Social Emotional Learning and Academic Success 1
Social Emotional Learning as an Early Warning Indicator of Academic Success and Failure
Social Emotional Learning and Academic Success 2
ABSTRACT
This study evaluated the degree to which a range of social emotional learning skills – academic self-efficacy, academic motivation, social connections, importance of school, and managing psychological and emotional distress and academic stress – could be used as an indicator of future academic outcomes. Using a sample of 4,797 from a large urban school district, we found that high school students classified as performing in the lowest 25% of their grade reported lower social emotional skills than students classified in the top 25% of academic performers by the end of the 8th grade. Two variables, perceived importance of attending college and psychological and physical stress, accounted for nearly 26% of the variance in cumulative high school GPA after controlling for 9th-grade GPA. Finally, the results indicated that a combination of 5 social emotional learning subscales effectively discriminated between students making positive progress towards high school graduation and those identified as having dropped out or failed more than 14% of their courses.
Social Emotional Learning and Academic Success 3
Middle and high schools optimize youth development when educators personalize
educational and social emotional learning opportunities for the changing needs of youth
(Battistich, 2010; Deci & Ryan, 2002; Eccles & Roeser, 2010; Lapan, Gysbers, & Petroski,
2001). Social emotional learning is the ability to acquire and apply the knowledge and skills
necessary to comprehend, manage, and articulate social and emotional information that is
associated with social behaviors such as maintaining positive relationships, recognizing
emotions, and making responsible decisions (Crick & Dodge, 1994; Elias et al., 1997; Nowicki
& Mitchell, 1998). School-based efforts to promote and enhance social emotional learning skills
are built on an understanding that academic success and better school performance emerge in the
context of supportive relationships (Eisenberg, 2006; Guerra & Bradshaw, 2008; Masten &
Coatsworth, 1998; Weissberg & Greenberg, 1998). However, in response to an increasingly
depersonalized learning environment, many youth disengage from middle and high school,
evidenced by lower academic achievement, higher absenteeism, and increased behavior
disruptions, all of which too often culminate in leaving school (Alexander, Entwisle, & Kabbani,
2001; Balfanz, Bridgeland, Moore, & Fox, 2010; Eccles, Midgley, Buchanan, Wigfield, Reuman,
& Mac Iver, 1993; Rumberger, 1995).
In their longitudinal study of Philadelphia schools, Neild and her colleagues identified four
early-warning indicators of dropout risk (Neild, Balfanz, & Herzog, 2007). These indicators
included receiving a final grade of F in either Mathematics or English, attendance below 80%,
and evidence of disruptive behavior. Subsequent research in a large urban school district found
that creating an “on-time to graduate” indicator consisting of grades and credits accumulated was
a good predictor of who was likely to graduate with 93% of those identified in the ninth grade as
on track eventually graduating while 50% of those ninth graders identified as not on track
Social Emotional Learning and Academic Success 4
eventually dropped out of school (Carl, Richardson, Cheng, Kim, & Meyer, 2013). Carl et al.
(2013), however, identified two problems with the early warning indicator methodology. One
problem was that nearly 30% of the students who graduated received cumulative math grade
point averages below 1.0 and an additional 30% averaged 2.0 and below across their math
courses. Although these students did graduate from high school, they did not possess the college
and career readiness skills to enter and succeed in a postsecondary setting. The second issue was
that accounting for both credits and quality GPA as a “total quality credits” indicator, they
concluded that “predicting dropouts is more complex for some students, and may even be driven
by external or non-academic factors” (Carl et al., p. 21).
In a subsequent study, Neild and colleagues evaluated whether two nonacademic factors -
student’s self-reported academic and social engagement – contributed to models estimating
future dropout risk (Neild, Stoner-Eby, & Furstenberg, 2008). Although the results indicated
that academic, social, and teacher engagement were associated with dropout risk, a logistic
regression analysis indicated that once academic engagement was entered into the model, social
and teacher engagement were no longer significant predictors.
Creating early warning systems that help schools more effectively support all youth to stay
on track toward graduation and prepare youth who graduate college- and career-ready (Achieve,
2010) has important economic implications. For society, the numbers of youth and the estimated
6.7 million out-of-school nonworking young adults aged 16-24 are expected to reduce the tax
base directly by $1.56 trillion and cost society, in aggregate, $4.75 trillion (Belfield, Levin, &
Rosen, 2012). The economic stakes of high school success and successfully entering
postsecondary educational or vocational training as a means to increasing both personal earnings
and occupational opportunities are paramount (Neild & Boccanfuso, 2010). Through this lens,
Social Emotional Learning and Academic Success 5
states are beginning to frame high school success as a foundation for their future economic
outlook as retirement numbers far outnumber the future supply of high school graduates
(Sullivan, 2012).
The purpose of this study was to investigate whether assessing nonacademic factors such as
social emotional learning skills could also offer an early warning system strategy for identifying
students who are currently or in the future likely to become at risk. Understanding whether and
how social emotional learning skills predict future academic performance and dropout risk,
schools are in a better position to use assessment strategies to design personalized intervention
strategies. Furthermore, the aim of this study was to evaluate whether middle school students’
ratings on a range of social emotional learning areas could serve as an early warning system for
differentiating high and low academic performers in high school. The remainder of the literature
review provides a rationale for the social emotional learning constructs being used in the study
and evidence for their construct, concurrent, and predictive validity.
Social Emotional Learning Constructs
Social emotional learning is the ability to acquire and apply the knowledge and skills
necessary to comprehend, manage, and articulate social and emotional information, associated
with social behaviors such as maintaining positive relationships, recognizing emotions, and
making responsible decisions (Bierman, 2004; Crick & Dodge, 1994; Elias et al., 1997; Nowicki
& Mitchell, 1998)). A recent meta-analysis of over 200 teacher-led schools found that classroom
strategies designed to promote social emotional learning skills contributed to higher academic
outcomes, better connections to school, and positive behavior (Durlak, Weissberg, Dymnicki,
Taylor, & Schellinger, 2011). Specific social emotional learning skills that were found to affect
educational outcomes included self-efficacy, decision-making and problem-solving skills, and
Social Emotional Learning and Academic Success 6
stress and health management. The six social emotional learning skill areas in this study include
self-efficacy, motivation, social connections, perceived importance or utility of education,
psychological and emotional distress, and academic stress (Bandura, 1986; Cohen & Wills,
1985; Dohrenwend & Dohrenwend, 1974; Eccles & Wigfield, 2002; Egeland, Carlson, & Sroufe,
1993; Hobfoll, 1989; Ryan & Deci, 2002; Werner & Smith, 1992).
Academic self-efficacy refers to perceived competence in performing a range of academic
activities needed to be successful within a school setting (Solberg, O’Brien, Villarreal, Kennel,
& Davis, 1993; Solberg et al., 1998). According to Bandura (1986; 1997), self-efficacy refers to
perceived ability to successfully perform a task or behavior. As a construct, academic self-
efficacy has been consistently found to be associated with a range of academic outcomes
(Schunk & Pajares, 2009) and meta-analysis has established an average medium effect size
between academic self-efficacy and school outcomes (Multon, Brown, & Lent, 1991; Schunk &
Pajares, 2009).
Academic motivation was defined with respect to whether students are motivated to attend
school because it is perceived as a relevant, meaningful, and/or enjoyable learning experience -
what is referred to as “autonomous motivation” (Close & Solberg, 2008; Ryan & Deci, 2002,
2008). Autonomous motivation refers to engaging in an activity because of its perceived
meaningfulness and relevance. It has been well established that youth develop self-determination
(i.e., intrinsic motivation and stronger competence beliefs) when educators create a personalized,
caring, and relational classroom environment in which the youth receive choices and engage in
autonomy-supportive interactions (Deci, Schwartz, Sheinman, & Ryan, 1981).
Social connections were conceptualized as the perceived availability of three relational
connections – family, peers, and teachers. From diathesis-stress research, it has been well
Social Emotional Learning and Academic Success 7
established that high levels of stress are associated with increases in psychological and emotional
distress that is mediated and moderated by social support systems (Cohen & Wills, 1985;
Dohrenwend & Dohrenwend, 1974). In a recent study of anxiety among African American
youth, Lewis, Byrd, & Ollendick (2012) found that stress was associated with increased anxiety
and availability of social support was associated with lower anxiety.
Importance of school was used to indicate the degree to which students perceive their
current school and future college education as having strong utility for helping them achieve
their desired life and career goals (Eccles & Wigfield, 2002). Perceived importance of school is
conceptualized as a subjective task value that contributes to both expectations for school success
and achievement related actions and choices (Wigfield, Tonks, & Klauda, 2009). Research has
demonstrated that youth gain better course outcomes when they perceive the subject matter as
relevant and supportive of future life goals (Eccles, 2005; Harackiewicz, Durik, Barron,
Linnenbrink-Gracia, & Tauer, 2008; Hulleman, Godes, Hendricks, & Harackiewicz, 2010;
Hulleman & Harackiewicz, 2009). Perceived relevance and importance of the subject matter are
not only important in motivating youth to succeed in their coursework, but also are malleable to
classroom-based interventions. For example, Hulleman and Harackiewicz (2009) randomly
assigned high school students to receive a utility enhancing intervention whereby they would
explore how science courses are important to helping them achieve desired life goals. They
found that students with a history of low academic performance in science reported higher
perceived relevance and end of term grades than youth in either the control condition or who
entered the class with a better science academic history.
Psychological and emotional distress is conceptualized as an absence of positive affect or the
presence of unpleasant emotions, which affect level of functioning (Suldo, Gormley, DuPaul, &
Social Emotional Learning and Academic Success 8
Anderson-Butcher, 2014). Literature has highlighted the risk of coexistence among emotional,
psychological, and academic dimensions in which a student with problems in one domain is
more inclined to concurrently have difficulties in other domains such as academic performance.
In contrast, youth with positive academic outcomes and minimal psychological distress tend to
be better adjusted (Valdez, Lambert, & Ialongo, 2011). Furthermore, literature has demonstrated
that students with low stress were more likely to continue their education and had a higher GPA
than those that experienced higher levels of stress (Dornbusch, Mont-Reynaud, Ritter, Chen, &
Steinberg, 1991; Gillock & Reyes, 1999; Windle & Windle, 1996). A subsequent longitudinal
study, in which student mental health was examined by well-being and psychological distress
indicators, found that students with a combination of low well-being and elevated psychological
distress were most at risk for descending GPA (Suldo, Thalji, & Ferron, 2011).
Academic stress is conceptualized according to Hobfoll’s (1989) conservation of resources
model. This model posits that an activity is perceived as stressful by people who do not feel they
have the skills, emotional, or time resources needed to effectively manage a given activity.
Academic stress was operationalized in terms of the perceived difficulty youth reported having
experienced in completing a range of academic activities.
In previous research, these social emotional learning constructs were evaluated in relation to
one another and academic outcomes using structural equation modeling. Close and Solberg
(2008) demonstrated that students reporting stronger relational connections with teachers and
peers reported school as being more relevant, meaningful, and enjoyable. Perceived relevance
was associated with higher reported academic self-efficacy, which was subsequently associated
with better reported health management. The combination of academic motivation, academic
self-efficacy, and health management was associated with recording better academic outcomes
Social Emotional Learning and Academic Success 9
during their initial entry into the ninth grade. Gillis (2011) extended this research by showing
that academic self-efficacy mediates the relationship between motivation and academic stress
and that perceived importance of education was predicted by a combination of academic self-
efficacy and motivation. This study extends this previous research by examining whether and
how social emotional learning skills can serve as an early warning indicator for future academic
success. Specifically, this study assessed whether: (a) social emotional learning skills assessed at
the end of middle school can effectively predict future high school academic success and (b) a
subset of social emotional learning skills could serve as an early warning indicator for future
dropout risk. The potential significance of this study is that social emotional learning skills may
offer schools an early warning indicator of whether and why students may become at risk for
future school failure.
Method
Participants
A total of 4,797 students were surveyed during the summer prior to beginning the ninth
grade as part of a summer transition-to-high-school program in a large western urban school
district. The total sample represents three annual cohorts. A total of 1,830 students participated
in Cohort 1 beginning in 2007, 1,728 participated in Cohort 2 beginning in 2008, and 1,214
participated in Cohort 3 beginning in 2009. A total of 35% of the students were Latino, 11%
were African American, 10% White, 1% American Indian, 2% Asian or Pacific Islander, and the
remaining were either not identified or identified as more than one race or ethnicity. For gender,
51% were identified as female.
Measures
Social Emotional Learning and Academic Success 10
Academic self-efficacy. This measure is a 22-item scale that assesses the degree to which an
individual believes he or she can successfully perform a number of academic and school-related
tasks (Gillis & Sedivy, 2008). Responses range from 1 (not at all confident) to 5 (extremely
confident), with positive scores indicating greater belief in the ability to perform academic and
school-related tasks. Principal components analysis using a national sample of students (Gillis &
Sedivy, 2008) identified three subscales: (a) social, (b) classroom, and (c) test taking. Internal
consistency coefficients for the subscales and the total scale using Cronbach’s alpha were .79,
.83, .69, and .91, respectively.
Academic Motivation. This measure is a 15-item scale that assesses the reasons students
attend school (Close & Solberg, 2008). It was derived using self-determination theory (Deci &
Ryan, 1985), which delineates two dimensions of internal motivation: performing a task because
it is perceived as meaningful and performing a task because it is deemed enjoyable. Students
indicate the extent to which they agree with a series of statements using a scale ranging from 1
(strongly disagree) to 5 (strongly agree). Principal components (Gillis & Sedivy, 2008) and
confirmatory factor analysis (Solberg, Howard, Gresham, & Carter, 2012) using a national
sample of students identified two internal control subscales: (a) attending school because it is
deemed enjoyable (enjoy school), and (b) attending school because it is deemed meaningful
(meaningfulness of school). Solberg et al. (2012) report internal consistency was .64 (enjoy
school) and .77 (meaningfulness of school), and the total scale was .79.
Social Connections. This measure consists of 16 items that relate to the perceived
availability of support from teachers, family, and peers. The items were modeled after the Social
Provision Scale (Russell & Cutrona, 1984), which assesses perceived availability of family
support. Students evaluated the degree to which they perceived availability of support using a
Social Emotional Learning and Academic Success 11
scale that ranging from 1 (strongly disagree) to 5 (strongly agree). Principal components
analysis using a national sample of students (Gillis & Sedivy, 2008) identified three subscales:
(a) family support, (b) teacher connections, and (c) peer connections. The three subscales and the
scale composite score were found to have adequate internal consistency, with coefficients of .80,
.81, .76, respectively, and .84 for the total scale.
Importance of School. This measure consists of 10 items that ask about individuals’ beliefs
about the importance of education in achieving their life goals. Students rated the importance of
each item using a scale ranging from 1 (strongly disagree) to 5 (strongly agree). The items were
analyzed using principal components analysis (Gillis & Sedivy, 2008) and two subscales were
identified: (a) importance of school, and (b) importance of college. The two subscales and the
scale composite score were found to have adequate internal consistency: .84 (school), .90
(college), and .92 (total scale).
Psychological and emotional distress. This 23-item scale assesses how often an individual
experiences emotional-psychological and physical health–related concerns (Gillis & Sedivy,
2008). Individuals provide responses on this measure to a series of statements indicating how
often they have experienced each health-related concern during the past month, using a scale
ranging from 1 (never) to 5 (always). Principal components analysis using a national sample of
students (Gillis & Sedivy, 2008) identified five subscales: (a) agitation, (b) eating problems, (c)
feeling blue, (d) sleeping problems, and (e) physical problems. Each of the subscales and the
total composite scale demonstrated adequate reliability, with alpha coefficients of .86, .83, .85,
.86, .75, and .95, respectively.
Academic stress. This 22-item scale assesses how often an individual experiences
difficulties completing a variety of academic-related tasks (Gillis & Sedivy, 2008). On this
Social Emotional Learning and Academic Success 12
measure, students evaluate the stressfulness of a series of tasks by indicating the degree of
difficulty they experience, with scores ranging from 1 (not at all difficult) to 5 (extremely
difficult). Principal components analysis using a national sample of students (Gillis & Sedivy,
2008) identified three subscales: (a) academic, (b) social, and (c) financial. The three subscales
and the scale composite score were found to have adequate internal consistency, with
coefficients of .86, .86, .88, and .94, respectively.
Dependent Variables. Three indicators of academic progress in high school were employed
as dependent variables. Cumulative grade point average (GPA), measured on a scale of 1 to 5,
corresponded to traditional course grades in all subjects (1= F, 4 = A, 5 = Advanced Placement
A). Second, a composite index of high school performance, academic success, was created
based on early warning indicators by Balfanz et al. (2010), using district-provided data for
attendance, suspension reports, and cumulative grade point averages for students across each of
the three cohorts. Each indicator was standardized separately by cohort, and the average of the
non-missing standardized values was computed in order that all three cohorts could be combined.
Because the indicator for behavior reports is a negative value with higher numbers indicating
more incidents, the average for behavior reports was multiplied by -1 in order to create a score
that reflected better behavior. Once average standardized ratings were computed separately for
attendance, behavior reports, and grades, the average of these three composites was generated to
create a single indicator of high school performance, with higher z-scores representing more
success. Students from the 2007 cohort had standardized scores from 3 academic years
averaged, the 2008 cohort had 2 years of standardized scores averaged, and the 2009 cohort had
1 year of standardized scores averaged. Finally, the academic-success indicator was divided into
quartiles with the upper 25% of the distribution comprising the high-success group and students
Social Emotional Learning and Academic Success 13
falling in the lower 25% of the distribution comprising the low-success group. Dividing the high
school success indicator into quartiles resulted in a total of 835 students scoring below the 25th
percentile (low-success Group) and 1001 students scoring above the 75th percentile (high-
success group).
A dichotomous dependent variable, progress toward graduation, was generated using the
ratio of credits earned to credits attempted. Students were classified as not making progress
toward graduation if they had failed 14% or more of their courses or if the district had identified
them as having dropped out. A total of 4.1% of the sample was identified by the district as
having dropped out. The total number of students classified as not making progress towards
graduation was 2,096 or 45.6% of the total sample.
Missing Data. Approximately 8% of cases were missing values for one or more
dependent variables. Missing values were not imputed for dependent variables; cases with
missing data for a dependent variable were dropped from the analysis involving that dependent
variable. Missing items within social emotional subscales ranged from 0.8% to 3.7% of cases,
depending upon the subscale. In those cases, if two or more items were answered, the mean of
the answered items was substituted for missing items within the subscale. If fewer than two of
the items for a scale were complete, the case was dropped from analysis.
Results
Social Emotional Learning Skills Predicting Academic Success
Table 1 reports the means and correlations among the variables. In order to evaluate
whether a student’s reported measures on the Success Highways (Gillis & Sedivy, 2008) 18
social emotional learning subscales were associated with established measures of academic
success, a one-way multivariate analysis of variance was conducted using high or low academic
Social Emotional Learning and Academic Success 14
success as the independent variable and the 18 social emotional learning subscales as the
dependent variable. First, a significant main effect was found (Wilks’ Lambda = .835, F [18,
1817] = 19.88, p < .000, eta2 = .17). The univariate tests reported in Table 2 indicated that
students in the high-success Group achieved significantly higher social emotional learning skills
than students in the low-success Group for all 18 subscales. The results indicated, therefore, that
as an early warning indicator of future academic performance, each of the 18 social emotional
learning subscales was able to effectively differentiate between students classified in the high
and low academic-success groups.
The second analysis employed multiple regression and path analysis to determine the
percent of variance in cumulative GPA attributable to the measured social emotional variables.
Bivariate correlations among the independent variables (Table 1) were examined to identify
variables with correlations greater than r = .3 that would contribute to multicollinearity if used
together in a regression analysis. The data set was then randomly divided into two equal-sized
sets, A and B, with A designated the exploratory data set and B the confirmatory data set. In the
exploratory data set, two variables, importance of college, and psychological and emotional
stress, emerged as the highest social-emotional bivariate correlates of cumulative GPA (r = .24, r
= .18 respectively). The remaining independent variables were omitted to avoid multicollinearity.
In combination in a multiple-regression analysis, they explained 6.9% of the variance in
cumulative GPA (R = .263, R2 = .069, p < .001). Path analysis was used to determine the unique
variance in cumulative GPA associated with importance of college and psychological and
emotional stress, controlling for first-year GPA (Figure 1). The path coefficients were .000 for
stress, and .375 (p. < .001) for importance of college, the latter accounting for 14.1% of variance
in cumulative GPA not associated with Year 1 GPA or stress. The same analysis conducted with
Social Emotional Learning and Academic Success 15
the confirmatory data set yielded path coefficients shown in parentheses in Figure 1. Both
importance of college and stress were significant predictors of cumulative GPA after controlling
for first-year GPA, and together accounted for 25.9% of the variance in cumulative GPA (R =
.509, R2 = .259, p < .001).
Social Emotional Learning Skills Predicting Progress Toward Graduation
Discriminant analysis was used to determine whether a subset of social emotional
learning skills could effectively predict whether students would be likely to make successful
progression towards graduation or whether they would be become at risk for dropping out of
high school. As in the previous analysis, the sample was randomly divided in half with one half
designated as Sample A for exploratory purpose and the other as Sample B for confirmatory
cross-validation. As part of the exploratory phase, an examination of the correlation matrix was
conducted with Sample A to reduce the number of subscales and the multicollinearity among
them prior to conducting the discriminant analysis, but a decision was made to retain one
subscale from each of the six constructs in order to test the full theoretical model. The subscale
from each of the six social emotional learning skills with the highest correlation with the
dependent variable (progress towards graduation) was included in the discriminant analysis. A
confirmatory cross-validation analysis was then conducted by applying the discriminant equation
generated from Sample A with Sample B.
In the exploratory phase, the six subscales that were selected for entry into the
discriminant analysis included: importance of college, meaningful motivation to attend school,
classroom confidence, family connections, physical symptoms of distress (negative), and
academic stress (negative). The initial discriminant analysis with Sample A indicated that family
connections was negatively associated with progress towards graduation which is not consistent
Social Emotional Learning and Academic Success 16
with previous research and was therefore deemed to have resulted from multicollinearity. Family
connections was therefore dropped from the analysis and a five-predictor discriminant analysis
was conducted which yielded a small effect size (canonical correlation = .228 explaining 5.2% of
the variance in progress towards graduation) and a significant fit (Wilks’ Lambda = .948, χ2 =
90.753, p < .001), indicating that the model including these five variables was able to
significantly discriminate between the two groups. A positive cutoff score of 0.10 was selected
to yield a higher hit rate for failing students (balanced by a lower hit rate for students on track for
graduation).
The unstandardized discriminant function generated in the exploratory phase was then
used to compute a predicted score for each student in Subsample B:
Ŷ = .816 (imp of college) + .335 (meaningful motivation) + .205 (classroom conf) - .252
(physical symptoms) -.508 (academic stress) – 4.124
The same cutoff score of 0.10 was then used in a confirmatory analysis used to predict
membership in progress toward graduation and correctly predicted 60.5% of students not making
progress and 56.5% of students making progress. This provides strong evidence that the
prediction formula is stable, and not dependent on a particular sample of students (χ2 = 47.82, p
< .001).
Discussion
Early detection of students who may become at risk for performing poorly in high school,
who may be at risk for dropping out of school, or both, continues as a critical issue with recent
estimates that nearly 39 million youth aged between 16 and 24 years old are out of school and
not employed (Belfield, Levin & Rosen, 2012). This study evaluated the degree to which social
Social Emotional Learning and Academic Success 17
emotional learning skills could be used as an indicator of future academic outcomes in three
complementary ways. First, a composite of academic success was created using indicators
identified by Balfanz et al. (2010) related to attendance, behavior, and course performance. We
found that eighth-grade indicators of social emotional learning subsequently differentiated
between students in the top 25% and bottom 25% academically in high school. Second, path
analysis was employed to predict cumulative high school GPA by first separating the entire data
set into two equal-sized random samples, and using the first to conduct an exploratory analysis
based on observed correlations and the second as a confirmatory analysis involving the same
variables. In both analyses, perceived importance of college was a significant predictor of
cumulative GPA after controlling for GPA in the first year of high school. Third, a measure of
progress towards graduation was generated using a combination of the percentage of high school
courses passed and whether the district had designated the student had dropped out of school.
We found that a combination of five social emotional learning subscales effectively
discriminated between students making positive progress towards high school graduation and
those identified as having dropped out or failed more than 14% of their courses. These social
emotional learning subscales included importance of college, meaningful motivation, classroom
self-efficacy, physical symptoms, and academic stress.
Each of the social emotional learning skills selected for the study are malleable and
therefore one of the implications of this research is that response to intervention (Duffy, 2007)
strategies could use social emotional learning skills assessment to tailor the types of tiered
prevention efforts being offered to students. The underlying logic is that improving social
emotional skills will enhance academic performance by helping students become more self-
regulated and engaged learners (Zimmerman, 2011) who perceive the relevance of their
Social Emotional Learning and Academic Success 18
education for achieving future goals (Hulleman et al., 2010; Hulleman & Harackiewicz, 2009).
In particular, the study supports the importance of helping students to articulate future goals
including attending college. The study also supports a strategy of assessing the social emotional
learning of middle and high school students and offering tailored intervention strategies to
address areas identified as low. Such opportunities could be implemented in small-group
settings unless additional information indicates that one-on-one services are advisable.
Caution should be made in generalizing from the results of this correlational study. The
sample represents one school district, and, although the district did find that the program
participants matched were representative of the school population, replication with other school
districts is needed. Also, social emotional learning was assessed using self-report data and
therefore all limitations associated with traditional survey research are warranted. Future
research could also evaluate whether the same or a different pattern of social emotional learning
subscales are predictive of progress toward graduation for students from different racial and
ethnic backgrounds, for students from different income levels, and for students with disabilities.
Social Emotional Learning and Academic Success 19
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Table 1
Summary of Intercorrelations, Means, and Standard Deviations for Scores on Social Emotional
Learning Measures and Progress Towards On-Time Graduation
Measure 1 2 3 4 5 6 1. Importance of College ---- 2. Meaningful
Motivation .439** --- 3. Classroom
Confidence .490** .404** ---
4. Physical Symptoms -.123** -.071** -.180** --- 5. Academic Stress -.194** -.132** -.320** .474** --- 6. Progress towards
Graduation .157** .120** .155** -.102** .142** --- M 4.43 3.73 4.14 1.92 2.22 0.55 SD 0.70 0.64 0.59 0.83 0.79 0.50
Note. * p < .05, ** p< .01, *** p < .001
Social Emotional Learning and Academic Success 28
Table 2
Univariate Tests for Scores on Social Emotional Learning Measures for the Low and High Success Group
Dependent Variable
Success Groups
F Statistic Sig.
Eta Squared
Low Mean Std. Error
High Mean Std. Error
School Importance
Importance of School 4.448 0.02 4.648 0.019 52.18 0.00 0.03
Importance of College 4.259 0.024 4.608 0.022 117.79 0.00 0.06
Academic Self-Efficacy
Social 3.908 0.022 4.035 0.02 18.84 0.00 0.01
Classroom 4.021 0.02 4.274 0.018 89.13 0.00 0.05
Test-Taking 3.93 0.023 4.085 0.021 24.1 0.00 0.01
Social Connections
Family Support 4.071 0.024 4.349 0.022 70.83 0.00 0.04
Teacher Connections 3.664 0.025 3.885 0.023 41.41 0.00 0.02
Peer Connections 4.279 0.025 4.455 0.023 26.73 0.00 0.01
Academic Stress
Academic Stress 2.401 0.027 2.021 0.025 107.08 0.00 0.06
Social Stress 2.161 0.027 1.888 0.024 57.51 0.00 0.03
Financial Stress 1.884 0.029 1.557 0.026 71.19 0.00 0.04
Distress
Sleeping Problems 2.279 0.035 2.025 0.032 29.15 0.00 0.02
Feeling Blue 2.187 0.034 1.918 0.031 34.73 0.00 0.02
Agitation 2.283 0.029 1.838 0.027 124.82 0.00 0.06
Social Emotional Learning and Academic Success 29
Eating Problems 2.057 0.032 1.773 0.029 42.322 0.00 0.02
Physical Symptoms 2.053 0.028 1.77 0.026 54.23 0.00 0.03
Motivation
Enjoy School 3.467 0.026 3.691 0.024 40.42 0.00 0.02
Meaningfulness 3.581 0.021 3.855 0.02 88.41 0.00 0.05
Note. * p<0.05, **p<0.01, ***p<0.001
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