effects of academic mindsets on college students￢ﾀﾙ...

Effects of Academic Mindsets on College Students’ Achievement and Retention

Cheon-woo Han, Susan P. Farruggia, Thomas P. Moss

Journal of College Student Development, Volume 58, Number 8, November2017, pp. 1119-1134 (Article)

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https://doi.org/10.1353/csd.2017.0089

https://muse.jhu.edu/article/678949

https://doi.org/10.1353/csd.2017.0089

https://muse.jhu.edu/article/678949

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Effects of Academic Mindsets on College Students’ Achievement and RetentionCheon-woo Han Susan P. Farruggia Thomas P. Moss

Noncognitive factors, such as academic self-efficacy, motivation, and sense of belonging, predict college students’ academic performance and retention. It is unclear if varying profiles of academic mindset are differentially associated with student success. We examined first-year college students’ academic mindsets (perceived academic self-efficacy, sense of belonging, and academic motivation) along with academic performance and first-to-second-year retention. Participants included 1,400 students enrolled at a diverse, urban research university. Cluster analysis identified 4 profiles of students: all high, self-efficacy-oriented, belonging-oriented, and all low. Students in the all high group were the most likely to succeed and students in the all low group were the least likely. Self-efficacy was more closely associated with academic performance, whereas belonging was more closely associated with retention. The results provide important intervention implications to improve college student success.

College success is often determined by two types of indicators: academic performance (grade point average, credits earned during specific periods) and academic retention and graduation (Robbins et al., 2004). Academic success and degree attainment of postsecondary students remain a major concern with dropout rates of up to 45% and the average time to attain a bachelor’s degree over 6 years

(Barefoot, 2004). Data from the National Center for Education Statistics indicate that in 2015 only 59% of the students who entered 4-year colleges and universities graduated within 6 years and over 21% of those students left after their first year (Kena et al., 2015). What variables are important to predicting and increasing college students’ success and academic performance? Researchers have identified some cognitive variables like academic ability—typically measured in terms of nationwide tests, such as the SAT or ACT—as key predictors of student success (Vasquez & Jones, 2006). In the same manner, other precollege academic variables, such as high school grades (Robertson & Taylor, 2009) and high school rank (Whalen, Saunders, & Shelley, 2009), are related to college student success and retention. Many educational improvement strategies are focused on the cognitive variables, and many institutions of higher education rely on them to evaluate incoming students’ college readiness (Farrington et al., 2012). However, students leave college prior to completion not only because of academic difficulties but for a variety of other reasons such as adjustment problems, uncertain goals, inadequate finances, and lack of student involvement (Robbins, Oh, Le, & Button, 2009). Researchers increasingly have been turning their attention to noncognitive factors—a

Cheon-woo Han is at The University of Texas at Tyler. Susan P. Farruggia and Thomas P. Moss are at the University of Illinois at Chicago. Research supported by the Morris and Mayer Kaplan Family Foundation, Office of the Vice Provost at the University of Illinois at Chicago, and an anonymous foundation grant. Thanks to Lakeshia Watson for support in managing the data collection.

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range of personality and motivational habits and attitudes that facilitate functioning well in school—to explain differences in academic performance (e.g., Robbins et al., 2004). Some have suggested that noncognitive factors might be as important as, or even more important than, cognitive skills in determining academic outcomes (Heckman, Stixrud, & Urzua, 2006). Among noncognitive factors, students’ academic mindsets play a principal role in predicting success in college. In an effort to bring together the wide-ranging research and theory on noncognitive factors, Farrington and colleagues (2012) developed a model with which they hypothesized how these factors are associated. Specifically, they hypothesized that academic mindsets are positively associated with learning persistence, better academic behavior, and improved performance in school. Farruggia, Han, Watson, Moss, and Bottoms (in press) tested a modified version of this model and found that students’ noncognitive factors, such as grit and academic mindsets in particular (i.e., academic self-efficacy, motivation, and sense of belonging), predict academic success in college. Consequently, we narrowed the scope of that research and examined how students’ academic mindsets are associated with their college performance and first-to-second-year retention.

THeoreTiCal FraMework

Academic mindsets are psychological and social attitudes or beliefs that an individual holds toward academic work. Previous research in academic mindsets is distributed extensively in areas such as achievement goal (Dweck, 1986), social learning theory (Bandura, 1986), attribution theory (Weiner, 1979), expectancy-value theory (Eccles et al., 1983), locus of control (Rotter, 1954), and sense of community (Sarason, 1974). Most studies about academic mindsets have focused on

the relationships between students’ observed performance and their measurements of psychological factors. Students with a positive academic mindset are likely to persist at their schoolwork, which may lead to better performance. Among the possible different constructs of academic mindsets, in this study we focused in depth on perceived self-efficacy, motivation, and sense of belonging.

Perceived Academic Self-EfficacySince its introduction by Bandura in 1977, the concept of self-efficacy has been a major topic of theoretical and empirical research. Self-efficacy is defined as a judgment of “how well one can execute courses of action required to deal with the prospective situation” (Bandura, 1982, p. 122). Academic self-efficacy, which denotes confidence in performing academic tasks, should be relevant for understanding individual academic mindsets because it leads to specific behaviors that can encourage or discourage academic performance and attainment. Students with high self-efficacy tend to view problems as challenges to be mastered instead of threats, be committed to the academic goals they set, have a task-diagnostic orientation instead of self-diagnostic, and increase their efforts in case of failure to achieve goals they have (Torres & Solberg, 2001; Zajacova, Lynch, & Espenshade, 2005). In particular, Locke, Frederick, Lee, and Bobko (1984) found that college students’ self-efficacy was strongly associated with both past and future performance in a nonacademic task. Also, researchers studying self-efficacy have reported that it is strongly related to actual academic performance such as mathematics tasks (Marsh, Hau, Artelt, Baumert, & Peschar, 2006). Some researchers have reported that self-efficacy affects more distal outcomes, such as higher persistence intentions (Multon, Brown, & Lent, 1991), selection of a major field of

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Academic Mindsets and College Success

study in college, or career choice (Betz & Hackett, 1981, 1983). When Bandura (1997) theorized the concept, learning persistence was considered one of three primary behavioral outcomes influenced by self-efficacy beliefs. That is, when students perceive a difficult college task as a challenges, stronger college self-efficacy expectations lower academic stress and maintain psychological and emotional health (Solberg, Gusavac, Hamann, & Felch, 1998). A decade ago, Robbins et al. (2004) conducted a meta-analysis of the relationships between self-efficacy beliefs and academic performance and retention. The results indicated moderate relationships between retention and academic goals, self-efficacy, and academically related skills. Also, Bean and Eaton (2001) argued that positive self-efficacy beliefs play a significant role in academic performance. However, the role of academic self-efficacy in predicting college success is less clear. Kahn and Nauta (2001) found that past academic performance (high school GPA and ACT score) and first-semester GPA significantly predicted first-to-second-year retention in college, but they did not find any associations with self-efficacy. The inconsistent relationship between academic self-efficacy and retention in college should be tested across diverse ethnicities and institutions.

academic MotivationMotivation can be defined as an intentional system that decides the direction of an indi-vidual’s behavior, energy, and performance maintenance (Han, 2016). Research on motivation and how it relates to achievement has a long and distinguished history. In particular, Pintrich and De Groot (1990), in their theoretical framework for con-cep tu alizing student motivation, proposed three moti vational components: expectancy, intrinsic value, and affect. The relations among the three general types of motivational

components have been examined, and the use of self-regulatory strategies in college has been explored. Researchers have typically focused on describing how different motivational components help to promote, sustain, or facilitate learning (Pintrich, 1999). Among them, the value component of academic motivation involves students’ goals for the task and their beliefs about the importance and interest of the task. Value can be variously defined as the importance of doing well on a task (attainment value), gaining enjoyment by doing a task (intrinsic value), or serving a useful purpose or meeting an end goal that is important by completing a task (utility value; Eccles et al., 1983). Although this component has been conceptualized in different ways, the motivational perspective essentially concerns students’ reasons for doing a task and affects their persistence in learning. That is, even if a student is confronted by harsh environments or negative outcomes, a positive intrinsic value makes the learner continue to engage in learning-related activities. Considerable evidence is accumulating to suggest that motivated learners have a greater chance to achieve positive performance outcomes. Dweck (1986) reported that motivational processes influence a learner’s acquisition, transfer, and use of knowledge and skills. She described how particular individual goals make children pursue cognitive tasks and shape their reactions to success and failure. Also, Elliot (1999) distinguished approach from avoidance motivation and its different effects on learning outcomes. More recently, some researchers have reported that intrinsic motivation predicts effort and persistence both directly and indirectly. French, Immekus, and Oakes (2005) found that motivation was related to engineering students persisting within their major. Morrow and Ackermann (2012) indicated that students’ motivational beliefs are significantly associated with retention in



college regardless of their major. Some studies, however, have shown inconsistent results on retention in college. D. Allen (1999) found that for ethnic minority students there was a significant relationship between motivation and retention, though this relationship was not significant for nonminority students. Further, Robbins, Allen, Casillas, Peterson, and Le (2006) found that performance-based motivation factors (academic discipline) were more associated with performance outcome (first-year GPA), whereas aspiration-based motivation factors (e.g., commitment to college) were more associated with retention outcomes (second-year retention). In summary, academic motivation may affect students’ learning outcomes differently based on its measurement and/or targeted population.

Sense of BelongingFeeling a sense of belonging to a community is an essential human need (Baumeister & Leary, 1995). Sense of belonging is defined as a psychological sense of identification and affiliation with a community. Given the importance and definition of sense of belonging, researchers have been interested in its role in educational processes and outcomes. For instance, Kirk and Lewis (2015) found that social connection and academic adjustment were related to college retention. Diverse studies have reported that students’ sense of belonging has a strong impact on their academic performance (Battistich, Solomon, Kim, Watson, & Schaps, 1995; Furrer & Skinner, 2003; Ryan & Deci, 2000; Wentzel & Caldwell, 1997). Similarly, other researchers (Beil, Reisen, Zea, & Caplan, 2000; Milem & Berger, 1997) have presented evidence that commitment to the university and involvement in campus activities are strongly related to retention. Among them, Tinto’s (1993) model of individual student departure is likely the most widely discussed

and frequently explored in higher education fields. He theorized that students’ retention was in large measure dependent upon their integration within an institution’s existing academic and social structures. In particular, student experiences during the first year in college have a more powerful influence on retention than do the characteristics they bring to institution. Using the same logic, Rendón, Jalomo, and Nora (2000) argued that if a student withdraws from a college, it is due to the student’s failure to integrate successfully, not institutional shortcomings. Belonging, indeed, is one factor that schools can address to improve the lives of their students across a variety outcomes (Resnick et al., 1997). The subjective sense of belonging also surpassed the effect of a number of objective factors typically associated with being at risk, such as low GPA. Furrer and Skinner (2003) reported that students’ sense of relatedness is vital to their classroom engagement. In contrast, students who feel unconnected should find it harder to become constructively involved in academic activities and should more easily become bored, worried, and frustrated. A college-based study also showed similar positive results of sense of belonging (Freeman, Anderman, & Jensen, 2007). The researchers found associations between students’ sense of belonging and their academic self-efficacy, intrinsic motivation, and task value. Further, sense of belonging is also related to distal but important academic outcomes such as amount of effort and number of absences in college (Sánchez, Colón, & Esparza, 2005).

THiS STudy

We examined the relationships between first-year students’ academic mindsets and their outcomes using an urban college sample. Academic mindsets included academic self-

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efficacy (students’ beliefs in their academic ability), academic motivation (intrinsic value), and sense of belonging (overall belonging in their college). Past research on academic mindsets has suggested that each of these indicators was related to academic performance (e.g., Cohen & Garcia, 2008; Eccles et al., 1983; Torres & Solberg, 2001). However, few researchers have explored more than one indicator of academic mindset simultaneously. In using this simultaneous examination, we recognized that students may not be consistent in their reported levels across the indicators, meaning that they may not be uniformly high or uniformly low. With this study we aimed to examine variations among differing levels of mindset indicators to determine if there were different “profiles” of academic mindset. To assess first-year college students’ academic success, we used their first semester GPA and first-to-second-year retention. Their writing course grades and number of credits earned during the first year were also included to attain a more comprehensive indicator of success and because previous researchers found that those were additional indicators of students’ academic performance and success in college (Berkovitz & O’Quin, 2006; Lau, 2003). Also, most studies on mindsets have used an overall sample or a single minority group and have not looked for variation among different groups of students. With this study we also aimed to determine if there are differences in profiles of academic mindsets based on student characteristics, such as race/ethnicity. Finally, we focused on first-year college students because an extensive body of research has indicated that experiences in a student’s first year are particularly important to academic attainment and retention at an institution (Boudreau & Kromrey, 1994; Engberg & Mayhew, 2007; Noble, Flynn, Lee, & Hilton, 2007). To meet the above aims, we addressed

the following research questions. First, how many different profiles of academic mindsets can be identified? More specifically, using a clustering analysis approach, how many profiles can be extracted for individual academic mindsets? We hypothesized that a series of cluster analyses could identify different groups of first-year college students based on their different academic mindsets profiles, which will be discussed in more detail in the following section. Second, are there observed meaningful differences in college students’ success based on his or her individual academic mindsets profiles? After classifying the meaningful clusters for students’ academic mindsets, academic performance and first-to-second-year retention were compared across these groups. We anticipated that the first-year college students in different clusters would show different patterns of learning attainment and persistence. Finally, we explored if different groups of students had varying rates of membership in the cluster groups. More specifically, are there differences in cluster group membership based on race/ethnicity, first generation in college status, and gender? No hypotheses were stated due to the exploratory nature of these research questions.

MeTHodParticipantsA total of 1,400 college students (653 males, 747 females) from a large, public university located in a major US city participated in this study. Ages of the participants ranged from 16 to 33 years (M = 18.66, SD = 0.75), and the sample was ethnically diverse: 7% African American, 27% Asian American, 29% Latino/a, 27% White, and 10% other. The other category consisted of students who did not fall into one of the four other racial/ethnic groups, such as multiracial/ethnic, international, and Native American



students or students who did not indicate a race/ethnicity. The demographics of the participating students were similar to those of that entire entering cohort from which the study sample was drawn. Thirty-three percent of the participants were first-generation college students, defined as neither parent having completed a 4-year college degree. Most participants were either first-generation (21%) or second-generation (49%) immigrant students, with 67% indicating that their mother was not born in the United States and 68% indicating that their father was not born in the United States. All participants were enrolled in a required first-year writing class during the 2013 Fall semester, and their majors were diverse.

Measures Demographic Variables. Individual student infor mation included age in years when the stu dent started at the university, gender (1 = female, 0 = male), and ethnicity (1 = African American, 2 = Asian American, 3 = Latino/a, 4 = other, 5 = White). Demographic family information included college generation status (1 = first generation/neither parent completed college, 0 = non-first generation/one or both parents completed 4-year college). Academic Mindsets. The student surveys included items measuring perceived self-efficacy, sense of belonging, and academic moti-vation. Perceived self-efficacy was measured using Solberg, O’Brien, Villareal, Kennel, and Davis’s (1993) Course Efficacy subscale to assess participants’ self-efficacy in different domains of academic tasks (e.g., writing a course paper, understanding the textbook). This scale consists of seven items (α = .81) on a three-point Likert-type scale with response options ranging from 1 (not confident) to 3 (a lot confident). The five-item Overall Sense of Belonging subscale (Johnson et al., 2007), with a 4-point response scale (α = .84) ranging from

1 (never) to 4 (very often), was used to measure perceived sense of belonging to college (e.g., I feel like a member of the campus community). To assess participants’ academic motivation, Pintrich and De Groot’s (1990) Intrinsic Value subscale, with a 3-point Likert-type response scale (α = .80) ranging from 1 (not at all true of me) to 3 (very true of me) was used. The academic mindset constructs were previously tested for measurement invariance by ethnic group using structural equation modeling through Mplus 7.11, and the results demonstrated that the constructs are equivalent across the ethnic groups (Farruggia, Han, Moss, & Bottoms, in press). Student Success. The indicators of academic performance and success for this study included participants’ first-term grade point average (GPA) in college on a 4.0 scale, writing course letter grade (on a 4.0 scale), first-to-second-year retention (1 = yes, 0 = no), and number of first-year credits earned based on 120 credits required for graduation. The GPA reflected the semester in which the student completed the survey. Writing course letter grade was included as it reflected the only course common among all students in the cohort. Credits earned in the first year were included to indicate how much on track a student was to graduation.

ProcedureData were collected from two sources: a student survey and institutional data. All procedures were approved by the university’s Institutional Review Board. A survey was administered to participants at the end of the 2013 Fall semester during their required first-year writing classes. Instructors distributed the survey packets, which included a scannable answer sheet, the survey, and a consent form, to their students. The instructor read a statement to the students that told them to read the consent form completely and

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highlighted key aspects of protection such as confidentiality and the voluntary nature of the survey. Once consent forms were signed, students were asked to complete the survey. A total of 1,739 students out of 3,344 students enrolled in the courses completed the survey, with 136 students excluded due to unsigned consent forms. Additionally, 52 participants were deleted for the analyses because they were transfer students from another institution, and 151 students were removed because they were not in their first year of college. After exclusions, the final sample size was 1,400 students, reflecting 45% of the entering Fall 2013 class (3,104 students). Approximately one quarter of the writing class instructors opted not to allow their students to participate. All institutional data were obtained from the university’s data warehouse after officially completing enrollment the following academic year.

Overview of Data Analysis StrategiesFirst, descriptive analyses were conducted on study variables. Next, hierarchical cluster analysis with Ward’s method was performed, and then k-means cluster analysis (two-stage cluster analysis) was conducted with the cluster information found. The combination technique of using two methods (two-stage cluster analysis) has been recommended by recent theorists because it can produce better validity for data structures and fulfill criteria (e.g., Panitz, 2010). The data from 55 participants who did not complete one or more of the scales representing academic mindsets were deleted from the cluster analyses, as any missing values on academic mindsets might have led to an incorrect cluster assignment. After identifying a meaningful number of clusters, each individual in the dataset was assigned to a cluster given his or her scores across indicators. Then, multivariate analysis of variance (MANOVA) and a series of ANOVA

tests were conducted to find significant differences among the clusters in academic performance and retention. In addition, cross-tabulation analyses for exploratory purposes were performed to find any differences in distribution of clusters by students’ demo-graphic information including gender, race/ethnicity, and parents’ education level.

reSulTSDescriptive AnalysisMeans and standard deviations or percentage for each variable of academic mindsets and student success are presented in Table 1. Students, on average, reported moderate levels on all three indicators of academic mindset. For student success, students performed moderately on indicators of student success and retention, and they performed slightly better than the entire first-year cohort, including those who did not participate in the study (first-term GPA: participants = 2.85, entire cohort = 2.73) Also, the generally moderate positive correlation among the independent and dependent variables is shown in Table 2.

TaBle 1.Descriptive Statistics of Key Study

Variables (N = 1,400)

Variable M (SD) %

Academic Mindsets

Self-efficacy 2.54 (0.41) Sense of belonging 2.82 (0.67) academic motivation 2.50 (0.35)

Student Success

1st semester GPa 2.85 (0.84) writing course letter grade 3.32 (0.79) 1st year credits earned 25.65 (6.57) 1st to 2nd year retention 85



Cluster AnalysisDifferent cluster solutions were tested with com bi na tions of possible memberships after Ward’s hierarchical technique. Ward’s cluster-ing procedure suggests that four profiles could account for academic mindsets. To confirm whether the Ward procedure’s solution had well-balanced groups, a series of k-means cluster analyses was conducted for three-, four-, and five-cluster solutions. First, three- and four-cluster solutions were assessed and compared. The four-cluster solution was better than the three-cluster, because the three-cluster solution did not have a well-balanced sample size for each membership (the smallest cluster comprised 17%, whereas the largest one comprised 45% of the total sample). In the same manner,

four- and five-cluster solutions were tested and compared. The five-cluster solution was slightly better than the three-cluster in terms of sample size for each group (the smallest cluster comprised 10%, whereas the largest comprised 30% of the total sample); however, the analysis still showed large differences between the smallest and largest sample sizes. Based on Ward’s hierarchical technique and two-stage cluster analysis suggestion, and to retain reasonably large and equal sample sizes in each cluster (Alexander & Murphy, 1998; Bråten & Olaussen, 2005), a four-cluster solution appeared to be most appropriate for the data. Final cluster centroids and sample size for the constructs of academic mindsets in cluster membership are presented in Table 3.

TaBle 2.Correlations among academic Mindset and academic Performance (N = 1,400)

SEFF BEL MOT GPA CREDIT GRADEBel .28***MoT .32*** .27***GPa .26*** .08** .16***CrediT .21*** .09** .11*** .59***Grade .27*** .04 .13*** .60*** .40***reTeNT .09** .11*** .05 .26*** .33*** .17***

Note. SEFF = self-efficacy, BEL = sense of belonging, MoT = academic motivation, GPA = first semester GPA, CREDIT = number of credits earned during the first year, Grade = writing course letter grade, RETENT = first-to-second-year retention.

** p < .01. *** p < .001.

TaBle 3.Cluster Centroids of academic Mindsets (N = 1,345)

Academic Mindset Indicators

Cluster 1: ALL High

Cluster 2: BEL oriented

Cluster 3: ALL Low

Cluster 4: SEFF oriented

SeFF 0.66 –1.23 –0.88 0.37Bel 0.95 0.41 –0.82 –0.60MoT 0.64 –0.05 –1.36 0.02n (%N) 432 (32%) 198 (15%) 242 (18%) 473 (35%)

Note. SEFF = self-efficacy, Bel = sense of belonging, MoT = academic motivation.

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Cluster 1 (n = 432, 32%) was characterized as an all high profile because students in the group reported high levels of self-efficacy, sense of belonging, and academic motivation, all of which had centroids greater than .60. Cluster 2 (n = 198, 15%) consisted of students with a belonging-oriented profile, for which only the centroid for sense of belonging was positive and greater than .40. Cluster 3 (n = 242, 18%), labeled all low, consisted of students who adopted all low academic mindsets, all of which were less than –.80. Finally, cluster 4 (n = 473, 35%), characterized by students who reported high levels of self-efficacy, was named self-efficacy-oriented. Multiple discriminant function analysis, performed to validate the presumed academic mindset character of the clusters, showed that overall group membership was accurately predicted for 97% of all participants. The prediction accuracy for the all high group was 96%, for

belonging-oriented 96%, for all low 97%, and for self-efficacy-oriented 97%. To test for differences among the clusters a MANOVA was conducted with cluster group as the independent variable and students’ writing course letter grade, first semester GPA, and credits earned during the first year as the dependent variables. The analysis indicated a significant overall difference between clusters, F(9, 2602) = 8.43, p < .001, Wilks’s Λ = .93, ηp

2 = .23; follow-up ANOVAs showed that there were significant univariate effects for all the dependent variables (see Table 4). Using Scheffe’s post hoc tests for the first semester GPA, credits earned during the first year, and writing class grade, the all high and self-efficacy-oriented groups had significantly higher scores than did the belonging-oriented and all low groups. There were no significant differences between the all high and self-efficacy-oriented groups for

TaBle 4.Univariate Effects of Academic Mindset on Indicators of Student Success

(N = 1,345)

Dependent Variable df df Error F ηp2 Cluster M (SD)

GPa 3 1,341 21.31*** .46 all high 3.04 (0.80)a

Bel oriented 2.62 (0.79)b

all low 2.60 (0.80)b

SeFF oriented 2.93 (0.86)a

CrediT 3 1,285 10.46*** .24 all high 26.56 (5.99)a

Bel oriented 24.08 (6.78)b

all low 24.35 (7.05)b


Grade 3 792 10.27*** .37 all high 3.42 (0.70)a

Bel oriented 3.00 (0.91)c

all low 3.22 (0.85)b


Note. GPA = first semester GPA, CREDIT = number of credits earned during the first year, GRADE = writing course letter grade, BEL = sense of belonging, SEFF = self-efficacy. Means with different superscripts significantly differ according to Scheffe’s post hoc tests: a > b > c

*** p < .001.



all academic performance indicators. There were also no significant differences between the all low and belonging-oriented groups on academic performance indicators, with the exception of the writing course grade, for which the belonging-oriented group had lower performance than did the all low group. Finally, a chi-square test of independence was performed to examine the relationship between cluster membership and first-to-second-year retention. The relationship between these variables was significant, χ2(3, n = 1,345) = 22.31, p < .001. Students in the all high academic mindset group were more likely to return for their second year than were those in other groups (all high, 90.3%; belonging-oriented, 86.3%; self-efficacy-oriented, 83.2%; all low, 76.9%). The belonging-oriented group, however, was second highest for first-to-second-year retention followed by the self-efficacy-oriented group and then the all low group.

Cluster Membership DifferencesTo explore differences in clusters based on students’ demographic information (race/ethnicity, gender, and college generation status), a series of chi-square tests were per formed (Table 5). Cluster membership did vary by student race/ethnicity, χ2(12, n = 1,345) =

25.03, p < .05. An additional series of chi-square independence tests was conducted to determine where the differences occurred. To prevent a Type I error, an adjusted p value was used for each cluster membership set of analyses. In the case of the all high cluster, African American students (40%) were compared to Asian American students (26%). Because the difference was significant, Asian American students were compared with Latino/a students (32%). Results indicated that African American students (40%), χ2(1, n = 433) = 7.62, p < .01, and White students, χ2(1, n = 720) = 4.74, p < .05, were more likely to have adopted all high cluster attributes than were Asian American participants (26%); Latino/a students did not differ significantly from the other three groups. Latino/a (18%), χ2(1, n = 776) = 7.80, p < .01, and Asian American students (17%), χ2(1, n = 720) = 5.33, p < .05, were more likely to be members of the belonging-oriented cluster than were White students (11%); African American students were not significantly different from the other three groups. The only significant difference found in the all low cluster, χ2(1, n = 493) = 3.86, p < .05, was between African American (22%) and White students (15%). Finally, Asian American, χ2(1, n = 433) = 8.25, p < .01, and White students, χ2(1, n = 493) = 6.69, p < .01, were

TaBle 5.Cross-Tabulation for Profiles by Participants’ Race/Ethnicity (N = 1,345)

All High (%)

BEL oriented (%)

All Low (%)

SEFF Oriented (%)

african american (n = 103) 40a 16 22a 22b

asian american (n = 330) 26b 17a 20 37a

Latino/a (n = 386) 32 18a 18 32white (n = 390) 35a 11b 15b 39a

other (n = 136) 33 12 19 36

Notes. Race/ethnicity χ2 = 25.03 (p < .05). SEFF = self-efficacy; BEL = sense of belonging. Within cluster type, means with different superscripts significantly differ according to χ2 tests: a > b

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more likely to have self-efficacy-oriented cluster attributes than were African American students. Gender and college generation status were not associated with students’ academic mindsets cluster membership.

diSCuSSioN

The purpose of this study was to examine relationships between students’ profiles of academic mindsets (self-efficacy, sense of belonging, and academic motivation) and their success in college, as indicated by grades, credits earned, and first-to-second-year retention. It built upon a theoretical framework (Farrington et al., 2012) and a previous study by Farruggia, Han, Moss, & Bottoms (in press) that found that academic mindsets were associated with academic performance and retention in college. Using a person-centered approach, cluster analysis identified four profiles for students’ academic mindsets, which were: all high, belonging-oriented, all low, and self-efficacy-oriented. As reflected by their first-term GPA, writing class letter grade, and first-year credits earned, students in the all high and self-efficacy-oriented groups earned higher grades and credits in the first year than did those in the belonging-oriented and all low groups. It appears, then, that students’ self-efficacy may be particularly important for their academic performance in the first year of college because students in the self-efficacy-oriented group showed similar results to those in the all high group, despite relatively lower motivation and sense of belonging. Students’ academic performance in the first year of college is connected to their beliefs concerning their ability to be academically successful. As pointed out earlier, self-efficacious beliefs make students set and achieve their academic goals in college (Bandura, 1993). There are many implications from this finding. For instance,

institutions and their students would benefit from early identification of students with all low profiles and provide interventions to promote self-efficacious beliefs. These interventions could be formal interventions, such as the college students’ self-efficacy enhancement intervention described by Luzzo, Hasper, Albert, Bibby, and Martinelli (1999). This relatively brief intervention increased math/science self-efficacy among college students. In addition, the intervention could be less formal, as seen in the interactions between students and their advisors. If advisors have knowledge of when their advisees have low self-efficacy, they can communicate with them in a manner to help promote efficacious beliefs. A different pattern was found for first-to-second-year retention. Students in the all high group still showed the highest retention rate, but those in the belonging-oriented group were next highest; thus, college students’ first-to-second-year retention appears to be associated with their sense of belonging, highlighting the importance of social adjustment to and social relationships in college. As described earlier, in a small number of studies the relation between sense of belonging and other positive educational outcomes has been examined but not with consistent findings. For instance, Thomas and Galambos (2004) reported that sense of belonging predicts general satisfaction with college, and Kember, Lee, and Li (2001) concluded that college students’ sense of belonging can make a significant difference between completing academic programs and dropping out. Walton and Cohen (2007) found that fostering students’ sense of belonging may be an effective means of improving college performance, but they did not explore whether the sense of belonging was associated with students’ retention. In addition, students’ sense of belonging can be linked to or shown to interact with diverse sources or factors in aca-demic or nonacademic communities. Faculty



or staff, for instance, could be very important in building students’ sense of belonging in their college. Kim and Lundberg (2016) reported that student–faculty interactions are related to greater levels of sense of belonging and facilitate students’ cognitive skills development. Most higher education institutions already have summer programming, such as orientation and bridge programming, that could provide an avenue for this support before students start college by implementing formal interventions or through training faculty and staff to engage in belonging and promoting interactions. In addition, facilitating engagement in student organizations may also help increase students’ sense of belonging. Hurtado and Carter (1997) found that students who belonged to organizations (e.g., religious, social or community, student government, sororities or fraternities) had a significantly stronger sense of belonging, likely because increased peer-group interactions is associated with a greater sense of belonging (Hausmann, Schofield, & Woods, 2007). The differences by race/ethnicity in cluster membership are interesting. African American students, to some degree, were overrepresented in both the all high group and the all low group and were underrepresented in the self-efficacy-oriented group. It was expected that African American students would be underrepresented in the all high cluster to show consistency in the pattern among the clusters. Similar to many higher education institutions, at the institution where this research was conducted, African American students, along with Latino/a students, have lower student success rates, as indicated by grades, retention, and graduation rates (Farruggia, Bottoms, Leighton, Wellman, & Moss, 2016). This all high pattern may suggest that the association between the noncognitive factors and student success may not be as strong for African American students as other

factors, such as finances, which may moderate the association (Alon, 2007). The finding that Latino/a and Asian American students were overrepresented in the belonging-oriented group may be partially explained by the university being designated as both a Hispanic Serving Institution and an Asian American and Native American Pacific Islander Serving Institution. Previous research has demonstrated the importance of racial/ethnic identity to success in college, likely with sense of belonging being the mechanism by which the identity–success relationship is fostered. For instance, W. Allen (1992) found that African American students attending historically Black colleges and universities (HBCUs) were better integrated, academically and socially, than were their peers at traditionally White institutions (TWIs). African American students have reported more positive relationships with faculty at HBCUs than have African American students at TWIs, helping to account for their high GPAs (Fries-Britt & Turner, 2002). Feagin and Sikes (1995) asserted that having the perception of being a racial/ethnic minority student and experiencing discrimination can foster a climate that can lead to disengagement and feelings of alienation, marginalization, and isolation. It seems, then, that Latino and Asian American students appear to benefit in the same manner as African American students in these previous studies did by having this critical mass of similar students. As racial/ethnic identity plays a role in sense of belonging, it is likely that other types of identity, such as gender identity, may also be important. Pizmony-Levy and Kosciw (2016) asserted that school experiences and gender identity of lesbian, gay, bisexual, and transgender students could affect their perception of school climate and belonging; that is, understanding more about both students’ academic experiences as well as their

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varying identities is central to improving their academic success and degree attainment rates. As intervention efforts are developed, it is clear that efforts to acknowledge the varying identities of students are essential.

limitations and Suggestions for Future researchAlthough this study demonstrates the impor-tance of examining mindsets simultaneously and considering varying levels of the variables, a number of limitations related to future research should be noted. First, although we adopted self-efficacy, motivation, and sense of belonging as indicators of noncognitive factors in college students’ learning outcomes and retention, other noncognitive variables, such as help-seeking behaviors (Karabenick & Knapp, 1991), theory of intelligence (Blackwell, Trzesniewski, & Dweck, 2007), self-regulated learning, learning strategies (Weinstein, Acee, & Jung, 2011), and achievement goals (Senko & Harackiewicz, 2005), may also be important. Based on students’ perceptions of their intelligence, for instance, they have different approaches to learning gains, success/failure, and retention. Relatedly, a more expanded conceptualization of sense of belonging to include identity (e.g., racial/identity, gender) would be warranted. Future researchers should explore these findings while adding other noncognitive factors into the profiles. Second, for this study we gathered parti-ci pants’ responses during their writing class at the end of the first semester. The tim ing of the data collection might have affected stu dents’ perception toward their noncog-ni tive characteristics, as they may have already experienced academic success or fail-ure. Also, students experiencing academic problems may have already withdrawn before the end of the first semester. Therefore, in future investigations data collected either prematriculation or closer to the beginning

of the semester may account for these. In addition, although one strength of this study was the racial/ethnic diversity of the sample, it also poses a limitation in terms of generalizability. Particularly, African American students comprised only 7% of all participants in this study. It is also unclear if these finding could be replicated at colleges and universities that are more homogenous, such as HBCUs or TWIs. Along these lines, it would be interesting to test if sense of belonging increases retention for other minority students such as those identifying with underrepresented gender identities and sexual orientations. In future studies, researchers should examine sense of belonging among these groups as a possible mechanism to increase retention. Future research also might include broader contextual variables, such as classroom experiences, to better understand if college student success is impacted by individual and contextual variables as well as the interaction between the two. For example, quality of classroom instruction may interact with varying academic mindset profiles, making some students more or less resilient to lower quality teaching. Finally, in this study we took the analytic approach of creating clusters based on the entire sample of students. Given the differences in cluster membership by race/ethnicity, it is unclear if different clusters would have emerged had the analyses been conducted separately for each racial/ethnic group. In future studies, researchers should conduct the analyses within groups to test for varying patterns in cluster formation by racial/ethnic group.

CoNCluSioN

We explored college students’ academic mindsets through cluster analyses and associ-ations between their profiles and academic success. A person-centered approach for explor ing students’ noncognitive variables was



adopted because it allowed for the simultaneous examination of multiple indicators of academic mindsets, which confirmed that students were not consistent in their academic mindset profiles, such as all high or all low. Based on the findings, higher education institutions should focus on identifying and promoting

both academic self-efficacy and belonging to increase student success on their campuses.

Correspondence concerning this article should be addressed to Cheon-woo Han, College of Education and Psychology, The University of Texas at Tyler, Tyler, TX 75799; [email protected]

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