Achievement Goals and Engagement With Academic Work in Early High School: Does Seeking Help From Teachers Matter?

Abstract

The aim of this longitudinal study was to examine seeking help from teachers as a mechanism mediating the relationship between achievement goals adopted by students early in the school year and their degree of behavioral and cognitive engagement in academic work almost 2 years later. A sample of 456 French Canadian students (215 boys; 240 girls; one unspecified) in Grade 7 (61%) and Grade 8 (39%) participated in the study. Results of structural equation modeling showed that mastery goals (approach and avoidance) were indirect predictors of both behavioral and cognitive engagement through seeking help from teachers. Performance goals (avoidance, but not approach orientation) were associated with cognitive engagement through help-seeking behaviors. Overall, these results suggest that achievement goals are key drivers of changes in academic engagement in early high school and that their contribution is explained by seeking help from teachers. Practical implications, limitations, and future research directions are discussed.

Keywords

achievement goals behavioral engagement cognitive engagement seeking help high school

Being actively engaged in academic work is seen as an essential ingredient for success, adaptation, and resilience in school (Fredricks, 2015; Skinner & Pitzer, 2012). Research rooted in achievement goal theory (Dweck, 1986; Elliot, 2005; Senko, 2016) suggests that student engagement in academic work conforms to the goals they adopt when they first undertake their work (Bong, 2009; Elliot, McGregor, & Gable, 1999; Liem, Lau, & Nie, 2008; Vrugt & Oort, 2008; Wolters, 2004). Despite these studies, questions remain concerning the exact nature of the relationship between achievement goals and engagement, particularly among students in early high school. To date, no studies have examined the role of achievement goals defined in the 2 × 2 model (Elliot & McGregor, 2001) in predicting changes in terms of student engagement in academic work. In addition, taking into account mediating mechanisms underlying the relationship between these motivational constructs has so far been ignored in the empirical literature.

The main objective of this prospective 2-year study was to address the abovementioned issues by examining the predictive association between achievement goals adopted by students in early high school and their degree of engagement in performing academic work. In this study, academic engagement refers specifically to behavioral and cognitive dimensions, which have been widely discussed in the literature (Anderman & Patrick, 2012; Fredricks, Blumenfeld, & Paris, 2004). Behavioral engagement refers to effort and perseverance in academic work (Birch & Ladd, 1997; Skinner, 2016), whereas cognitive engagement refers to using cognitive (memorization, rehearsal) and metacognitive (planning, self-regulation) learning strategies to understand and master academic work (Appleton, Christenson, & Furlong, 2008; Boekerts, 2016). The study’s second objective was to explore the mediating role of seeking help from teachers. Help seeking in the classroom shows promise as a mediating mechanism because it was associated with both achievement goals and engagement in academic work (Bong, 2009; Middleton & Midgley, 1997).

Achievement Goals

Achievement goal theory (Ames, 1992; Dweck, 1986; Elliot, 2005) is a social-cognitive theory of motivation that focuses on the aims that guide students in a learning task when personal competence is at stake and success is uncertain. Achievement goals are viewed as future-oriented, internal cognitive structures that provide a purpose for the engagement and are involved in the regulation of cognitions, emotions, and behaviors during task performance (Elliot & McGregor, 2001; Senko, 2016). Theorists and researchers postulate that these goals arise from a basic motivational state characterized by the tendency to approach or avoid emotionally valenced achievement situations (Elliot, 1999; Elliot & Church, 1997). This state has appetitive (motivated to approach a desired state) and aversive (motivated to avoid an undesired state) forms depending on whether students anticipate success or failure in performing a task (Elliot, 2006, 2008; Elliot & McGregor, 2001).

The integration of the approach-avoidance dichotomy caused a major conceptual revision that gradually led to the development of a taxonomy including four distinct achievement goals (the 2 × 2 framework): mastery approach (MAp), mastery avoidance (MAv), performance approach (PAp), and performance avoidance (MAv) (Elliot, 1999; Elliot & McGregor, 2001; Pintrich, 2000). MAp goals focus on the development and mastery of skills. Behavior in an achievement situation is regulated by anticipation of a desired result (learning, mastery), and the skill is judged on an absolute/intrapersonal standard (e.g., past achievements). PAp goals focus on demonstrating competence and superiority. Behavior is also guided by anticipation of a desired result (appearing intelligent, being the best), but assessment of the skill is based on a normative/interpersonal standard (e.g., other students). MAv goals involve avoiding not understanding a task or doing worse than before. These goals guide behavior toward the anticipation of an undesired result (e.g., not understanding everything), and competence is defined in absolute/intrapersonal terms. PAv goals focus on the need to avoid showing incompetence. Behavior is directed by the possibility of a negative result (appearing incompetent), and the skill is based on a normative/interpersonal criterion. The validity of these four types of goals has been supported by the literature with samples of adolescents (e.g., Bong, 2009; Hackel, Jones, Carbonneau, & Mueller, 2016). Significant positive correlations were also found between these goals (e.g., Hackel et al., 2016; Jowkar, Kojuri, Kohoulat, & Hayat, 2014; Michou, Vansteenkiste, Mouratidis, & Lens, 2014).

Achievement Goals and Academic Engagement: What We Know, Do Not Know, and Need to Know

The empirical relationships between achievement goals and different academic processes and consequences have been extensively studied over the past two decades (for reviews, see Anderman & Patrick, 2012; Maher & Zusho, 2009; Senko, 2016). Among these studies, some have particularly focused on behavioral and cognitive dimensions of student engagement in academic work. The following paragraphs briefly review the main results of these studies.

Behavioral Engagement

The picture that emerges of the link between approach goals (mastery and performance) and behavioral engagement is relatively consistent in all of the studies reviewed. For example, these goals have been positively associated with effort and perseverance in academic tasks and activities, to an equal degree for students in high school and in postsecondary education (Elliot et al., 1999; Liem, 2016; Miller, Greene, Montalvo, Ravindran, & Nichols, 1996; Wolters, 2004). The MAp goals were also positively associated with behavioral engagement in the classroom (Ruzek et al., 2016) and negatively with disengagement from tasks (Grant & Dweck, 2003; Liem et al., 2008). The scant research data on PAv goals show no link with behavioral engagement or a positive relationship with disengagement, and a negative relationship with effort and perseverance (Elliot et al., 1999; Gonida, Voulala, & Kiosseoglou, 2009; Liem, 2016; Liem et al., 2008; Wolters, 2004). Finally, a recent study found that MAv goals were negatively associated with effort and academic perseverance (Liem, 2016).

Cognitive Engagement

Studies that examined the links between achievement goals and student cognitive engagement are more numerous than those involving behavioral engagement. It has consistently been shown that approach goals (mastery and performance) promote better cognitive engagement in performing academic work among students of all ages (e.g., Bong, 2008, 2009; Elliot et al., 1999; Howell & Watson, 2007; King & McInerney, 2016; Liem et al., 2008; Miller et al., 1996; Vrugt & Oort, 2008; Wolters, 2004; Wolters, Yu, & Pintrich, 1996). For example, these goals have been positively associated with the use of cognitive and metacognitive strategies to regulate learning (Bong, 2008, 2009; Howell & Watson, 2007; Wolters, 2004; Wolters et al., 1996). Furthermore, MAp goals were negatively related to disorganization in exam preparation (Elliot et al., 1999; Howell & Watson, 2007). Regarding PAv and MAv goals, the nature of their relationships with cognitive engagement was more ambiguous. For example, PAv goals were positively associated with the use of cognitive and metacognitive strategies (Bong, 2008; Howell & Watson, 2007) and negatively with disorganization (Elliot et al., 1999). Conversely, they were also positively associated with disorganization (Howell & Watson, 2007) and negatively associated with the use of metacognitive strategies (Wolters, 2004). As for MAv goals, the two studies reviewed (Bong, 2009; Howell & Watson, 2007) reported positive associations with cognitive and metacognitive strategies, surface processing (memorization), and disorganization.

Although past studies have shown significant relationships between achievement goals and engagement in academic tasks or activities, many important gaps limit our knowledge of the associations among these constructs. First, most previous studies have used cross-sectional designs. Thus, it is not possible to know the extent to which goals predict changes with regard to the degree of student behavioral and cognitive engagement over more than 1 school year. This information is critical to make a better inference about the real involvement of these goals with a view to preventing student academic disengagement. Second, there is a substantial empirical gap regarding the contribution of MAv goals to explain engagement in academic work. Among the studies reviewed, only one was related to behavioral engagement (Liem, 2016) and only two with cognitive engagement (Bong, 2009; Howell & Watson, 2007). It is necessary to determine the predictive value of these goals in academic engagement. Third, with the exception of a few studies (e.g., Bong, 2009; Wolters, 2004; Wolters et al., 1996), the relationships between goals and academic engagement were mainly examined with samples of students in Grade 9 and above. It would thus be reasonable to examine whether the patterns in the reported results can be generalized to younger students (i.e., Grade 7 and Grade 8). Early high school offers an interesting window of opportunity to study these links, as research has documented a normative decline in academic engagement during this period (see Eccles & Roeser, 2011; Wang & Eccles, 2012). Clarifying the contribution of individual factors, such as achievement goals, could provide a better understanding of why students disengage during this period. Finally, the processes allowing us to understand why achievement goals would be associated with engagement in academic work have not been explored. This study examined the possibility that seeking help from teachers could act as one of the core mechanisms underlying this relationship.

Seeking Help From Teachers: A Promising Mediator

Help seeking in the classroom is considered a crucial self-regulatory approach in the repertoire of actions promoting learning (Butler, 2006; Karabenick & Newman, 2009; Newman, 1994, 2000; Ryan & Pintrich, 1998). When students have difficulty performing a complex task, help seeking in order to learn and understand the material (called instrumental help seeking) provides the opportunity to receive support to reengage and autonomously complete the task (Sideridis & Stamovlasis, 2016). Many students, however, choose not to seek help when they need explanations (Butler, 2008; Karabenick, 2004, 2006; Newman, 1994), thus compromising their learning and performance in academic work (Urdan, Ryan, Anderman, & Gheen, 2002). Researchers (Karabenick, 2004; Kessels & Steinmayr, 2013; Newman, 2000; Ryan & Pintrich, 1997) have suggested that this deliberate decision may reflect the belief that help seeking inevitably has a high cost in terms of self-worth and social reputation because it may imply incompetence that may incur negative judgments from others.

Seeking help from teachers and classmates has been the subject of a considerable number of studies in relation to achievement goals. To date, the most robust results have involved MAp and PAv goals. While MAp goals have been associated positively with help seeking (Federici, Skaalvik, & Tangen, 2015; Kaplan, Lichtinger, & Gorodetsky, 2009; Karabenick, 2004; Roussel, Elliot, & Feltman, 2011) and negatively with avoiding help (Bong, 2008, 2009; Gonida, Karabenick, Makara, & Hatzikyriakou, 2014; He, Gou, & Chang, 2015; Karabenick, 2004; Middleton & Midgley, 1997; Ryan & Pintrich, 1997; Shih, 2007), inverse relationships have been reported for PAv goals (Bong, 2008, 2009; Federici et al., 2015; Gonida et al., 2014; Kaplan et al., 2009; Middleton & Midgley, 1997; Roussel et al., 2011; Shih, 2007). For their part, the results for PAp and MAv goals have been inconsistent. The PAp goals have been linked positively (Federici et al., 2015; Kaplan et al., 2009) and negatively (Bong, 2009) to help seeking, in addition to being linked positively (Bong, 2008, 2009; Gonida et al., 2014; Karabenick, 2004; Middleton & Midgley, 1997; Ryan & Pintrich, 1997) and negatively (He et al., 2015; Shih, 2007) to avoiding help. The MAv goals have been positively related to help seeking (Kaplan et al., 2009; Roussel et al., 2011) and to avoiding help (Karabenick, 2004).

In this study, help seeking in the classroom has been examined as a mediator in the relationship between achievement goals and engagement in academic work. We specifically focused our attention on seeking help from teachers. Research has shown that students who perceive their teacher as being available for help, caring, and supportive are more intrinsically motivated, engaged, and effective in academic tasks (e.g., Duchesne & Larose, 2007; Goodenow, 1993; Lam et al., 2014; Tucker et al., 2002). Based on the literature relating achievement goals to help seeking, it is plausible that the contribution of achievement goals to explaining effort, perseverance, and cognitive regulation in academic work occurs through seeking help from teachers. Thus, students whose achievement goals are oriented toward competence and success (MAp and PAp goals) could maintain and increase their engagement over time because they are more inclined to ask their teachers for help when they experience difficulties in their academic work. Support from teachers, characterized by caring, relevant information, advice, and constructive feedback, could boost students’ self-competence and engagement, particularly when the support is aligned with the students’ perspectives and their need for autonomy (e.g., providing opportunities for choice making); when it is directed toward efforts, progress, and skills development rather than abilities; when it is enacted through effective strategies for task achievement and problem solving; and when the emphasis is placed on individual rather than normative assessment standards (e.g., Dweck & Master, 2009; Newman, 2000; Wentzel, 2016). Conversely, students oriented toward avoidance of incompetence and fear of failure (MAv and PAv goals) may be less inclined to seek help from their teachers in order to get the support they need to overcome difficulties, which would gradually decrease their engagement in tasks.

The Present Study

The aim of this prospective longitudinal study was to estimate the contribution of the four goals of the 2 × 2 achievement goal model to explaining behavioral and cognitive engagement in academic work, after the initial levels of engagement have been controlled. Seeking help from teachers was examined as one of the possible mediating mechanisms. Based on achievement goal theory and the findings presented above, two major patterns of association were expected: one pattern showing that approach goals (mastery and performance) positively predict engagement in academic work through the tendency of students to seek help from their teachers; and the second pattern showing that avoidance goals (mastery and performance) negatively predict engagement via the tendency of students to avoid or limit help seeking. It is possible that other scenarios could emerge for the avoidance goals because data on MAv are scarce, and the results involving PAv are inconclusive. For example, the PAv could predict an increase in cognitive engagement over time (Bong, 2008); this increase could be explained by the tendency of students not to seek help from their teachers when they face difficulties. Structural equation modeling (SEM) was used to test these hypotheses in one model.

Students’ gender was also controlled in the model tested because differences have been reported in previous studies on achievement goals (e.g., Duchesne, Ratelle, & Feng, 2014, 2017; Middleton & Midgley, 1997), instrumental help seeking (e.g., Roussel et al., 2011), and engagement in academic work (e.g., Miller et al., 1996; Wolters et al., 1996). Generally, girls tend to have higher scores than boys for MAp goals, instrumental help seeking, effort, and cognitive engagement but lower scores for performance goals (approach and avoidance orientations).

Method

Participants and Procedure

The sample used in this study consisted of 456 French Canadian students (215 boys; 240 girls; one unspecified) in Grade 7 (61%) and Grade 8 (39%). By the end of the period covered by the study, they were in Grades 8 and 9, respectively. These students participated in a longitudinal study on the motivational processes involved in socioacademic adaptation to high school. They attended 25 high schools throughout the Québec Metropolitan Community (average number of students in these schools = 682). Three waves of data were available at the time of writing this article: early in the school year (September/October; Time 1 [T1]), end of the same school year (May/June; Time 2 [T2]), and end of the next school year (May/June; Time 3 [T3]). At T1, the average age of students was 13.19 years (SD = 0.59), and the majority (62%) lived with both biological parents. The median family income was between Can$70,000 and Can$79,999. By comparison, the middle-class household income in the province of Québec is Can$72,240 (Statistics Canada, 2015). Participation in the study required the written consent of the student and one parent.

Measures

Achievement goals at T1

Early in the school year, students completed the Achievement Goal Questionnaire (AGQ; Elliot & McGregor, 2001). This questionnaire consists of four subscales with three items each measuring four types of goals: MAp, PAp, MAv, and PAv goals. The MAp subscale taps the degree to which students focus on learning, understanding, and task mastery (e.g., “I desire to completely master the material presented”). The PAp subscale measures the extent to which students are trying to surpass their classmates (e.g., “It’s important for me to do better than other students”). The MAv subscale evaluates the degree to which students are concerned about the possibility of not learning or understanding all of the content that is presented (e.g., “I worry that I may not learn all that I possibly could”). The PAv subscale measures how much students seek to avoid performing poorly (e.g., “I just want to avoid doing poorly”). Items of the AGQ are rated on a 7-point Likert-type scale that ranged from 1 (not at all true of me) to 7 (very true of me). In the present study, Cronbach’s alphas were .79, .86, .87, and .71 for the MAp, PAp, MAv, and PAv goal subscales, respectively.

Seeking help from teachers at T2

At the end of the first school year, the students’ help-seeking behaviors in their relationships with teachers, in general, were assessed using the Seeking Help from Teacher subscale of the Test of Reactions and Adaptation in College (TRAC; Larose & Roy, 1995). Students had to rate, on a 7-point Likert-type scale ranging from 1 (never) to 7 (always), the extent to which they agreed with the five items of this subscale (e.g., “When I do not understand an idea, I avoid asking the teacher for clarification”—reverse coded). In this study, Cronbach’s alpha for this subscale was .82.

Behavioral and cognitive engagement in academic work at T1 and T3

Twenty-three items from Wolters’s (2004) questionnaire were used to evaluate the degree of behavioral and cognitive engagement in academic work reported by students at T1 (early in the school year – control variables) and T3 (end of the next school year – outcome variables). Students were asked about the extent to which each item applied to them using a 7-point Likert-type scale ranging from 1 (strongly disagree) to 7 (strongly agree). The behavioral dimension of engagement includes six items measuring the effort exerted and perseverance to complete academic work despite distractions, boredom, and difficulties. A sample item is “I always work as hard as I can to finish my course assignments.” The cognitive dimension of engagement includes 17 items assessing two types of learning strategies reported by students for completing academic work: cognitive strategies (memorization, rehearsal, and elaboration; eight items) and metacognitive strategies (planning, monitoring, and regulating; nine items). Samples of items include “To learn the material for my classes, I rehearse the important material until I know it” (cognitive) and “I start my assignments without really planning out what I want to get done (reverse coded)” (metacognitive). In this study, Cronbach’s alphas at T1 and T3 were .81/.88 for behavioral engagement, .85/.87 for cognitive strategies, and .86/.89 for metacognitive strategies.

Statistical Analyses

SEM was used for the main analyses using Version 7.11 of the Mplus statistical software program (Muthén & Muthén, 2015). The coefficients of the tested models were obtained using the maximum likelihood robust (MLR) method of estimation. The model fit was determined by the comparative fit index (CFI), the Tucker-Lewis index (TLI, also known as the nonnormed fit index), and the root mean square error of approximation (RMSEA). For the CFI and TLI, values above .90 are considered desirable, and an RMSEA value of .08 or lower is indicative of a “close fit” (Byrne, 2012; Hu & Bentler, 1999). The chi-square (χ²) test statistic was also reported, but it was not used to assess the suitability of the model due to its high sensitivity to the sample size (Byrne, 2012). Latent factors were assessed by their respective individual indicators (observed variables), and each factor was scaled by fixing one factor loading to 1.0. The indirect effects (or mediators) of achievement goals (T1) on engagement in academic work (T3) through seeking help from teachers (T2) were estimated with the bootstrap resampling procedure (Hayes, 2013; Marcoulides & Schumacker, 2013). Bootstrapping uses a number of samples, k, randomly drawn from the original sample. For each subsample, the bootstrap produces bias-corrected 95% confidence intervals (CIs) around the product of the nonstandardized path coefficient of the estimated indirect effect. Significant indirect effects are determined by CIs that do not contain zero (Hayes, 2013). In this study, the 95% CI was computed from 1,000 bootstrap resamples.

Missing Values

The proportion of missing data for each of the variables used in this study ranged from 0.21% (gender and cognitive strategies at T1) to 34.21% (metacognitive strategies at T3). Little’s (1988) test was performed on all of these variables to verify whether the missing data mechanism was missing completely at random (MCAR). The p value was statistically nonsignificant, χ²(237) = 224.18, p = .72, indicating that the data were MCAR. In addition, we examined whether students with complete data for these variables (56% of the sample) differed from those with incomplete data. Analyses were performed by gender, grade level (Grade 7 or 8), and achievement goals. Results showed that students in the subsample for whom data were complete were not different from students for whom data were incomplete in regard to gender, χ² = 1.10, df = 1, p = .29; grade level, χ² = 0.01, df = 1, p = .97; and achievement goals, Wilks’ λ(4,421) = .99, p = .53.

In this study, the missing values were treated in SEM analyses with the full information maximum likelihood (FIML; Graham, 2003; Muthén & Muthén, 2015) procedure. The FIML procedure is considered superior to conventional procedures (e.g., listwise deletion, mean substitution) because it produces unbiased estimates in the presence of MCAR data (Baraldi & Enders, 2010; Davey, Shanahan, & Schafer, 2001; Peugh & Enders, 2004).

Results

Preliminary Analyses

Data screening

Before the main analyses, data were screened for normality and multivariate outliers following the guidelines of Tabachnick and Fidell (2013). Kurtosis and skewness values for all the variables remained below the set cutoff point of ±2.00. A total of 19 multivariate outliers (4.17% of the sample) were identified through the Mahalanobis distance, whose influence was controlled for by the estimation method (MLR). Bivariate correlations, means, and standard deviations for all variables are presented in Table 1.

Table 1.

Bivariate Correlations and Descriptive Statistics for Variables of the Study.

	1	2	3	4	5	6	7	8	9	10	11	M (SD)
1. Mastery approach (T1)	—											5.84 (1.08)
2. Mastery avoidance (T1)	−.01	—										3.39 (1.57)
3. Performance approach (T1)	.23	.16	—									4.00 (1.76)
4. Performance avoidance (T1)	.25	.11	.20	—								5.55 (1.27)
5. Behavioral engagement (T1)	.51	−.24	.10	.18	—							5.45 (0.98)
6. Cognitive strategies (T1)	.42	−.15	.11	.15	.60	—						4.89 (1.12)
7. Metacognitive strategies (T1)	.46	−.12	.18	.17	.58	.69	—					4.73 (1.13)
8. Seeking help (T2)	.19	−.28	.08	−.03	.22	.21	.20	—				5.00 (1.45)
9. Behavioral engagement (T3)	.43	−.17	.05	.22	.52	.39	.41	.31	—			5.30 (1.07)
10. Cognitive strategies (T3)	.38	−.15	.09	.13	.37	.50	.46	.35	.63	—		4.75 (1.22)
11. Metacognitive strategies (T3)	.39	−.08	.11	.12	.34	.40	.48	.31	.59	.61	—	4.62 (1.21)

Note. All variables scored on a 7-point scale. Correlations greater than or equal to .10 are significant at p < .05 or less. T1 = Time 1 (beginning of school year), T2 = Time 2 (end of the same school year), T3 = Time 3 (end of the following school year).

Data nesting

We checked the need to control for variations between the schools attended by the participants. Thus, intraclass correlations (ICCs) and the effect of independence violations on standard error estimated (i.e., design effects or DEFF) were calculated for all the variables. Results of the ICC analysis showed coefficients ranging from .002 to .095. The DEFF values were examined using the following equation: 1 + p(n – 1), where p is the ICC and n is the average number of participants per cluster (McCoach & Adelson, 2010; Peugh, 2010). The calculation of DEFF produced values between 1.01 and 1.64. Although the ICC and DEFF values were relatively small, we controlled for the nested nature of the data in the main analyses by using the type = COMPLEX option in Mplus.

Factorial structure

The factorial structure of the tested model was evaluated with exploratory structural equation modeling (ESEM; Marsh et al., 2009; Marsh, Nagengast, & Morin, 2013). Unlike confirmatory factor analysis (CFA), this approach allows items to freely load on multiple latent constructs and to estimate all rotated cross-loadings (Marsh et al., 2009). For example, it is reasonable to assume that certain items that are intended to measure a given factor (e.g., cognitive strategies) could also load on another latent factor (e.g., metacognitive strategies).

The measurement model included 11 latent factors: achievement goals (four factors at T1), behavioral engagement (one factor at T1/one factor at T3), cognitive engagement (cognitive strategies: one factor at T1/one factor at T3; metacognitive strategies: one factor at T1/one factor at T3), and seeking help (one factor at T2). The results indicated that the model provided a poor fit to the data, χ²(1315, N = 456) = 2,378.57, p < .01; CFI = .90; TLI = .86; RMSEA = .04 (90% CI = [0.039, 0.045]). Although the vast majority of items loaded more strongly on the theoretically expected factor, some items appeared problematic: Two items for behavioral engagement (T1) and two items for metacognitive strategies (T3) had a low loading (<.15); one item for metacognitive strategies (T1) and four items for cognitive strategies (T3) loaded more strongly on another factor than their targeted factor. We reran the ESEM model by removing the nine problem items this time, in addition to the nine items corresponding to the other measurement time (e.g., the same two behavioral engagement items at T3 were removed). Fit indices for this second model indicated satisfactory relationships between latent factors and their indicators, χ²(550, N = 456) = 782.93, p < .01; CFI = .97; TLI = .95; RMSEA = .03 (90% CI = [0.025, 0.035]). Factor loadings for the latent factors varied from .34 to .87, with two items loading at .21 (behavioral engagement at T1) and .18 (metacognitive strategies at T1). At T3, these same two items loaded more strongly on their respective factors (i.e., .39 and .40). Because removing these items did not change the fit indices of the model, we kept them.

The Bayesian information criterion (BIC) was used as a comparison index to select the model that better fits the data (e.g., Haughton, Oud, & Jansen, 1997). The model with the lowest BIC is considered to be the preferred model. In this case, the model with some items removed clearly had a lower BIC value (61373.66) compared with the model with all the items (85383.84). The internal consistency coefficients of the constructs for which items had been removed (i.e., behavioral engagement, cognitive strategies, and metacognitive strategies) ranged from .79 to .88. Overall, these findings suggest that the latent factors can be adequately assessed by the items retained in the second model (see the appendix for items used). Special attention must be paid to the four items used for cognitive strategies, which mainly focus on memorization and rehearsal strategies.

Interfactor correlations obtained with ESEM analysis showed that the relationships between MAp, PAp, and MAv goals; help seeking; and the dimensions of engagement in academic work were in the hypothesized directions, with correlation coefficients varying from −.20 to .43 (see Table 2). However, contrary to expectation, positive but weak correlations were found between PAv goals and the engagement dimensions.

Table 2.

Correlations Among Latent Variables Using Exploratory Structural Equation Modeling (N = 456).

	1	2	3	4	5	6	7	8	9	10	11
1. Mastery approach (T1)	—
2. Mastery avoidance (T1)	−.01	—
3. Performance approach (T1)	.13	.13	—
4. Performance avoidance (T1)	.22	.05	.11	—
5. Behavioral engagement (T1)	.40	−.15	.04	.16	—
6. Cognitive strategies (T1)	.27	−.07	.07	.12	.37	—
7. Metacognitive strategies (T1)	.27	−.08	.07	.09	.33	.43	—
8. Seeking help (T2)	.14	−.20	.04	−.02	.06	.10	.07	—
9. Behavioral engagement (T3)	.30	−.09	.04	.15	.34	.19	.22	.16	—
10. Cognitive strategies (T3)	.24	−.07	.02	.09	.16	.25	.22	.19	.40	—
11. Metacognitive strategies (T3)	.23	−.01	.03	.02	.10	.15	.22	.12	.36	.32	—

Note. Correlations greater than or equal to .08 are significant at p < .05 or less. T1 = Time 1 (beginning of school year), T2 = Time 2 (end of the same school year), T3 = Time 3 (end of the following school year).

Gender differences

Regression analyses were performed to detect possible differences between boys and girls in all latent factors. Results showed that girls were more oriented toward MAp goals (β = .14, p < .01). They were also more behaviorally (T1: β = .21, p < .01; T3: β = .15, p = .011) and cognitively engaged in their academic work at T1 and T3 (cognitive strategies at T1: β = .30, p < .01; cognitive strategies at T3: β = .32, p < .01; metacognitive strategies at T1: β = .29, p < .01; metacognitive strategies at T3: β = .15, p = .007). These results provide support for testing the mediation model while controlling for gender.

Main Analyses

Direct effects in the model

We tested the proposed model without cross-loadings by controlling for gender and the temporal stability of behavioral and cognitive engagement (i.e., cognitive and metacognitive strategies). Model fit indices indicated that the model fit the data well, χ²(934, N = 456) = 1566.60, p < .01; CFI = .92; TLI = .91; RMSEA = .039 (90% CI = [0.035, 0.042]). Standardized regression coefficients and standard errors are presented in Table 3. As shown in Figure 1, the MAp goals at T1 positively predicted seeking help from teachers at T2 (β = .24, p < .01) and cognitive engagement in academic work at T3 (cognitive strategies: β = .20, p = .01; metacognitive strategies: β = .27, p < .01), over and beyond the contribution of gender and engagement variables at T1. MAv (β = −.35, p < .01) and PAv goals (β = −.13, p < .05) at T1 also contributed to seeking help from teachers at T2. Finally, seeking help from teachers at T2 predicted an increase in behavioral (β = .23, p < .01) and cognitive engagement (cognitive strategies: β = .27, p < .01; metacognitive strategies: β = .22, p < .01) in academic work at T3, above and beyond control variables. The proportion of variation accounted for by the model (R²) was .18 for help seeking, .45 for behavioral engagement, .45 for cognitive strategies, and .37 for metacognitive strategies.

Table 3.

Standardized Regression Coefficients (and Standard Errors) for the Mediation Model (N = 456).

Predictors	Mediator	Dependant variables
Predictors	Seeking help (T2)	Behavioral engagement (T3)	Cognitive strategies (T3)	Metacognitive strategies (T3)
Mastery approach (T1)	.24 (0.06)	.15 (0.11)	.20 (0.08)	.27 (0.09)
Mastery avoidance (T1)	−.35 (0.05)	.00 (0.07)	−.01 (0.07)	.05 (0.08)
Performance approach (T1)	.08 (0.05)	−.01 (0.06)	.00 (0.05)	−.04 (0.07)
Performance avoidance (T1)	−.13 (0.06)	.10 (0.06)	.07 (0.06)	−.04 (0.06)
Behavioral engagement (T1)	NE	.44 (0.10)	NE	NE
Cognitive strategies (T1)	NE	NE	.32 (0.07)	NE
Metacognitive strategies (T1)	NE	NE	NE	.35 (0.08)
Seeking help (T2)	—	.23 (0.06)	.27 (0.05)	.22 (0.07)
Gender^a	NE	.02 (0.05)	.19 (0.06)	.01 (0.06)

Note. Coefficients greater than or equal to .13 are significant at p < .05 or less. NE = not estimated in the model. T2 = Time 2 (end of the same school year), T3 = Time 3 (end of the following school year), T1 = Time 1 (beginning of school year).

Girls serve as the reference group.

The results also showed that most achievement goals were positively associated with each other (MAp-PAp: β = .24, p < .01; MAp-PAv: β = .35, p < .01; MAv-PAp: β = .18, p < .01; PAp-PAv: β = .16, p = .01). In addition, the path coefficients indicated that the three dimensions of academic engagement were moderately stable over time (behavioral engagement, T1-T3: β = .44, p < .01; cognitive strategies, T1-T3: β = .32, p < .01; metacognitive strategies, T1-T3: β = .35, p < .01). The cross-sectional association between cognitive and metacognitive strategies was high at T1 (β = .82, p < .01) and moderate at T3 (β = .48, p < .01). Behavioral engagement at T1 was positively linked to cognitive and metacognitive strategies at T1 (cognitive: β = .66, p < .01; metacognitive: β = .65, p < .01). These associations were also observed at T3 (behavioral-cognitive: β = .54, p < .01; behavioral-metacognitive: β = .54, p < .01). Moreover, behavioral engagement at T1 was positively related to MAp (β = .62, p < .01) and PAv goals (β = .25, p < .01) but negatively to MAv goals (β = –.24, p < .01). Similar patterns were found for cognitive and/or metacognitive strategies at T1 for MAp (cognitive: β = .52, p < .01; metacognitive: β = .57, p < .01), PAp (metacognitive: β = .18, p = .01), PAv (cognitive: β = .18, p < .01; metacognitive: β = .17, p = .01), and MAv (cognitive: β = –.18, p < .01; metacognitive: β = –.14, p < .05) goals. Finally, being a girl was positively associated with MAp goals (β = .14, p < .01), behavioral engagement at T1 (β = .23, p < .01), cognitive strategies at T1 and T3 (β = .30, p < .01, and β = .19, p < .01, respectively), and metacognitive strategies at T1 (β = .29, p < .01).

Indirect effects in the model

Results based on the bootstrap procedure showed eight indirect effects in the tested model. Mastery goals (approach and avoidance) were involved in six of these effects. Specifically, the MAp goals at T1 positively predicted seeking help from teachers at T2, which in turn contributed to increased levels of behavioral engagement (95% CI = [0.017, 0.11]), cognitive strategies (95% CI = [0.034, 0.168]), and metacognitive strategies (95% CI = [0.022, 0.15]) at T3, over and beyond the contribution of gender and behavioral and cognitive engagement at T1. Conversely, MAv goals at T1 negatively predicted seeking help from teachers at T2, which contributed to decreased behavioral engagement (95% CI = [−0.098, −0.02]), cognitive strategies (95% CI = [−0.15, −0.043]), and metacognitive strategies (95% CI = [−0.127, −0.024]) at T3. The last two effects highlight PAv goals and cognitive engagement. The PAv goals at T1 were associated with increased cognitive and metacognitive strategies at T3 through decreased help seeking at T2 (cognitive: 95% CI = [−0.099, −0.002]; metacognitive: 95% CI = [−0.085, −0.003]).

Discussion

The present study extends prior investigations by examining student achievement goals in early high school as predictors of change in the behavioral and cognitive dimensions of their engagement in academic work. In doing so, we focused on seeking help from teachers as one of the core mechanisms that can potentially connect these constructs through time. These longitudinal relationships were estimated after controlling for gender and the baseline levels of behavioral and cognitive engagement (cognitive and metacognitive strategies). The results have largely supported the study’s general hypothesis, which was based on the temporal sequence of achievement goals (early in the school year) → seeking help (end of the school year) → engagement (end of the next school year). Thus, MAp goals (positive) and MAv goals (negative) were found to be indirect predictors of both behavioral and cognitive engagement through help seeking. In addition, PAv goals positively predicted cognitive and metacognitive strategies, but not behavioral engagement, through the same mediator. Caution is, however, required in the interpretation of the sequences involving these goals. For their part, PAp goals did not predict any of the dimensions of academic engagement. A discussion and implications of these findings, limitations of this study, and future research directions are presented below.

Achievement Goals and Behavioral Engagement in Academic Work

The results showed that when achievement goals were simultaneously considered in the same SEM analysis, only mastery goals (approach and avoidance) emerged as indirect predictors of behavioral engagement in academic work through seeking help from teachers. The contribution of MAp goals appears consistent with research that positively links these goals to behavioral engagement (e.g., Bong, 2009) and to help seeking in the classroom (e.g., Roussel et al., 2011). However, this study provides a more complete picture by suggesting that students in early high school who are oriented toward the development and mastery of competence would be more inclined to increase their efforts and persevere to successfully complete their work because they could ask teachers to help them overcome obstacles. Many achievement goal researchers (see Anderman & Patrick, 2012; Elliot, 2005) contend that actions taken by MAp-oriented students are mainly regulated in achievement situations by the anticipation of a desired result (e.g., increased competence). Seeking help from teachers could be part of the set of actions considered and used by these students. Help that is received as advice, feedback, and hints might allow them to quickly and efficiently reengage in their work, and be confident of reaching the desired result (being competent) by increasing their efforts and perseverance (e.g., Wentzel, 2016).

The results also showed that MAv goals predicted a decrease in behavioral engagement almost 2 years later and that this relationship was explained by the tendency to not seek help from teachers. This result is important because no known study has yet established a link between these goals and behavioral engagement. However, MAv goals have been positively associated with seeking (Kaplan et al., 2009; Roussel et al., 2011) and avoiding help (Karabenick, 2004). Whereas the actions of MAv-oriented students are regulated by the anticipation of an undesired result (e.g., not understanding all of the material), seeking help from teachers could be a serious threat to self-esteem (e.g., Karabenick, 2004), which would confirm that the fear anticipated at the time of undertaking the task was justified. By deliberately depriving themselves of additional explanations and clarifications during the school year, these students’ problems might accumulate and intensify, which would gradually decrease their motivation, effort, and perseverance to carry out the required work.

The results indicated that PAp goals were neither directly nor indirectly related longitudinally to student behavioral engagement. The lack of a relationship between these constructs could reflect a tendency among students motivated by the importance of surpassing others to believe that more effort will not improve their performance, in addition to being a sign of incompetence. This hypothesis seems plausible in light of work on implicit theories of intelligence, suggesting that students oriented toward performance are more inclined to believe that there is little change in a person’s level of intelligence or abilities even when making an effort (e.g., Dweck & Master, 2009).

Finally, the pattern of relationships involving PAv goals was the same as for PAp goals. In clear, no direct or indirect relationship was detected longitudinally. However, a significant, positive cross-sectional association was observed with behavioral engagement at T1. This relationship is contrary to what has been observed in the few studies that have reported significant links between these constructs (Gonida et al., 2009; Wolters, 2004). It should be noted that contrary to these studies, the subscale used in the present study to measure PAv goals included an explicit, negative affective component (i.e., the fear of performing poorly). One possible explanation is that students motivated by a fear of poor results or failure might be inclined, early in the school year, to exert effort and persevere in their academic work to avoid a dreaded result. In the longer term, this motivational orientation, however, would not be associated with behavioral engagement, once the initial level of engagement has been taken into account. At first glance, these results suggest that PAv goals would not compromise efforts and perseverance to carry out academic tasks early in high school and could even be beneficial in the short term. However, we must keep in mind that these goals have most often been associated with academic difficulties (e.g., Anderman & Patrick, 2012; Senko, 2016) and harmful emotions such as anxiety (e.g., Duchesne et al., 2014). Over time, behavior motivated by a fear of failure and the intention to avoid performing poorly could come at a considerable cost to students.

Achievement Goals and Cognitive Engagement in Academic Work

MAp goals early in the school year predicted an increased use of cognitive and metacognitive strategies in performing academic work at the end of the next school year. These links are explained by actively seeking help from teachers. Cross-sectional studies have already linked MAp goals to cognitive engagement (e.g., Bong, 2009; Elliot et al., 1999; Liem et al., 2008; Miller et al., 1996; Wolters, 2004) and help seeking (e.g., Federici et al., 2015), but none have examined these relationships longitudinally, controlling for the initial level of engagement, and tested seeking help from teachers as a mediator. The explanatory hypothesis provided above concerning the relationship between MAp goals and behavioral engagement could also be applied here. It seems that students whose behavior is directed toward the possibility of a desired result would be more inclined to increase the use of diverse cognitive and metacognitive strategies to achieve the goal. Obtaining help from teachers would enable these students to reach their goals. However, it is important to note that help seeking does not appear to be a prerequisite because MAp goals were also directly associated with cognitive and metacognitive strategies.

The results showed that the MAv goals predicted a decrease in cognitive and metacognitive strategies through avoiding seeking help from teachers. The few studies that have examined the relationship between MAv goals and cognitive engagement in academic work have produced mixed results (Bong, 2009; Howell & Watson, 2007). In light of this study’s results, cognitive disengagement when performing learning tasks could possibly be due to certain negative emotions overwhelming students whose actions are guided by a fear of not learning or understanding. Evidence supporting this hypothesis comes from studies that have reported positive links between MAv goals and anxiety during engagement in a task (Elliot & McGregor, 2001; Putwain & Daniels, 2010). Negative emotions like anxiety could disrupt certain cognitive functions crucial to learning (e.g., working memory) by focusing students’ attention on their concerns rather than on the task (Ashcraft, 2002; Eysenck & Calvo, 1992; Park, Ramirez, & Beilock, 2014). With repeated difficulties in performing complex work, these students, who do not ask their teachers for help to protect their self-image (see above), might feel overwhelmed and become disorganized, which would lead them to decrease their use of effective cognitive (e.g., memorization) and metacognitive (e.g., regulation) strategies to do what is asked of them.

Our results also indicated that PAv goals have been associated with increased cognitive and metacognitive strategies in academic work. These longitudinal relationships could be explained by a decrease in seeking help from teachers. These results are consistent with research, indicating that these goals are positively linked with cognitive engagement (Bong, 2008; Howell & Watson, 2007) and negatively with help-seeking behaviors in the classroom (e.g., Bong, 2008, 2009; Federici et al., 2015; Gonida et al., 2014; Middleton & Midgley, 1997; Roussel et al., 2011). However, the literature has also shown that PAv goals were negatively linked to cognitive engagement (Wolters, 2004) and positively to disorganization (Howell & Watson, 2007). It may not be surprising that some students, when faced with a task where their competence and social image are threatened by failure, would be less inclined to seek help from their teachers to overcome obstacles. Help seeking would simply reveal their incompetence (e.g., Karabenick, 2004; Ryan & Pintrich, 1997). Here, it is most surprising that the degree of cognitive engagement of these students increases because they limit help seeking. The possibility that this increased engagement “compensates” for the fact that they avoid help seeking and receive little support cannot be completely ignored. However, the quality of this engagement is questionable because PAv goals have been associated with disorganization (Howell & Watson, 2007) and several other academic problems (Anderman & Patrick, 2012; Maher & Zusho, 2009). It is thus possible that these students use several cognitive and metacognitive strategies, but not necessarily in a manner that is logical or adapted to the demands of the task. In other words, the quantity of strategies used by these students could override their quality.

The absence of contribution of PAp goals on cognitive engagement is contrary to what has been hypothesized. Another study (Wolters, 2004) also showed that these goals were not associated with cognitive and metacognitive strategies, once other variables were taken into account (e.g., MAp and PAv goals; gender). At first glance, this result may seem difficult to reconcile with research that has found a positive association between PAp goals and academic achievement (e.g., Elliot et al., 1999; Howell & Watson, 2007; Wolters et al., 1996). If these goals are beneficial to success, why would they not also favor the use of learning strategies that are deemed crucial for this success? We believe that part of the answer comes from research based on a multiple goals perspective (e.g., Harackiewicz, Barron, Pintrich, Elliot, & Thrash, 2002; see Senko, 2016). In particular, these studies have indicated that it is possible to simultaneously pursue the two approach goals, which would have distinct and positive consequences on academic functioning. In light of our findings, we cannot rule out the possibility that some students can manage MAp and PAp goals successfully. In this way, MAp goals would stimulate interest and cognitive engagement in academic tasks in general (which was measured in this study), while PAp goals would promote the triggering of cognitive efforts in specific tasks (e.g., those evaluated formally or perceived necessary to succeed and surpass others).

Gender Differences

Results of preliminary analyses showed that girls were more oriented toward the mastery of skills than boys and were more inclined to exert effort and persevere in their academic work and to use varied learning strategies. These results are consistent with other studies (e.g., Duchesne et al., 2017; Miller et al., 1996; Wolters et al., 1996). Although there is no clear explanation as to the origin of these differences, it is legitimate to assume that MAp goals and academic engagement could operate in a “synergistic” manner with perceived competence and academic achievement. Studies conducted on early high school have reported that, compared to boys, girls reported higher levels of perceived academic competence (Duchesne et al., 2017) and showed better academic achievement (Duchesne & Larose, 2007). Thus, because they have a more positive perception of their skills, girls may also be willing to give more importance to mastering these skills and engaging in actions to achieve them. Academic success could signal to them that they are succeeding, which would have the effect of reinforcing their adopted goals and their level of engagement in tasks.

Practical Implications

The results of this study highlight the importance of creating conditions that guide students toward adopting MAp goals and encourage them to seek help from teachers when faced with uncertainty or difficulties in their academic work. Evidence indicates that it is possible for teachers to work toward this by making an effort to structure the learning environment around tasks that are meaningful, diversified, and adapted to their students’ skill level, by providing the opportunity to make choices and participate in decision making, by acknowledging effort and progress, by giving precise feedback on strategies used, by avoiding social comparisons, and by encouraging collaborative work among peers (e.g., Ames, 1992; Givens Rolland, 2012; Wolters, 2004). Implementing these practices could be particularly crucial early in high school, as this period is characterized by a decrease in MAp goals and help seeking by students (e.g., Anderman & Midgley, 1997; Duchesne et al., 2014; Ryan & Pintrich, 1998).

Limitations and Future Directions

Although this study has several methodological strengths that distinguish it from previous studies (e.g., prospective longitudinal design, sophisticated data analyses, controlling for the initial levels of academic engagement), a few limitations need to be considered: First, the correlational nature of the research design does not allow firm conclusions to be drawn about the direction of the postulated relationships (i.e., achievement goals → seeking help → engagement). Other relational patterns between variables might also be plausible. To this end, a recent study that focused only on MAp goals provided evidence of reciprocal effects between these goals and metacognitive learning strategies among high school students (King & McInerney, 2016). Thus, a suggestion for future research is to test a full transactional model with bidirectional and recursive relations over time between achievement goals, help seeking, and the dimensions of academic engagement. Second, this study relies exclusively on self-report data that may have been affected by common method variance (e.g., social desirability). Replication studies would benefit from using other informants (e.g., teachers) and multiple methodologies (e.g., observations) to limit this problem. Third, the sample used in this study was relatively homogeneous (White students from urban, middle-class backgrounds). Future research should attempt to confirm this study’s results with a more diverse sample in terms of sociodemographic characteristics. Fourth, the literature has shown that classroom goal structures and parenting behaviors were significant predictors of achievement goals and academic engagement (e.g., Anderman & Patrick, 2012; Duchesne & Ratelle, 2010; Wolters, 2004). Future studies should consider testing the potential moderating effects of environmental factors.

In summary, this study showed that among students in early high school, mastery goals (approach and avoidance) adopted early in the school year helped to explain the degree of behavioral and cognitive engagement in academic work at the end of the next school year through seeking help from teachers. At the same time, PAv goals were only associated with the cognitive dimension of engagement through help-seeking behaviors. All these links were significant after controlling for gender and the initial levels of engagement. These results have implications for teachers who can contribute, by virtue of their practices and policies implemented in the classroom, to the orientation of young adolescents’ achievement goals in such a way as to promote their academic engagement and, ultimately, their academic success in high school.

Footnotes

Appendix

Items Assessing Behavioral and Cognitive Engagement in Academic Work (Items Retained After Exploratory Structural Equation Modeling [ESEM] Analyses are Highlighted in Bold).

Behavioral engagement

I always work as hard as I can to finish my course assignments.

I always put a lot of effort into doing my work.

I get distracted very easily when I am studying for my courses. (Reverse item)

I get started on doing my work for my courses but often don’t stick with it for very long. (Reverse item)

Even my course work is dull or boring, I keep at it until I am finished.

I often begin my course assignments but give up before I am done. (Reverse item)

Cognitive strategies

When I study for my courses, I read my notes, my homework, and the textbook over and over.

To learn the material for my classes, I rehearse the important material until I know it.

I spend my time memorizing key equations or facts when I study for my courses.

When I study for my courses, I practice saying the material over and over to myself.

When doing work for my courses, I try to relate what I’m learning to what I already know.

When I study for my courses, I try to connect what I am learning with my own experiences.

I try to make all the different ideas fit together and make sense when I study for my courses.

I make up my own examples to help me understand the important concepts in my courses.

Metacognitive strategies

Before starting my assignments, I try to figure out the best way to do it.

Before I begin to study for my courses, I think about what I want to get done.

I start my assignments without really planning out what I want to get done. (R)

For my course assignments, I double check my work to make sure I am doing it right.

When I’m working on my assignments, I stop once in a while and go over what I have been doing.

In general, I keep track of how much I understand the work, not just if I am getting the right answers.

I try to change the way I study to fit the type of material I am trying to learn.

I try to adapt how I do my course assignments to fit with what the teacher wants or expects.

If what I am working on for my courses is difficult to understand, I change the way I learn the material.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the Social Sciences and Humanities Research Council of Canada (SSHRCC) and the Fonds de Recherche du Québec–Société et Culture (FRQSC).

Author Biographies

Stéphane Duchesne is professor of educational psychology at Laval University (Canada). His main fields of research and teaching are child and adolescent motivation, emotional problems, school transitions, and parent-child relationships. He is coprincipal investigator of prospective and longitudinal studies on the role of individual and social factors involved in school motivation and academic persistence and adjustment.

Simon Larose is professor of educational psychology at Laval University. His main areas of work are adolescent and young adult development, attachment and family relationships, mentoring, school transitions, and academic adjustment.

Bei Feng is a statistician at the Research Unit on Children’s Psychosocial Maladjustment. Her research focuses on normative and problematic social development.

References

Ames

(1992). Classrooms: Goals, structures, and student motivation. Journal of Educational Psychology, 84, 261-271.

Anderman

E. M.

Midgley

(1997). Changes in achievement goal orientations and perceived classroom goal structures across the transition to middle level schools. Contemporary Educational Psychology, 22, 269-298.

Anderman

E. M.

Patrick

(2012). Achievement goal theory, conceptualization of ability/intelligence, and classroom. In Christenson

S. E.

Reschly

A. L.

Wylie

(Eds.), Handbook of research on student engagement (pp. 173-191). New York, NY: Springer.

Appleton

J. J.

Christenson

S. L.

Furlong

M. J.

(2008). Student engagement with school: Critical conceptual and methodological issues of the construct. Psychology in the Schools, 45, 369-386.

Ashcraft

M. H.

(2002). Math anxiety: Personal, educational, and cognitive consequences. Current Directions in Psychological Science, 11, 181-185.

Baraldi

A. N.

Enders

C. K.

(2010). An introduction to modern missing data analyses. Journal of School Psychology, 48, 5-37.

Birch

S. H.

Ladd

G. W.

(1997). The teacher-child relationship and children’s early school adjustment. Journal of School Psychology, 35, 61-79.

Boekerts

(2016). Engagement as an inherent aspect of the learning process. Learning and Instruction, 43, 76-83.

Bong

(2008). Effects of parent-child relationships and classroom goal structures on motivation, help-seeking avoidance, and cheating. The Journal of Experimental Education, 76, 191-217.

10.

Bong

(2009). Age-related differences in achievement goal differentiation. Journal of Educational Psychology, 101, 879-896.

11.

Butler

(2006). An achievement goal perspective on student help seeking and teacher help giving in the classroom: Theory, research, and educational implications. In Karabenick

S. A.

Newman

R. S.

(Eds.), Help seeking in academic settings: Goals, groups, and contexts (pp. 15-44). Mahwah, NJ: Lawrence Erlbaum.

12.

Butler

(2008). Ego-involving and frame of reference effects of tracking on elementary school students’ motivational orientations and help seeking in math class. Social Psychology of Education, 11, 5-23.

13.

Byrne

B. M.

(2012). Structural equation modeling with Mplus: Basic concepts, applications, and programming (Multivariate Applications Series, 1st ed.). New York, NY: Routledge.

14.

Davey

Shanahan

M. J.

Schafer

J. L.

(2001). Correcting for selective nonresponse in the National Longitudinal Survey of Youth using multiple imputation. Journal of Human Resources, 36, 500-519.

15.

Duchesne

Larose

(2007). Adolescent parental attachment and academic motivation and performance in early adolescence. Journal of Applied Social Psychology, 37, 1501-1521.

16.

Duchesne

Ratelle

C. F.

(2010). Parental behaviors and adolescents’ achievement goals at the beginning of middle school: Emotional problems as potential mediators. Journal of Educational Psychology, 101, 497-507.

17.

Duchesne

Ratelle

C. F.

Feng

(2014). Developmental trajectories of achievement goal orientations during the middle school transition: The contribution of emotional and behavioral dispositions. Journal Early Adolescence, 34, 485-516.

18.

Duchesne

Ratelle

C. F.

Feng

(2017). Psychological need satisfaction and achievement goals: Exploring indirect effects of academic and social adaptation following the transition to secondary school. The Journal of Early Adolescence, 37, 1280-1308.

19.

Dweck

C. S.

(1986). Motivational processes affecting learning. American Psychologist, 41, 1040-1048.

20.

Dweck

C. S.

Master

(2009). Self-theories and motivation: Students’ beliefs about intelligence. In Wentzel

K. R.

Wigfield

(Eds.), Handbook of motivation in school (pp. 123-140). New York, NY: Taylor & Francis.

21.

Eccles

J. S.

Roeser

R. W.

(2011). Schools as developmental contexts during adolescence. Journal of Research on Adolescence, 21, 225-241.

22.

Elliot

A. J.

(1999). Approach and avoidance motivation and achievement goals. Educational Psychologist, 34, 149-169.

23.

Elliot

A. J.

(2005). A conceptual history of the achievement goal construct. In Elliot

A. J.

Dweck

C. S.

(Eds.), Handbook of competence and motivation (pp. 52-72). New York, NY: Guilford Press.

24.

Elliot

A. J.

(2006). The hierarchical model of approach avoidance motivation. Motivation and Emotion, 30, 111-116.

25.

Elliot

A. J.

(2008). Handbook of approach and avoidance motivation. New York, NY: Taylor & Francis.

26.

Elliot

A. J.

Church

M. A.

(1997). A hierarchical model of approach and avoidance achievement motivation. Journal of Personality and Social Psychology, 72, 218-232.

27.

Elliot

A. J.

McGregor

H. A.

(2001). A 2 × 2 achievement goal framework. Journal of Personality and Social Psychology, 80, 501-519.

28.

Elliot

A. J.

McGregor

H. A.

Gable

(1999). Achievement goals, study strategies, and exam performance: A mediational analysis. Journal of Educational Psychology, 91, 549-563.

29.

Elliot

A. J.

Murayama

(2008). On the measurement of achievement goals: Critique, illustration, and application. Journal of Educational Psychology, 100, 613-628.

30.

Eysenck

M. W.

Calvo

M. G.

(1992). Anxiety and performance: The processing efficiency theory. Cognition and Emotion, 6, 409-434.

31.

Federici

R. A.

Skaalvik

E. M.

Tangen

T. N.

(2015). Students’ perceptions of the goal structure in mathematics classrooms: Relations with goal orientations, mathematics anxiety, and help-seeking behavior. International Education Studies, 8, 146-158.

32.

Feldlaufer

Midgley

Eccles

J. S.

(1988). Student, teacher, and observer perceptions of the classroom environment before and after the transition to junior high school. The Journal of Early Adolescence, 8, 133-156.

33.

Fredricks

J. A.

(2015). Academic engagement. In Wright

(Ed.), The international encyclopedia of social and behavioral sciences (2nd ed., pp. 31-36). Oxford, UK: Elsevier.

34.

Fredricks

J. A.

Blumenfeld

P. C.

Paris

A. H.

(2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74, 59-109.

35.

Givens Rolland

. (2012). Synthesizing the evidence on classroom goal structures in middle and secondary schools: A meta-analysis and narrative review. Review of Educational Research, 82, 396-435.

36.

Gonida

E. N.

Karabenick

S. A.

Makara

K. A.

Hatzikyriakou

G. A.

(2014). Perceived parent goals and student goal orientations as predictors of seeking or not seeking help: Does age matter? Learning and Instruction, 33, 120-130.

37.

Gonida

E. N.

Voulala

Kiosseoglou

(2009). Students’ achievement goal orientations and their behavioral and emotional engagement: Co-examining the role of perceived school goal structures and parent goals during adolescence. Learning and Individual Differences, 19, 53-60.

38.

Goodenow

(1993). Classroom belonging among early adolescent students: Relationships to motivation and achievement. The Journal of Early Adolescence, 13, 21-43.

39.

Graham

J. W.

(2003). Adding missing-data-relevant variables to FIML-based structural equation models. Structural Equation Modeling, 10, 80-100.

40.

Grant

Dweck

C. S.

(2003). Clarifying achievement goals and their impact. Journal of Personality and Social Psychology, 85, 541-553.

41.

Hackel

T. S.

Jones

M. H.

Carbonneau

K. J.

Mueller

C. E.

(2016). Re-examining achievement goal instrumentation: Convergent validity of AGQ and PALS. Contemporary Educational Psychology, 46, 73-80.

42.

Harackiewicz

J. M.

Barron

K. E.

Pintrich

P. R.

Elliot

A. J.

Thrash

T. M.

(2002). Revision of achievement goal theory: Necessary and illuminating. Journal of Educational Psychology, 94, 638-645.

43.

Haughton

D. M.

Oud

J. H.

Jansen

R. A.

(1997). Information and other criteria in structural equation model selection. Communications in Statistics: Simulation and Computation, 26, 1477-1516.

44.

Hayes

A. F.

(2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York, NY: Guilford Press.

45.

T. H.

Gou

W. J.

Chang

S. M.

(2015). Parental involvement and elementary school students’ goals, maladaptive behaviors, and achievement in learning English as a foreign language. Learning and Individual Differences, 39, 205-210.

46.

Howell

A. J.

Watson

D. C.

(2007). Procrastination: Associations with achievement goal orientation and learning strategies. Personality and Individual Differences, 43, 167-178.

47.

L. T.

Bentler

P. M.

(1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6, 1-55.

48.

Jowkar

Kojuri

Kohoulat

Hayat

A. A.

(2014). Academic resilience in education: The role of achievement goal orientations. Journal of Advances in Medical Education & Professionalism, 2, 33-38.

49.

Kaplan

Lichtinger

Gorodetsky

(2009). Achievement goal orientations and self-regulation in writing: An integrative perspective. Journal of Educational Psychology, 101, 51-69.

50.

Karabenick

S. A.

(2004). Perceived achievement goal structure and college student help seeking. Journal of Educational Psychology, 96, 569-581.

51.

Karabenick

S. A.

(2006). Introduction. In Karabenick

S. A.

Newman

R. S.

(Eds.), Help seeking in academic settings: Goals, groups, and contexts (pp. 1-13). Mahwah, NJ: Lawrence Erlbaum.

52.

Karabenick

S. A.

Newman

R. S.

(2009). Seeking help as an adaptive response to learning difficulties: Person, situation, and developmental influences. In Wosnitza

Efklides

Karabenick

S. A.

Nenniger

(Eds.), Contemporary motivation research: From global to local perspectives (pp. 24-47). Amsterdam, The Netherlands: Hogrefe.

53.

Kessels

Steinmayr

(2013). Macho-man in school: Toward the role of gender role self-concepts and help seeking in school performance. Learning and Individual Differences, 23, 234-240.

54.

King

R. B.

McInerney

D. M.

(2016). Do goals lead to outcomes or can it be the other way around? Causal ordering of mastery goals, metacognitive strategies, and achievement. British Journal of Educational Psychology, 86, 296-312.

55.

Lam

S. F.

Jimerson

Wong

B. P.

Kikas

Shin

Veiga

F. H.

. . . Stanculescu

(2014). Understanding and measuring student engagement in school: The results of an international study from 12 countries. School Psychology Quarterly, 29, 213-232.

56.

Larose

Roy

(1995). Test of Reactions and Adaptation in College (TRAC): A new measure of learning propensity for college students. Journal of Educational Psychology, 87, 293-306.

57.

Liem

G. A. D.

(2016). Academic and social achievement goals: Their additive, interactive, and specialized effects on school functioning. British Journal of Educational Psychology, 86, 37-56.

58.

Liem

G. A. D.

Lau

Nie

(2008). The role of self-efficacy, task value, and achievement goals in predicting learning strategies, task disengagement, peer relationship, and achievement outcome. Contemporary Educational Psychology, 33, 486-512.

59.

Little

R. J.

(1988). A test of missing completely at random for multivariate data with missing values. Journal of the American Statistical Association, 83, 1198-1202.

60.

Maher

M. L.

Zusho

(2009). Achievement goal theory, the past, present, and future. In Wentzel

K. R.

Wigfield

(Eds.), Handbook of motivation at school (pp. 77-104). New York, NY: Routledge.

61.

Marcoulides

G. A.

Schumacker

R. E.

(2013). Advanced structural equation modeling: Issues and techniques. New York, NY: Psychology Press.

62.

Marsh

H. W.

Muthén

Asparouhov

Lüdtke

Robitzsch

Morin

A. J.

. . .Trautwein

(2009). Exploratory structural equation modeling, integrating CFA and EFA: Application to students’ evaluations of university teaching. Structural Equation Modeling: A Multidisciplinary Journal, 16, 439-476.

63.

Marsh

H. W.

Nagengast

Morin

A. J.

(2013). Measurement invariance of big-five factors over the life span: ESEM tests of gender, age, plasticity, maturity, and la dolce vita effects. Developmental Psychology, 49, 1194-1218.

64.

McCoach

D. B.

Adelson

J. L.

(2010). Dealing with dependence (Part I): Understanding the effects of clustered data. Gifted Child Quarterly, 54, 152-155.

65.

Michou

Vansteenkiste

Mouratidis

Lens

(2014). Enriching the hierarchical model of achievement motivation: Autonomous and controlling reasons underlying achievement goals. British Journal of Educational Psychology, 84, 650-666.

66.

Middleton

M. J.

Midgley

(1997). Avoiding the demonstration of lack of ability: An under-explored aspect of goal theory. Journal of Educational Psychology, 89, 710-718.

67.

Miller

R. B.

Greene

B. A.

Montalvo

G. P.

Ravindran

Nichols

J. D.

(1996). Engagement in academic work: The role of learning goals, future consequences, pleasing others, and perceived ability. Contemporary Educational Psychology, 21, 388-422.

68.

Muthén

L. K.

Muthén

B. O.

(2015). Mplus for Windows 7. 31. Los Angeles, CA: Author.

69.

Newman

R. S.

(1994). Adaptive help seeking: A strategy of self-regulated learning. In Schunk

D. H.

Zimmerman

B. J.

(Eds.), Self-regulation of learning and performance: Issues and educational applications (pp. 283-301). Hillsdale, NJ: Lawrence Erlbaum.

70.

Newman

R. S.

(2000). Social influences on the development of children’s adaptive help seeking: The role of parents, teachers, and peers. Developmental Review, 20, 350-404.

71.

Park

Ramirez

Beilock

S. L.

(2014). The role of expressive writing in math anxiety. Journal of Experimental Psychology: Applied, 20, 103-111.

72.

Peugh

J. L.

(2010). A practical guide to multilevel modeling. Journal of School Psychology, 48, 85-112.

73.

Peugh

J. L.

Enders

C. K.

(2004). Missing data in educational research: A review of reporting practices and suggestions for improvement. Review of Educational Research, 74, 525-556.

74.

Pintrich

P. R.

(2000). The role of goal orientation in self-regulated learning. In Boekaerts

Pintrich

P. R.

Zeidner

(Eds.), Handbook of self-regulation: Theory, research, and applications (pp. 451–502). San Diego, CA: Academic Press.

75.

Putwain

D. W.

Daniels

R. A.

(2010). Is the relationship between competence beliefs and test anxiety influenced by goal orientation? Learning and Individual Differences, 20, 8-13.

76.

Roussel

Elliot

A. J.

Feltman

(2011). The influence of achievement goals and social goals on help-seeking from peers in an academic context. Learning and Instruction, 21, 394-402.

77.

Ruzek

E. A.

Hafen

C. A.

Allen

J. P.

Gregory

Mikami

A. Y.

Pianta

R. C.

(2016). How teacher emotional support motivates students: The mediating roles of perceived peer relatedness, autonomy support, and competence. Learning and Instruction, 42, 95-103.

78.

Ryan

A. M.

Pintrich

P. R.

(1997). “Should I ask for help?” The role of motivation and attitudes in adolescents’ help seeking in math class. Journal of Educational Psychology, 89, 329-341.

79.

Ryan

A. M.

Pintrich

P. R.

(1998). Achievement and social motivational influences on help seeking in the classroom. In Karabenick

S. A.

(Ed.), Strategic help seeking: Implications for learning and teaching (pp. 117-139). Mahwah, NJ: Lawrence Erlbaum.

80.

Senko

(2016). Achievement goal theory: A story of early promises, eventual discords, and future possibilities. In Wentzel

K. R.

Miele

D. B.

(Eds.), Handbook of motivation at school (2nd ed., pp. 75-95). New York, NY: Taylor & Francis.

81.

Shih

S. S.

(2007). The role of motivational characteristics in Taiwanese sixth graders’ avoidance of help seeking in the classroom. The Elementary School Journal, 107, 473-495.

82.

Sideridis

G. D.

Stamovlasis

(2016). Instrumental help-seeking as a function of normative performance goal orientations: A “catastrophe.” Motivation and Emotion, 40, 82-100.

83.

Skinner

E. A.

(2016). Engagement and disaffection as central to processes of motivational resilience and development. In Wentzel

K. R.

Miele

D. B.

(Eds.), Handbook of motivation at school (2nd ed., pp. 145-168). New York, NY: Taylor & Francis.

84.

Skinner

E. A.

Pitzer

J. R.

(2012). Developmental dynamics of student engagement, coping, and everyday resilience. In Christenson

S. L.

Reschly

A. L.

Wylie

(Eds.), Handbook of research on student engagement (pp. 21-44). New York, NY: Springer.

85.

Statistics Canada. (2015). Median total income, by family type, by province and territory. Retrieved from http://www.statcan.gc.ca/tables-tableaux/sum-som/l01/cst01/famil108a-eng.htm

86.

Tabachnick

B. G.

Fidell

L. S.

(2013). Using multivariate statistics (6th ed.). Boston, MA: Pearson.

87.

Tucker

C. M.

Zayco

R. A.

Herman

K. C.

Reinke

W. M.

Trujillo

Carraway

. . . Ivery

P. D.

(2002). Teacher and child variables as predictors of academic engagement among low-income African American children. Psychology in the Schools, 39, 477-488.

88.

Urdan

Ryan

A. M.

Anderman

E. M.

Gheen

(2002). Goals, goal structures, and avoidance behaviors. In Midgley

(Ed.), Goals, goal structures, and patterns of adaptive learning (pp. 55-83). Mahwah, NJ: Lawrence Erlbaum.

89.

Vrugt

Oort

F. J.

(2008). Metacognition, achievement goals, study strategies and academic achievement: Pathways to achievement. Metacognition and Learning, 3, 123-146.

90.

Wang

M. T.

Eccles

J. S.

(2012). Adolescent behavioral, emotional, and cognitive engagement trajectories in school and their differential relations to educational success. Journal of Research on Adolescence, 22, 31-39.

91.

Wentzel

K. R.

(2016). Teacher-student relationships. In Wentzel

K. R.

Miele

D. B.

(Eds.), Handbook of motivation at school (2nd ed., pp. 211-230). New York, NY: Taylor & Francis.

92.

Wolters

C. A.

(2004). Advancing achievement theory: Using goal structures and goal orientations to predict students’ motivation, cognition, and achievement. Journal of Educational Psychology, 96, 236-250.

93.

Wolters

C. A.

S. L.

Pintrich

P. R.

(1996). The relation between goal orientation and students’ motivational beliefs and self-regulated learning. Learning and Individual Differences, 8, 211-238.