Adaptability, personal best (PB) goals setting, and gains in students’ academic outcomes: A longitudinal examination from a social cognitive perspective

Accepted Manuscript Adaptability, Personal Best (PB) Goals Setting, and Gains in Students’ Academic Outcomes: A Longitudinal Examination from a Social Cognitive Perspective Emma C. Burns, Andrew J. Martin, Rebecca J. Collie PII: DOI: Reference:

S0361-476X(17)30235-7 https://doi.org/10.1016/j.cedpsych.2018.02.001 YCEPS 1671

To appear in:

Contemporary Educational Psychology

Please cite this article as: Burns, E.C., Martin, A.J., Collie, R.J., Adaptability, Personal Best (PB) Goals Setting, and Gains in Students’ Academic Outcomes: A Longitudinal Examination from a Social Cognitive Perspective, Contemporary Educational Psychology (2018), doi: https://doi.org/10.1016/j.cedpsych.2018.02.001

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Adaptability, Personal Best (PB) Goals Setting, and Gains in Students’ Academic Outcomes: A Longitudinal Examination from a Social Cognitive Perspective

Emma C. Burns, Andrew J. Martin, Rebecca J. Collie School of Education University of New South Wales, Australia

January 2018

Requests for further information about this investigation can be made to Scientia Professor Andrew J. Martin, School of Education, University of New South Wales, NSW 2052, AUSTRALIA. E-Mail: [email protected]. Phone: +61 2 9385 1952. Fax: +61 2 9385 1946.

Acknowledgements: The authors thank Dr Marianne Mansour for assisting with data collection. Funding: This study was funded by the Australian Research Council (Grant #DP140104294).

Submission Date: January 27th, 2018 Abstract The present investigation examines how two novel constructs, adaptability (for selfregulation) and PB goal setting (for goal setting), operate alongside the more “traditional” constructs of the triadic model of social cognitive theory (SCT; Bandura, 1986) to predict students’ academic gains over time. Given that the triadic model highlights the importance of

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self-regulation and goal setting in human motivation, it is important to revisit classic models (such as SCT) to ascertain the role and validity of these new and relevant constructs in seminal conceptualizing. A longitudinal process model explored the extent to which: social support from parents, peers, and teachers (environmental factors) predicted gains in students’ self-efficacy, perceived control, adaptability, and PB goal setting (personal factors); selfefficacy, perceived control, and adaptability predicted growth in students’ PB goal setting; and, PB goal setting predicted academic growth in engagement and achievement (behavioral factors). Data were collected via survey one year apart across the 2014 and 2015 academic years from N=1,481 students in nine Australian high schools. Longitudinal structural equation modelling indicated that parent, peer, and teacher social support significantly predicted gains in adaptability and self-efficacy; adaptability, self-efficacy, and teacher support significantly predicted gains in PB goal setting; and PB goal setting significantly predicted gains in both academic engagement and achievement. These findings extend and augment previous work by providing support for the positive role adaptability and PB goal setting play in student academic functioning over time. Similarly, this investigation confirms the viability of including adaptability and PB goal setting within SCT’s triadic model and provides evidence for their impact within the larger psycho-educational terrain. Keywords: social cognitive theory; adaptability; goals; engagement; achievement

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Adaptability, Personal Best (PB) Goals, and Gains in Students’ Academic Outcomes: A Longitudinal Examination from a Social Cognitive Perspective 1. Introduction The triadic model is proposed under social cognitive theory (SCT; Bandura, 1986) to explain the factors that influence human agency. This model examines the interactions among environmental (e.g., social support), personal (e.g., self-factors and self-strategies), and behavioral (e.g., academic outcomes) factors that shape human functioning. Although past investigations of human agency using the triadic model have emphasized self-efficacy (e.g., Bandura, 1991, 2006), the model also articulates the importance of self-regulation and goal setting (Bandura, 1991). Importantly, recent research has shed new light on these two constructs that may hold renewed relevance to SCT and its conceptual elements (e.g., Martin, 2006; Martin, Nejad, Colmar & Liem, 2012, 2013). Accordingly, harnessing the triadic model (Bandura, 1986, 2001), the present investigation explores how adaptability (a recently proposed construct under the self-regulation umbrella; e.g., Martin, Nejad et al., 2012, 2013) and personal best (PB) goal setting (a recently proposed construct under the goal setting umbrella; e.g., Martin & Liem, 2010) impact gains in students’ academic engagement and achievement. Adaptability is a specific form of self-regulation regarding students’ ability to psycho-behaviorally adjust in the face of change, novelty, or uncertainty (Martin, Nejad et al., 2012, 2013). PB goal setting represents a growth-oriented and self-referenced approach to goal setting (Martin & Elliot, 2016a). As new psycho-educational constructs (such as adaptability and PB goal setting) are identified, there can be yields in revisiting classic models to ascertain the role of novel constructs in seminal conceptualizing. Indeed, given the demonstrated predictive validity of the triadic model in high school populations (the student population of interest for this investigation; Alliman-Brissett, Turner, & Skovholt, 2004; Bong 2001, 2004; Zimmerman,

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Bandura, & Martinez-Pons, 1992) and the well-documented motivational decline in high school students (e.g.,Abbott-Chapman et al., 2014; Cooper, 2013; Wang & Eccles, 2012; Wigfield, 1994; see also Lepper, Corpus, & Iyengar, 2005 for evidence of even earlier declines), there are empirical grounds to examine how two recent motivational constructs may function within this theoretical space and population. As such, this investigation provides an opportunity to examine how adaptability and PB goal setting impact high school students’ academic outcomes, as well as augment current understanding of the triadic model in this population. Following cross-sectional work by Author 1, Co-Author, and Co-Author (in press) and drawing on Bandura’s (1986) triadic framework, we examine a process by which: social support from parents, peers, and teachers (environmental factors) predict students’ selfefficacy, perceived control, adaptability (“self-factors” within personal factors), and PB goal setting (“self-strategies” within personal factors); self-efficacy, perceived control, and adaptability also predict PB goal setting; and, PB goal setting predicts academic engagement and achievement (behavioral factors). Figure 1 demonstrates. Extending prior research (Author 1, Co-Author, & Co-Author, in press), we employed a longitudinal approach that enables us to more clearly ascertain the unique role of adaptability and PB goal setting in predicting gains in students’ academic engagement and achievement. Indeed, because SCT argues that the comparative influence of each factor must be examined over time (Bandura, 1986), the present longitudinal investigation further adds to current understanding of the triadic process. 2. Adaptability, PB Goal Setting, and SCT 2.1. Adaptability: Definition and Development Adaptability refers to one’s capacity to cognitively, behaviorally, and emotionally self-regulate in response to changing, novel, or uncertain circumstances (Martin, Nejad et al.,

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2012, 2013). This is referred to as the tripartite (cognition, behavior, emotion) perspective of adaptability. A student’s ability to effectively manage their response to change, novelty, and uncertainty is critical for managing and engaging with academic demands. Adaptability is considered especially relevant to students because the classroom is a dynamic environment subject to frequent change. Indeed, past work has found that adaptability positively predicts students’ academic engagement, achievement, and well-being (Martin et al., 2013). Similarly, recent work has found that adaptability is predictive of student outcomes beyond the effects of other cognate factors (e.g., self-regulation, buoyancy; Martin et al., 2013; Martin, Yu, Ginns, & Papworth, 2016). The present study extends this work by exploring adaptability’s predictive role alongside novel predictors (viz. self-efficacy and perceived control). Two frameworks central to the development of adaptability are self-regulated learning (Zimmerman, 1990) and life-span development theory (Baltes, 1987). Self-regulated learning refers to the strategies by which students organize and direct their own learning to meet learning goals (Zimmerman, 2002). Through these processes, self-regulated learners monitor, evaluate, and re-evaluate their learning approaches and habits in order to improve upon them (Zimmerman, 2002). Adaptability is generally considered to be under the self-regulation umbrella (Collie & Martin, 2017). However, adaptability expands on this traditional framework by explicitly focusing on self-regulatory mechanisms in changing, novel, or uncertain circumstances; traditionally, self-regulation is operationalized in reference to general academic tasks and demands (Callan & Cleary, 2017; Collie & Martin, 2017). As such, adaptability is considered a specific form of self-regulation that captures how students cognitively, behaviorally, and emotionally respond and adjust to change, novelty, and uncertainty (Martin et al., 2013). Life-span development theory argues that psycho-behavioral mechanisms are acquired, developed, and shaped throughout one’s life (Baltes, 1987). Moreover, the context and

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phenomena that shape these mechanisms are highly unique to the individual (Heckhausen, Wrosch, & Schulz, 2010). In the academic context, change, novelty, and uncertainty are recurring phenomena. Given that adaptability is a psycho-behavioral mechanism critical for managing these academic phenomena, it is considered a critical skill to develop and apply in school and beyond. 2.2. Adaptability within SCT SCT argues that self-regulation is a collection of psychological sub-functions that are central to human agency and critical for self-directed change (Bandura, 1991). The central sub-functions include self-observation, -evaluation, and -reaction. These sub-functions direct cognitive, behavioral, and emotional resources to inform plans of action (i.e., goal setting) and enable desired outcomes (Bandura, 1991). Adaptability, as a form of self-regulation, is expected to operate in a similar way. Like the sub-functions described by SCT, adaptability strengthens and directs a student’s cognitive, behavioral, and emotional resources in order to maintain self-direction in the face of change, novelty, and uncertainty (Collie, Holliman, & Martin, 2017). 2.3. PB Goal Setting: Definition and Development PB goal setting is a goal setting approach focused on specific, challenging, and competitively self-referenced goals towards which individuals strive (Martin, 2006). PB goal setting encourages students to focus on personal improvement and striving to outperform their best past personal efforts, rather than the efforts of others or achieving against the absolute criteria of the task (Martin & Elliot, 2016b). The emphasis on self-competition engenders the use of positive learning strategies and skills to strive towards personal excellence. Indeed, PB goal setting has been found to positively predict engagement, achievement, growth mindsets, and decreased self-handicapping, both in cross-sectional and cross-lag panel analyses (Author 1, Co-Author, & Co-Author, in press, Martin & Liem, 2010;

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for review, see Martin, 2014). Similarly, recent research has shown PB goal setting to be predictive of academic outcomes beyond that of other adaptive goal setting (e.g., mastery goals; Martin & Elliot, 2016a, 2016b). The present study builds on this work by exploring the predictive role of PB goal setting in the context of other predictors (viz. adaptability, selfefficacy, perceived control). Goal setting theory is a key conceptual underpinning of PB goal setting1 (Locke & Latham, 1990). Goal setting theory has identified that the core features of productive goals are specificity and difficulty; these goals are frequently associated with positive academic and personal outcomes (for a review, see Locke & Latham, 2013). In like fashion, PB goals are designed to be optimally personally challenging and to orient students toward what is required to outperform past personal efforts (Martin & Liem, 2010). Similarly, past work has found that goals involving challenging standards create a motivating internal pressure that is more likely to lead to desired outcomes (Senko, Hulleman, & Harackiewicz, 2011). The personally referenced nature of PB goal setting, which focuses on self-competition, is likely to create such an internal pressure. This may in turn help direct students’ attention and resources towards goal-directed behaviors and efforts conducive to outperforming past performances (Martin & Liem, 2010). Thus, the emphasis of PB goal setting on personal improvement is likely to positively impact students’ academic experience and outcomes. 2.4. PB Goal Setting within SCT SCT argues that self-improvement is derived through a cycle of personal discrepancy production and reduction (Bandura, 1991). Bandura argues that individuals are naturally inclined towards self-improvement, and thus are likely to identify how their current state/performance differs from their desired state/performance (discrepancy production). In order to reduce this discrepancy, individuals set self-referenced challenging goals (Bandura, 1991). In doing so, individuals achieve new standards of performance, which in turn starts the

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process again. PB goal setting embodies much of this mechanism. PB goal setting is a strategy by which individuals recognize areas for personal improvement, strive to achieve it, and set new standards for personal excellence (Author 1, Co-Author, & Co-Author, in press). 3. SCT and a Proposed Longitudinal Model SCT utilizes the triadic model to argue that there are three central factors that impact human functioning: environmental, personal, and behavioral (Bandura, 1986). Following well-established operationalizing and ordering of the triadic model (e.g., Alliman-Brissett et al., 2004; Zimmerman et al., 1992), environmental factors are examined by way of parent, peer, and teacher social support in the current investigation. Personal factors comprise “selffactors” and “self-strategies”. Self-factors include self-beliefs and cognitive skills, and are operationalized in the current investigation as self-efficacy, perceived control, and adaptability (as a form of self-regulation). Self-factors can be considered as antecedents of self-strategies as they provide the foundational beliefs and skills that inform self-strategies. Self-strategies, operationalized as PB goal setting, are also included within the personal factors domain, as following from self-factors, because they represent the essential transitional step between the largely cognitive processes of self-factors to the overtly active behavioral factors (Bandura, 1991). Behavioral factors, which refer to students’ choices and outcomes, are operationalized in the current investigation as student engagement and achievement. Although cross-sectional research provides the foundational knowledge of the relationships among these constructs, Bandura (1986) argues that it is critical to examine these relationships across time to assess the initial impact of each factor and the resultant gains (Bandura, 1986). As such, the proposed model examines one integrative analytic longitudinal framework that demonstrates the predictive relationships from environmental factors to personal factors, within personal factors, and personal to behavioral factors, as presented in Figure 1. Below, we explain each of the model’s links.

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3.1. Environmental Factors and Connections to Personal Factors One’s social context is a key environmental factor (Bandura, 1986). Similarly, given that schooling is an inherently social experience, it is important to account for the role of interpersonal relationships in students’ academic functioning (Wentzel, 1994, 1999). Indeed, past research has demonstrated the significant impact of positive and negative social support in high school students’ academic lives (e.g., Cook, Deng, & Morango, 2007; Martin & Dowson, 2009; Maulana, Opdenakker, & Bosker, 2014; Reeve, 2009). As such, we examine how students’ social relationships may impact personal factors (i.e., self-factors and selfstrategies). 3.1.1. Social support to self-factors. As shown in Figure 1, we hypothesize that students’ relationships with their parents, peers, and teachers will predict key self-factors (self-efficacy, perceived control, and adaptability). With regards to self-efficacy, past research has found that positive social relationships with parents (e.g., Grolnick, Ryan, & Deci, 1991; Schunk & Pajares, 2002), peers (e.g., Cook et al., 2007; Schunk & Pajares, 2002), and teachers (e.g., Diseth, Danielsen, & Samdal, 2012; Usher & Pajares, 2008) are associated with higher self-efficacy. Indeed, Author 1, Co-Author, and Co-Author (in press) found that all forms of social support positively predicted student self-efficacy. However, theirs was cross-sectional work, so the present longitudinal study is an opportunity to explore potential shifts in self-efficacy across time. In regard to perceived control, past work has identified positive links with parent (e.g., Grolnick et al., 1991; Trusty & Lampe, 1997; Vargas, Dayuma, Galambos, Krahn, & Lachman, 2015) and teacher (e.g., Skinner, Wellborn, & Connell, 1990; Soenens & Vansteenkiste, 2005) social support (see also Author 1, Co-Author, & Co-Author, in press). In this way, students who experience positive relationships with their teachers and parents, are more likely to experience higher perceived control. However, there is little literature

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regarding peer support and perceived control, and even less on exploring shifts in control over time (as is the focus in the present investigation). As such, we hypothesize that social support will predict gains in perceived control. There has been no longitudinal work examining the relationships between social support and adaptability. However, past research into self-regulation and social support has shown that students’ self-regulation is positively influenced by quality relationships with parents, peers, and teachers (e.g., Bandura, 1991, Black & Deci, 2000; Grolnick & Ryan, 1989). We might thus infer that social support will similarly positively support students’ adaptability. Indeed, cross-sectional correlational findings by Author 1, Co-Author, and CoAuthor (in press) were consistent with this, finding that students with more positive relationships with parents, peers, and teachers also demonstrated higher adaptability. What is not known, however, is the role of social support in predicting shifts in adaptability across time—the focus of the present study. 3.1.2. Social support to PB goal setting. According to SCT, personal standards and goal setting are influenced by those who are significant to the individual, such as parents, peers, and teachers (Bandura, 1991; see also Wentzel, 1999). Thus, students who experience more positive relationships with parents, peers, and teachers may be more likely to utilize PB goal setting. While past work by Collie, Martin, Papworth, and Ginns (2016) found that all forms of social support predicted PB goal setting, their model did not examine these alongside the predictive role of self-factors, such as self-efficacy, perceived control, and adaptability (consistent with the SCT framework). Investigating social support alongside these other factors is important for understanding the unique associations that social support may have with PB goal setting (i.e., the role of social support in predicting PB goal setting beyond the predictive effects of self-efficacy, etc.). 3.2. Self-factors to Self-strategies

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As shown in Figure 1, it is contended that self-factors underpin self-strategies. Bandura (1991, 1997) argues that self-factors provide the foundational beliefs (e.g., selfefficacy, perceived control) and forethought, monitoring, and management (e.g., adaptability, as self-regulation) that guide and inform purposeful plans of action (i.e., goal setting). Given that self-beliefs tend to crystallize in high school and have been shown to impact post high school success (e.g., Hazari, Sonnert, Sadler, & Shanahan, 2010; Jones, 2008), it is important to understand the impact of self-factors on self-strategies in high school students. In line with SCT, we hypothesize that these self-factors (self-efficacy, perceived control, adaptability) are likely to positively predict PB goal setting (a self-strategy). In practice, for example, students who are high in self-efficacy and inclined to self-regulate in the face of change, novelty, and uncertainty, are hypothesized to upwardly regulate their goals (PB goal setting) in a way that mirrors their self-belief. 3.2.1. Self-efficacy to PB goal setting. Past work has clearly established the positive link between students’ self-efficacy and positive goal setting (e.g., Miller, Behrens, Greene, & Newman, 1993; Locke & Latham, 1990; Seifert & O’Keefe, 2001; Zimmerman, 2000; Zimmerman et al., 1992). Students who believe they have the capacity to accomplish tasks through effort and personal resources are more likely to set personally relevant goals (Bandura & Locke, 2003; Zimmerman et al., 1992). We therefore hypothesize that students higher in self-efficacy are more likely to utilize PB goal setting to set personally challenging targets over time. 3.2.2. Perceived control to PB goal setting. Perceived control enhances individuals’ capacity to direct their personal resources towards goals they feel are personally relevant, as well as prioritize goals they feel are personally significant (Schindler & Tomasik, 2010). As such, and in line with work by Bandura and Wood (1989), those with a higher sense of perceived control may be more likely to set goals that are personally challenging (i.e., PB

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goal setting). Interestingly, Author 1, Co-Author, and Co-Author (in press) found a negative relationship between perceived control and PB goal setting. They were unsure if this was the result of a suppression effect; thus, the present study is an opportunity to see if this unexpected result is replicated. 3.2.3. Adaptability to PB goal setting. SCT articulates the important role that selfregulation plays in personal goal setting (Bandura, 1991), such that self-regulation provides the forethought, monitoring, and management mechanisms necessary for setting and pursuing personal goals (Bandura, 1997; Cleary, 2015). As a form of self-regulation, adaptability is also expected to be a similar antecedent to personal goal setting. Students who are more capable of being self-directed in the face of change, novelty, and uncertainty may be more inclined to set self-directed goals, such as PB goals (Author 1, Co-Author, & Co-Author, in press). Also, because PB goal setting entails adjustment and regulation of one’s self-set targets (Martin & Elliot, 2016a), it follows that students who are inclined to adjust and adapt will also be more likely to utilize PB goal setting. 3.3. Self-strategies (PB Goal Setting) to Behavioral Factors Finally, SCT argues that personal goal setting is the essential translation of the largely cognitive processes of self-factors to intentional and purposeful behavior that enables individuals to achieve their desired outcomes (Bandura, 1991, 2006). As such, the impact of self-factors on behavioral factors is modeled via personal (PB) goal setting. By enabling students to visualize their desired future state, goal setting provides meaning to students’ behavior and this promotes or propels behavior (Bandura, 2006). Thus, it follows that students who employ PB goal setting to set personally relevant goals, may be more likely to behave in a way that is most conducive to achieving personal outcomes. Past work among high school student samples has found that PB goal setting predicts academic engagement (e.g., Martin & Elliot, 2016a) and achievement (e.g., Liem, Ginns, Martin, Stone, & Herrett,

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2012). Limited work has examined PB goal setting, engagement, and achievement within a single model (Martin & Liem, 2010) and alongside other substantive predictors (Author 1, Co-Author, & Co-Author, in press). In our longitudinal model, we are able to explore the novel role of PB goal setting in predicting shifts (hypothesized gains) in engagement and achievement. 4. Overview of Present Investigation Utilizing a longitudinal design to examine the relationships theorized under the triadic model of SCT, the present study examines (1) how two novel constructs, adaptability and PB goal setting, function alongside the well-established SCT constructs in the triadic model (viz. social support, self-efficacy, perceived control), and (2) the unique impact of these factors, especially adaptability and PB goal setting, on shifts in students’ academic outcomes by way of engagement and achievement. The present investigation is the first to examine all these factors in a longitudinal set-up in which it is hypothesized that: social support from parents, peers, and teachers (environmental factors) predicts gains in students’ self-efficacy, perceived control, adaptability, and PB goal setting (personal factors); self-efficacy, perceived control, and adaptability predict gains in PB goal setting; and, PB goal setting predict gains in academic engagement and achievement (behavioral factors). 5. Methods 5.1. Participants and Sampling Data were collected across two consecutive academic years (once in 2014 and once in 2015) from nine Australian high schools. The schools in the sample were local catholic or private schools across New South Wales, Victoria, and Western Australia. These schools were chosen based on the second author’s professional networks in the education community and aimed at capturing a nationally generalizable sample. The longitudinal sample (N=1,481) comprised students who were present at both data collections (both 2014 and 2015). This

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represented a 72% retention rate, which is considered acceptable for survey data (Taris, 2000). Sampling was ultimately determined by which students received parent/guardian consent for participation and were present at both data collections. For parsimony and clarity, the socio-demographic factors reported here refer to the data reported at time 2 (T2). Of the students sampled, just over half were female (53%). The mean age was 14.44 years (SD=1.00), with 564 students (38%) in grade 8, 398 students (27%) in grade 9, and 519 students (35%) in grade 10. In regard to non-English speaking background (NESB), 323 (22%) primarily spoke a non-English language at home. Students were also asked if they had been formally diagnosed with a learning disability (viz., reading, writing, or math difficulty, and/or attention-deficit/hyperactivity disorder; ADHD). Onehundred (7%) had at least one learning disability. In regard to prior achievement, students were asked to report their most recent score on a national standardized test (National Assessment Program-Literacy and Numeracy; NAPLAN). NAPLAN measures students’ literacy and numeracy, and estimates if students are meeting national academic benchmarks. Although students reported their literacy and numeracy scores on each survey, for continuity and to more clearly assess gains, the time 1 (T1) reported score was used. NAPLAN scores for both literacy and numeracy range on a scale of 1 (low) to 10 (high). The mean NAPLAN literacy score was 7.60 (SD=1.73) and the mean NAPLAN numeracy score was 7.63 (SD=1.81); 27 students (2%) did not report their numeracy NAPLAN scores and 29 students (2%) did not report their literacy NAPLAN scores. The literacy and numeracy NAPLAN scores were found to be significantly correlated (r=.68, p<.001). From these two responses a combined mean NAPLAN score, scaled from 1 to 10, was derived. The literacy and numeracy NAPLAN scores demonstrated reliability (α=.83) for the combined prior achievement score. The mean combined NAPLAN score (out of 10) was 7.50 (SD=1.57).

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Additionally, to test the validity of the self-reported NAPLAN scores, the T1 combined NAPLAN measure (time point used in the model) was compared with the published NAPLAN scores for each school sampled (available on the MySchool’s website); these scores were collated to create a single overall score from the MySchool data. MySchool is a subset of the Australian Curriculum Assessment Reporting Authority (ACARA), an independent curriculum body in Australia; the MySchool website contains data regarding a school’s funding, NAPLAN performance, and general descriptors. Results of a one-sample ttest indicate that there was not a significant difference between our sample’s self-reported NAPLAN score and their schools’ published NAPLAN score: t(1454)=-1.08, p=.28. Thus, the self-reported NAPLAN measures can be considered a reliable measure of prior achievement. The schools included in the sample were slightly above the national average for socio-economic status and NAPLAN scores (ACARA, 2011). 5.2. Materials All measures were included in a single survey that assessed target factors (adaptability, PB goal setting), substantive predictors (social support, self-factors) and substantive outcomes (engagement, achievement). All factors, excluding achievement, were measured using scales ranging from 1 (Strongly Disagree) to 7 (Strongly Agree). Achievement was assessed via objective numeracy and literacy tests (detailed below). Descriptive statistics, reliabilities, and factor loadings are presented in Table 1. Reliability for all substantive factors (T1, T2) was assessed via Cronbach’s α, with values of .65 as the minimally accepted level (Anastasi & Urbina, 1997; Sattler, 2001); the reliabilities reported in the following sections refer to the T1 and then T2 reliability scores (e.g., α=T1α; T2α). 5.2.1. Parent support. The Self-Description Questionnaire II (SDQ II) scale (parent self-concept items; Marsh, 2007) measured parent support (4 items; e.g., “My parents understand me”). This scale demonstrated reliability (α=.91; .90). Past investigations have

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demonstrated the internal and external validity of this measure (see Marsh, 2007 for a review; Martin, Marsh, McInerney, Green, & Dowson, 2007). 5.2.2. Peer support. The SDQ II (opposite- and same-sex peer relationship items) measured peer support (4 items; e.g., “Overall, other students are interested in me, what I do, and what I think”). This scale demonstrated reliability (α=.85; .86). As stated above, the internal and external validity of the SDQ II has been demonstrated (Marsh, 2007). 5.2.3. Teacher support. Teacher support (4 items; e.g., “In general, my teachers really listen to what I have to say”) was measured using items developed by Martin and Marsh (2008). This scale demonstrated reliability (α=.89, .90). Previous work has demonstrated the validity of this measure (Collie et al., 2016; Martin & Marsh, 2008). 5.2.4. Self-efficacy. The Motivation and Engagement Scale-High School (MES-HS; Martin, 2009) was used to assess self-efficacy (4 items; e.g., “If I try hard, I believe I can do my schoolwork well”). This scale demonstrated reliability (α=.81; .86). The MES-HS has demonstrated internal and external validity across a range of samples in numerous studies (see Liem & Martin, 2012 for a review). 5.2.5. Perceived control. The MES-HS (Martin, 2009) measured perceived control via uncertain control (inversely scored; 4 items; e.g., “When I get a good mark I’m often not sure how I’m going to get that mark again”). This scale demonstrated reliability in this investigation (α=.83; .85). As stated above, the internal and external validity of the MES-HS has been demonstrated (see Liem & Martin, 2012 for a review). 5.2.6. Adaptability. The 9-item Adaptability Scale (Martin et al., 2013) was used to assess cognitive, behavioral, and emotional facets of adaptability. Examples of survey items are: “I am able to think through a number of possible options to assist me in a new situation” (cognitive), “I am able to seek out new information, helpful people, or useful resources to effectively deal with new situations” (behavioral), and “I am able to reduce negative

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emotions (e.g., fear) to help me deal with uncertain situations” (emotional). A first order adaptability factor was employed and estimated by way of the 9 items. Thus, adaptability is a multi-dimensional construct that comprises cognitive, behavioral, and emotional components. This factor demonstrated reliability (α=.91; .91; reliabilities for the individual cognitive, behavioral, and emotional components are reported in Table S.2 in Supplementary Materials). Martin, Nejad, Colmar, and Liem (2012) demonstrated the reliability and validity of the Adaptability Scale, as well as evidence that a single adaptability factor is empirically sound and operationally preferable. Similarly, Collie and Martin (2017) demonstrated external validity for the single adaptability factor, as student self-reports of adaptability were significantly correlated with teacher reports of student adaptability. Lastly, in line with SCT, the single first order adaptability factor was used to align with how self-regulation (its cognate construct in the triadic framework) is operationalized in SCT (e.g., Clearly & Chen, 2009; Zimmerman, 2002). 5.2.7. PB goal setting. The PB Goal Scale (Martin & Liem, 2010) measured student PB goal setting (4 items; e.g., “When I do my schoolwork I try to improve on how I’ve done before”; “When I do my schoolwork I try to do the best that I’ve ever done”). This scale demonstrated reliability (α=.90; .91). Past work has provided evidence of this scale’s reliability and validity (Martin & Liem, 2010). 5.2.8. Engagement. Fredricks, Blumenfeld, and Paris’s (2004) tripartite approach was used to represent engagement. Twelve items were administered to assess cognitive, behavioral, and emotional engagement (cognitive, 4 items; e.g., “I want to continue with and complete school”; behavioral, 4 items; e.g., “I participate in activities and discussions”; emotional, 4 items; e.g., “I enjoy school”). Due to high inter-factor correlations (e.g., emotional engagement with cognitive engagement; this indicated an underlying general construct), a higher order engagement factor was employed; this aligns with previous

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research (e.g., Archambault, Janosz, Fallu, & Pagani, 2009; Jang, Reeve, & Deci, 2010; Reeve & Lee, 2014). The higher order factor demonstrated reliability (α=.93; .93; reliabilities for the individual cognitive, behavioral, and emotional components are reported in Table S.2 in Supplementary Materials). Previous work has also demonstrated the reliability and validity of these items (e.g., Martin & Liem, 2010). 5.2.9. Achievement. An objective 10-item numeracy and a 10-item literacy test were administered upon completion of the above survey items to measure achievement. The literacy test is a brief adaptation of a literacy test validated by Green and colleagues (2012), and the numeracy test is a subset of a longer form validated by Martin, Anderson, Bobis, Way, and Vellar (2012). The instruments were shortened (due to time constraints) and formatted for online completion. Both tests followed a graduated design, such that question difficulty increased with each question. As a result, the tests are more sensitive to and can better differentiate between grade levels (Martin, Anderson et al., 2012). These tests, after controlling for prior achievement scores (self-reported T1 NAPLAN), offer an objective measure of student achievement over time. The in-survey achievement test was a means of collecting contemporaneous achievement data within the conditions of an in-class survey, while the self-reported NAPLAN score was a prior assessment traversing a wider range of literacy and numeracy achievement. Each achievement measure was thus considered the most ideal means to represent students’ performance within the parameters of the study. It is important to note the mean scores and correlation between the literacy and numeracy achievement scores that are the basis of the substantive overall achievement measure at both T1 and T2. For T1, the mean literacy score was 8.93 (SD=1.41) and the mean numeracy score was 7.46 (SD=1.72); these scores were significantly correlated (r=.39, p<.001) and demonstrated sound reliability (α=.70). For T2, the mean literacy score was 9.08 (SD=1.36) and the mean numeracy score was 7.89 (SD=1.66); these scores were significantly

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correlated (r=.38, p<.001) and demonstrated sound reliability (α=.72). A single overall achievement score was employed and calculated via the combined means of the literacy and numeracy quizzes within each time point. The correlation between T1 and T2 overall achievement is reported as prior variance in Table 2b. The reliability and validity of the tests, as well as their ability to differentiate across grade levels has been demonstrated in recent work (Tarbetsky, Collie, & Martin, 2016), as has the use of the higher order factor (Author 1, Co-Author, & Co-Author, in press). For the current investigation, Cronbach’s alpha (as derived from the literacy and numeracy scores) for T1 was α=.70 and for T2 was α=.72. 5.2.10. Covariates. To effectively assess the substantive issues in the present investigation, we controlled for variance attributable to socio-demographic and other background factors. The covariates included in this investigation were chosen because prior work has found each to be significantly associated with some (or all) of the substantive factors. As such it is necessary to account for their impact in the model. The following covariates were included: age (operationalized as a continuous variable), gender (0=female; 1=male), non-English speaking background (NESB) status (0=English speaking; 1=nonEnglish speaking), socio-economic status (a continuous measure of Australian Bureau of Statistics relative disadvantage and advantage based on students’ home postcode and where a higher score represents higher SES), learning disability (0=no formal diagnosis of reading, writing, mathematics, or ADHD difficulties; 1= formal diagnosis of at least one of these difficulties), and prior achievement (NAPLAN literacy and numeracy band scores between 1 [low] and 10 [high]). It is important to note that the T1 covariate measures were used in all analyses for consistency. Controlling for these covariates, alongside other substantive factors, enabled us to partial out the variance attributable to target and substantive factors, and thus ascertain the unique role of adaptability and PB goal setting on students’ academic gains. 5.3. Data Analysis

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A structural equation modelling approach was utilized for central data analysis, with confirmatory factor analysis (CFA) conducted to test the underlying measurement properties and structural equation modelling (SEM) conducted to test the substantive hypotheses that were central to this investigation. Invariance testing and indirect effects testing were conducted as subsidiary tests. All analyses were completed in Mplus v7.30 (Muthén & Muthén, 2015). 5.3.1. CFA and SEM. CFA was used to ensure the appropriate factor loading on substantive constructs and to determine the latent correlations among covariates and substantive factors. SEM was then employed to examine the strength of the predictive paths among the covariates and substantive factors hypothesized in Figure 1. The CFA and SEM estimation procedures were maximum likelihood with robustness to non-normality (MLR; Chou & Bentler, 1995). To account for the clustering of students within schools, the “cluster” and “complex” commands were used in Mplus to adjust standard errors. Missing data were handled using FIML, the default in Mplus. FIML produces model fit estimations based on the maximum amount of data available from individual cases and has been shown to produce unbiased results (Arbuckle, 1996). To test for significant patterns of attrition, invariance tests were conducted to compare students who had taken the survey at both times (longitudinal sample) and students who had only taken it once (sampled at only T1 or T2). Two sets of invariance tests were conducted to compare the factor structure of the (1) those present at both T1 and T2 and those present only at T1, and (2) those present at both T1 and T2 and those present only at T2. Because this study is about associations among factors in the model, the chief point on which to examine for differential patterns of attrition is on these inter-relationships. Thus, two consecutive models were run: the first model constrained all parameters, but not correlations among factors; the second model constrained all parameters, including correlations among factors.

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Invariance was determined based on the changes in the comparative fit index (CFI) and root mean square error of approximation (RMSEA) across these models (∆CFI no greater than .01, Cheung & Rensvold, 2002; ∆RMSEA no greater than .015, Chen, 2007). For the T1-T2 sample compared to T1-only sample: unconstrained correlations, χ² (864)=3328.75, p<.000; CFI=.943; RMSEA=.041, and constrained correlations, χ² (900)=3417.95, p<.000; CFI=.942; RMSEA=.040. For the T1-T2 sample compared to with the T2-only sample: unconstrained correlations, χ² (864)= 4503.76, p<.000; CFI=.934; RMSEA=.043, and constrained correlations, χ² (900)= 4560.64, p<.000; CFI=.934; RMSEA=.042. Based on changes in CFI and RMSEA (Chen, 2007; Cheung & Rensvold, 2002), we concluded that the T1-T2 students (the sample examined here) can be considered broadly representative of the students (including those only at one of the two time points) at the nine schools sampled. For both CFA and SEM, the CFI and RMSEA were the examined fit indices. CFI values greater than .90 indicate adequate fit and those above .95 indicate excellent fit; RMSEA values below .08 indicate adequate fit and below .06 indicate excellent fit (Hu & Bentler, 1999). The ² test statistic is reported but not relied on, given its sensitivity to large sample sizes (Bryant & Yarnold, 1995). It is important to note that the CFA included all T1 and T2 substantive factors. Similarly, the SEM included the auto-regressions of the T2 substantive factors on their T1 counterparts. That is, T1 (e.g., T1 adaptability) predicted its T2 counterpart (T2 adaptability) so that effects for that T2 construct are controlled for (prior variance). Doing so allows for the salience of the unique predictive pathways among substantive factors to be more clearly seen (i.e., the role of various constructs predicting T2 adaptability beyond prior T1 variance in adaptability). Moreover, having controlled for prior variance, the predictive paths can be considered as indicative of predicted gains or declines in the dependent variable, such that the structural relationships (beta values) between the T2 substantive variables indicate the

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predictive gains (positive) or declines (negative) in the substantive factors (McArdle, 2009). Thus, for example, having controlled for T1 variance in adaptability, we can consider significant predictors of T2 adaptability as predictive of gains (or declines) in adaptability. This enables the assessment of growth across the academic year of study. 5.3.2. Subsidiary tests. Invariance testing (multi-group CFA and SEM) and indirect effects testing were conducted to provide a more thorough understanding of the relationships within the model. For multi-group CFA and SEM, the model was examined for invariance across key subgroups to ensure data could be pooled rather than examined as a function of subgroup—and to know that significant effects in the model are not due to underlying differences in measurement properties as a function of sample sub-groups. The subgroups examined were: younger students (<13.47 years old; based on a mean split) and older students (>13.47); males and females; students with and without learning disabilities; English and non-English speaking background students; low (<7.50 NAPLAN score, based on mean split) and high prior achievement (>7.50 NAPLAN score). For multi-group CFA, six consecutive models were run, progressively constraining factor loadings, intercepts, residuals, and correlations. For multi-group SEM, two consecutive models were run: the first was an unconstrained model with all betas freely estimated (while all other parameters were constrained across subgroups); the second model constrained all parameters (including betas). For both multi-group CFA and SEM, the RMSEA and CFI were monitored to ensure changes did not exceed .015 and .01, respectively, to conclude invariance (Chen, 2007; Cheung & Rensvold, 2002). Following multi-group analyses, indirect effects testing was conducted to examine the extent to which factors centrally located within the hypothesized model might mediate the relationship between antecedent and outcome factors. Indirect effects were assessed via nonparametric bootstrapping with 1000 draws (Bollen & Stine, 1990; Shrout & Bolger, 2002).

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Additionally, although our model specifications rest on formal conceptualizing under SCT, we recognize other paths are possible. For example, it is possible that self-efficacy would predict achievement directly and not just via PB goal setting. Accordingly, we conducted tests (via modification indices) for any additional paths that should be freed. 6. Results 6.1. Descriptive and Psychometric Analyses The descriptive statistics (mean, standard deviations, skew, kurtosis), reliabilities (Cronbach’s α), and factor loadings for multi-item factors are reported in Table 1. The statistics for skewness and kurtosis indicate that each factor demonstrated approximately normal distribution. The reliability scores, as based on a Cronbach’s α greater than .65 (Anastasi & Urbina, 1997; Sattler, 2001), demonstrate the internal consistency of all factors. The factor loadings for each multi-item substantive factor were also found to be acceptable. 6.2. Confirmatory Factor Analysis The CFA was employed to test the factor integrity (i.e., examine cross-loading) of each latent construct, as well as examine the bivariate correlations between the latent constructs and covariates for potential collinearity. The CFA tested a 17-factor model, comprising 8 first order factors (parent support, peer support, teacher support, self-efficacy, perceived control, adaptability, and achievement), 1 higher order factor (engagement), and 8 covariates (age, gender, NESB, SES, parent education, parent occupation, learning disability, and prior achievement). The CFA model (including all substantive factors and covariates) yielded adequate fit: χ2(4478)=10668.83, p<.001, RMSEA=.031, CFI=.921. All latent correlations are reported in Tables 2a and 2b (for completeness, individual CFAs for substantive factors and alternative factor structures can be found in Table S.1 in Supplementary Materials). In the text to follow we report all significant correlations (at p<.001) as relevant to the hypothesized substantive (T2 factors) relationships in Figure 1.

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Table 2a shows all correlations among substantive factors and covariates; Table 2b shows all correlations among substantive factors. Parent, peer, and teacher support, respectively, correlated with self-efficacy (r=.53; r=.53; r=.61), perceived control (r=.21; r=.23; r=.26), adaptability (r=.60; r=.70; r=.69), and PB goal setting (r=.52; r=.53; r=.57)2. Self-efficacy (r=.74), perceived control (r=.19), and adaptability (r=.72) correlated with PB goal setting. PB goal setting correlated with engagement (r=.74) and achievement (r=.20). Regarding prior variance shared with counterpart factors (auto-correlations), all T2 factors were significantly (p<.001) correlated with their T1 counterparts. The results of the confirmatory factor analysis indicated sound factor integrity and provided correlational evidence to support the structural equation modelling. 6.3. Structural Equation Modelling The SEM (including both substantive factors and covariates) yielded a good fit to the data: χ2(4562)=11719.89, p<.001, RMSEA=.033, CFI=.909. In the text to follow, we report all significant beta paths as relevant to the proximal paths (T2) in Figure 1. Figure 2 presents significant beta paths for all substantive factors, including their T1 auto-regression. All beta paths, both significant and non-significant, for covariates and substantive factors are shown in Table 3 (for completeness and to better understand the relationships among the substantive factors without covariates, see Table S.3 in Supplementary Materials). It is also important to note that the modification indices did not signal the need to free additional paths beyond those originally hypothesized. As discussed in Methods, having controlled for prior variance (auto-regression), the beta values between the T2 substantive factors can be considered as predicting gains or declines in the subsequent substantive factor—thus, capturing change over time. Parent support predicted gains in self-efficacy (β=.18, p<.001) and adaptability (β=.21, p<.001). Peer support predicted gains in self-efficacy (β=.08, p<.05) and adaptability

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(β=.29, p<.001). Teacher support predicted gains in self-efficacy (β=.30, p<.001), adaptability (β=.25, p<.001), and PB goal setting (β=.06, p<.05). As relevant to personal factors, self-efficacy (β=.39, p<.001) and adaptability (β=.33, p<.001) predicted growth in PB goal setting. Perceived control predicted reduced PB goal setting across time (β=-.09, p<.01). PB goal setting predicted growth in engagement (β=.65, p<.001) and achievement (β=.10, p<.001). Because each T2 factor was significantly predicted by its T1 counterpart (p<.001), the substantive parameters described above (e.g., between self-efficacy and PB goal setting) are effects beyond the significant role played by their T1 counterparts (auto-regression). Although not central to our investigation, for completeness, we report the covariates that significantly (at p<.001) predicted substantive factors. Prior achievement significantly predicted peer support (β=.06), perceived control (β=.10), adaptability (β=.05), and achievement ((β=.22), such that students with higher prior achievement evinced more growth on each of these factors. 6.4. Invariance Testing Multi-group CFA and SEM were used to examine invariance across key subgroups (age, gender, NESB, learning disabilities, and prior achievement). Tables 4a and 4b present the changes in CFI and RMSEA by subgroup for the multi-group CFA and SEM, respectively. The change in CFI and RMSEA was monitored as parameters within the model were progressively constrained. Multi-group CFA and SEM was conducted in both crosssectional samples, as well as the longitudinal sample. To account for invariance across time, the T1-T2 correlations are constrained in the latter models. The longitudinal findings are discussed here. For each multi-group analysis that constrained parameters as a function of each subgroup, analyses indicated that the the RMSEA and CFI did not change beyond acceptable levels (viz., ΔRMSEA≤.015; Chen, 2007; ΔCFI≤.01; Cheung & Rensvold, 2002). This suggests that the relationships in the hypothesized model did not function differently as

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a result of age, gender, language background, learning disability, and prior achievement in the longitudinal sample. We therefore infer that key relationships within our model generalize across each of these subgroups. 6.5. Indirect Effects Bootstrapping techniques were employed to examine the extent to which the relationship between antecedent and outcome factors were mediated by centrally located factors (Bollen & Stine, 1990). Here we report the indirect effects significant at p<.001; all indirect paths are shown in Table 4. Indirect effects leading to PB goal setting are: Parent support → Self-efficacy → PB goal setting (β= .07), Teacher support → Self-efficacy → PB goal setting (β=.12), Parent support → Adaptability → PB goal setting (β=.07), Peer support → Adaptability → PB goal setting (β=.10), and Teacher support → Adaptability → PB goal setting (β= .08). Indirect effects leading to engagement are: Parent support → Self-efficacy → PB goal setting → Engagement (β=.04), Teacher support → Self-efficacy → PB goal setting → Engagement (β=.07), Parent support → Adaptability → PB goal setting → Engagement (β=.04), Peer support → Adaptability → PB goal setting → Engagement (β=.06), Teacher support → Adaptability → PB goal setting → Engagement (β=.05), Selfefficacy → PB goal setting → Engagement (β=.24), Perceived control → PB goal setting → Engagement (β=-.06), and Adaptability → PB goal setting → Engagement (β=.20). Indirect effects leading to achievement are: Adaptability → PB goal setting → Achievement (β=.03). As is evident, in many of these indirect effects, adaptability and PB goal setting play a salient role. As Table 5 also shows, there were statistically significant indirect effects involving adaptability, PB goal setting, and achievement (at p<.05 and p<.01), but these did not attain significance at the conservative p<.001 level. 7. Discussion

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As informed by the triadic model of SCT, this longitudinal investigation examined the role of adaptability and PB goal setting in shifts (gains or declines) in students’ academic outcomes. The findings largely supported the hypothesized model. Major findings and their implications for theory, research, and practice are discussed in turn. 7.1. Environmental Factors to Self-factors and Self-strategies 7.1.1. Social support to adaptability. Parent, peer, and teacher support were predictive of growth in adaptability. This suggests that positive experiences of parent, peer, and teacher support play an important role in students’ ability to cognitively, behaviorally, and emotionally regulate over time. Similarly, given that social support predicts adaptability beyond the effects of T1 measurement, the findings suggest that social support is impactful over time, such that the more positive experiences one has with parents, peers, and teachers, the more likely one’s adaptability is to develop over time As Author 1, Co-Author, and CoAuthor (in press) suggested, it may be that these supportive relationships instill a belief and a capacity to regulate effectively in the face of change, novelty, and uncertainty. Given that past work has largely focused on the intrapersonal predictors of adaptability (e.g., personality, implicit theories, socio-demographic factors; Martin et al., 2013; Martin et al., 2016), these findings provide more information about the environmental factors (interpersonal) that may impact adaptability. These findings also align with SCT, which argues that an individual’s self-regulation is most influenced by those with whom they spend the most time and those who set social standards (Bandura, 1991). Given that students spend the majority of their time among their peers and teachers, and social standards are often set by parents, peers, and teachers, this study provides evidence that a student’s adaptability, as a form of selfregulation, is likely to be influenced by this relational support. 7.1.2. Social support to self-efficacy and perceived control. Self-efficacy was found to be significantly predicted by parent, peer, and teacher support. These findings provide

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longitudinal evidence for the continued influence of social support on self-efficacy development (e.g., Schunk & Pajares, 2002; Usher & Pajares, 2008). Interestingly, however, perceived control was not predicted by parent, peer, or teacher support. It may thus be that students’ sense of control is derived from other sources. Skinner (1996), for example, has suggested that the complexity of the task may impact perceived control, such that more complex tasks may reduce a sense of control. Further research is needed to explore the extent to which this may be the case. 7.1.3. Social support to PB goal setting. Only teacher support directly predicted growth in PB goal setting over time (although past cross-sectional work has found all forms of social support to significantly predict PB goal setting; Author 1, Co-Author, & Co-Author, in press; Collie et al., 2017). This finding indicates that teacher-student relationships play a supportive and promotive role in students’ PB goal setting. This aligns with previous research, which demonstrates that teacher support positively promotes adaptive goal setting, such as pro-social goal setting (Wentzel, Baker, & Russell, 2012) and mastery goals (e.g., Patrick, Ryan, & Kaplan, 2007). However, these findings extend previous work in two notable ways. First, this finding extends the importance of teacher support to growth goal setting, such that students who feel more supported by their teachers may be more likely to use PB goal setting. Second, this relationship was examined simultaneously with parent and peer support, as well as students’ self-beliefs. This provides a more contextualized understanding about the unique variance explained by teacher support (beyond other social and personal factors) in PB goal setting. 7.2. Self-factors to Self-strategies. 7.2.1. Adaptability to PB goal setting. Adaptability was found to significantly predict students’ PB goal setting over time. This suggests that, as a form of self-regulation, adaptability may be an explanatory mechanism of PB goal setting. The fact that self-

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regulation is considered a vital antecedent of goal pursuit (Locke & Latham, 1990) suggests that one’s ability to regulate thoughts, behaviors, and emotions also impacts the capacity to set and pursue goals (Bandura, 1991; Locke & Latham, 1990). Indeed, Bandura (1991) argues that self-regulation is critical to self-directed change and growth. As such, these findings suggest that those who are capable of upwardly or downwardly regulating their cognitive, behavioral, and emotional patterns, may be better able to upwardly regulate their goals such that they are personally challenging and self-directed, like those of PB goal setting. This may be especially true for changing, novel, and uncertain circumstances, during which goals must also be adjusted to meet task and situational demands. As such, our longitudinal findings (that explicitly explore such shifts) suggest that those with higher levels of adaptability, as a form of self-regulation, may be more likely to set personally challenging and self-directed goals, including growth in PB goal setting over time. 7.2.2. Self-efficacy and perceived control to PB goal setting. Self-efficacy was found to be significantly predictive of growth in PB goal setting. These findings provide further evidence for the well-established link between self-efficacy and personal goal setting (e.g., Zimmerman et al., 1992). However, while past work has shown that those with higher levels of self-efficacy are more likely to set more personally challenging goals (Bandura, 1991; Martin & Elliot, 2016a), our findings provide evidence for the significant positive impact of self-efficacy on growth in PB goal setting over time. Counter to expectations, perceived control was found to be a significant (albeit minimally) negative predictor of PB goal setting over time. Although this finding was unexpected (but see Author 1, Co-Author, & Co-Author, in press), it is in line with work suggesting that students with high levels of perceived control may adopt maladaptive strategies to protect their sense of control (Skinner, 1995). However, given the small parameter estimate of this relationship, it may be that, when compared alongside other

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predictors, perceived control has a less apparent role in PB goal setting. Future work may consider looking at the relationships between the predictors of PB goal setting to better understand this relationship. 7.3. PB Goal Setting to Behavioral Factors PB goal setting was found to predict significant growth in student engagement and achievement. These findings provide further support for past cross-sectional work regarding the relationship between PB goal setting, engagement, and achievement (e.g. Author 1, CoAuthor, & Co-Author, in press; Martin & Elliot, 2016a) and also confirm longitudinal work that has examined some of these relationships (e.g., Martin & Liem, 2010). However, it is worth considering why PB goal setting more strongly predicted gains in engagement than in achievement. Given that PB goal setting focuses on self-referenced goals, it may be that they are more closely related to processes and activities that require frequent personal investment, such as class participation, future academic intent, and school enjoyment. As such, PB goal setting may be more associated with engagement factors than it is with achievement (Locke & Latham, 2013). Another possible reason is that the achievement measures used in this investigation are domain-specific achievement measures (literacy and numeracy)—that were aggregated to approximate a domain-general achievement index, whereas the engagement measure was domain-general. Thus, the relationship between domain-general PB goal setting and domain-general engagement may be stronger than the relationship between domaingeneral PB goal setting and an achievement index that was domain-general by statistical aggregation. These findings provide further evidence that goals create a mental framework through which individuals understand and evaluate the choices, behaviors, and outcomes associated with their goals, especially in how their goals relate to engagement behaviors (Deshon & Gillespie, 2005). PB goal setting utilizes personally referenced targets that are likely to create

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a personally referenced mental framework that emphasizes personal growth and excellence. As such, students who employ PB goal setting may evaluate and choose behaviors that are more conducive to personal growth, such as engagement and achievement striving. Similarly, SCT argues that personal goal setting enables individuals to continually selfimprove. SCT posits a personal discrepancy production and reduction mechanism, which holds that individuals set and pursue personally challenging goals to reach their desired future states (Bandura, 1991). Once a desired state has been reached, the process begins again. It may be that these personally challenging goals, such as those of PB goal setting, perpetuate this cycle, leading to desired gains in areas such as engagement and achievement—as indicated by our data. 7.4. Implications for SCT SCT is a theoretical framework that seeks to understand the mechanisms underlying human motivation and competence (Bandura, 1986). The aim of the current investigation was to examine how recent developments in self-regulation (viz., adaptability) and goal setting (viz. PB goal setting) function within the SCT framework alongside well-established constructs. As new psycho-educational constructs are developed, it is important to examine potential yields from revisiting classic models, such as the triadic model, to ascertain the role of these new constructs in these seminal theories. Our investigation supports the viability of including adaptability and PB goal setting within the SCT framework. In addition, it may also be that these recently developed constructs provide potentially new insights into SCT. For example, Bandura (1991) emphasized the role and influence of context on human motivation and functioning across time. As a construct specifically oriented to changing, novel, and uncertain environments, adaptability may provide a different perspective on the extent to which situational context affects self-regulation over time. As discussed above, SCT poses the personal discrepancy production and reduction mechanism. To our knowledge, the PB

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Goal Scale is among the first explicitly developed survey instruments that may enable researchers to empirically assess this discrepancy production/reduction mechanism. 7.5. Implications for Classroom Practice The findings of this investigation suggest that adaptability and PB goal setting play a positive role in student academic functioning, and thus may be considered as important points of intervention in the classroom. Our findings also provide further evidence for the importance of promoting social support, especially teacher support, and self-efficacy. The following section is a discussion of the implications of our study for educational practice. Moving through the hypothesized model (Figure 1), beginning with social support, it is important to consider how to promote quality relational dynamics in the academic context. “Connective instruction” (Martin & Dowson, 2009) has been developed as an approach to optimize teacher-student relationships. Here, teachers seek to connect to students via personal (e.g., showing interest in the student), substantive (e.g., presenting relevant and interesting subject matter), and pedagogical (e.g., clear, thoughtful, structured communication) channels. To optimize peer relationships in the academic context, cooperative learning is a viable consideration (Johnson & Johnson, 1999). With regards to self-factors, the findings suggest that students’ self-efficacy and adaptability positively impact students’ use of positive academic strategies (e.g., PB goal setting). To promote self-efficacy, teachers may consider reframing classroom activities and instruction to provide more opportunities for success (hence, perceived competence). Teachers may consider restructuring activities into smaller tasks, so as to allow students to experience a greater sense of accomplishment and efficacy (Author & Co-Author, 2014). Self-efficacy development can also be supported through communicating positive belief in the student, such as through direct messages from the teacher (Usher & Pajares, 2008).

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Adaptability may be fostered by encouraging a positive attitude towards change, novelty, and uncertainty. Given that school environments are often variable by nature, it is important to encourage students to view such changes as opportunities, rather than as barriers (Martin et al., 2013). Similarly, teachers may refocus students so that they reflect on the aspects of the situation they can control (e.g., effort, attitude, strategy), rather than those they cannot (e.g., luck, assignment difficulty). Martin and colleagues (2013) proposed an adaptability intervention that helps students recognize and adjust to change, novelty and uncertainty, emphasizing the importance of self-regulating in such situations. The findings also indicate that students’ use of PB goal setting has a significant impact on growth in both engagement and achievement. This suggests that promoting PB goal setting (and potentially other growth-oriented strategies) within the classroom may have positive academic outcomes. Martin (2006) identified intervention suggestions to employ PB goal setting. Working with their teacher, students are encouraged to identify, develop, and plan clear, realistic, and personally challenging goals that promote personal growth (Martin, 2006). Recent experimental work provides support for the effectiveness of PB goal setting interventions (Martin & Elliot, 2016b). 7.6. Implications for Australian High School Students Although the findings of this study are considered generalizable (large sample size, alignment with previous related work, etc.), it is important to consider the implications of adaptability and PB goal setting for our sample: Australian high school students. In the past decade, there has been an increasing focus on national standardized testing in Australian schools. NAPLAN, as discussed previously, is a national annual assessment measuring literacy and numeracy achievement among Australian students. Although originally intended as a low-stakes test, schools and other stake-holders have increasingly focused on NAPLAN scores as a means to compare school performance, and two states (NSW and WA) have

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introduced minimum NAPLAN scores requirements for “higher school certificate” (final school qualification) eligibility. Given this increasing focus on standardized testing, it is likely that inter-student comparison may increase (McNeil, 2000). As such, it is important to consider motivational constructs, such as adaptability and PB goal setting, that may buffer against the welldocumented negative side-effects of inter-student comparison (e.g., Frenzel, Pekrun, & Goetz, 2007). Adaptability, as a self-regulatory mechanism critical for handling change, novelty, and uncertainty, may be beneficial for helping students develop strategies to navigate unexpected questions during the test (cognitive and behavioral adaptability), as well as adverse emotions related to both inter-student comparison and testing (emotional adaptability). PB goal setting may be similarly beneficial given its emphasis on personal excellence and striving. As such, encouraging students to focus on PB goal setting may help them behave in ways conducive to maintaining self-direction and remaining focused on personal striving (as demonstrated by our findings), rather than demonstrating achievement as relative to others. Moreover, to the extent these contentions relate to Australian students who are increasingly subjected to the exigencies of national assessment and benchmarking exercises, they are also relevant to students in other national or jurisdictional contexts where similar assessment regimes exist. 7.7. Limitations and Future Directions The present investigation has potential limitations that are important to consider in regard to the interpretation of the findings and possible direction for future research. First, although an objective achievement measure was used at both T1 and T2, other substantive factors were measured using self-report. In addition, even though self-report data are widely used and effective for measuring personal and intra-psychic phenomena (Brener, Billy, & Grady, 2003), there are some known limitations, such that students may misinterpret

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questions, under- or over-report their responses, or not answer in line with their actual behavior (Karabenick et al., 2007). Future work may look at gathering information from other sources, such as observation, interviews, and teacher and/or parent reports. For example, as relevant to the present study, Collie and Martin (2017) collected teacher reports of students’ adaptability as a means to collect objective data on a construct that is predominantly accessed via self-reports. Second, student attrition across the two data collections does impact which students contributed data to this study. While reasons for attrition may vary and the attrition in this study was not high, it is possible that the reasons why students did not participate at T2 may be substantively relevant; however, invariance tests for significant patterns of attrition did not demonstrate significant differences between these groups. Third, our study focused on only the Zimmerman model of self-regulated learning as underpinning adaptability. Because this model only provides one perspective on selfregulated learning, it is important that future work explore alternative, but complementary, theories of self-regulation (e.g., Winne & Hadwin, 2008; Panadero, 2017). Fourth, although this study focused on substantive factors and covariates outlined by SCT, we recognize that there are other factors that could have been considered. Regarding environmental factors, we followed Bandura’s (1991) advice that social support is a key environmental factor, but future research might also consider factors, such as classroom climate and goal structures, that have also been identified as notable environmental factors (Meece, Anderman, & Anderman, 2006; Reeve, 2009). We also recognize that there are other covariates important to account for in future research. For example, ethnicity has been represented in other triadic research (Bong, 1997; Usher & Pajares, 2006) and would be useful to consider in the context of the factors studied here. In a similar vein, although our present investigation focuses on goal setting (Locke & Latham, 2013) and not achievement goal theory (Elliot, 2005), there are other goal constructs, such as mastery and performance goals and the recently proposed self-based goals

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under the 3x2 framework (Elliot, Murayama, & Pekrun, 2011), that may be implicated. Future work should investigate this model alongside these goals in order to assess their unique impact on student engagement and achievement. Fifth, while longitudinal data provide evidence for the directional nature of the substantive relationships within the model, other statistical analyses, such as latent growth modelling, may provide further information about the nature of these relationships; these types of analyses require more than two time points. Finally, the present study was a domain-general one; further research might investigate these factors and processes within specific school subjects. 8. Conclusion As new psycho-educational constructs are identified, there can be yields in revisiting classic theories to ascertain the role of these new constructs in seminal conceptualizing. The present investigation examined how two such constructs, adaptability and PB goal setting, operated alongside more “traditional” constructs in SCT’s triadic model. Findings demonstrated the positive impact of adaptability and PB goal setting on gains in students’ academic outcomes. They have also added to the growing body of work in self-regulation (for adaptability) and goal setting (for PB goal setting) and hold implications for future research, theory, and practice.

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Footnote 1

Although considered a goal setting strategy, it is important to note how PB goal setting differs from and complements achievement goal theory (Elliot, 2005). Following the 3x2 framework, (Elliot, Murayama, & Pekrun, 2011), PB goal setting can be differentiated from the other achievement goals in that PB goal setting is most closely aligned with self-based goals (i.e., criteria and expectations set by the individual), with the important distinction being that PB goal setting focuses on improving beyond one’s past best effort, whereas selfbased goals focus on improving beyond one’s typical effort (Martin & Elliot, 2016a). PB goal setting can be further distinguished from the classic dichotomous goals in that mastery goals are task-based (i.e., absolute criteria of the task) and performance goals are other-based (i.e., performance in comparison to peers; Martin & Elliot, 2016a). As such, PB goal setting can be considered as one way to operationalize the self-based goals of the 3x2 framework. 2

It is important to note that peer and teacher support demonstrated high correlations with engagement (r=.82, p<.001 and r=.86, p<.001, respectively). As Abbott-Chapman and colleagues (2014) discuss, interpersonal relationships and engagement are likely to be correlated, as in-school relationships have been found to be strong determinants of school enjoyment (emotional component of our engagement measure) and class participation (behavioral component). This correlation may be due in part to the use of “we” in some classparticipation items (e.g., “I participate when we discuss things in class”), as it references the social aspect of the classroom. However, given the sound factor loadings and that issues were not indicated in the SEM via modification indices, these correlations do not appear to be problematic.

Adaptability, PB Goal Setting, and SCT: A Longitudinal Examination

a.

50

Personal

Environmental

Behavioral

(Self-factors →Self-strategies)

b. Parent Support + +

+

SelfEfficacy

+

Engagement +

T1 AutoRegressions

+ +

Covariates: Age Gender Learn Dis. SES NESB Pr. Achv.

Peer Support

Perceived Control

+ +

PB Goal Setting

+

+

+ +

Achievement Adaptability +

+

+

+

Teacher Support

Figure 1. Examined relationships stemming from social cognitive theory’s triadic model (upper part of figure) and the hypothesized longitudinal model corresponding to SCT model (lower part of figure). The dashed lines represent non-substantive relationships, but which were investigated for completeness. Auto-regressive pathways between T1 and T2 constructs were also included but not shown for clarity. Note: Learn Dis.=Learning Disability, NESB=non-English speaking background, SES=socio-economic status, Pr. Achv.=Prior Achievement

Adaptability, PB Goal Setting, and SCT: A Longitudinal Examination

51

Table 1. Descriptive, Reliability, and Factor Analytic Statistics for Substantive Factors Mean T1

SD T2

T1

Skew T2

T1

Kurtosis T2

T1

T2

Loading Range (Mean)

Cronbach's α T1

T2

T1

T2

Environmental Factors Parent Support

6.03

5.87

1.30

1.33

-1.61

-1.21

2.48

1.08

.91

.90

.84 (.82-.87)

.83 (.78-.86)

Peer Support

5.50

5.35

1.26

1.32

-1.04

-0.91

1.23

0.88

.85

.86

.78 (.72-.81)

.78 (.76-.82)

Teacher Support

5.51

5.34

1.29

1.33

-1.08

-0.92

1.41

0.96

.89

.90

.82 (.77-.85)

.83 (.81-.86)

Self-efficacy

5.90

5.81

1.19

1.21

-1.31

-1.14

2.08

1.38

.81

.86

.72 (.67-.79)

.78 (.75-.84)

Perceived Control

3.54

3.60

1.79

1.73

0.21

0.17

-0.93

-0.87

.83

.85

.74 (.68-.80)

.77 (.71-.82)

Adaptability

5.25

5.14

1.06

1.07

-0.40

-0.36

0.07

0.19

.91

.91

.74 (.56-.84)

.73 (.51-.84)

5.55

5.36

1.24

1.27

-0.80

-0.65

0.51

0.34

.90

.91

.83 (.80-.87)

.85 (.84-.86)

Engagement

5.70

5.48

1.16

1.23

-1.10

-0.88

1.10

0.61

.93

.93

.86 (.81-.92)

.84 (.80-.90)

Achievement

8.19

8.49

1.56

1.51

-1.47

-1.82

3.37

4.88

.70

.72

Personal Factors: Self-factors

Personal Factors: Self-strategy PB Goal Setting Behavioural Factors

-

-

Adaptability, PB Goal Setting, and SCT: A Longitudinal Examination

52

Table 2a. Estimated Latent Correlations between Demographic Covariates and Substantive Factors

Age

Gender (M)

NESB

SES

Parent Education

Parent Occupation

Learning Disability

Prior Achievement

Environmental Factors Parent Support

-.10***

-.07

-.02

-.04

.03

.03

-.11***

.02

-.09*

.01

.02

.06

.13***

.07**

-.12**

.15***

-.04

-.01

.02

.01

.10**

.05**

-.13***

.14***

Self-efficacy

-.12***

-.03

.02

.02

.09***

.06*

-.16***

.23***

Perceived Control

-.12***

.05

-.11***

.10**

.06**

.07*

-.11***

.26***

Adaptability

-.10***

.05*

.05**

-.05

.06***

.01

-.13***

.17***

-.15***

-.02

.04

-.07*

.05

.03

-.13***

.14**

-.11***

-.03

.04**

.06

.16***

.09***

-.14***

.21***

.01

.02

.09*

.05

-.21***

.41***

Peer Support Teacher Support Personal Factors: Self-factors

Personal Factors: Self-strategy PB Goal Setting Behavioural Factors Engagement

Achievement -.04 -.13* Note: NESB=non-English speaking background, SES=socio-economic status, M=male *p < .05, **p < .01, ***p < .001.

Adaptability, PB Goal Setting, and SCT: A Longitudinal Examination

53

Table 2b. Estimated Latent Correlations among Substantive Factors

Prior Variance

Parent Support .57***

Peer Support .55***

Teacher Support .58***

Selfefficacy .60***

Perceived Control .58***

Adaptability .55***

PB Goal Setting .62***

Engagement .69***

Achievement .53***

Environmental Factors Parent Support

1.00

Peer Support

.57***

1.00

Teacher Support

.58***

.75***

1.00

Self-efficacy

.53***

.53***

.61***

1.00

Perceived Control

.21***

.23***

.26***

.33***

1.00

Adaptability

.60***

.70***

.69***

.70***

.29***

1.00

.52***

.53***

.57***

.74***

.19***

.72***

1.00

.71***

.82***

.86***

.75***

.29***

.81***

.74***

1.00

.19***

.19***

.20***

.33***

.26***

.19***

.20***

.30***

Personal Factors: Self-factors

Personal Factors: Self-strategy PB Goal Setting Behavioural Factors Engagement Achievement *p < .05, **p < .01, ***p < .001

1.00

Adaptability, PB Goal Setting, and SCT: A Longitudinal Examination

54

Table 3. SEM Findings: Standardised Beta Coefficients for Paths in the Hypothesised Model Parent Support

Peer Support

Teacher Support

Self-efficacy

Perceived Control

Adaptability

.56***

.51***

.53***

.35***

.51***

.25***

Age

-.06*

-.04

.01

-.05**

-.08**

Gender (M)

-.05*

-.02

-.01

-.02

NESB

-.01

.04

.02

SES

-.01

.02

Parent Education

.02

Parent Occupation

Prior Variance

PB Goal Setting

Engagement

Achievement

.23***

.25***

.39***

-.02

-.04*

.03

.01

.03

.06*

-.01

-.05*

-.10***

.01

-.04

.02

-.02

.04*

-.01

.01

.02

.03

-.06**

-.03

.09**

.01

.07*

.06

-.01

-.05*

-.01

-.02

.08**

-.01

.02

-.01

-.01

-.01

-.01

-.04

.02

-.01

.01

Learning Disability

-.05

-.03

-.06*

.01

-.01

-.01

.01

-.02

-.06

Prior Achievement

-.02

.06***

.04

.08*

.10***

.05***

-.03

.05

.22***

.18***

.03

.21***

.03

.08*

.01

.29***

.02

.30***

.10

.25***

.06*

.65***

.10***

72***

34***

Covariates

Environmental Factors Parent Support Peer Support Teacher Support Personal Factors: Self-factors Self-efficacy

.39***

Perceived Control

-.09**

Adaptability

.33***

Personal Factors: Self-strategy PB Goal Setting Variance Explained (%) 33*** 30*** 32*** 53*** 36*** 64*** Note: NESB=non-English speaking background, SES=socio-economic status, M=Male; *p < .05, **p < .01, ***p < .001

71***

Adaptability, PB Goal Setting, and SCT: A Longitudinal Examination

55

Table 4a. Measurement Invariance Testing Results from the Longitudinal Multi-Group CFAs Configural

Metric

Scalar

Residual

Structural 1

Structural 2

CFI

RMSEA

CFI

RMSEA

CFI

RMSEA

CFI

RMSEA

CFI

RMSEA

CFI

RMSEA

Age

.909

.035

.905

.036

.905

.036

.906

.035

.906

.035

.906

.035

Gender

.905

.038

.900

.039

.897

.039

.898

.039

.897

.039

.898

.039

NESB

.905

.037

.901

.038

.900

.038

.899

.038

.900

.038

.899

.038

Learning Disability

.861

.050

.857

.051

.857

.051

.853

.051

.856

.051

.853

.051

Prior Achievement .914 .034 .909 .035 .908 .035 .904 .036 .908 .035 .904 .036 Note: NESB=non-English speaking background; Configural=baseline, all parameters free; Metric=constrains factor loading; Scalar=constrains factor loading and intercepts; Residual=constrains factor loadings, intercepts, and residuals; Structural 1=constrains factor loading, intercepts, and correlations; Structural 2=constrains factor loading, intercepts, residuals, and correlations

Adaptability, PB Goal Setting, and SCT: A Longitudinal Examination

56

Table 4b. Measurement Invariance Testing Results from the Longitudinal Multi-Group SEMs Model 1

Model 2

CFI

RMSEA

CFI

RMSEA

Age

.890

.037

.890

.037

Gender

.883

.040

.883

.040

NESB

.883

.039

.883

.039

Learning Disability

.820

.055

.820

.055

Prior Achievement .887 .037 .887 .037 Note: NESB=non-English speaking background; Model 1=baseline, all parameters, excluding beta values, are constrained; Model 2=all parameters, including beta values, are constrained

Adaptability, PB Goal Setting, and SCT: A Longitudinal Examination Personal Factors: Selffactors

Environmental Factors

Parent Support

Personal Factors: Selfstrategy

Behavioural Factors

T1

.18***

T1

.35***

.56***

.25***

T1 .21***

T1

Self-Efficacy

T1

.39***

Engagement

.23***

.51***

.65***

.08*

Peer Support

Perceived Control

-.09**

PB Goal Setting

.10***

.29*** .51***

.33***

Achievement Adaptability .30***

T1 .39***

T1 .25***

.53***

.25***

T1

T1

Teacher Support

.06*

Figure 2. Longitudinal beta model of significant pathways, p < .05, p < .01, and p < .001. Table 3 displays all pathways, including all covariate parameters. Note: T1=Prior variance (auto-regression) in a given factor

57

Adaptability, PB Goal Setting, and SCT: A Longitudinal Examination

58

Table 5. Results of Indirect Effect Testing (Bootstrapping Approach) Pathway Parent Support  Self-efficacy  PB Goal Setting Peer Support  Self-efficacy  PB Goal Setting Teacher Support  Self-efficacy  PB Goal Setting

Coefficient .07*** .03 .12***

Parent Support  Adaptability  PB Goal Setting Peer Support  Adaptability  PB Goal Setting Teacher Support  Adaptability  PB Goal Setting

.07*** .10*** .08***

Parent Support  Self-efficacy  PB Goal Setting  Engagement Peer Support  Self-efficacy  PB Goal Setting  Engagement Teacher Support  Self-efficacy  PB Goal Setting  Engagement

.04*** .02 .07***

Parent Support  Self-efficacy  PB Goal Setting  Achievement Peer Support  Self-efficacy  PB Goal Setting  Achievement Teacher Support  Self-efficacy  PB Goal Setting  Achievement

.01* .01 .01**

Parent Support  Adaptability  PB Goal Setting  Engagement Peer Support  Adaptability  PB Goal Setting  Engagement Teacher Support  Adaptability  PB Goal Setting  Engagement

.04*** .06*** .05***

Parent Support  Adaptability  PB Goal Setting  Achievement Peer Support  Adaptability  PB Goal Setting  Achievement Teacher Support  Adaptability  PB Goal Setting  Achievement

.01** .01** .01**

Self-efficacy  PB Goal Setting  Engagement Perceived Control  PB Goal Setting  Engagement Adaptability  PB Goal Setting  Engagement Teacher Support  PB Goal Setting  Engagement

.24*** -.06*** .20*** .04

Self-efficacy  PB Goal Setting  Achievement Perceived Control  PB Goal Setting  Achievement Adaptability  PB Goal Setting  Achievement Teacher Support  PB Goal Setting  Achievement *p < .05, **p < .01, ***p < .001.

.04** -.01** .03*** .06

Adaptability, PB Goal Setting, and SCT: A Longitudinal Examination

59

Adaptability, Personal Best (PB) Goal Setting, and Gains in Students’ Academic Outcomes: A Longitudinal Examination from a Social Cognitive Perspective

Highlights 

This study explores the role of adaptability and personal best (PB) goal setting in gains in students’ academic outcomes



These two novel constructs are considered in the context of “classic” social cognitive theorizing



Adaptability significantly predicted gains in PB goal setting



PB goal setting significantly predicted gains in academic outcomes



Findings also reaffirmed the importance of self-efficacy and social support, especially teacher support, in academic outcomes