Person-centered approach in achievement goals represents multiple goal pursuit which complement variable-centered findings and expand the consideration of multiple goals beyond mastery-approach and performance-approach goals alone. The approach is meaningful to provide a clearer understanding of which combinations of goals typically emerge in a population. There has been extensive work on classifying achievement goal orientations, which comprise five main combinations: a basic mastery profile [i.e., mastery- and learning-oriented (Tapola and Niemivirta, 2008; Tapola et al., 2014; Peixoto et al., 2016)], a basic performance profile [i.e., performance-oriented and low-mastery/high-performance (Tuominen-Soini et al., 2012; Gonçalves et al., 2017)], a mastery and performance-approach goals combined profile [i.e., success-oriented, multiple goals cluster, and approach-oriented group (Daniels et al., 2008; Tuominen-Soini et al., 2008, 2011; Luo et al., 2011; Zhang et al., 2016)], a moderate or low profile [i.e., indifferent, average goals (Tuominen-Soini et al., 2008, 2011; Jansen In de Wal et al., 2016) low-mastery/low-performance, low-motivation, and disaffected (Daniels et al., 2008; Gonçalves et al., 2017)], and a profile emphasizing avoidance [i.e., avoidance-oriented students (Daniels et al., 2008; Tuominen-Soini et al., 2008, 2012)]. In this study, we employed the latent profile analysis (LPA), a person-centered approach, to classify the research objects. LPA is useful in describing the types of achievement goal orientations and is optimally suited to identify which individuals may benefit from different combination of goals to adapt the learning environment. Therefore, the present study aimed to do the similar classification in Chinese freshmen. We expected to detect several meaningful types of achievement goal orientations, which highlighted either a single or a combination of goal orientations. Based on the previous literature, we hypothesized that we would identify groups of students with five dominant tendencies of mastery-oriented, performance-oriented, approach-oriented, avoidance-oriented, and low-motivation (Hypothesis 1). We identified common patterns of goals profiles first and then compared the benefits of endorsing different goal profiles with learning engagement and academic adjustment.

The achievement goal theory is one of the most influential theories to explain and predict the direction and intensity of individuals’ behavior in school-related situations (Xiang et al., 2017), which has experienced a 30year change in processes. Initially, achievement goal orientations were characterized by students’ aims for task engagement, which were differentiated into two distinct types: mastery goals that focused on the development of competence and performance goals that were concerned with the demonstration of competence (Dweck, 1986). Subsequently, Elliot and Harackiewicz (1996) refined achievement goal orientations and established two types of performance goals. Elliot and McGregor (2001) proposed a more detailed extension that further divided mastery goals into approach and avoidance components, and a 2×2 model of achievement goal orientations was developed and tested. The quartered model emphasized two directions: the definition of competence and the valence of competence. The former refers to whether the individual considers their ability selectable (mastery and performance goals), whereas the latter refers to whether an individual pays attention to achieving positive outcomes (i.e., success; approach-oriented) or avoiding negative outcomes (i.e., failure; avoid-oriented). Specifically, mastery-approach goals are related to the motivation for improving knowledge, skills, and learning, whereas mastery-avoidance goals are concerned with the purpose of avoiding misunderstandings or failing to accomplish a task. Performance-approach goals focus on the endeavors to outperform others and demonstrate their superiority, whereas performance-avoidance goals represent the desire to avoid performing more poorly than others.

Recently, from the perspective of the variable-centered approach, investigators confirmed the effectiveness of the dimensions of achievement goal orientations on academic adjustment (Linnenbrink-Garcia and Wormington, 2019). These studies established that the direct relationships between the mastery-approach and performance-avoidance goals on academic adjustment are relatively consistent; however, some uncertainty remains regarding the relationship between the mastery-avoidance and performance-approach goals and academic adjustment. Specifically, mastery-approach goals are positively associated with adaptive coping strategies and positive behavior patterns, such as high levels of effort, persistence at a task, and adoption of deep learning strategies (Honicke et al., 2020). Therefore, we proposed Hypothesis 2a: Mastery-approach goals will positively predict academic adjustment.

Performance-avoidance goals are mainly associated with unfavorable results and maladaptive adjustment patterns (Van Yperen et al., 2014, 2015). Students who possessed performance-avoidance goals have a tendency to avoid learning, choose disorganized study strategies, or fail to use learning strategies. They exhibit high rates of burnout, lower self-efficacy, inactive learning interest, anxiety, shame, and hopelessness (Pekrun et al., 2006; Auvinen et al., 2015; Nadon et al., 2020). Thus, we proposed Hypothesis 2b: Performance-avoidance goals negatively predict academic adjustment.

Several studies have revealed that mastery-avoidance goals are linked to adaptive outcomes, such as learning interest (Baranik et al., 2010), whereas other studies have suggested that mastery-avoidance goals are more likely to be related to maladaptive outcomes, such as procrastination, disorganization, and declines in performance (Howell and Watson, 2007; Van Yperen et al., 2009). Thus, we proposed Hypothesis 2c: Mastery-avoidance goals negatively predict academic adjustment.

There is another ambiguous relationship between performance-approach goals and academic adjustment. Meece et al. (2006) showed that performance-approach goals are positively correlated with self-handicapping strategies and lower academic help-seeking. However, other researchers have found that these goals were positively correlated with adaptive behavior patterns, such as using learning strategies and making effort (Mouratidis et al., 2013; Tian et al., 2017). In collectivist cultural contexts, performance-approach goals are more likely to be associated with positive learning outcomes (Cheng and Lam, 2013; Datu, 2018). Therefore, taken together, we proposed Hypothesis 2d: Performance-approach goals positively predict academic adjustment.

From the person-centered approach perspective and based on LPA results, researchers have explored the relationships between the profiles of achievement goal orientations and academic adjustment and found that the combination of goals predicting relevant adaptive outcomes of education remains controversial (Tuominen-Soini et al., 2012). Firstly, students with high mastery-oriented goals showed stronger ability to adapt to consequences and showed more adaptive patterns of motivation and academic wellbeing than those who possessed weak mastery ability. At the same time, mastery-oriented students may engage more time studying (Tapola and Niemivirta, 2008; Tuominen-Soini et al., 2012). Secondly, students who focused on both mastery- and performance-approach goals (approach-oriented goas) had two-sided adaptive outcomes (Daniels et al., 2008; Tuominen-Soini et al., 2008, 2011, 2012; Luo et al., 2011). On the one hand, several studies have suggested that these students are more likely to use cognitive strategies and make effort to achieve better academic performance and encounter difficulties than students with low motivation. Also, approach-oriented students are higher on self-efficacy, time management, and meta-cognitive self-regulation than are mastery-oriented students. On the other hand, other studies reported that these students experienced several positive outcomes, such as lower levels of anxiety and negative affect. Thirdly, because of the stronger concerns with performance, students with performance-oriented goals showed more school burnout, higher test anxiety, and higher negative affect than students in approach-oriented and mastery-oriented goals groups (Luo et al., 2011; Tuominen-Soini et al., 2012). Finally, students with low-mastery performance [low-motivation cluster (Daniels et al., 2008) and indifferent students (Tuominen-Soini et al., 2008, 2011)] or avoidance-oriented goals (Daniels et al., 2008; Tuominen-Soini et al., 2008, 2012) were similarly to those who were low in motivation and academic adjustment. Given the above-mentioned evidence, we hypothesized that from high to low level of goals’ scores on adaptive academic achievement, these types are approach-oriented, mastery-oriented, and performance-oriented goals. The avoidance-oriented and low-motivation goals negatively predict academic adjustment (Hypothesis 3). However, the mechanisms underlying the relationship between achievement goal orientations and academic adjustment remain to be elucidated. It is important to gain insight into how achievement goal orientations affect academic adjustment. Therefore, this study sought to provide guidance for educational practitioners by exploring the effects of the mediating variable of learning engagement.

The integrative development-in-sociocultural-context model elaborates on the function of engagement in the key facilitators and consequences of identity engagement (Wang et al., 2019). The model emphasizes that engagement plays a dynamic developmental and reciprocal role in shaping youth’s learning processes. In this model, engagement is conceptualized as an enriching multidimensional construction shaped by interactions between the individual and the environment. On the one hand, personal beliefs (e.g., competence beliefs) have been established as antecedents of engagement (Wang and Eccles, 2013). On the other hand, longitudinal studies have shown that engagement predicts children’s educational outcomes, such as academic achievement (Li et al., 2019).

Learning engagement is a multifaceted construct in nature and mainly reflects individuals’ varying patterns in cognition, emotion, and behavior (Phan, 2016). Schaufeli et al. (2002) were interested in the multifaceted composition of learning engagement and defined it as a persistent, positive, fulfilling, and learning-related state of mind that consists of three major motivation-related components: vigor, dedication, and absorption. Vigor refers to a high level of energy, resilience, willingness, and ability to study, and persistence in the face of difficulties. Dedication refers to a strong involvement in studying, accompanied by a sense of significance, enthusiasm, pride, and inspiration. Absorption refers to a pleasant state of complete immersion in studying, which is characterized by being unable to detach oneself from studying. Learning engagement is defined and highlighted by Schaufeli et al. (2002) as focusing on the positive aspects of a person’s study and having educational implications. Studies have found that learning engagement is a principal determining factor of students’ adjustment and achievement in educational settings (García-Ros et al., 2018; León et al., 2021; León and García-Martínez, 2021). Therefore, we would more fully explore the learning engagement and how learning engagement interacts with other education-related factors.

Studies using the variable-centered approach have demonstrated that learning engagement contributes to academic adjustment. Students who are highly engaged in work follow an overall positive adolescent development trajectory (Upadyaya and Salmela-Aro, 2013). Learning engagement is correlated with several academic aspects, such as adaptive coping strategies (e.g., meta-cognitive strategies), self-efficacy, performance, and academic success, and it is negatively correlated to test anxiety and ill-being (Upadyaya and Salmela-Aro, 2013; García-Ros et al., 2018; Vizoso et al., 2018). High learning engagement also improves students’ wellbeing, which includes positive affect and life satisfaction (Upadyaya and Salmela-Aro, 2013).

According to the development-in-sociocultural-context model, developmental competencies and motivational beliefs work together to shape engagement (Wang et al., 2019). Achievement goal orientations as motivation-related constructs (Wigfield and Cambria, 2010) are predictors of adolescents’ learning engagement (Shih, 2012), and important, albeit limited, empirical evidence has established such links. Specifically, both mastery-approach goals (Shih, 2012; Poortvliet and Perdeck, 2014; Nerstad et al., 2020; van Dam et al., 2020) and performance-approach goals (Shih, 2012; Barashev and Li, 2017) are positively related to learning engagement. Mastery-avoidance goals are unrelated to learning engagement (Poortvliet and Perdeck, 2014); however, studies have frequently found that they are positive predictors of work exhaustion (de Lange et al., 2010; Poortvliet et al., 2015). Furthermore, performance-avoidance goals negatively predict learning engagement (Shih, 2012; Barashev and Li, 2017).

For the person-centered approach, Wormington and Linnenbrink-Garcia (2017) developed person-centered approaches to study achievement goals using meta-analytical techniques. They found that mastery- and approach-oriented profiles are adaptive profiles, and students with mastery-oriented goals had higher engagement than those with approach-oriented goals. Average and low all-goal profiles have shown the lowest levels of engagement. Moreover, the effects of high-performance approach and performance-avoidance profiles on engagement did not differ. However, Liu et al. (2020) reported that in Chinese educational contexts, the approach-oriented profile is the most adaptive to learning engagement, followed by the mastery-oriented profile.

In summary, from the perspective of variables, we proposed the following hypotheses: Mastery-approach and performance-approach goals positively and independently predict academic adjustment through learning engagement (Hypotheses 4a and b); mastery-avoidance and performance-avoidance goals negatively and independently predict academic adjustment through learning engagement (Hypotheses 4c and d). At the individual level, we predicted that approach-oriented (Hypothesis 5a) and mastery-oriented goals (Hypothesis 5b) would positively influence academic adjustment through learning engagement.

We estimated sample size by G*Power and calculated the minimum sample size. Based on our preliminary research, we set an alpha of 0.05, power (1−β) of 0.80, and an effect size of 0.04. The sample size was 191. In this study, a total of 590 college students were recruited from universities in northeast China using random sampling. Due to missing or invalid responses, 12 participants were not included in the analyses. Therefore, the final sample consisted of 578 participants (338 females, 58.5%). Mean age was 18.29 years [standard deviation (SD)=1.04]. During the first month of the new semester (in October 2018), the first author with the assistance of teachers used questionnaires to assess freshmen’s goals, learning engagement, and academic adjustment at a public university located in China. Participants were informed that all information would be kept confidential and that they could withdraw from the study at any time. Questionnaires took 5min to complete. The study was approved by the Academic Ethics Committee of the College of Psychology of Northeast Normal University.

Mardia’s Skewness and Mardia’s Kurtosis were used to assess multivariate distribution by the online WebPower (Liu, 2013). Results showed Skewness (b=2.75, z=264.68, p<0.05) and Kurtosis (b=57.23, z=11.33, p<0.05), and indicated the absence of multivariate normality. Knief and Forstmeier (2018) suggested that comparing with other violations or alternative approaches, violations of multivariate normality may be less detrimental to the interpretation of findings, so we choose to process with the analyses using 1,000 bootstraping (Pek et al., 2018). To test our hypotheses, we performed five steps to conduct the variable-centered and person-centered analyses and controlled for sex. Preliminary analyses included descriptive statistics and correlation analysis. Second, we used LPA to identify profiles along the dimensions of achievement goal orientations using Mplus8.0 (Muthén and Muthén, 1998–2015) and validated the efficacy of the classifiers. Third, we performed ANOVAs and post-hoc comparisons (Bonferroni adjustment) to analyze the relationships between achievement goal orientations types, learning engagement, and academic adjustment. Fourth, we performed a mediation analysis among the three variables using MEDIATE in SPSS 22.0 (Hayes and Scharkow, 2013). Finally, we tested the mediation effect of learning engagement on achievement goal orientations profiles and academic adjustment using the bootstrap method by running the PROCESS plugin in the SPSS 22.0 software (Hayes, 2018). In the PROCESS, we used the ordinary least squares (OLS) to estimate the parameter in mediation model.

To explore the potential profiles of freshmen’s achievement goal orientations, potential profiles were analyzed by fitting a model that varied in solutions from 2 to 5 classes. Following Nylund’s (2007) suggestion, model fit comparisons are based on the following indicators: Akaike’s information criterion (AIC), Bayesian information criterion (BIC), adjusted BIC (aBIC), Lo-Mendell-Rubin likelihood ratio (LMR-LRT), bootstrap likelihood ratio (BLRT), and entropy. The model with a smaller AIC, BIC, and ABIC is better. If values of p of the LMR-LRT and BLRT are less than 0.05, this implies that the k-class model is significantly better than the k-1-class model. Higher entropy indicates few classification errors.

We investigated subsequent mediation effects by comparing the effects of profiles and learning engagement on academic adjustment. Based on the LPA, we explored the mediating role of learning engagement in achievement goal orientations profiles and academic adjustment (hypotheses 3 and 5). When the independent variable was multi-categorical, we used the method that integrated relative and omnibus mediation to analyze the mediation effect (Iacobucci, 2012). The first step was to implement an omnibus mediation analysis. Following previous methods, if the omnibus mediation effect is not significantly different from zero, the k-1 relative mediation effect is zero, where k reflects the number of profiles. Otherwise, the second step is applied, which involves conducting a relative mediation analysis that is aimed at determining the relative mediation effect. If the effect is significantly different from zero, then, the third step should be implemented. Otherwise, the mediation analysis is complete. In the third step, the relative direct effects are reported. Combining the characteristics and analysis results of achievement goal orientations profiles, we selected the average profile as the reference profile that was considered maladaptive (Tuominen-Soini et al., 2008), and the remaining profiles were compared with profile 3.

A previous study found that girls were superior to boys in achievement goal orientations, which indicated that girls possessed higher mastery-approach goals and lower performance-avoidance goals (Mouratidis et al., 2017). Male students are more performance-oriented than females (Butler, 2014; Yu and McLellan, 2019). Moreover, girls were more apt at engaging in learning than are boys (Li et al., 2011; Salmela-Aro and Upadaya, 2012; Wang and Eccles, 2012). From this, we controlled for sex for all data analyses. The relationships between all variables were analyzed using partial correlation analysis. Means, SDs, and zero-order correlations among variables are shown in Table 1. As shown in Table 1, mastery-avoidance goals did not correlate with learning engagement or academic adjustment; therefore, we excluded it from the mediation analysis of the variable-centered approach.

Researchers classify the latent profiles and understand the proportion of people of the various categories throughout the group according to the answer mode on the individual external test topic, rather than determine the number of classifications a priori. LPA is particularly suitable for exploratory research questions and offers several advantages (Stern and Hertel, 2020). This probabilistic model-based classification method can not only guarantee the largest difference between the divided categories and the smallest difference within the categories but also can be measured by objective statistical indicators. As shown in Table 2, the AIC, BIC, aBIC, entropy, and LMR-LRT results for the different classes indicated that the five-class solution did not fit the data better than did the four-class solution; thus, we chose the four-class solution.

Figure 1 shows that the values for each variable were standardized scores per profile. Following the profile division method of achievement goal orientations used by Luo et al. (2011), we used a standardized score of 0.50 to divide and name the goals. We defined three levels: high (>0.50 SDs), average level (0.50–0.50 SDs), and low (<0.50 SDs). Profile 1 (11.61%) was characterized by low levels across all indicators of achievement goal orientations. We defined this class as low-motivation goals. Profile 2 (10.38%) included goals where scores of the two approach goals were both more than twice the 0.50 SD (the average level); the scores of the mastery-avoidance goals were contained within the average level, and performance-avoidance scores were sufficiently below the low level. We defined this class as approach-oriented goals. Profile 3 was the most prevalent (50.90%) and showed that the dimension scores of achievement goal orientations all fell within the average level. We defined this class as average goals. Profile 4 was the opposite of profile 1 and described 27.12% of the sample. This profile showed that mastery-approach goal scores were slightly below the high level, and the scores of the other three dimensions of achievement goal orientations were above the high level. We defined this class as multiple goals. Overall, LPA of achievement goal orientations revealed four groups: low-motivation (profile 1, 11.1%), approach-oriented (profile 2, 9.5%), average (profile 3, 52.8%), and multiple (profile 4, 26.6%).

To assess the validity of the classification of the profiles, we examined the association with the dimensions of achievement goal orientations using analyses of covariance. Table 3 shows the means and SDs of each goal orientation.

Results showed that there were significant differences in learning engagement [F(3,574)=9.74, p<0.001, η²=0.05] and academic adjustment [F(3,574)=27.26, p<0.001, η²=0.13] between the achievement goal orientations profiles. As shown in Table 3, post-hoc Bonferroni tests found that profile 2 (approach-oriented goals) had the highest scores for learning engagement and academic adjustment, and the academic adjustment score of profile 1 was significantly higher than that of profiles 3 and 4. According to the results of ANOVAs and post-hoc comparisons analyses, approach-oriented approach had the highest score for academic adjustment, low-motivation goals had the second highest score, and the rest of two goals had the lowest scores that were not significant. In the score of learning engagement, approach-oriented goals had the highest score, the rest three goals had low scores and revealed no significant differences.

Because of the continuous variables, we used a simple mediation handler to test hypotheses 2 and 4 using the MEDIATE macro (Hayes and Scharkow, 2013). Figure 2 shows that mastery-approach goals were positively correlated with learning engagement (β=0.88, p<0.001) and academic adjustment (β=0.34, p<0.001), performance-avoidance goals were negatively correlated with learning engagement (β=−0.16, p=0.001) and academic adjustment (β=−0.19, p<0.001), and performance-approach goals were not significantly correlated with learning engagement (β=−0.02, p=0.81) or academic adjustment (β=0.01, p=0.80). In addition, learning engagement was positively correlated with academic adjustment (β=0.11, p<0.001).

Further statistical tests revealed that learning engagement mediated the relationship between mastery-approach goals and academic adjustment [indirect effect=0.09, 95% confidence interval (CI)=(0.06, 0.14)]. The mediation effect accounted for 26.47% of the total effect. Learning engagement mediated the relationship between performance-avoidance goals and academic adjustment [indirect effect=−0.02, 95% CI=(−0.03, −0.01)]. The mediation effect accounted for 11.11% of the total effect. From these results, we can make the conclusion that learning engagement mediated the effects of the mastery-approach and performance-avoidance goals on academic adjustment.

Because of the classified independent variable, the PROCESS macro in SPSS22.0 was used to analyze the mediation effect (Hayes, 2018). The omnibus mediation effects [F(3, 573)=27.08, p<0.001] and the relative mediation effect [F(3, 572)=19.72, p<0.001] were significantly different from zero.

Results of the relative mediation analysis showed that low-motivation goals directly predicted academic adjustment. However, the mediation results were not significant because the CIs included zero. Therefore, they are not reported. However, the 95% bootstrap CIs (0.06, 0.19) of the relative mediation of approach-oriented goals excluded 0, which indicated that the relative mediation effect was significant (a₁=0.75, b=0.17, a₁b=0.13). That is, freshmen with approach-oriented goals had 0.75 times greater learning engagement level than those with approach-oriented goals (a₁=0.75), and their academic adjustment level also increased by 0.17 (b=0.17) with learning engagement (Figure 3). The relative direct effect of approach-oriented goals was significant (c'₁=0.39, p<0.001), which indicated that academic adjustment in students with approach-oriented goals was 0.39 higher than that of students with average goals. Moreover, the relative total effect was significant (c₁=0.51, p<0.001), and the relative mediation effect a₁b was 25.49%. From these results, we can make the conclusion that learning engagement played a mediated role between approach-oriented goals and academic adjustment.

We explored the latent profiles of achievement goal orientations by conducting an LPA. Inconsistent with hypothesis 1, the LPA revealed four profiles of achievement goal orientations in freshmen: low-motivation, approach-oriented, average, and multiple. This inconsistency may be due to unknown factors that resulted in individual variations (Lo et al., 2017). The average profile had the highest number of freshmen (50.9%), and the approach-oriented students had the lowest number (10.4%). These results can be deceiving for the following three reasons. First, the cultural background of Chinese Confucian-collectivism emphasizes harmony and balance over individual achievement and is focused on the interconnectedness of everyone and everything (Gardiner et al., 2020). Second, students’ hard work and mastery of knowledge are aimed at showcasing their talents or gaining recognition from external factors (e.g., parental expectations), which are likely to result in a lack of initiative. External factors may further increase students’ fear of academic failure, which may cause them to refuse to pursue specific goals (Lin et al., 2003; Shih, 2008). Third, holding multiple goals is common in school because students may seek to follow personal interests and respond to external demands (Tuominen et al., 2020).

In addition, we revealed other achievement goal orientations profiles. Multiple profile students (27.1%) focused more on achieving better grades than other students and avoiding failure and mistakes. This profile emphasized not only learning and mastering knowledge in school but also achieving good grades and performing better than others. However, low-motivation profile students (11.6%) had relatively low scores across all dimensions of achievement goal orientations and did not focus on specific goals, such as mastering knowledge, performing better than others, or avoiding failure. Overall, these results indicated that different dimensions of achievement goal orientations can coexist in a student, and the variable-centered approach for exploring achievement goal orientations may lead to inappropriate conclusions (Ryan and Shim, 2006, 2008).

In the current study, we tested the relationships between achievement goal orientations and academic adjustment, and the results partially supported our hypotheses. Specifically, mastery-approach goals positively predicted academic adjustment (hypothesis 2a), and performance-avoidance goals showed the opposite pattern (hypothesis 2b). These results support Dweck’s (1986) point that an individual who has mastery-approach goals develops adaptive behavior, whereas one who has performance-avoidance goals mainly develops maladaptive behavior (Gillet et al., 2015).

However, performance-approach goals were negatively correlated with academic adjustment, which was contrary to hypothesis 2d. A possible explanation is that goal realization is brought about contrary results by the different self-determination reasons behind goals (Elliot and Murayama, 2008). For example, the autonomous reasons underlying performance-approach goals are closely related to the adaptive results (e.g., persistence), whereas the control reasons underlying performance-approach goals are closely associated with maladaptive results (e.g., negative affect; Vansteenkiste et al., 2010). In the future, distinguishing achievement goals from achievement goal orientations and examining the underlying reasons for adopting and pursuing achievement goals is warranted.

Mastery-avoidance goals were shown to be unrelated to academic adjustment, which rejects hypothesis 2c. This may be because mastery-avoidance goals have both positive (emphasizing learning and striving toward goals that imply self-improvement and growth) and negative (avoiding failing) characteristics (Michou et al., 2016). The interaction between mastery-avoidance goals and outcomes depends on the characteristics of the individual that are more prominent (Elliot, 1999); therefore, the results are inconsistent. Another possible reason is that compared with freshmen, higher-grade students are more likely to experience or perceive some loss of skills or abilities with increasing grade due to the increasing amount of course content and difficulty. Therefore, it can be predicted academic adjustment more accurately in older students (de Lange et al., 2010; Senko and Freund, 2015).

From the person-centered approach, we found that students with approach-oriented goals had the highest scores for academic adjustment, and goals directly predicted academic adjustment. Results were concordant with hypothesis 3a and consistent with previous research by Tuominen-Soini et al. (2008). In addition, low-motivation goals directly predicted academic adjustment, whereas multiple goals did not. Notably, if scores of the approach-oriented goals are largely in line with (see low-motivation) or below (see multiple goals) scores of the avoidance-oriented goals, motivation-oriented goals may or may not positively predict academic adjustment. However, when approach-oriented goal total scores are higher than those of avoidance-oriented goals, it may predict higher academic adjustment (see approach-oriented goals). As mentioned in the Introduction, achievement goal orientations refer to the channels through which achievement-related motive dispositions manifest in students during specific achievement situations, and in the hierarchical model of achievement motivation (Elliot and Church, 1997; Elliot, 1999), motive dispositions reflect competence- and affect-based constructs that promote individuals to adopt achievement goals and orient them toward success (approach-oriented) or away from failure (avoidance-oriented) in specific achievement contexts and situations. Approach-oriented goals are focused on the constructive aspects of achievement, whereas avoidance-oriented goals are focused on the negative aspects (Job et al., 2009). Therefore, we can infer that regardless of the avoidance-oriented goals that the individual adopts, as long as it is accompanied by similar or a higher degree of approach-oriented goals, both motivation-oriented goals have a positive impact on the adaptation of the individual. Another notable finding is that individuals with mastery-approach goals perform better in predictive adaptation. It appeared that the adoption of these goals overcame any potential negative effects that were exerted by the other three goals (see motivation and approach goals). This may be because mastery-approach goals are primarily linked to a pattern of adaptive educational outcomes (Sommet and Elliot, 2017). An alternative reason is that students who possess mastery-approach goals have fewer negative psychological responses to wrong social comparisons (Kamarova et al., 2017). This study confirmed that mastery-approach goals are a remarkably powerful goal type (Shih, 2012) that considerably boosts academic adjustment.

From the perspective of the variable-centered approach, we found that mastery-approach goals of freshmen increased learning engagement, which was positively related to academic adjustment, and performance-avoidance goals reduced learning engagement, which was negatively associated with academic adjustment. That is, learning engagement mediated the relationship between mastery-approach/performance-avoidance goals and academic adjustment. Therefore, why freshmen with different goal orientations are likely to explain different levels of academic adjustment can be explained by learning engagement. These findings support the integrative development-in-sociocultural-context model (Wang et al., 2019), which assumes that learning engagement is a pathway or process through which personal factors (e.g., motivational beliefs) shape learning outcomes.

In addition to the overall mediation result, we focused on a separate link within the mediation model. On the one hand, our findings support the notion that mastery-approach goals are related to increased academic adjustment and that performance-avoidance goals are the opposite of academic adjustment. Competence beliefs have been established as antecedents of engagement (Wang et al., 2019). This finding is consistent with the mindset theory, which posits that judgments of students regarding their performance have a significant influence on engagement (Weiner, 1985). Students who hold mastery-approach goals are likely to have higher learning engagement, whereas students with performance-avoidance goals will have lower learning engagement. On the other hand, we revealed that learning engagement was positively correlated with academic adjustment, which is consistent with previous studies that showed that learning engagement predicts academic achievement (Wang and Eccles, 2012), academic effort (Strauser et al., 2012), and better adaptation (Reschly and Christenson, 2012). Therefore, learning engagement may be used to assess the adjustment of freshmen in school and identify areas for intervention.

From the person-centered perspective, only approach-oriented goals increased learning engagement, which further increased academic adjustment. That is, learning engagement mediated the link between approach-oriented goals and academic adjustment. Emmons (1989) pointed out that for students, specific goals are important for adapting to the current environment. People with clear achievement goals (approach-oriented) will be courageous enough to make effort, face problems, and adopt positive learning strategies. Our results are in line with Luo et al.’s (2011) study that demonstrated that approach-oriented students have more adaptive results for learning motivation (e.g., self-efficacy), learning engagement, and academic emotions [e.g., test anxiety (Luo et al., 2011; Lo et al., 2017)]. Moreover, students achieving goals facilitate meeting their internal needs, obtaining happiness (Job et al., 2009), and more easily adapting to the learning environment.

Despite the theoretical and practical implications, the limitations of our work should be recognized. Firstly, we only tested freshmen, and thus, our results do not generalize across all students. In the future, studies should further consider the relationships between the dimensions and profiles of achievement goal orientations and academic adjustment in senior college students or primary and middle school students. Secondly, our study was based on a Chinese sample. The levels of achievement goal orientations vary between cultures. Therefore, students’ achievement goal orientations should be compared between collectivism and individualism cultures to determine whether our findings generalize to other cultures. Thirdly, we did not observe a relationship between mastery-avoidance goals and outcomes, and the conclusions for mastery-avoidance and performance-approach goals were inconsistent. The reasons underlying these findings require further exploration, which may include investigations on autonomy and control, achievement motivation, and independent support, which will extend the achievement goal research field and offer future directions for research.

Our findings have both theoretical and practical implications. In terms of theoretical significance, we integrated variable-centered (concerning individual changes) and person-centered (concerning individual differences) approaches, which provided an important opportunity to advance the understanding of achievement goal orientations and demonstrated a comprehensive account of the influence factors and underlying mechanisms of academic adjustment. Furthermore, we provided further evidence of how academic adjustment is shaped. The person-centered approach allowed us to reveal different motivation patterns of freshmen and complemented the knowledge gained from traditional variable-centered quantitative methods for studying individual differences.

In regard to the practical implications, our findings highlighted the importance of mastery-approach and approach-oriented goals in promoting adaptive academic adjustment. Given the pivotal role of academic adjustment in student success (van Rooij et al., 2018), improving approach-oriented goals may help to increase academic adjustment in freshmen. It is essential for students to establish approach-oriented goals by accumulating experience by applying learning methods, such as inquiry-based learning, which is associated with goal orientations (Ramnarain and Ramaila, 2016). Moreover, by establishing a mediation model, our findings offer advice for practitioners to understand how achievement goal orientations are linked to academic adjustment. These findings may help in the development of appropriate educational practices. Freshmen who only have approach-oriented goals have greater learning engagement and academic adjustment; therefore, teachers should pay more attention to discovering potential at-risk groups and encourage them to explore their sense of competence to ensure that these students do not neglect their schooling. Furthermore, it is vital for teachers to reduce the emphasis on comparisons between students and minimize the negative impact of competitive environments.