factor related to students leaving engineering disciplines
altogether (Marra et al. 2012). Thus, the inclusion of self-efficacy was considered to be
appropriate in our evaluation of belonging and engagement. Demographic characteristics
including family background (Ostrove and Long 2007) and gender (Good et al. 2012) were
also considered in our analyses.
Research Questions
In seeking to demonstrate the validity of our research model in STEM classrooms and
majors, we addressed two research questions using data collected from five different
institutions and multiple STEM majors:
1. Which levels of belonging are most consistently associated with behavioral
engagement as well as emotional engagement after controlling for relevant factors
such as self-efficacy?
2. What are the similarities and differences among the different types of institutions in
terms of the relationships between belonging levels and engagement?
The first research question examined whether class, academic major, or university
belonging had the most consistent pattern of significant relationships to various forms of
engagement. The second question focused on the patterns of significance that were similar
or different across the schools in terms of the relationships among the levels of belonging
and types of engagement. Because our research questions highlighted patterns of
Fig. 1 Research model
Res High Educ (2015) 56:750–776 755
123
relationships, it was not relevant to test for differences between the institutions in the
strength of significant relationships. Rather, the goal was to discern patterns of significance
across the five schools, which would provide evidence on the generality of the findings
across settings as well as the variations between institutional contexts in the relationships
between belonging and engagement.
This investigation was part of a larger, 5 years, five-institution research study that
examined connection, community, and engagement in STEM education. In this larger
study, described in Floyd-Smith et al. (2010), patterns of belonging, connection to com-
munity, and related affective outcomes were investigated with the goal to predict and
improve engagement and connection to community across a diverse range of institutions,
students, teaching styles, and faculty. In the component of the study discussed here, the
goal was to identify the nature of belonging in three contexts (class, major, and university)
and then to understand how the types of belonging were associated with behavioral and
emotional academic engagement. The five participating institutions, their Carnegie Clas-
sifications (2010), and their key characteristics as drawn from institutional data are pre-
sented below.
• HBCU (Bac-Div): An historically black, small private university institution which
serves approximately 4,000 students and confers over 600 degrees annually. Five
engineering and computer science undergraduate degrees are offered along with a wide
range of non-engineering STEM degrees including agricultural and environmental
sciences, animal science, biology, chemistry, physics, and math.
• Private/Faith Based (Masters L): A small teaching institution in the Pacific Northwest
with 4,000 students, which offers four engineering and computer science degrees as
well as degrees in biology, chemistry, math, and physics. Students attend lower
division classes ranging between 20 and 75 students with upper division classes
typically enrolling 10–20 students. Students begin taking courses in their intended
major as early as freshman year.
• Research (RU/VH): A large research institution and flagship university in the Pacific
Northwest which serves over 43,000 students and confers over 3,150 degrees in STEM
fields annually. This institution offers ten engineering and computer science
undergraduate degrees and a wide range of non-engineering STEM degrees including
multiple degree options in physics, chemistry, math, biology, and environmental
studies.
• Teaching (Masters L): A medium-sized institution of approximately 15,000 students in
the Midwest serving a regional student population. The school combines an emphasis
on teaching with emerging innovations in research and offers over ten undergraduate
degrees in engineering and computer science as well as degrees in biology, chemistry,
physics, information technology, math, and statistics within STEM. Class sizes
typically average 25 students, with upper division classes averaging about 15 students.
• Women’s (Masters L): A small women’s college of approximately 1,900 students in the
Northeast with fifty majors, including computer science, biology, biochemistry, bio-
statistics, chemistry, environmental science, health informatics, mathematics, and
physics degrees in STEM, but no engineering degrees. This institution offers a liberal
arts education for its undergraduates integrated with professional work experience.
Class sizes are typically 6–12 students, with the largest class size around 20.
These five institutions were selected to capture a diverse range of undergraduate ex-
periences across different institution types that vary significantly by size (enrollments),
variety of STEM majors, institutional culture, and diversity of undergraduates on campus.
756 Res High Educ (2015) 56:750–776
123
The comparisons across such diverse settings allowed us to explore both generalities in the
relationships between belonging and engagement and the variations at distinct institutions.
Research Methods
Participants
The sample consisted of 403 sophomores, 633 juniors, and 471 seniors in STEM majors
from the five institutions described above. Approximately one-third of the sample was
female (n = 497; males = 1001). The self-reported ethnicity of the students was primarily
White (50 %), Asian/Asian American (25 %), African American/Black (13 %) and other
(12 %). The sample characteristics varied by school and are presented in Table 1.
Procedure
The sample was recruited over a two-year period from Fall 2010 through Fall 2012
primarily in STEM courses or through STEM activity groups. For the first term of re-
cruitment, the survey was administered by paper-and-pencil. Thereafter, an electronic
version was available. Most of the students (65 %) completed the survey in class when
instructors provided time. Otherwise, students completed the survey outside of class.
Participation in the study was voluntary and student assent was obtained. Most participants
received an incentive for completing the survey ranging from a small amount of extra
credit for the course in which they completed the survey to monetary compensation
ranging from $5 to a $20 gift card for the campus bookstore or amazon.com.
Table 1 Participant characteristics by school—N (%)
Private HBCU Women’s Teaching Research
Participants: N 108 176 63 274 886
Gender: N
Females 36 (33 %) 84 (48 %) 63 (100 %) 36 (13 %) 278 (31 %)
Males 72 (67 %) 92 (52 %) 0 (0 %) 238 (87 %) 599 (69 %)
Year in school: N
Sophomore 56 (51 %) 80 (45 %) 18 (29 %) 59 (22 %) 190 (21 %)
Junior 20 (19 %) 57 (32 %) 28 (44 %) 137 (50 %) 391 (44 %)
Senior 32 (30 %) 39 (22 %) 17 (27 %) 78 (28 %) 305 (34 %)
Self-reported Ethnicity: N (%)
African American/Black 2 (2 %) 158 (90 %) 5 (8 %) 12 (4 %) 9 (1 %)
Asian American/Asian 13 (12 %) 0 (0 %) 9 (14 %) 21 (8 %) 327 (37 %)
Hispanic 2 (2 %) 1 (1 %) 1 (2 %) 4 (1 %) 14 (2 %)
White/Caucasian 74 (69 %) 0 (0 %) 39 (62 %) 217 (80 %) 419 (47 %)
Other 16 (15 %) 17 (10 %) 9 (14 %) 16 (6 %) 97 (11 %)
Missing 1 (1 %) 0 (0 %) 0 (0 %) 2 (1 %) 20 (2 %)
Res High Educ (2015) 56:750–776 757
123
Measures
Measures used in this study were adapted from a variety of other studies in both K-12 and
higher education in order to evaluate the research model described in Fig. 1.
Belonging
Belonging was measured at three levels (class, academic major, and university). To
measure belonging to class and academic major, items were adapted from the belonging
scale (Anderson-Butcher and Conroy 2002) to assess a student’s feelings of support and
acceptance in a STEM class or major. For each scale, students responded to four items
using a 5-point Likert scale (strongly disagree to strongly agree). At the class level,
statements included ‘‘I feel that I am a part of this class’’ and ‘‘I feel that I am accepted in
this class.’’ Students who took the survey outside of class were instructed to select one of
their basic STEM courses as the reference for the class items. Academic major belonging
items included ‘‘I feel comfortable in this major’’ and ‘‘I feel that I am a part of this
major.’’ The belonging scale has demonstrated strong internal consistency (alpha = 0.93)
and solid validity for an adolescent population (Anderson-Butcher and Conroy 2002). In
this study, acceptable internal consistency was also demonstrated (Belonging Class, al-
pha = 0.89; Belonging Major, alpha = 0.84).
University belonging was measured using items from the Collegiate Psychological
Sense of Community (PSC) scale (Lounsbury and De Neui 1996). University belonging
assessed the students’ sense of belonging and affinity for the school they were attending.
Students responded to four statements using a 5-point Likert scale (strongly disagree to
strongly agree). Items in the university belonging subscale included statements like ‘‘I feel
like I really belong at this university/college.’’ Within college samples, the PSC has
demonstrated strong reliability (alpha = 0.90) and established construct validity (Louns-
bury and De Neui 1996). In this study the subscales demonstrated solid internal consis-
tency with an alpha of 0.86 for the university belonging scale.
Behavioral Engagement
The behavioral engagement scale was designed to measure students’ effort and par-
ticipation with regard to work in their classes. The behavioral engagement items were
adapted from Miserandino (1996). The original scales measuring behavioral engagement
have demonstrated adequate internal consistency and construct validity (Miserandino
1996). Students responded to a 5-point Likert scale (strongly disagree to strongly agree) for
each item. Effort evaluated how hard students try to work in their classes and lab/study
groups. This scale included 5 items and had an alpha of 0.86. The scale consisted of the
items such as ‘‘I try hard to do well in this class’’ and ‘‘In my major classes, I work as hard
as I can.’’ Participation measured how students think about their participation in class
discussions in their classes and lab/study groups. This scale included two items and
demonstrated an alpha of 0.74. Items in this scale were ‘‘When I’m in classes in my major,
I participate in class discussions with my classmates and instructors’’ and ‘‘When I’m in
this class, I participate in class discussions with my classmates.’’ The original scales
measuring behavioral engagement have demonstrated adequate internal consistency
(Miserandino 1996). For this study, the subscales demonstrated strong psychometric
properties as well (Effort, alpha = 0.86; Participation, alpha = 0.74).
758 Res High Educ (2015) 56:750–776
123
Emotional Engagement
The emotional engagement scale was designed to measure students’ affective responses to
the work in their classes. This scale was adapted from Miserandino (1996). Students
responded to each item using a 5-point Likert scale (strongly disagree to strongly agree).
Positive Emotional Engagement measured the level of positive affect that students felt
about learning and being in their major classes and lab/study groups. The scale consisted of
six items such as ‘‘I enjoy learning new things in my major classes/lab/study groups,’’
‘‘When I’m in classes in my major, I feel good,’’ and ‘‘In my major classes/lab/study
groups, when we work on something I feel interested.’’ The scale demonstrated solid
internal reliability with an alpha of 0.84.
Negative Emotional Engagement, also adapted from Miserandino (1996) measured the
negative feelings that students felt about learning and being in their classes and lab/study
groups. The measure contained six items and demonstrated solid internal reliability with an
alpha of 0.83. Items included statements like ‘‘When we work on something in this class/
my lab/study groups, I feel discouraged,’’ and ‘‘When I’m in classes in my major/my lab/
study groups, I feel worried.’’
Self-efficacy was included to represent students’ academic orientations and was defined
as the self-appraisal of one’s ability to master a task. The measure included judgments
about one’s ability to accomplish a task as well as one’s confidence in one’s skills to
perform that task. Self-efficacy was measured at the academic major level using five items
from the Motivated Strategies for Learning Questionnaire (MSLQ) (Pintrich and DeGroot
1990; Pintrich et al. 1993). Items included statements such as ‘‘I’m certain I can understand
the most difficult material taught in the classes in my major’’ and ‘‘I believe I will receive
excellent grades in the classes in my major.’’ Each item was scored on a 5-point Likert
scale (strongly disagree to strongly agree). The original scale demonstrated strong internal
consistency (alpha = 0.89) and validity (Pintrich and DeGroot 1990; Pintrich et al. 1993).
In this study, the scale demonstrated strong psychometric properties as well with an alpha
of 0.87.
Background Characteristics
Year in school was based on the students’ self-reports of their university classification as
freshman, sophomore, junior, or senior. The classifications were assigned numerical codes
for the analyses (freshman = 1; senior or 5th year seniors = 4). Only sophomores, juniors,
and seniors were included in the sample because of school variations in policies regarding
admission to STEM majors.
Parental education was assessed separately for mothers and fathers or for primary
caregivers. Students selected one of seven options that indicated the highest level of
education completed by each parent or primary caregiver. The options ranged from ‘‘Did
not finish high school (1)’’ to ‘‘Doctoral or professional degree such as Ph.D., J.D., M.D.,
etc. (7).’’
Gender
Students indicated their gender as either female (1) or male (2).
Res High Educ (2015) 56:750–776 759
123
Plan of Analysis
We first examined the data to check for outliers and normality of distributions. All vari-
ables were found to have met the assumptions of normality and homoscedasticity
(Tabachnick and Fidell 2012). Mahalanobis and Cook’s distances were evaluated to de-
termine the presence of outliers in the data set (Stevens 2009). Although some cases were
identified as potential outliers, the removal of these cases did not significantly impact the
results; consequently the identified cases were not removed.
Bivariate correlations among the study variables were then calculated for each school to
determine the strength of the relationships among the variables. The descriptive statistics
are presented in the Appendix.
The primary analyses employed simultaneous multiple regression analyses using SPSS
18. Four separate multiple regression models evaluated the unique contributions of the
three belonging variables (Class Belonging, Major Belonging, and University Belonging)
to the behavioral (Effort and Participation) and emotional (Positive and Negative) en-
gagement variables for each school. The four models were tested on each school separately
in order to evaluate the significance of the belonging factors to the multiple indicators of
engagement for each school and to assess the patterns of significance across the schools.
This approach allowed us to evaluate the significance of the model for each school with the
greatest clarity regardless of size. As such, the school differences in the absolute levels of
the standardized regression coefficients were not tested directly and were not central to our
goal of assessing the patterns of significance across schools.
Initial analyses revealed that gender and parental education did not contribute to the
models; therefore, these parameters were dropped from further consideration. In the final
models, the four independent variables were year in school, self-efficacy, class belonging,
major belonging, and university belonging. The dependent variable in each model was one
of the four engagement measures (effort, participation, positive emotional engagement, or
negative emotional engagement). The ratio of participants to predictors was adequate
(Stevens 2009). The ratio was at or above 15:1 for four of the schools and was at 10:1 for
the Women’s college.
The multiple analyses involved in this study had the potential to increase Type 1 errors.
To address this issue, we considered each school as an independent set of analyses because
the samples were independent and non-overlapping. However, four analyses were com-
pleted for each school on the same sample. Therefore, Bonferroni’s correction was used to
control for Type I error inflations within each school. As such, the a per model was set at
0.05/4 analyses = 0.0125. Thus, all significance tests were evaluated at a = 0.0125.
Results
The correlations among the study variables were calculated for each school and are pre-
sented in the Appendix. The pattern of results revealed that except for year in school, the
belonging and engagement measures were significantly intercorrelated at each of the
schools. The correlations were positive except for the relationships with negative emo-
tional engagement, in which case, greater belonging at all levels was related to less
negative emotional engagement, as predicted. The correlations typically ranged from
moderate (0.25–40) to strong (0.50–0.75), but were not at the levels that have been con-
sidered problematic for multicollinearity (0.90) according to Tabachnick and Fidell (2012).
760 Res High Educ (2015) 56:750–776
123
We used multiple regression analyses to answer our first research question on which
levels of belonging accounted for the variance in the engagement measures. Results from
each multiple regression analysis for each type of engagement have been presented in
separate tables to enhance cross-school comparisons. Even though the correlations among
the belonging variables were moderate to strong, a review of indices related to multi-
collinearity revealed that multicollinearity was not a statistical problem in any of the
equations.
Behavioral Engagement: Effort
The results for Effort are presented in Table 2. The regression models were significant at
four of the schools although the amount of variance (R2) accounted for ranged from good
(Private & Teaching) to modest (Research). A review of the standardized Beta coefficients
indicated that several factors were related to student effort. Class Belonging made sig-
nificant contributions to Effort for three of the five schools. Self-efficacy and major be-
longing were significant predictors for two of the schools. The general trend was for higher
levels of self-efficacy, class belonging, and major belonging to be associated with greater
academic effort.
Behavioral Engagement: Participation
Table 3 presents the multiple regression results for self-reported class participation. The
regression models were significant at all the schools and R2 values ranged from substantial
(0.44) to good (0.21). The most consistent relationship emerged for Class Belonging.
Students who reported greater levels of class belonging indicated a greater likelihood of
participating in class discussions at all five campuses. This result was evident at all of the
schools.
Emotional Engagement: Positive
The significant multiple regression models for each school revealed that several factors
were correlates of positive emotional engagement. The amount of variance accounted for
Table 2 Links between belonging and behavioral engagement: effort
School
Private HBCU Women’s Teaching ResearchN = 100 N = 173 N = 61 N = 251 N = 850
Year in school -0.21 0.02 -0.09 -0.11 -0.11***
Self-efficacy -0.32 0.30** 0.10 0.17** 0.00
Belonging—class level 0.28** 0.29** 0.21 0.03 0.21***
Belonging—major level 0.50** -0.07 0.30 0.34*** 0.07
Belonging—university level 0.03 0.11 -0.17 0.10 0.10**
F 9.26*** 11.09*** 2.47 22.01*** 21.60***
R2 0.31 0.25 0.18 0.30 0.11
Standardized regression coefficients by school for simultaneous multiple regression
** p\ .01; *** p\ .001
Res High Educ (2015) 56:750–776 761
123
(R2) in each analysis was substantial (range 0.63–0.40). A review of Table 4 indicated that
self-efficacy and major belonging were significantly related to positive engagement for at
least three of the campuses. Class belonging and university belonging were significantly
related to positive engagement for two of the campuses. Students who felt efficacious or
who reported higher levels of major belonging were also more likely to report greater
positive affect in academic experiences in their major.
Emotional Engagement: Negative
Self-efficacy and Class Belonging were the primary correlates associated with negative
emotional engagement in the consistently significant multiple regression models. The R2
values ranged from strong (0.50) to good (0.27). At nearly all the schools, negative affect
was greater among students who reported less self-efficacy and class belonging. At the two
larger schools (Teaching, Research), a greater sense of university level belonging was
associated with less negative emotional engagement (Table 5).
Table 3 Links between belonging and behavioral engagement: participation
School
Private HBCU Women’s Teaching ResearchN = 100 N = 173 N = 61 N = 251 N = 850
Year in school -0.13 0.07 -0.10 0.09 -0.06
Self-efficacy -0.11 0.32*** 0.36 0.12 0.11**
Belonging—class level 0.59*** 0.37*** 0.41** 0.36*** 0.35***
Belonging—major level 0.21 0.04 -0.08 0.26*** 0.06
Belonging—university level -0.02 0.03 0.02 -0.09 0.00
F 16.12*** 21.39*** 4.97*** 31.24*** 45.27***
R2 0.44 0.39 0.30 0.37 0.21
Standardized regression coefficients by school for simultaneous multiple regression
** p\ .01; *** p\ .001
Table 4 Links between belonging and emotional engagement: positive
School
Private HBCU Women’s Teaching ResearchN = 100 N = 173 N = 61 N = 251 N = 850
Year in school -0.10 -0.01 -0.12 -0.09 -0.03
Self-efficacy 0.07 0.31*** 0.14 0.31*** 0.14***
Belonging—class level 0.35*** 0.15 0.19 0.13 0.24***
Belonging—major level 0.45*** 0.10 0.53*** 0.42*** 0.29***
Belonging—university level 0.05 0.28*** 0.02 0.31 0.15***
F 34.58*** 24.50*** 16.45*** 68.20*** 119.9***
R2 0.63 0.42 0.59 0.57 0.40
Standardized regression coefficients by school for simultaneous multiple regression
** p\ .01; *** p\ .001
762 Res High Educ (2015) 56:750–776
123
Summary of Results
The findings from all the multiple regression analyses were summarized in Table 6 in order
to integrate the results across the schools for the four engagement variables and answer
Question 2 about patterns of similarity and differences among the schools. In Table 6, each
cell indicates the number of schools with significant relationships between the predictor
variables and the various engagement outcomes. The maximum possible number in each
cell is 5, except the summary cell at the far right, which has a maximum total of 20.
The Total column in Table 6 indicates that both self-efficacy and class belonging most
consistently contributed to engagement in STEM coursework across the five schools
whereas significant relationships between year in school and belonging–university level
were lacking among the five schools. Patterns of significant relationships between be-
longing and engagement by institution can be observed in Fig. 2.
The pattern of relationships shown in Fig. 2 indicated that among the three belonging
measures, class belonging was most consistently linked to engagement in STEM course-
work across the five schools while university belonging was linked least consistently.
Specifically, greater class belonging was significantly linked to higher levels of reported
participation at all five schools and less negative emotional engagement at four of the five
schools.
The pattern of significant results also revealed that the sense of belonging to an aca-
demic major was a significant factor linked to engagement for some of the schools. At the
Teaching institution, major belonging was related positively to three types of engagement:
effort, participation, and positive emotional engagement, whereas at the Private institution,
only effort and positive emotional engagement were positively and significantly related to
major belonging. At the Women’s and Research institutions, only one of these relation-
ships was significant (major belonging and positive emotional engagement). Thus, there is
evidence that at several institutions, major belonging was associated with feeling positive
about the learning experiences in major classes while relationships between major be-
longing and behavioral engagement were far less common. Interestingly, major belonging
was not significantly related to negative emotional engagement at any of the five schools.
Finally, the significant contributions of university belonging to engagement were evi-
dent primarily for the Research institution as evidenced by the significant Beta weights for
Table 5 Links between belonging and emotional engagement: negative
School
Private HBCU Women’s Teaching ResearchN = 100 N = 173 N = 61 N = 251 N = 850
Year in school 0.05 0.09 -0.12 -0.06 0.04
Self-efficacy -0.35** -0.27** -0.47** -0.27*** -0.30***
Belonging—class level -0.18 -0.25** -0.41*** -0.18** -0.20***
Belonging—major level -0.02 -0.03 0.06 -0.02 -0.08
Belonging—university level -0.10 -0.07 -0.05 -0.21*** -0.19***
F 7.94*** 12.51*** 11.43*** 22.42*** 89.08***
R2 0.28 0.27 0.50 0.30 0.34
Standardized regression coefficients by school for simultaneous multiple regression
** p\ .01; *** p\ .001
Res High Educ (2015) 56:750–776 763
123
effort, positive and negative emotional engagement. This result could be connected to the
size of the university or its particular culture within its STEM fields.
Like class belonging, self-efficacy was also consistently linked to engagement measures
at the five institutions in this study. While the link between class belonging and par-
ticipation was the only one that was significant for all five schools in Fig. 2, the link
Table 6 Significant relationships for each predictor variable by type of engagement
Number of statistically significant predictors
Behavioral engagement Emotional engagement Total
Effort Participation Positive Negative
Year in school 1 0 0 0 1
Self-efficacy 2 2 3 5 12
Belonging—class level 3 5 2 4 14
Belonging—major level 2 1 4 0 7
Belonging—university level 1 0 2 2 5
Fig. 2 Patterns of linkages between belonging and engagement. Shown are links between various forms ofbelonging and various forms of engagement that were statistically significant in the multiple regressionanalyses for each school. The nature of the linkage (positive or negative) is shown in parenthesis after eachschool. Arrows show direction of input to output in the regression and are not causal
764 Res High Educ (2015) 56:750–776
123
between self-efficacy and negative emotional engagement was the only one that was
significant for all five schools in Fig. 3. Thus, there was evidence to suggest that as
students perceived themselves to be less capable in their classes, they also reported more
negative emotions in those classes, regardless of the school they attend.
Discussion
A primary goal of this research was to evaluate the linkages between belonging and
academic engagement for STEM undergraduate students. Our findings contribute to the
literature on academic engagement in STEM fields by providing additional evidence that a
sense of belonging, especially class belonging, is related to behavioral and emotional
engagement among undergraduate STEM students. Given the multitude of studies that
relate engagement to persistence (Hughes and Pace 2003; Nelson Laird et al. 2008; Kinzie
et al. 2008), efforts to enhance belonging among undergraduates in individual classes are
likely to result in increased persistence in majors represented by students in those classes.
The results of this research also underscore the relevance of nonacademic factors such as
belonging for academic engagement and persistence in STEM education (Marra et al.
2012).
Several aspects of the research bolster the validity and relevance of this work. First, the
relationships between belonging and academic engagement were evident even when
considering other factors that are known correlates of engagement such as self-efficacy.
When other factors were included in the model, classroom belonging was a strong con-
tributor to the variance in the academic engagement indices. Belonging, then, is not simply
reducible to feelings of academic competence such as self-efficacy. Rather, belonging
represents a distinct attribute that reflects the experiences of students in the STEM envi-
ronment and has implications for what they do in class (effort and participation) and how
they feel about their experiences in class and their major (positive and negative emotions).
These results hold regardless of gender as indicated by the lack of significance of gender in
any of the models and give further credence to the centrality of the need for belonging for
all students.
The second element of this research that contributes to the significance of the work is
the fact that it is a multi-institutional study undertaken at five distinct universities and
Fig. 3 Patterns of linkagesbetween self-efficacy andengagement. Shown are linksbetween self-efficacy and variousforms of engagement that werestatistically significant in themultiple regression analyses foreach school. The nature of thelinkage (positive or negative) isshown in parenthesis after eachschool. Arrows show direction ofinput to output in the regressionand are not causal
Res High Educ (2015) 56:750–776 765
123
colleges. The schools were selected for their diverse characteristics so that we could
examine similarities and differences in the relationships between belonging and engage-
ment. We suggest that the consistency of the significant contribution of classroom be-
longing in the multiple regression equations is evidence that belonging plays a role in the
academic engagement of students in STEM fields regardless of the nature of the school.
These findings should alert faculty and administrators to the importance of attending to
belonging issues and classroom experiences when striving to engage and retain students in
the STEM fields.
The third element of the work that enhances its contribution is the evidence for variation
among schools in the patterns of significance between engagement and two types of
belonging, namely, major and university belonging. Major belonging was a significant
factor related to engagement at only three of the institutions and primarily for effort and
positive emotional engagement. These institutions were quite varied in terms of the size of
the student body and the types of academic majors offered. In each case, however, either
the small size of the academic units or cohort programs within the majors could have
contributed to the importance of the major as a mechanism for encouraging a sense of
belonging.
It was only at the large research institution that university belonging was a consistent
contributor to engagement. These results could be connected to the size of the research
institution or its particular culture within STEM fields. It is also possible that the pattern
could be related to the university’s nationally successful Division 1 intercollegiate sports
teams which amplifies the brand and stature of the school and influences student con-
nection to the university community (Getz et al. 2010). Alternately, the large sample size
and resultant statistical power for the Research University analyses may have made it more
likely to detect the contributions of university belonging to engagement.
These results highlight the importance of considering contextual variations that can
shape the sense of belonging and its relationship to engagement. Life beyond the classroom
as represented by major belonging and university belonging has the potential to contribute
to engagement depending on the culture of the individual institution and should be given
additional consideration. The sense of belonging to a STEM major or to the university
reflects variation between the schools and could be additional pathways to engagement and
persistence depending on the school culture.
Self-efficacy also emerged as a central variable related to different types of en-
gagement in that there was a consistent pattern of significant relationships across the five
institutions. Our findings are consistent with previous investigations that have revealed
linkages between self-efficacy, academic performance, and persistence (Marra et al.
2009, 2012; Ponton et al. 2001). The consistent negative linkage between self-efficacy
and negative emotional engagement among all five schools studied suggests that students
withdraw from the academic experience when they feel less capable, regardless of the
institution they attend. On the other hand, mixed patterns of connection between self-
efficacy and more positive forms of engagement (effort, participation, and positive
emotion) suggest that institutional factors may play a stronger role in how students
choose to engage more in their studies. In both negative and positive linkages, the
consistency of the self-efficacy pattern of significance underscores the need to include
self-efficacy in future research on belonging and engagement in STEM education to
better understand the independent contributions of both belonging and self-efficacy to
academic engagement.
766 Res High Educ (2015) 56:750–776
123
Limitations
Although this research has provided evidence on the linkages between belonging and
engagement at multiple institutions of higher education, there are limitations that need to
be acknowledged. It could be that the findings may not generalize to institutions of higher
education other than the five institutions included in study. The participating institutions
were selected to represent a broad array of types of institutions so that the consistency of
the results gives support to the generalizability of the findings. Still there are two levels of
concern related to generalizability across institutions. First, each type of participating
institution is represented by a single case. It is unknown if these results generalize to other
institutions representing Private, HBCU, Women’s, Teaching, or Research universities.
Second, even though we identified similar patterns of relationships (especially for class
belonging and engagement) across the five schools, we do not know if the relationships
function in similar ways across institutional types. Third, additional types of institutions
that were not included may have different patterns of relationships. Further research should
be attentive to these issues to increase generalizability of the research.
Another limitation of the research is that we do not provide information on the ways in
which race may affect the relationships between belonging and engagement. There is a
body of research that has indicated the importance of belonging for minority students and
their persistence in attending college and majoring in STEM fields (Johnson 2011; Walton
and Cohen 2011). Unfortunately, we were not able to examine these issues in the current
study. Although the participating institutions were of diverse types, the racial composition
of the students at each school and the participants in the study were not similarly or
sufficiently diverse across all the schools. It was therefore not possible to include race in
each of the statistical models. The issue of the relationships among belonging and en-
gagement indicators for minority students, especially underrepresented minorities, thus
remains to be investigated systematically.
Another limitation of the study centers on the data. The participant responses were
gathered at one point in time, which means the relationships are based on concurrent
responses. As such, the direction of effects between variables cannot be determined. We do
not know definitively if a strong sense of belonging increases engagement or if engagement
leads to a heightened sense of belonging. Longitudinal research is needed to explicate the
direction of effects issues and to better understand the reciprocal effects that emerge over
time between belonging and engagement.
Implications
This study highlights the importance of supporting and strengthening belonging among
STEM students. However, belonging within an individual class or classroom seems
especially important as demonstrated by the commonality of significant links between class
belonging and engagement among all five institutions considered in this study. Although
this study is not longitudinal, it nevertheless has some implications for practitioners in
applying proven practices to increase belonging among students in the classroom.
Although still in its infancy, research on improving classroom belonging at the college
level has highlighted several techniques that generate significant improvements in student
belonging. For example, Walton and Cohen (2011) showed that a simple, short interven-
tion stabilized sense of belonging and was related to academic performance over a three-
year period. This intervention involved presenting a report of a survey to study participants
Res High Educ (2015) 56:750–776 767
123
from upperclassmen from all ethnic groups. The report indicated that although the up-
perclassmen reported worrying about whether they were accepted in the college commu-
nity during their first year, this worry lessened over time. This seemingly simple
intervention was related to an increase in GPAs of the African-American students in the
intervention group compared to the control group. Jordan et al. (2012) conducted a similar
intervention with engineering students in multiple fields across three institutions and found
that these students’ sense of belonging increased over the course of a single semester as a
result of the intervention. These interventions increased awareness for both students and
faculty alike of the importance of belonging and connecting to community and also shifted
the focus of struggles with fit, acceptance, and belonging from a unique struggle for the
individual student to a normal experience for all students.
Additional studies in higher education classrooms (Freeman et al. 2007; Rocca 2010) have
shown that sense of belonging is highest in classes where instructors learn students’ names,
encourage students to participate, are perceived as warm and helpful, and are well organized
and prepared for their classes. Negative experiences within the classroom can also diminish
belonging and engagement such as when instructors are overly critical, sarcastic, conde-
scending, or verbally aggressive (Rocca 2010). Instructors may find pursuing the positive
strategies influential in strengthening the belonging that students experience in their classes.
Conclusions
In conjunction with other recent studies, the results of this study reinforce the central im-
portance of belonging in the undergraduate STEM academic experience. Among five uni-
versities and colleges studied, significant links between multiple measures of belonging and
multiple measures of both behavioral and emotional engagement are numerous and varied,
even when controlling for other known and significant correlates to engagement such as self-
efficacy. The most consistent relationships occur between class belonging and the multiple
forms of engagement. This result speaks to the importance of highly contextualized con-
nections to peers and faculty for academic engagement. No matter what the institutional
culture, no matter what the geographical location, no matter what the school size, this study
has revealed evidence that a sense of belonging cultivated in a class is strongly related to the
way the students feel, how hard they try, and how willing they are to participate in a class.
Although a longitudinal study is required to confirm the direction of influence, these results
nevertheless suggest that individual faculty and instructors can support students’ ability to
learn not just through their own teaching, but also through supporting opportunities to build
community and belonging in class. Belonging, then, is not just something immutable that
students bring into the classroom that is resistant to change, but instead, appears to be a more
malleable construct that is strongly linked to engagement and can potentially be as strong a
contributor to persistence in STEMfields as self-efficacy and intrinsic interest in these fields.
Acknowledgments The authors would like to gratefully acknowledge the National Science Foundation fortheir support of this work under the REESE program (Grant numbers DRL-0909817, 0910143, 0909659,0909900, and 0909850). Any opinions, findings, and conclusions or recommendations expressed in thismaterial are those of the author(s) and do not necessarily reflect the views of the National ScienceFoundation.
Appendix
See Tables 7, 8, 9, 10 and 11.
768 Res High Educ (2015) 56:750–776
123
Table
7Correlationtable
fortheprivateuniversity
M(SD)
n1
23
45
67
89
Yearin
school
2.78
.88
108
–
Self-efficacy
19.10
3.71
108
-.11
–
Class
belonging
16.27
2.56
108
.08
.43***
–
Majorbelonging
16.99
3.02
108
.07
.73***
.59***
–
University
belonging
17.31
3.00
108
-.07
.31***
.46***
.60***
–
BEeffort
21.02
3.65
108
-.11
.20*
.44***
.44***
.37***
–
BEparticipation
7.55
1.80
108
-.05
.31**
.65***
.46***
.35***
.43***
–
EEpositive
23.81
3.63
108
-.04
.57***
.66***
.73***
.51***
.53***
.54***
–
EEnegative
10.59
3.29
108
.08
-.48***
-.38***
-.44***
-.31**
-.10
-.39***
-.41***
–
N=
108
BEbavioralengagem
ent,EEem
otional
engagem
ent
*p\
.05;**p\
.01;***p\
.001
Res High Educ (2015) 56:750–776 769
123
Table
8Correlationtable
forhistoricallyblack
university
M(SD)
n1
23
45
67
89
Yearin
school
2.77
.79
176
–
Self-efficacy
20.03
3.24
176
-.01
–
Class
belonging
15.81
2.90
176
-.14
.41***
–
Majorbelonging
16.61
2.78
176
-.04
.68***
.60***
–
University
belonging
16.47
3.04
176
-.10
.24**
.52***
.47***
–
BEeffort
20.75
3.29
176
-.03
.39***
.42***
.35***
.29***
–
BEparticipation
7.78
1.65
176
.01
.51***
.53***
.49***
.30***
.55***
–
EEpositive
23.79
3.24
176
-.07
.51***
.49***
.53***
.48***
.59***
.47***
–
EEnegative
10.51
3.25
176
.14
-.41***
-.43***
-.40***
-.29***
-.29***
-.30***
-.31***
–
N=
176
BEbavioralengagem
ent,EEem
otional
engagem
ent
*p\
.05;**p\
.01;***p\
.001
770 Res High Educ (2015) 56:750–776
123
Table
9Correlationtable
forwomen’s
college
M(SD)
n1
23
45
67
89
Yearin
school
2.98
.75
63
–
Self-efficacy
18.92
3.77
63
.16
–
Class
belonging
15.15
3.04
63
-.08
.26*
–
Majorbelonging
16.25
2.37
63
-.12
.75***
.41***
–
University
belonging
15.85
3.56
63
-.03
.52***
.37**
.67***
–
BEeffort
21.70
3.22
63
-.12
.28*
.30*
.36**
.17
–
BEparticipation
7.57
1.86
63
-.06
.38**
.47***
.35**
.27*
.54***
–
EEpositive
24.19
3.62
63
-.18
.57***
.46***
.74***
.52***
.28*
.34**
–
EEnegative
12.14
4.25
63
-.17
-.58***
-.52***
-.48***
-.41***
-.23
-.27*
-.54***
–
N=
63
BEbavioralengagem
ent,EEem
otional
engagem
ent
*p\
.05;**p\
.01;***p\
.001
Res High Educ (2015) 56:750–776 771
123
Table
10
Correlationtable
forteachinguniversity
M(SD)
n1
23
45
67
89
Yearin
school
3.07
.70
274
–
Self-efficacy
18.72
3.42
274
.10
–
Class
belonging
15.28
2.74
269
.10
.45***
–
Majorbelonging
16.15
2.53
274
.09
.56***
.66***
–
University
belonging
15.46
3.16
274
-.02
.41***
.49***
.55***
–
BEeffort
20.04
3.63
274
-.09
.40***
.37***
.48***
.40***
–
BEparticipation
7.22
1.79
274
.11
.40***
.55***
.49***
.31***
.45***
–
EEpositive
23.48
3.66
274
-.01
.62***
.55***
.69***
.49***
.58***
.49***
–
EEnegative
10.78
3.39
274
-.13*
-.41***
-.42***
-.39***
-.33***
-.08
-.18**
-.34***
–
N=
274
BEbavioralengagem
ent,EEem
otional
engagem
ent
*p\
.05;**p\
.01;***p\
.001
772 Res High Educ (2015) 56:750–776
123
Table
11
Correlationtable
forresearch
university
M(SD)
n1
23
45
67
89
Yearin
school
3.13
.74
886
–
Self-efficacy
17.88
3.54
885
.02
–
Class
belonging
14.21
3.02
884
-.06
.37***
–
Majorbelonging
14.90
3.05
886
.02
.51***
.55***
–
University
belonging
15.38
3.42
885
-.07*
.26***
.37***
.44***
–
BEeffort
20.34
3.37
886
-.13***
.13***
.29***
.22***
.22***
–
BEparticipation
6.59
1.68
886
-.08*
.27***
.43***
.31***
.20***
.38***
–
EEpositive
22.56
3.65
886
-.04
.41***
.51***
.56***
.41***
.40***
.42***
–
EEnegative
12.27
3.75
886
.06
-.47***
-.43***
-.42***
-.37***
-.01
-.17***
-.37***
–
N=
886
BEbavioralengagem
ent,EEem
otional
engagem
ent
*p\
.05;**p\
.01;***p\
.001
Res High Educ (2015) 56:750–776 773
123
References
Anderson-Butcher, D., & Conroy, D. E. (2002). Factorial and criterion validity of scores of a measure ofbelonging in youth development programs. Educational and Psychological Measurement, 62(5),857–876.
Astin, A. W. (1984). Student involvement: A developmental theory for higher education. Journal of CollegeStudent Personnel, 25(4), 297–308.
Astin, A. W. (1993a). What matters in college? Four critical years revisited. San Francisco: Jossey-Bass.Astin, A. W. (1993b). What matters in College? Liberal Education, 79(4), 4–15.Bandura, A. (1997). Self-efficacy: The exercise of control. New York: W. H. Freeman.Baumeister, R. F., & Leary, M. R. (1995). The need to belong: desire for interpersonal attachments as a
fundamental human motivation. Psychological Bulletin, 117(3), 497–529.Bollen, K. A., & Hoyle, R. H. (1990). Perceived cohesion: A perceptual and empirical examination. Social
Forces, 69, 479–504.Brainard, S. G., & Carlin, L. (1998). A six-year longitudinal study of undergraduate women in engineering
and science. Journal of Engineering Education, 87(4), 369–375.Carini, R. M., Kuh, G. D., & Klein, S. P. (2006). Student engagement and student learning: Testing the
linkages. Research in Higher Education, 47(1), 1–32.Carnegie Foundation (2010). The Carnegie Classification of Institutions of Higher Education. Retrieved
from: http://classifications.carnegiefoundation.org/.Cheryan, S., Plaut, V. C., Davies, P. G., & Steele, C. M. (2009). Ambient belonging: How stereotypical cues
impact gender participation in computer science. Journal of Personality and Social Psychology, 97(6),1045–1060.
Chief Information Officers Council. (2010). Net generation guide: Preparing for change in the federalinformation technology workforce. Retrieved from: http://dodcio.defense.gov/Portals/0/Documents/netgenimages/NetGenerationGuide.pdf.
Connell, J. P., & Wellborn, J. G. (1991). Competence, autonomy, and relatedness: A motivational analysis ofself-system processes. Gunnar, Megan R. (Ed); Sroufe, L. Alan (Ed), in Self processes and development.The Minnesota symposia on child psychology, vol. 23., (pp. 43-77). Hillsdale, NJ: Lawrence ErlbaumAssociates, Inc.
DeNeui, D. L. (2003). An investigation of first-year college students’ psychological sense of community oncampus. College Student Journal, 37(2), 224–234.
Fantz, T., Siller, T., & DeMiranda, M. (2011). Pre-collegiate factors influencing the self-efficacy of engi-neering students. Journal of Engineering Education, 100(3), 604–623.
Floyd-Smith, T., Wilson, D., Campbell, R., Veilleux, N., Bates, R., Plett, M., et al. (2010). A multiinsti-tutional study of connection, community, and engagement in STEM education: Conceptual modeldevelopment. In Proceedings of the ASEE (American Society for Engineering Education) Conference,Arlington, VA, June 2010.
Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, stateof the evidence. Review of Educational Research, 74(1), 59–109.
Freeman, T. M., Anderman, L. H., & Jensen, J. M. (2007). Sense of belonging in college freshmen at theclassroom and campus levels. The Journal of Experimental Education, 75(3), 203–220.
Gasiewski, J. A., Eagan, M. K., Garcia, G. A., Hurtado, S., & Chang, M. J. (2012). From gatekeeping toengagement: A multicontextual, mixed method study of student academic engagement in introductorySTEM courses. Research in Higher Education, 53, 229–261.
Gellin, A. (2003). The effect of undergraduate student involvement on critical thinking: A meta-analysis ofthe literature 1991-2000. Journal of College Student Development, 44(6), 746–762.
Getz, M., Siegfried, J., & Australia, S. (2010). What does intercollegiate athletics do to or for colleges anduniversities? In Leo Kahane & Stephen Shmanske (Eds.), The oxford handbook of sports economics,Volume 1: The economics of sports. New York: Oxford University Press.
Good, C., Rattan, A., & Dweck, C. S. (2012). Why do women opt out? Sense of belonging and women’srepresentation in mathematics. Journal of Personality and Social Psychology, 102(4), 700.
Goodenow, C. (1993). The psychological sense of school membership among adolescents: Scale develop-ment and educational correlates. Psychology in the Schools, 30(1), 79–90.
Handelsman, M. M., Briggs, W. L., Sullivan, N., & Towler, A. (2005). A measure of college student courseengagement. The Journal of Educational Research, 98(3), 184–192.
Hausmann, L. R., Schofield, J. W., & Woods, R. L. (2007). Sense of belonging as a predictor of intentions topersist among African American and White first-year college students. Research in Higher Education,48(7), 803–839.
774 Res High Educ (2015) 56:750–776
123
Hughes, R., & Pace, C. R. (2003). Using NSSE to study student retention and withdrawal. AssessmentUpdate, 15(4), 1–2.
Hurtado, S., Chang, J. C., Saenz, V. B., Espinosa, L. L., Cabrera, N. L., & Cerna, O. S. (2007). Predictingtransition and adjustment to college: Minority biomedical and behavioral science students’ first year ofcollege. Research in Higher Education, 48(7), 841–887.
Johnson, D. R. (2011). Examining sense of belonging and campus racial diversity experiences amongwomen of color in STEM living-learning programs. Journal of Women and Minorities in Science andEngineering, 17(3), 209–223.
Jordan, K. L., & Sorby, S. A. (2012). Pilot intervention to improve sense of belonging of minorities inengineering. In Proceedings from ASEE 2012: American Society of Engineering Education Confer-ence. Atlanta, Georgia.
Kinzie, J., Gonyea, R., Shoup, R., & Kuh, G. (2008). Promoting persistence and success of underrepresentedstudents: Lessons for teaching and learning. New Directions for Teaching and Learning, 115, 21–38.
Kuh, G. D., Cruce, T. M., Shoup, R., Kinzie, J., & Gonyea, R. M. (2008). Unmasking the effects of studentengagement on first-year college grades and persistence.The Journal of Higher Education, 79(5), 540–563.
Kuh, G.D., Kinzie, J., Buckley, J.A, Bridges, B. K., & Hayek, J C. (2006). What matters to student success:A review of the literature. Retrieved from http://nces.ed.gov/npec/pdf/kuh_team_report.pdf.
Locks, A. M., Hurtado, S., Bowman, N. A., & Oseguera, L. (2008). Extending notions of campus climateand diversity to students’ transition to college. The Review of Higher Education, 31(3), 257–285.
Lounsbury, J. W., & De Neui, D. (1996). Collegiate psychological sense of community in relation to size ofcollege/university and extroversion. Journal of Community Psychology, 24(4), 381–394.
Lounsbury, J. W., & DeNeui, D. (1995). Psychological sense of community on campus. College StudentJournal, 29, 270–277.
Marra, R. M., Rodgers, K. A., Shen, D., & Bogue, B. (2009). Women engineering students and self-efficacy:A multi-year, multi-institution study of women engineering student self-efficacy. Journal of Engi-neering Education, 98, 27–37.
Marra, R. M., Rodgers, K. A., Shen, D., & Bogue, B. (2012). Leaving engineering: A multi-year singleinstitution study. Journal of Engineering Education, 101(1), 6–27.
Maslow, A. H., & Lowry, R. (1968). Toward a psychology of being. New York: Van Nostrand Reinhold.McCarthy, M. E., Pretty, G. M., & Catano, V. (1990). Psychological sense of community and student
burnout. Journal of College Student Development, 31(3), 211–216.Miserandino, M. (1996). Children who do well in school: Individual differences in perceived competence
and autonomy in above-average children. Journal of Educational Psychology, 88(2), 203–214.Museus, S. D., & Maramba, D. C. (2010). The impact of culture on Filipino American students’ sense of
belonging. The Review of Higher Education, 34(2), 231–258.Nelson Laird, T. F. N., Chen, D., & Kuh, G. D. (2008). Classroom practices at institutions with higher-than-
expected persistence rates: What student engagement data tell us. New Directions for Teaching andLearning, 115, 85–99.
O’Brien, L. T., Mars, D. E., & Eccleston, C. (2011). System-justifying ideologies and academic outcomesamong first-year Latino college students. Cultural Diversity and Ethnic Minority Psychology, 17(4),406.
Ostrove, J. M., & Long, S. M. (2007). Social class and belonging: Implications for college adjustment.Review of Higher Education, 30(4), 363.
Pajares, F. (1996). Self-efficacy beliefs in academic settings. Review of Educational Research, 66(4),543–578.
Pajares, F. (2002). Overview of social cognitive theory and of self-efficacy. Retrieved from: http://www.emory.edu/EDUCATION/mfp/eff.html.
Pascarella, E. T., & Terenzini, P. T. (2005). How college affects students: A third decade of research. SanFrancisco: Jossey-Bass.
Pike, G., & Killian, T. (2001). Reported gains in student learning: Do academic disciplines make a dif-ference? Research in Higher Education, 42(4), 429–454.
Pintrich, P. R., & DeGroot, E. V. (1990). Motivational and self-regulated learning components of classroomacademic performance. Journal of Educational Psychology, 82(1), 33–40.
Pintrich, P. R., Smith, D. A., Garcia, T., & McKeachie, W. J. (1993). Reliability and predictive vailidity ofthe motivated strategies for learning questionnaire (MSLQ). Educational and Psychological Mea-surement, 53(3), 801–813.
Ponton, M., Edmister, J., Ukeiley, L., & Seiner, J. (2001). Understanding the role of self-efficacy inengineering education. Journal of Engineering Education, 90(2), 247–251.
Rocca, K. A. (2010). Student participation in the college classroom: An extended multidisciplinary literaturereview. Communication Education, 59(2), 185–213.
Res High Educ (2015) 56:750–776 775
123
Sansone, C., & Thoman, D. B. (2005). Does what we feel affect what we learn? Some answers and newquestions. Learning and Instruction, 15, 507–515.
Stevens, J. (2009). Applied multivariate statistics for the social sciences. New York: Routledge.Tabachnick, B. G., & Fidell, L. S. (2012). Using multivariate statistics (6th ed.). New York: Pearson.Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of
educational research, 45(1), 89–125.Walton, G. M., & Cohen, G. L. (2011). A brief social-belonging intervention improves academic and health
outcomes of minority students. Science, 331(6023), 1447–1451.Walton, G. M., Cohen, G. L., Cwir, D., & Spencer, S. J. (2012). Mere belonging: The power of social
connections. Journal of Personality and Social Psychology, 102, 513–532.Wolf-Wendel, L., Ward, K., & Kinzie, J. (2009). A tangled web of terms: The overlap and unique con-
tribution of involvement, engagement, and integration to understanding college student success.Journal of College Student Development, 50(4), 407–428.
Zumbrunn, S., McKim, C., Buhs, E., & Hawley, L.R. (2014). Support, belonging, motivation, and en-gagement in the college classroom: A mixed method study. Instructional Science. doi:10.1007/s11251-014-9310-0.
776 Res High Educ (2015) 56:750–776
123