J Youth Adolescence DOI 10.1007/s10964-013-0085-4

EMPIRICAL RESEARCH

Academic Achievement in the High School Years: The Changing Role of School Engagement Paul A. Chase • Lacey J. Hilliard • G. John Geldhof Daniel J. A. Warren • Richard M. Lerner

•

Received: 12 September 2013 / Accepted: 20 December 2013 Ó Springer Science+Business Media New York 2014

Abstract School engagement is an important theoretical and practical cornerstone to the promotion of academic accomplishments. This article used a tripartite—behavioral, emotional, and cognitive—model of school engagement to assess the relationship between school engagement and academic success among high school students, and to determine whether a reciprocal relationship exists between these constructs. Data were derived from 710 youth (69 % female) who took part in Waves 6 through 8 (Grades 10 through 12) of the 4-H study of positive youth development. Longitudinal confirmatory factor analyses confirmed the invariance of the tripartite model of school engagement. Results of a structural equation model showed that the components of school engagement and academic achievement were mutually predictive and that these predictions varied from grade to grade. Future possibilities for evaluating the relationship between school engagement and academic achievement, as well as the implications for educational policy and practice, are discussed. Keywords Academic achievement
School engagement
Confirmatory factor analysis
Longitudinal structural equation modeling

P. A. Chase
L. J. Hilliard
D. J. A. Warren
R. M. Lerner (&) Eliot-Pearson Department of Child Development, Institute for Applied Research in Youth Development, Tufts University, 301 Lincoln Filene Building, Medford, MA 02155, USA e-mail: [email protected] P. A. Chase e-mail: [email protected] G. John Geldhof Department of Human Development and Family Sciences, Oregon State University, Corvallis, OR, USA

Introduction For many American youth, the transition from middle school to high school involves adapting to the outcomesoriented curriculum used in these latter school grades (Eccles and Midgley 1989). In addition to greater academic expectations, youth must simultaneously cope with a new school environment and social setting (Crosnoe et al. 2004). In U.S. public schools, most incoming high school students must also adjust to a noticeably larger student population compared with the number of students in middle school (McNeeley et al. 2002). Taken together, these characteristics make the transition to high school challenging for even the most well-prepared and resilient student. Many high school students are able to meet these challenges successfully, however others fail to succeed academically. Students who struggle academically are more likely to experience unemployment, substance use, and delinquency as adults (Chavez et al. 1994; Henry et al. 2012). These outcomes may occur because students with poor academic outcomes have fewer opportunities for traditional career success and therefore become less invested in society, leading to the potential for a hefty burden on themselves, their families, and their communities at large (e.g., Balfanz et al. 2010). Therefore, it is important to examine the potential determinants of students’ academic success or failure. Theoretical Framework A student’s academic success is the product of many factors, both individual and contextual (Li et al. 2010). Students do not exist in a vacuum, and thus cannot succeed academically without contextual supports such as strong

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families, neighborhoods, and schools (Woolley and Grogan-Kaylor 2006). The positive youth development (PYD) perspective provides a theoretical framework in support of this premise. PYD is centered on the notion that, in order to thrive, youth must capitalize on ecological assets. Concurrently, adults who hope to serve youth, such as parents, teachers, and mentors, must build on the individual strengths of youth. With such contextual support, youth acquire the knowledge and skill they need to become healthy and productive adults (Lerner et al. 2005). Several relevant strengths that youth must develop to be successful during adolescence include self evaluations, long-term planning, use of effective learning strategies, and prioritizing goals (Wigfield 1994). These skills all contribute to the meaningful engagement of students in their own learning and in attaining their academic aspirations (Wigfield and Cambria 2010). Through practicing these skills, students can become cognitively, emotionally, and behaviorally active participants in their own learning, which positively predicts academic achievement (Zimmerman and Schunk 2001). Youth cannot achieve academic success on their own, however. Previous efforts aimed at enhancing academic achievement have focused on capitalizing on adolescents’ positive social relationships in the school context (Greenberg et al. 2003). Accordingly, this article uses a PYD framework to examine how students relate to their academic context, and how this person-context relation impacts academic achievement among high school students. However, the focus here is not only on social relationships but, more broadly, on the construct of school engagement. School engagement (i.e., the degree to which students are involved in and committed to the academic and social activities in school) plays an influential role in preventing academic failure, promoting competence, and influencing a wide range of adolescent outcomes (Eccles and Roeser 2011).

Rusk (2011) found that emotional engagement in school is related to intrinsic motivation. Larson and Rusk argued that an internal desire to succeed in school is as important as behavioral engagement because it gives students the motivation and agency to regulate themselves toward their goals of academic achievement. However, the link between school engagement and academic achievement is neither simple nor universal. Early studies of school engagement indicated a positive association between emotional engagement and achievement (Fredricks et al. 2004; Voelkl 1997). More recent studies found that the strength of the association between engagement and academic achievement varied depending on how achievement was measured and the racial/ethnic and socio-economic composition of the participants in the study (Shernoff and Schmidt 2008). These studies found that school engagement was less predictive of GPA for African-American students than European American students in their sample. Similarly, school engagement was less predictive of academic success for students from low income communities than for students from high income communities. Although the above studies attempted to explain the unidirectional predictive relationship of school engagement on academic achievement, we must also consider the opposite direction of effects—the effect of academic achievement on school engagement. Such a bidirectional relationship would mean that the more students are engaged, the more that they learn in school. The more that students are successful in school, the more efficacious they feel, which, in turn, increases their engagement (HauserCram et al. 2006). Previous research using longitudinal panel models found reciprocal effects of academic achievement and academic self-concept (Marsh 1990; Marsh and O’Mara 2008). These reciprocal pathways will be tested in the current study to determine the direction of effect between three components of school engagement and academic achievement.

School Engagement and Academic Achievement The Tripartite Model of School Engagement Several researchers have documented the effects of school engagement on academic achievement. Balfanz and Byrnes (2006) investigated this relationship among Philadelphia public school students and found that aspects of behavioral engagement, such as student attendance, problem behavior, and indicators of effort, all independently and significantly predicted academic achievement. Previous research has made a strong case that that behaviors related to school engagement are critical in predicting academic outcomes (Dotterer and Lowe 2011; Kindermann 2007). For example, disengagement behaviors, such as being inattentive or disruptive in class at Grade 8, predicted lower grades in high school (Finn and Rock 1997). Similarly, Larson and

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Recent research has found that school engagement is a multi-dimensional construct (Christenson et al. 2012). Specifically, in this article, we focus on the tripartite— behavioral, emotional, and cognitive—components of school engagement and how engagement in the school context relates to academic achievement throughout high school. In order to elucidate the changing impact of a student’s relationship with his or her school, we draw from data collected in the 4-H study of PYD (Lerner et al. 2005). The 4-H study of PYD is a longitudinal investigation that began assessing 5th Grade students and their parents in 2002. Data collection for Grade 12 was completed in 2011,

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and the study involves about 7,000 youth and 3,500 of their parents from 42 states to date. Using this data set, Li et al. (2010) sought to understand the ways youth engage with their school contexts and how school engagement related to both immediate and later developmental outcomes. Li et al. first used structural equation modeling to examine the role of school engagement in predicting academic competence. Using data from 960 participants (45.6 % boys) from Waves 1 and 2 (Grades 5 and 6) of the 4-H study of PYD, Li et al. (2010) found that emotional and behavioral school engagement mediated the relationships between developmental assets and self-reported academic competence. In addition, Li completed a longitudinal confirmatory factor analysis which established the tripartite model of school engagement from Waves 5–7 (Grades 9–11) of the 4-H study of PYD (Li 2010). Using latent growth curve analysis, Li and Lerner also demonstrated that the mean levels of behavioral engagement, as well as change in cognitive school engagement across Grades 9 through 11 were positively associated with GPA at Grade 11 (Li and Lerner 2011). More recently, Li and Lerner used these same waves of the 4-H study of PYD to demonstrate the longitudinal relationships between the three distinct behavioral, emotional, and cognitive components of school engagement (Li and Lerner 2013). The tripartite measure of school engagement gained further support from a study by Scheidler (2012), which demonstrated that a tripartite measure of school engagement positively predicted standardized test scores among 8th Grade students. However, to date, these tripartite models have overwhelmingly emphasized the unidirectional effects of school engagement on academic achievement. The possibility of a reciprocal relationship between the tripartite model of school engagement and academic achievement has not been thoroughly explored and thus will be the focus of the current article.

Current Study The tripartite model of behavioral, emotional, and cognitive school engagement captures how students feel, behave, and think when interacting with their dynamic school environment. Li and Lerner’s (2011) tripartite model of school engagement represents the basis for the current study. We hypothesize that we will be able to replicate this model in a different, older sample of participants in the 4-H study of PYD in order to use longitudinal panel modeling procedures to parse out both the strength and direction of the relationship between behavioral, emotional, and cognitive school engagement and academic achievement among students through the end of high school.

Methods As noted above, the current study was part of a larger, ongoing longitudinal investigation of youth development in the United States that began in 2002. The 4-H study of PYD is a longitudinal investigation that began by assessing 5th grade youth in the United States and their parents. Full details of the 4-H study of PYD have been presented elsewhere (Lerner et al. 2005, 2009a, b, 2010, 2011; Theokas and Lerner 2006; Phelps et al. 2007). Therefore, we present here only the features of the methods relevant to the present research, which includes data from Waves 6 through 8. A discussion of the overall method of the 4-H study is provided in the introductory article of this special issue (Bowers et al. in press). Participants The larger 4-H study examined various domains of individual development and ecological resources during adolescence. The current study focused on variables related to school engagement and academic achievement. The sample for the 4-H study was recruited from schools and afterschool programs across the country in order to reflect the racial/ethnic and socioeconomic diversity of the United States. Schools and districts were contacted across a wide array of cities, and targeted schools were recruited based on professional contacts of the research team. Youth were also recruited from after-school programs and 4-H-affiliated clubs and organizations, as well as other out-of-school-time programs such as Boys and Girls Clubs. Once agreement from a school or youth organization was obtained, recruitment information, including a flyer describing the study and a consent form, was sent or given out to youth and their parents. This information was distributed through school and site staff (e.g., participating after-school programs) to youth and their parents. Participants for the current analyses came from three waves (Waves 6–8) of the larger study, principally representing youth in Grades 10 through 12, respectively (Table 1). In the overall sample for Grade 10, data were collected from a total of 2,357 youth in Grade 10, 1,324 youth in Grade 11, and 1,030 youth in Grade 12. For the longitudinal study, analyses were conducted with 710 adolescents (mean age at Wave 6 = 15.7 years, SD = 0.73 years) all of whom participated in at least two out of three waves of assessment. This requirement for wave participation was implemented to minimize the threat to validity that occurs as a result of wave non-response error (Lindner et al. 2001). Participants in the longitudinal sample were predominantly European American, with the following racial/ethnic backgrounds: 83.1 % were European American, 4.7 % were Latino/a, 4.7 % were African American, 2.8 % were Asian

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J Youth Adolescence Table 1 Sample characteristics for cross-sectional and longitudinal participants from Grades 10, 11, and 12 of the 4-H study of PYD

Characteristics

Grade 10

Grade 11

Grade 12

Longitudinal

2,357

1,324

1,030

710

Sex Female (%)

1,487 (63.1)

898 (67.9)

770 (75.2)

493 (69.4)

Age (M, SD)

15.72 (1.37)

16.80 (1.42)

17.65 (1.18)

15.7 (0.73)

Race/ethnicity European American (%)

1,790 (75.9)

1,054 (79.6)

770 (74.8)

587 (83.1)

African American (%)

131 (5.6)

68 (5.1)

53 (5.4)

33 (4.7)

Latino (%)

148 (6.3)

35 (2.6)

42 (4.3)

33 (4.7)

Asian (%)

37 (1.6)

29 (2.2)

29 (2.8)

20 (2.8)

Native American (%)

22 (0.9)

16 (1.2)

10 (1.1)

11 (1.6)

Multiethnic or multiracial (%)

52 (2.2)

17 (1.3)

11 (1.0)

18 (2.5)

Not available (%)

17 (0.7)

6 (0.5)

8 (0.8)

4 (0.6)

Urban (%)

202 (8.6)

281 (21.2)

172 (16.7)

134 (18.9)

Suburban (%) Rural (%)

291 (12.3) 712 (30.2)

324 (24.5) 707 (53.4)

283 (27.5) 549 (53.3)

156 (21.9) 412 (58.1)

Not available (%)

8 (1.1)

Urbanity

1,152 (48.9)

12 (0.9)

14 (1.1)

Avg. Household income

$69,363.90

$71,792.26

$70,508.08

$71,782.07

Avg. Household income for lower quartile—25 %)

$41,873.03

$43,590.84

$44,201.91

$43,518.37

Avg. Household income for upper quartile—75 %)

$92,659.28

$94,811.20

$95,019.23

$95,142.76

Mother’s education level (highest reported)

14.67 (2.26)

14.75 (2.30)

14.37 (2.29)

14.56 (2.31)

American, 2.5 % were multiracial or multiethnic, and 1.6 % were Native American. In addition, 1.2 % of youth either reported their race/ethnicity inconsistently or did not report it. Table 1 summarizes the demographic characteristics of the cross-sectional samples for Grades 10–12, as well as our longitudinal sample. While no significant differences were found in racial/ ethnic composition, sex, or socio-economic status between the cross-sectional and longitudinal samples, as shown in Table 1, both the longitudinal sample and the three crosssectional samples (Grades 10–12) were slightly overrepresented by female participants. The longitudinal sample was comprised of 69.4 % females. Rural participants (58.1 %) and European American participants (83.1 %) were also overrepresented in both the cross-sectional and the longitudinal sample, as compared with the U.S. population (U.S. Census Bureau 2010). In addition, participants in our sample came from families from more advantaged socioeconomic backgrounds in terms of family income ($71,782.07, compared with $50,054 in the overall U.S. population) and levels of maternal education (42.7 % bachelor’s degree or higher, compared with 28.6 % in the overall population) (U.S. Census Bureau 2010). The average GPA of students in the longitudinal sample was 3.53 (SD = 0.56), significantly higher than the overall sample 3.31 (SD = 0.75), (p \ .05). This higher GPA in the longitudinal sample may indicate that generalizations cannot

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be made as readily about lower academically performing students. Procedure In Waves 1 through 3 of the 4-H study, data collection from youth was conducted by trained study staff or, at more distant locations, hired assistants. A detailed protocol was used to ensure that data collection was administered uniformly and to ensure the return of all study materials. After Wave 1, youth who were absent on the day of the survey or were from schools or programs that did not allow on-site testing were contacted by e-mail, mail, or phone, and were asked to complete and return the survey to us. Beginning in Wave 5, youth completed the survey online unless they requested a paper survey. Parents completed online or paper surveys. Paper surveys were delivered to their homes by their children or through the mail (in the latter case, return postage was provided). Measures The items used in our analysis came from the Student Questionnaire of the 4-H study of PYD. To measure the tripartite school engagement constructs accurately, we used the fifteen-item Behavioral-Emotional-Cognitive School Engagement Scale (BEC-SES) (Li 2010).

J Youth Adolescence Table 2 The Behavioral-Emotional-Cognitive School Engagement Scale (BEC-SES) Item

Response format

Behavioral school engagement How often do you come to class unprepared (homework unfinished, forget to bring books or other materials, etc.)?*

0 = Never to 3 = Always

How often do you complete homework on time?

0 = Never to 3 = Always

How often do you skip classes without permission?*

0 = Never to 3 = Always

How often do you actively take part in group (class) discussions?

0 = Never to 3 = Always

How often do you work hard to do well in school?

0 = Never to 3 = Always

Emotional school engagement I feel part of my school

0 = Strongly disagree to 3 = Strongly agree

I care about the school I go to

0 = Strongly disagree to 3 = Strongly agree

I am happy to be at my school

0 = Strongly disagree to 3 = Strongly agree

I don’t find school fun and exciting*

0 = Strongly disagree to 3 = Strongly agree

I enjoy the classes I am taking

0 = Strongly disagree to 3 = Strongly agree

Cognitive school engagement I want to learn as much as I can at school

0 = Strongly disagree to 3 = Strongly agree

I think it is important to make good grades

0 = Strongly disagree to 3 = Strongly agree

I think the thing I learn at school are useful

0 = Strongly disagree to 3 = Strongly agree

I think a lot about how to do well in school

0 = Strongly disagree to 3 = Strongly agree

School is very important for later success

0 = Strongly disagree to 3 = Strongly agree

* Items are reverse-coded

Behavioral School Engagement The behavioral engagement subscale includes five items indicating shallow engagement (attendance) and items tapping deeper engagement (effort). More specifically, items regarding contribution to class discussion, preparation, skipping class, and finishing homework on time are included. The measure focuses on students’ voluntary behaviors within the school context to minimize possible confounding effects of non-student related variables. In addition, the items are balanced in terms of valence, even though indicators of active disengagement may not be highly related to the items that denote varying levels of active engagement. The response format ranged from 0 (never) to 3 (always). A sample item is: ‘‘How often do you actively take part in group (class) discussions?’’ Sources of these items include the National Educational Longitudinal Study (NELS: 1988) (Finn and Rock 1997; Finn and Voelkl 1993), Murray and Greenberg (2001), and Heaven et al. (2002). Emotional School Engagement The emotional engagement subscale included five items that assessed students’ sense of belonging and their affect toward school. Sense of belonging was measured by one item asking the extent to which students feel like a part of their schools. Happiness, excitement, and enjoyment were

used to measure three related yet distinct types of positive affect. A sample item is: ‘‘I care about the school I go to.’’ Items used to tap school connectedness, belonging, and bonding was modified to assess different aspects of the emotional relationships students have with their school and classes. The response format ranged from 0 (strongly disagree) to 3 (strongly agree). Items from the Psychological Sense of School Membership (PSSM, Goodenow 1993) and Add Health (McNeeley et al. 2002; Resnick et al. 1997) were the major sources of items. Cognitive School Engagement We measured cognitive engagement with five items designed to assess the extent to which students valued education and things learned at school, as well as their thoughts about learning. More specifically, goal orientation, identification with school, and perceptions of the link between students’ lives and school were included as core indicators of cognitive engagement. One item, ‘‘I want to learn as much as I can at school,’’ tapped participants’ goal orientation. Two items that asked whether school learning is meaningful and important were used to measure students’ perceptions and beliefs. Another item was used to evaluate the extent to which a student is an intentional learner. Given the scarcity of measures of cognitive engagement in the literature, the items were developed primarily based on the definitions, instead of measures, of

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J Youth Adolescence Fig. 1 Hypothesized model of school engagement and academic achievement in Grades 10–12 of the 4-H study. (Dotted lines are correlations, solids lines are regression coefficients)

cognitive engagement (Skinner et al. 2009). A sample item is ‘‘School is very important for later success.’’ The response format was also a four-point Likert scale, ranging from 0 (strongly disagree) to 3 (strongly agree). All scales of school engagement demonstrated adequate reliability, with Cronbach’s alphas across the scales ranging from .76 to .90. For the complete BEC-SES see Table 2. Academic Achievement We operationalized academic achievement in our model by self-reported grade point average (GPA), with the single item, ‘‘What grades do you earn in school?’’ The scale ranged from 1 ‘‘(0.5) = Mostly below Ds’’ to 8 ‘‘(4.0) = Mostly As’’. This single item was deemed a sufficient proxy for academic achievement, as GPA has been shown to be a highly reliable self-report measure among adolescents (Cassady 2001). Analysis Technique For our longitudinal confirmatory factor analyses, we analyzed the factor structure of behavioral, emotional, and cognitive school engagement at each of the three waves of the 4-H study of PYD within our longitudinal sample

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from Waves 6 through 8. Each school engagement construct was operationalized by five manifest indicators per wave of measurement. Academic Achievement was operationalized by a single item, self-reported GPA, at each of the three waves of measurement (see the theoretical model in Fig. 1). In order to address missing data in our analysis, we used full information maximum likelihood (FIML) procedures (see Wothke 2000 for a more detailed description of FIML). To verify the tripartite model of behavioral, emotional, and cognitive school engagement within the current sample, a confirmatory factor analysis was conducted using Mplus Version 6.11 (Muthe´n and Muthen 2010). We began by attempting to demonstrate that the loadings and intercepts of each indicator were equivalent across waves. Establishing this invariance gave us a basis to assume that, because the constructs were defined in the same operational manner for each group, the construct’s variance, correlations, and mean differences could be compared meaningfully and with quantitative accuracy (Little 1997, 2013). After we established a measurement model through longitudinal confirmatory factor analyses, we proceeded with a structural equation model to determine the most parsimonious relationship between the latent variables of behavioral, emotional, and cognitive school engagement and academic achievement, specifically the

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direction and magnitude of effects as measured by crosslag relationships across high school.

Results The purpose of this study was to determine how a tripartite model of behavioral, emotional, and cognitive school engagement related to academic achievement, operationalized by GPA, in later waves of the 4-H study of PYD (Grades 9 through 12). Originally, we intended to use four waves in our model, Waves 5–8 (Grades 9–12). This original measurement model failed to demonstrate sufficient model fit [v2(1746) = 4,966.02, p \ .001, RMSEA = .08, 90 % CI (.07, .08); CFI = .73, TLI = .71]. After evaluating the model, we determined that Wave 5 (Grade 9) failed to demonstrate strong model fit on its own [v2(95) = 217.51, p \ .001, RMSEA = .09, 90 % CI (.08, .09); CFI = .91, TLI = .91], and contributed to poor fit in our original Wave 5–8 longitudinal model. Therefore, Wave 5 was dropped from the remainder of our analyses, and our final measurement model consisted of data from participants from Waves 6 through 8. This measurement model demonstrated sufficient fit [v2(972) = 2,047.69, p \ .001, RMSEA = .04, 90 % CI (.04, .04); CFI = .91, TLI = .92]. Two further modifications were made to the model. First, constraints on residual variances were relaxed between two cognitive school engagement items (Items 2 and 5), allowing their residuals to covary across waves (please see Table 2 for item numbers). This modification was deemed justifiable, as these items were worded similarly. In addition, residual variances for two emotional school engagement items (Items 1 and 2) were allowed to covary in Wave 7 only. This was also deemed justifiable, given that Items 1 and 2 were worded similarly as well. After these adjustments to the model, the new measurement model demonstrated good model fit, and sufficient configural invariance in the longitudinal sample [v2(969) = 1,933.64, p \ .001, RMSEA = .04, 90 % CI (.04, .04); CFI = .93, TLI = .92]. We next tested the model for loading invariance. This test was conducted to determine whether items load onto their factors similarly across waves of measurement and was necessary to ensure that meaningful comparisons could be made across multiple waves of testing (Brown 2006). The loading (weak) invariance model had sufficient fit [v2(993) = 1,968.85, p \ .001, RMSEA = .04, 90 % CI (.04, .04); CFI = .93, TLI = .92], Therefore, the model met the requirements for loading (weak) invariance, with a change of CFI of \.01 (Cheung and Rensvold 2002). The next model tested for intercept (strong) invariance in the longitudinal model. Testing for intercept invariance is important to determine

the latent mean structure across waves of measurement. Once again, the model was found to be invariant across the 3 waves of data [v2(1,017) = 2,038.35, p \ .001, RMSEA = .04, 90 % CI (.04, .04); CFI = .93, TLI = .92]. The factorial invariance of the behavioral, emotional, and cognitive school engagement model had been demonstrated in previous studies of school engagement that used the 4-H data set (Li 2010). The findings in the current sample verify the previous findings of Li (2010), in that a tripartite model of school engagement remained strong in later high school years. However, there is a caveat that this model did not include Wave 5 (Grade 9) as originally designed due to poor fit in the longitudinal sample (see Tables 3, 4, 5 for correlations, intercepts and fit indices, respectively). Having established weak and strong invariance, we next tested a structural equation model based on our original configural measurement model (see Fig. 1). In this longitudinal structural equation model, we hypothesized direct associations from the behavioral, emotional, and cognitive school engagement constructs in Grades 10 and 11 to the single-item construct of academic achievement (self-reported GPA) in Grades 11 and 12, controlling for the constructs in the prior grades. To saturate the latent correlation matrix fully, we additionally estimated correlations between all other pairs of latent constructs within waves. In order to maximize model parsimony, non-significant latent regression paths were removed one at a time, until a significant change in Chi square values was obtained, and only significant latent regression paths remained in the final model. Our final model after pruning nonsignificant paths demonstrated adequate fit [v2(981) = 1,946.79, p \ .001, RMSEA = .04, 90 % CI (.04, .04); CFI = .93, TLI = .92] (see Fig. 2). The final model showed significant relationships among aspects of Grade 10 through 12 behavioral, emotional, and cognitive school engagement and Grade 10 through 12 GPA, as hypothesized. However, the nature of the relationship was more complex than our theoretical model. As seen in Fig. 2, the final model included significant regression coefficients from Grade 10 Behavioral School Engagement to Grade 12 GPA, as well as Grade 10 Emotional School Engagement to Grade 11 GPA. Grade 10 GPA significantly predicted Grade 11 Cognitive School Engagement, as well as all three aspects of Grade 12 School Engagement. The two strongest relationships were found between Grade 10 Behavioral School Engagement and Grade 12 GPA (b = .19) and Grade 10 GPA on Grade 12 Behavioral School Engagement (b = .21; see Fig. 2 for additional regression coefficients). In addition, strong auto-regressive relationships were found within all latent constructs across waves. All reported regressions were found to be significant, at p \ .05.

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J Youth Adolescence Table 3 Latent correlations of variables included in model testing

BSE = Behavioral school engagement. ESE = Emotional school engagement. CSE = Cognitive school engagement. GRAD = Selfreported GPA * p \ .05

1

2

3

5

6

7

8

9

10

11

G10BSE

1.00

G10ESE

0.47*

1.00

G10CSE

0.68*

0.54*

1.00

G11BSE

0.76*

0.41*

0.56*

1.00

G11ESE

0.42*

0.68*

0.38*

0.47*

1.00

G11CSE

0.65*

0.44*

0.60*

0.72*

0.58*

1.00

G12BSE

0.67*

0.42*

0.56*

0.74*

0.42*

0.63*

1.00

G12ESE

0.45*

0.50*

0.31*

0.38*

0.63*

0.38*

0.52*

1.00

G12CSE

0.56*

0.34*

0.48*

0.52*

0.41*

0.52*

0.75*

0.67*

1.00

G10GRAD

0.68*

0.31*

0.40*

0.55*

0.26*

0.39*

0.52*

0.24*

0.28*

1.00

G11GRAD

0.58*

0.30*

0.32*

0.60*

0.31*

0.39*

0.55*

0.26*

0.28*

0.75*

1.00

G12GRAD

0.58*

0.27*

0.30*

0.57*

0.26*

0.35*

0.57*

0.27*

0.35*

0.65*

0.78*

Discussion School engagement plays an influential role in promoting academic competence and positive social relations with teachers and students (Eccles and Roeser 2011; Fredricks and Eccles 2006; Li and Lerner 2011). The influence of school engagement is both logical and intuitive. Adolescents who perceive that they are in a positive and supportive educational atmosphere may have a greater ability to capitalize on that support, leading to academic achievement (Klem and Connell 2004; Meehan et al. 2003). Although most students receive enough contextual support and show sufficient effort and ability to succeed in school, many young people become disengaged, decreasing their likelihood of academic success. Disengaged high school students who do not experience academic success are more likely to experience unemployment, substance use, and delinquency as adults (Chavez et al. 1994; Henry et al. 2012). Balfanz and Byrnes (2006) documented the effects of several indicators of school engagement on academic achievement among students in Philadelphia schools in which student attendance, behavior, and effort all independently and significantly predicted multiple measures of academic achievement. In order to understand and more widely generalize the relationship between school engagement and academic achievement, researchers have attempted to parse out the roles of various aspects of school engagement in predicting academic achievement (Li 2010; Li and Lerner 2013; Scheidler 2012). Despite such interest in the academic effects of school engagement, researchers have not measured whether the relationship between the tripartite model of school engagement (i.e., one that examines the unique contributions of behavioral, emotional, and cognitive components) and academic achievement is, indeed, bidirectional. The current study sought to determine the magnitude and direction of the relationships among students’

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4

12

1.00

self-reported GPA and behavioral, emotional, and cognitive school engagement across 3 years of high school. As hypothesized, bidirectional relationships were found between the three aspects of school engagement and GPA. behavioral school engagement at Grade 10 predicted GPA at Grade 12, and emotional school engagement at Grade 10 predicted GPA at Grade 11. GPA at Grade 10 positively predicted Cognitive School Engagement at Grade 11, as well as all three aspects of School Engagement at Grade 12. These findings support a bidirectional, reciprocal relationship between school engagement and academic achievement (Finn and Rock 1997; Li et al. 2010). This bidirectional relationship demonstrates that GPA is not only an outcome, but can also predict the degree to which students are engaged in high school. We found that the greatest predictor of GPA in the model was behavioral school engagement at Grade 10 on Grade 12 GPA. This finding is consistent with previous studies that demonstrated the importance of behavioral school engagement (Dotterer and Lowe 2011). Youth who display appropriate behaviors of successful students (such as doing homework and participating in class) earn higher GPAs. In addition, emotional engagement at Grade 10 significantly predicted students’ GPA at Grade 11, which supports previous studies related to emotional engagement (e.g., Larson and Rusk 2011). However, the regression coefficient for Grade 10 emotional engagement on Grade 11 GPA was not as strong as Grade 10 behavioral engagement on GPA at Grade 12. Previous studies supported this finding as well, such that when behavioral engagement is included in the model, emotional engagement’s relationship with GPA accounts for a smaller share of the variance (Li 2010). Significant latent correlations (shown in Table 3) between emotional and cognitive school engagement and GPA give support to an underlying relationship between these three latent constructs.

J Youth Adolescence Table 4 Means and variances of indicator variables

Table 4 continued

Mean

Variance

G10Grade

3.508

.025

G11Grade

3.539

.023

G12Grade

3.548

.024

G10BEI1

2.353

.026

G10BEI2

2.338

.031

G10BEI3

2.855

.017

G10BEI4

1.897

.036

G10BEI5

2.436

.031

G10EEI1

2.085

.030

G10EEI2

2.137

.030

G10EEI3 G10EEI4

2.083 1.818

.032 .036

G10EEI5

2.056

.029

G10CEI1

2.371

.026

G10CEI2

2.584

.023

G10CEI3

2.215

.028

G10CEI4

2.312

.029

G10CEI5

2.590

.024

G11BEI1

2.366

.025

G11BEI2

2.250

.032

G11BEI3

2.794

.021

G11BEI4

1.966

.035

G11BEI5

2.373

.031

G11EEI1

2.116

.031

G11EEI2

2.108

.032

G11EEI3

2.111

.031

G11EEI4 G11EEI5

1.744 2.051

.034 .028

G11CEI1

2.321

.027

G11CEI2

2.536

.026

G11CEI3

2.130

.030

G11CEI4

2.282

.029

G11CEI5

2.550

.025

G12BEI1

2.364

.030

G12BEI2

2.293

.034

G12BEI3

2.720

.027

G12BEI4

1.975

.038

G12BEI5

2.349

.035

G12EEI1

2.054

.036

G12EEI2

2.097

.035

G12EEI3

2.085

.035

G12EEI4 G12EEI5

1.814 2.100

.040 .031

G12CEI1

2.347

.031

G12CEI2

2.513

.030

G12CEI3

2.200

.033

G12CEI4

2.228

.035

G12CEI5

Mean

Variance

2.514

.029

BE = Behavioral school engagement. EE = Emotional school engagement. CE = Cognitive school engagement. Grade = Selfreported GPA. G10, G11, and G12 represent Grades 10 through 12 respectively

Nevertheless, behavioral school engagement emerged as the strongest predictor of GPA in the high school years. This may reinforce the notion that study skills and effort in school, key components of behavioral engagement, could contribute to academic success. It is important to note that GPA is not determined by mastery alone. In many schools, the effort that a student shows toward completing the required tasks, such as homework and participation, are included in a students’ GPA. Our findings show that, even if students think that school is important (demonstrating cognitive engagement), if they fail to behave in a way that allows them to do what is required of them, they tend not to succeed academically. Conversely, a student could have little understanding of how school may affect his or her future, but still work hard in school and get good grades anyway. The finding that GPA predicts all three aspects of school engagement suggests that students who thrive in school academically may be encouraged by their successes, which may influence all three components of engagement in the school context. A possible explanation for the lack of influence of cognitive school engagement on GPA is a ceiling effect, and overall stability of academic achievement in the high school years. Heckman and Masterov (2006) argued that, by the time a student has reached high school, his or her study habits and attitudes towards school are entrenched to such a degree that very little will alter that student’s academic achievement outcomes so late in their academic career. In the current study, this phenomenon of stable attitudes and performance may have manifested itself as well, in that students who demonstrated high levels of school engagement had already demonstrated high academic ability. For this reason, a change in academic success may not have been as readily predictable as a function of earlier school engagement in late high school students. Indeed, the longitudinal sample had an extremely high and consistent mean GPA across the 3 years of measurement (Grades 10–12). This consistency and potential ceiling effect may have diminished cognitive school engagement’s impact on academic achievement in the current study, due to the fact that the GPAs were already quite high for many participants.

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J Youth Adolescence Table 5 Fit indices for the confirmatory factor analyses and structural equation modeling Model

v2

Configural model before modifications

2,047.69

df

972

p

Dv2

Ddf

Dp

\.001

Configural model after modifications

1,933.64

969

\.001

Weak invariance model

1,968.85

993

\.001

35.21

24

\.001

104.71

48

\.001

Strong invariance model

2,038.35

1,017

\.001

Full path model

1,933.64

969

\.001

Final model

1,946.79

981

\.001

13.15

12

\.001

RMSEA

RMSEA 90 % CI

CFI

0.040

0.037–0.042

0.925

0.038

0.035–0.040

0.932

0.037

0.035–0.040

0.932

0.038

0.035–0.040

0.929

0.038

0.035–0.040

0.932

0.037

0.035–0.040

0.932

Fig. 2 Final model of Grades 10 through 12 school engagement and academic achievement (dotted lines are correlations, solids lines are regression coefficients). Correlational values were omitted for clarity. v2 test of model fit (df = 981) = 1,946.79. RMSEA = .037 (.035–.040). CFI = 0.932

To more fully understand the relationship between the three aspects of school engagement and academic achievement, future studies may consider additional sources of information regarding academic achievement to parse out potential social effects. Including standardized test scores such as college admissions assessments (e.g., the SAT) would give a more diverse measure of student achievement (Scheidler 2012). Although the SAT and other standardized tests have their own biases and limitations, such measures may show achievement in students who were not necessarily engaged in the practices of a school and yet have strong regard for and connection to learning (Cameron 1989). Furthermore, it may show that even though a student may not enjoy his or her current context, he or she is making efforts that would increase his or her future academic opportunities.

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The question still remains, however, about the importance of the freshman year (Wave 5) on engagement. Since these Wave 5 data were removed from the model because of poor longitudinal representativeness and misfit from the original longitudinal model, it is uncertain whether school engagement levels during the first high school year have the same, or potentially greater, implications than engagement during the 10th Grade. Exploring this possibility further, and highlighting the transition to high school, may allow for a better understanding of the relationship between the various aspects of engagement and academic success over the high school years. Future studies may also benefit from focusing on participants with lower academic achievement at early grades and evaluating their changes with regard to academic achievement and school engagement to better recognize patterns that may emerge in lower

J Youth Adolescence

achieving students. These youth with lower GPAs may have the most to gain from an engaging academic environment. An additional limitation is demonstrated by the demographic composition of the participants in our sample, which was significantly different from the U.S. population as a whole, most noticeably with regard to sex, rural–urban neighborhood, and SES, as noted in the description of participants. We must therefore temper our findings with the acknowledgement that our longitudinal sample may have unidentified differences in model fit and structure as compared with the overall sample in the 4-H study, as well as in the population. For example, our minimal urban sample within the current sample of 4-H study participants limited the generalizability of our findings. These demographic differences relating to the ethnic and socio economic composition of the present sample may also impact generalizability, as previous studies have demonstrated their relationship with academic achievement (Shernoff and Schmidt 2008). Finally, as noted earlier, the strength of the association between school engagement and GPA has been found to vary significantly depending on how engagement is measured, for instance, involving whether school engagement is self-reported (as in the current study) or teacher reported (Shernoff and Schmidt 2008). Future studies may consider a triangulation of measures from both teachers and students in order to evaluate these constructs in adolescence more effectively.

Conclusion The current study demonstrated that the tripartite model of school engagement and academic achievement are reciprocally linked, and school engagement positively predicts the GPA of high school students. In addition, GPA plays a role in subsequent behavioral, emotional, and cognitive school engagement. Regardless of the existing academic skills of the student, researchers must continue to acknowledge the importance of student engagement within the context of learning, and the relationship that students develop with their educational context, including the behavioral, emotional, and cognitive connections to their academic environment. As such, educational policy makers and school personnel should develop initiatives to promote enhanced school engagement, focused perhaps on the strongest predictor of GPA in our model (behavioral engagement), in order to promote academic achievement. Policies and programs may be aimed at ‘‘winning the hearts and minds’’ (the emotional and cognitive facets of school engagement) of high school students. Nevertheless, it may be that initiatives that promote intrinsic motives for behavioral

engagement, coupled perhaps, at least initially, with extrinsic incentives for such engagement, may be the best approach for capitalizing on the reciprocal relations between school engagement and academic achievement. Such efforts may serve to create a developmental cascade of positive, bidirectional relations between a young person whose actions involve engagement and whose levels of achievement reflect high competence in his or her educational context. Acknowledgments This research was supported in part by grants from the National 4-H Council, the Altria Corporation, the Thrive Foundation for Youth, and the John Templeton Foundation. Authors’ contributions Paul A. Chase participated in the study’s design and coordination, performed the statistical analyses, and drafted the manuscript; Lacey J. Hilliard participated in the coordination and editing of the manuscript; G. John Geldhof participated in the design and interpretation of the data analyses; Daniel J. A. Warren participated in the coordination and editing of the manuscript; Richard M. Lerner conceived of the study, participated in its design and coordination, and helped to draft the manuscript. All authors read and approved the final manuscript.

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Paul A. Chase is a doctoral student at the Institute for Applied Research in Youth Development at Tufts University. His research interests involve the study of positive youth development and intentional self regulation. Lacey J. Hilliard is a research assistant professor at the Institute for Applied Research in Youth Development at Tufts University. Her work integrates the study of positive youth development and gender roles. G. John Geldhof is an assistant professor in Human Development and Family Sciences at Oregon State University. His research emphasizes the integration of diverse theoretical approaches to selfregulation and examines how self-regulation develops across the life span. Daniel J. A. Warren is a doctoral student at the Institute for Applied Research in Youth Development at Tufts University. His research interests involve the study of positive youth development and educational programs. Richard M. Lerner is the Bergstrom Chair in Applied Developmental Science and the Director of the Institute for Applied Research in Youth Development at Tufts University. He received his Ph.D. in developmental psychology from the City University of New York. His work integrates the study of public policies and community-based programs with the promotion of positive youth development and youth contributions to civil society.

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