Motivation is a critical determinant in student outcomes, including achievements (Vansteenkiste et al., 2009), well-being (Gagne et al., 2015; Ryan & Deci, 2017), creativity, and learning (Vansteenkiste et al., 2009). It influences whether an individual embraces the opportunity to learn or resists (Siegle et al., 2017, 2018), sustains oneself through failures or abandons the task (Subotnik et al., 2011) and reaches their potential or falls short (Siegle et al., 2017). It is a universal construct, meaning that it is essential for all students within the increasingly diverse classrooms that comprise schools today. Recent studies (e.g., Martin et al., 2017) suggest that no single motivation leads to substantial change and that different motivations relate in various ways to outcomes (e.g., well-being vs. ill-being [Ryan & Deci, 2017], creativity vs. uninspired thinking [Csikszentmihalyi et al., 2018], and vitality vs. fragmentation [Orsini et al., 2018]). This variation in student outcomes implies that educators must be familiar with various motivational constructs (Worrell, 2018) to develop adaptive motivational patterns in students. In other words, more motivation is not necessarily better if the motivation is poor quality and associated with detrimental outcomes (e.g., cheating, stress).

Additionally, it is essential to understand the combined effects of different types of motivation at the individual level (Litalien et al., 2019). For instance, self-determination theory posits that motivation comprises specific beliefs (interests, values, pressures, rewards) and a global self-determined motivation which may configure in ways to form patterns within individuals. Studies have examined motivational patterns (or profiles) across a range of contexts, including employment (Graves et al., 2015; Howard et al., 2021), university (Litalien et al., 2019), exercise (Lindwall et al., 2017) and school (Vansteenkiste et al., 2009). Findings suggest patterns of autonomous motivations (e.g., interests, values) are associated with better outcomes than controlled (e.g., ego, rewards, punishments) motivations. However, there is yet to be a clear answer on the benefits of a combined profile typified by autonomous and controlled motivations.

Additionally, most studies still need to integrate global and specific dimensions in research instead of focussing on specific motivations (i.e., interests, values, ego, rewards) at the expense of the global motivation (e.g., Corpus & Wormington, 2014) or vice versa (e.g., Ommundsen & Kvalø, 2007). An exception, Howard et al., (2021) simultaneously considered global and specific motivations and reported profiles that differed across each of these dimensions, with the global dimension representing the most influential factor in employee motivation. Thus, there remains a need to understand further the relative importance of global and specific motivations in characterising profiles and predicting student outcomes.

To the researcher’s knowledge, no studies have explored motivational profiles in a high-achieving population. Instead, there is an ipso facto assumption that high-achieving students are all highly motivated – a myth this study intends to address. Moreover, this study accounts for the dual nature of motivation proposed by self-determination theory to understand how high-achieving students differ quantitatively (global self-determination) and qualitatively (interest, value, ego, rewards). An area that remains unexplored. Surprisingly, few studies have explored motivational profiles at a domain-specific level (i.e., within a single subject), with most considering general motivations towards studying (e.g., Vansteenkiste et al., 2009) college (e.g., Litalien et al., 2019), or employment (e.g., Graves et al., 2015; Howard et al., 2021). Considering how motivational patterns are situated within a specific context is essential. One’s motivations towards science may differ from one’s motivations towards history, and this study intends to address this by investigating profiles within the context of science.

The following research questions emerged:

• Which motivational profiles emerge in high-achieving students?
• How do perceptions of the climate of the classroom predict motivational profiles?
• How do motivational profiles predict student engagement?

This study used a cross-sectional, non-experimental design to collect survey data from 414 high-achieving students in Years 9 and 10. Relevant ethical approval was obtained from the New South Wales Department of Education and the University of New South Wales, Australia (HREC210175). All survey responses were anonymous to protect student confidentiality. Based on their relevance to the research aims and psychometric rigour, three existing scales were collectively used to develop the survey. These included the Comprehensive Relative Autonomy Index (CRAI; Sheldon et al., 2017), the Teacher as Social Context (classroom climate; Belmont et al., 1992), and the Math and Science Engagement Scale (engagement, Wang et al., 2016). First, preliminary analyses were conducted to ensure the data were in a suitable numerical format, cleaned, described, and met distributional assumptions for subsequent analyses (e.g., assumptions of normality). Next, bifactor-ESEM was conducted to evaluate the dimensionality of student responses to the C-RAI. This involved estimating and comparing several measurement models to explore possible sources of multidimensionality in data (e.g., confirmatory factor model vs. exploratory structural equation model vs. bifactor confirmatory model vs. bifactor exploratory model). Selection of the final measurement model involved an examination of fit indices, a detailed inspection of the parameter estimates (i.e., factor correlations, cross-loadings, the definition of factors) and a reflection of the theoretical conformity of each model (e.g., Guay et al., 2015; Howard et al., 2018; Litalien et al., 2017; Marsh et al., 2009; Morin et al., 2016). Finally, latent variable modelling was used to model heterogeneity in the population. Motivation factor scores from the bifactor-ESEM model were used as latent profile indicators to provide partial control for measurement error (Diallo et al., 2016; Peugh & Fan, 2013) to define profiles by global self-determination, intrinsic motivation, identified regulation, introjection approach, introjection avoidance, and external regulation. To select the best fitting model, statistical criteria (e.g., AIC, BIC, CAIC, LMR, BLRT, entropy, posterior probabilities; Nylund-Gibson et al., 2007; Nylund-Gibson & Choi, 2018) were evaluated alongside the substantive meaning and theoretical interpretability of the profiles (Bauer & Curran, 2004; Marsh et al., 2009; Muthén, 2003; Nylund-Gibson et al., 2019). The manual three-step procedure was used to explore how perceptions of the climate of the classroom predicted membership into profiles and how profiles subsequently predicted student engagement in class.

Four motivational profiles emerged, characterised by unique patterns of specific motivations and global self-determination. This suggested that important motivational information would be lost if one were to consider student motivation only in quantitative (i.e., how much motivation) or qualitative (i.e., which type of motivation) terms or view students as motivationally homogenous. Global self-determination (b = .67, p <.05) and intrinsic motivation (b = .11, p <.05) were powerful predictors of engagement. However, contrary to SDT expectations, introjection approach partnered with autonomous motivations and was a positive predictor of engagement (b = .16, p <.05). Thus, for high-achieving students, the desire to boost one’s ego, feel proud, and experience a sense of accomplishment was a positive motivational driver. This implies that motivational dynamics may be more important than considering motivations in isolation, as it is possible that introjection approach was adaptive only when coupled with high self-determination. Results supported the benefits of autonomy-supportive teaching (structure, autonomy, involvement) in predicting adaptive motivations and engagement in science. Thus, autonomy-supportive teaching holds much promise. Research has supported the global relevance of teacher professional learning in autonomy-supportive practices to incorporate culturally-informed, responsive, sensitive, and relevant education for all learners (Reeve & Cheon, 2021). Findings challenged theory by illustrating the distinction between approach and avoidance forms of introjected regulation in the analyses (e.g., motivation to boost vs. motivation to protect one’s ego). Thus, there may be a need to re-evaluate the SDT continuum and the motivations that comprise it and examine under which circumstances students endorse the more maladaptive avoidance form of introjection as opposed to introjection approach. This investigation's methodological contribution relates to a thorough evaluation of the dimensionality of motivation before estimating profiles in person-centred analyses. This is important to achieve greater clarity and accuracy in understanding the structure of motivation (variable-centred) and within-person dynamics of motivation (person-centred; Morin et al., 2016). The findings make visible the motivational diversity within classrooms to challenge assumptions of homogeneity and understand the complex dynamics at the person-centred level –relevant to all educators who wish to use motivational diversity as a starting point for effective curriculum design for all learners.

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