Does Classroom matter? : A longitudinal multilevel perspective on students’ achievement orientation profiles during lower secondary school

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https://helda.helsinki.fi

Does Classroom matter? : A longitudinal multilevel perspective þÿon students achievement orientation profiles during lower secondary school

Ketonen, Elina E.

2023-06

Ketonen , E E , Hienonen , N , Kupiainen , S & Hotulainen , R 2023 , ' Does Classroom þÿmatter? A longitudinal multilevel perspective on students achievement orientation profiles

during lower secondary school ' , Learning and Instruction , vol. 85 , no. 85 , 101747 . https://doi.org/10.1016/j.learninstruc.2023.101747

http://hdl.handle.net/10138/356513

https://doi.org/10.1016/j.learninstruc.2023.101747

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Learning and Instruction 85 (2023) 101747

Available online 26 February 2023

Does classroom matter? - A longitudinal multilevel perspective on students ’ achievement goal orientation profiles during lower secondary school

Elina E. Ketonen

^a^,^*

, Ninja Hienonen

^b

, Sirkku Kupiainen

^b

, Risto Hotulainen

^b

aCentre for University Teaching and Learning, Faculty of Educational Sciences, University of Helsinki, Finland

bCentre for Educational Assessment, Faculty of Educational Sciences, University of Helsinki, Finland

A R T I C L E I N F O Keywords:

Achievement goal orientations Goal stability

Compositional effect Multilevel longitudinal study Person-oriented analysis

A B S T R A C T

The present study adds to earlier person-oriented research by investigating differences in students’ achievement goal orientation (AGO) profiles and their development using a simultaneous consideration of classroom patterns with longitudinal multilevel methods. The sample of almost 10,000 lower secondary school students, representing over 600 classrooms, was surveyed on their AGOs in the 7th and 9th grade. Multilevel latent profile analyses (MLPAs) and transition analysis (MLTA) revealed similar student profiles in AGOs in both grades:

success-oriented, moderate multiple goals and avoidance-oriented, as well as two classroom types: success-oriented and mixed orientation classrooms with varied relative proportions of different student-level profiles and patterns of likely transitions. Stability of profiles was more typical than change. Maladaptive transitions were related to lower, and stable and adaptive transitions to higher GPA in the end of 9th grade. In success-oriented classrooms, it was more common to maintain or adopt the success-orientation across lower secondary school compared to the other classroom type.

1. Introduction

Among motivational theories, achievement goal theory is one of the more widely used frameworks for conceptualising achievement motivation, particularly in educational contexts (Senko, Hulleman, & Har- ackiewicz, 2011; Urdan & Kaplan, 2020). According to the theory, it is the purpose of engaging in (or avoiding) academic behaviour, as construed by the student, that affects achievement motivation. For example, the motive might be the activity itself (e.g., curiosity and enjoyment to learn new things), the outcome of the activity (e.g., accomplishing a school task, getting good grades) or just taking the easy way out (e.g., avoiding any extra effort in school-related work). This tendency, namely achievement goal orientation, refers to the general motivational approach (or avoidance) students take to academic work. It often comprises a range of dispositions, tendencies, processes, aims and outcomes, and the chosen approach or a combination of approaches affects the learning process and related outcomes (for a meta-analyses, see, e.g., Huang, 2012; Payne, Youngcourt, & Beaubien, 2007; Wor- mington & Linnenbrink-Garcia, 2017). Students bring their individual motivational tendencies to achievement situations, but the adoption of such orientations can also be promoted by the academic environment

(Urdan & Schoenfelder, 2006). Building on these lines of research, the present study investigates the development of students’ achievement goal orientations by considering both individual tendencies and the social context of classroom and peers. More precisely, the study exam- ines the development of students’ achievement goal orientations, their association with academic achievement, and most importantly, the extent to which students’ goal orientation patterns and their development differ across classrooms.

1.1. Achievement goal orientations

In achievement goal research, at least two lines of research exist: one regarding task-specific goals and one that focuses on goal-directed behaviour at a more general level (Wormington & Linnenbrink-Garcia, 2017). For the present study, we focused on the latter, that is, achievement goal orientations. Achievement goal orientations describe the broad reasons for engaging in – or avoiding – tasks in achievement settings. Instead of specific goals and objectives which a person seeks to attain, achievement goal orientations reflect individual differences in the more general motivational tendency to aspire to certain outcomes, such as to do better than last time or to avoid competitive situations (e.

* Corresponding author. University of Helsinki, Faculty of Educational Sciences, P.O. Box 9, 00014, Finland.

E-mail address: elina.e.ketonen@helsinki.fi (E.E. Ketonen).

Contents lists available at ScienceDirect

Learning and Instruction

journal homepage: www.elsevier.com/locate/learninstruc

https://doi.org/10.1016/j.learninstruc.2023.101747

Received 8 January 2022; Received in revised form 21 December 2022; Accepted 12 February 2023

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g., Niemivirta, Pulkka, Tapola, & Tuominen, 2020). Achievement goal research also divides which kinds of goals are taken into consideration.

These classifications have formed the grounds for goal orientation research as well (Wormington & Linnenbrink-Garcia, 2017). In original theorising on achievement goals, researchers distinguished three achievement goal orientations that students have about learning and studying: mastery, performance, and work avoidance (e.g., Ames, 1992;

Dweck, 1986; Nicholls, 1984). The central distinction between the first two is between engaging in achievement behaviour for the purpose of developing one’s competence versus demonstrating it. When students are oriented towards mastery goals (i.e., the mastery approach), their goal or motivation in an achievement setting is to learn, understand, and master the new knowledge or skills the situation affords. Learning is perceived as inherently interesting and valuable in itself, regardless of external standards or expectations. On the other hand, when inclined to performance orientation, students’ goal is to appear competent with focus on the achievement itself and on one’s ability (i.e., performance approach).¹Finally, work avoidance has been investigated as one goal orientation alongside mastery and performance orientation (e.g., Skaalvik, 1997; Tuominen, Niemivirta, Lonka, & Salmela-Aro, 2020).

Students pursuing a work avoidance goal prefer easy assignments, exert as little effort as possible in academic activities and avoid school-related work.

Whereas in the mastery approach, goals are purely self-referenced, a performance goal orientation, which focuses achievement, may involve the element of social comparison but may also imply a state in which performing or completing a task successfully is important in its own right, not as a means to establishing one’s superiority over others (Brophy, 2005). Some definitions of performance approach emphasise appearing capable to others or outperforming others (i.e., a normative, social-comparative standard), whereas others emphasise appearing capable to oneself or obtaining a desirable outcome (i.e., an internal standard; see Hulleman, Schrager, Bodmann, & Harackiewicz, 2010).

All these conceptualisations are present in the literature, with varying labels. For instance, Elliot, Murayama, and Pekrun (2011) differentiate between so-called self-approach and other-approach goals, self-approach goals referring to the pursuit of competence as indicated by intra-personal standards as compared to the normative standards of the social comparison of other-approach goals. In a similar vein, when the emphasis is on achievement but not on comparison, Grant and Grant and Dweck, 2003 refer to outcome goals (e.g., the goal of wanting to do well on a particular task) and conclude that goals of this kind can be a part of both a learning (mastery) orientation and a performance orientation (see also Bong, Woo, & Shin, 2013). Also, Niemivirta (2002) refers to the category of goals in which a student wants to do well and tends to rely on external criteria (such as grades) when evaluating goal accom- plishment, but they perceives these as a mastery-extrinsic orientation rather than performance orientation. In the present study, a performance-related orientation which has a strong emphasis on achieving success but not involving social comparison is called success orientation, as suggested by Brophy (2005). Compared to mastery orientation, such orientation focuses on the rewards of achievement more than attaining competence itself.

1.2. Person-oriented approaches in goal orientation research

While achievement goal theory distinguishes between independent goal orientations, students are often driven by multiple motives

simultaneously (i.e., multiple goal pursuit, see Barron & Harackiewicz, 2001; Pintrich, 2000). Hence, a person-oriented approach is frequently used in achievement goal orientation research. The method reveals common patterns of multiple goal endorsement by classifying students who display similar combinations of goal orientations together (goal profiles) and can be also used to study individual differences (or simi- larities) within and over time (Wormington & Linnenbrink-Garcia, 2017). Review articles indicate that similar profiles or patterns are identified across studies and that they are relatively stable over time (see Niemivirta et al., 2020; Wormington & Linnenbrink-Garcia, 2017).

In those studies which cluster students into qualitatively different achievement goal orientation profiles, researchers usually differentiate between mastery and performance orientation, and some also include work avoidance. Different conceptualisations and numbers of included orientations, different methods of analysis, and different participant populations make generalisation of the results challenging but most studies have identified three or four profiles (Niemivirta et al., 2020).

Certain profiles seem to repeatedly occur in all studies, even across age-groups or levels of schooling. (For a review, see Niemivirta et al., 2020; Wormington & Linnenbrink-Garcia, 2017). These include a combined mastery and performance-approach profile (i.e., success-or- iented or high approach profile), which in some studies is further divided into a primarily mastery and a primarily performance profile. This group of success-oriented students aims at learning but is also likely to pursuit performance-related goals, such as good grades. Another often identified profile is students with moderate or low levels of all achievement goals (i.e., indifferent, students with moderate multiple goals or low-motivation).

Students in this group do not orient dominantly towards any specific goal orientation and often do what is expected, but without much extra effort. Finally, if a work avoidance orientation is included, often a profile with stronger-than-average work avoidance, combined with relatively low values for both mastery and performance approaches, is found (i.e., avoidance-oriented). Compared to others, avoidance-oriented students show clearly lower aspiration to learn or even perform well while aiming to minimise both the effort and time spent on schoolwork.

Studies profiling students based on their achievement goal orientations have also found some gender differences in profile memberships (see e.g., Madamürk, Tuominen, Hietaj¨ ¨arvi, & Salmela-Aro, 2021;

Tuominen, Juntunen, & Niemivirta, 2020; Tuominen-Soini, Salmela-Aro, & Niemivirta, 2010). These studies suggest that, overall, girls emphasised learning more, while boys emphasised outperforming others and avoidance tendencies more. For instance, girls were significantly more likely than boys to be members of the mastery-oriented group (M¨adamürk et al., 2021, see also Tuominen, Juntunen, & Nie- mivirta, 2020) or the success-oriented group (Tuominen-Soini et al., 2010). In terms of academic achievement, success-oriented students who combine mastery and performance-related goals usually do better in school than avoidance-oriented or indifferent students, sometimes even better than solely mastery-oriented (Tuominen-Soini, Salmela-Aro,

& Niemivirta, 2011; see also Barron & Harackiewicz, 2001), whereas

indifferent students (with moderate multiple goals) still show higher achievement than the avoidance-oriented students across numerous studies (see Niemivirta et al., 2020). Besides lowest GPA, avoidance-oriented students also had the lowest SES in a study of lower secondary school students, whereas mastery- and success-oriented students had highest GPA and SES (M¨adamürk et al., 2021).

Although the findings on the stability and change in goal orientation profiles are less consistent than the qualitative differences between the profiles extracted and their achievement related outcomes, some con- clusions can be made. The proportion of students displaying stable profiles in lower secondary school has varied from 57 percent to 80 percent (Tuominen-Soini et al., 2011 and Madamürk et al., 2021, ¨ respectively). Moreover, 75 percent of students showed profile stability across an educational transition from elementary to lower secondary school (Tuominen, Niemivirta, et al., 2020) and 50 percent even from lower to upper secondary school (Tuominen-Soini, Salmela-Aro, &

1 Some goal researchers further differentiate the first two goal orientations based on an approach and an avoidance dimension, such that students may strive to learn as much as possible (mastery approach), avoid not learning or avoid losing skills they once had (mastery avoidance), or to demonstrate their competence (performance approach), or avoid demonstrating incompetence (performance avoidance) (e.g., Elliot, 1999; Pintrich, 2000).

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Niemivirta, 2012). When not stable, students’ profiles tended to change to a neighbouring group with a fairly similar profile such as from indifferent to avoidance-oriented (Lo, Chen, & Lin, 2017; Tuominen;

Niemivirta et al., 2020; Tuominen-Soini et al., 2011, 2012), a maladaptive change (i.e., from success-oriented to indifferent) being more common than an adaptive change (i.e., from indifferent to success-oriented) (e.g., M¨adamürk et al., 2021; Tuominen, Niemivirta, et al., 2020). Even if students’ goal orientation profiles seem to be stable more often than to show significant changes, previous studies have identified patterns of change that might indicate a contextual effect. Yet, the multi-layered nature of educational data, indicated by the specification of a classroom or peer level is seldom acknowledged or utilised in the person-oriented research of students’ achievement goal orientations.

Accordingly, there is little data on whether all class- or peer-groups are the same in terms of the occurrence of different goal orientation profiles and whether there are group-dependent differences in the development of these.

1.3. The role of the classroom and peers in the development of achievement goal orientations

Two general theoretical approaches have focused on overall development of motivational orientations in adolescence. According to these, motivational changes may occur as the consequence of cognitive development and contextual changes occurring during the course of schooling. Both approaches can also be applied to the more specific definition of the development of students’ achievement goal orientations (see e.g., Bong, 2009). The first perspective propose that students experience some significant changes in their competence beliefs during adolescence, resulting from the cognitive development. These ability perceptions are closely related to achievement goal adoption. In- dividuals who have incremental beliefs regarding ability or who believe ability is malleable are more likely to adopt mastery approach, whereas individuals who hold entity beliefs about ability tend to adopt performance approach. As younger students tend to believe more in the incremental nature of ability, they are more often oriented towards the mastery approach. As entity beliefs increase during middle to late childhood along with changes to more comparative assessments with peers, a growth in performance orientation is also expected (Dweck, 2002).

The second perspective addresses the decline in adolescents’

achievement motivation in terms of contextual changes in the classroom (e.g., Wigfield & Eccles, 2002). As the learning environment gradually becomes more normative in terms of achievement standards, it is reasonable to assume a relative decrease in the mastery approach over time compared with a relative increase in goal orientations that focus on normative comparison (e.g., Wolters, 2004). At the same time, classrooms are characterised by a greater emphasis on teacher control and fewer opportunities for student autonomy. The exposure to learning environments offering a poor fit to one’s psychological needs (e.g., growing need for autonomy in choosing what, when and how to learn) may have negative motivational consequences for the student (Eccles &

Roeser, 2009), indicated as an increase in avoidance. Overall, both theoretical approaches suggest that achievement motivation declines (in terms of quantity) during adolescence.

From its onset, the achievement goal theory has included the idea that students’ motives for achieving could be influenced not only by their own dispositional tendencies, but also by their school, classroom, peers and the cultural context (Urdan & Kaplan, 2020; see also Hieno- nen, Hotulainen, & Jahnukainen, 2021). Several of the founders of achievement goal theory explicitly argued that influencing students’

achievement goals requires altering the goals that are emphasised in the broader context (e.g., Ames, 1992; Nicholls, 1984; for a meta-analysis, see Bardach, Oczlon, Pietschnig, & Lüftenegger, 2020). Peers have been shown to affect student’s learning, motivation, school adjustment, and achievement (Altermatt & Pomerantz, 2003; Kindermann, 2007).

Earlier studies have shown peer group membership to be related, among other things, to school valuing (e.g., Ryan, 2001) and to students’ academic effort at school (e.g., Kindermann, 2007). Studies have pointed out that peer group effects are somewhat different depending on the student’s relative or perceived relative academic performance within the class. Belonging to the group of high performers in the class has usually boosting motivational effects for performance through higher self-concept. In contrast, a lower relative position in class has been shown to be related to a more negative academic self-concept, especially when students enter a new class or group, due to the well-known Big-- Fish-in-Little-Pond Effect (Marsh, Kong, & Hau, 2000).

On the other hand, studies have shown that in some settings, studying with higher performing classmates could be beneficial for low- performing student’s achievement (e.g., Hienonen et al., 2018). For low-achievers, belonging in a group valuing learning may help them to develop higher achievement motivation through basking in reflected glory of the perceived accomplishments of their peers (Marsh et al., 2000). In addition, research on achievement goals indicates that the quality of students’ motivation in the classroom depends on how success is understood in the classroom or school, thereby influencing the goals that students adopt (Urdan & Schoenfelder, 2006). Longitudinal research on adolescence has also evidenced the importance of peers on motivational outcomes, referred to as a group contagion effect (e.g., Burgess, Riddell, Fancourt, & Murayama, 2018; Shin & Ryan, 2014).

One conclusion that can be drawn from these studies is that students who spend more time together gradually become more alike in terms of academic behaviour. An interesting question is how classroom composition may interact with and influence the development of students’

achievement goal orientation profiles. That is, does peer group matter in goal orientation profile formation?

1.4. The present study

Earlier research on the number and characteristics of achievement goal orientation profiles as well as their associations with academic achievement have been quite consistent. By contrast, the possible impact of the classroom context or peers on the occurrence and development of the orientations remains unspecified. Previous person- oriented studies on students’ achievement goal orientations have seldom considered the nestedness of the data, leaving the role of classroom composition unchartered in their focus on individual differences (e.g., Lo et al., 2017; Tuominen-Soini et al., 2011). The present study adds to earlier research by investigating differences in students’ achievement goal profiles at both the individual and the group levels with longitudinal multilevel methods. This allows us to understand how classrooms may differ in both the occurrence and the development of students’ achievement goal orientations, and in the association of this development with academic achievement.

In the current study, we considered two self-referenced (non- normative) motivational pursuits: the mastery approach with an emphasis on the acquiring of new knowledge and learning, and the success approach, with an emphasis on obtaining acknowledged success even if not explicitly comparing oneself to others, and one (self-referenced) avoidance orientation, namely work avoidance, indicating the aim to avoid or minimise all school-related work. In a large sample of almost 10,000 lower secondary school students, who were surveyed on their achievement goal orientations at the beginning and the end of lower secondary education (7th and 9th grade), we first applied multilevel latent profile analyses (MLPAs) on both grades to examine the number of different student and classroom profiles in achievement goal orientations. Hereby, we examined whether specific goal orientation profiles were replicated over time at either student or classroom level or at both. To assess patterns of profile changes, that is, the stability or changes in students’ profile-membership, multilevel latent transition analysis (MLTA) was conducted on students who participated in the study at both time points. We investigated the extent to which different

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achievement goal orientation profiles/patterns (either stable or changing) differ across classrooms and how the stability or change in students’

goal orientation patterns were related to their school achievement (grade point average (GPA)). We addressed the following research questions.

(1) What types of achievement goal orientation profiles can be found at grade 7 at student and at classroom level, and are the same profiles valid still at grade 9?

In line with a multitude of previous studies applying person-oriented analyses (for a review, see Niemivirta et al., 2020), we expected to identify three qualitatively different achievement goal orientation profiles at the student level: success-oriented, students with multiple goals, and avoidance-oriented. Since we did not include a performance dimension with an explicit comparison to others, we anticipated that the first group would not be divided into subgroups (i.e., purely mastery-oriented vs. purely performance-oriented). Furthermore, since found goal orientation profiles have yielded consistent patterns over time in previous longitudinal studies (e.g., Lo et al., 2017; Tuominen, Niemivirta, et al., 2020), we expected to find the same number and types of profiles at both 7th and 9th grade. Given the lack of previous research at classroom level, we did not formulate specific expectations for classroom profiles.

(2) How students’ goal orientation profiles change during lower secondary school and are there group-dependent differences in this development? (That is, are there classrooms where certain kinds of patterns occur more often than in others)?

We expected an overall decline in adolescents’ achievement motivation over time (e.g., Wigfield & Eccles, 2002), that is, a relative decrease in mastery and success orientation and an increase in avoidance orientation. In terms of goal orientation profiles, we expected that students would show stable profile patterns more often than changing ones over time based on a comprehensive summary of prior longitudinal person-oriented studies (Tuominen, Niemivirta, et al., 2020). However, changes in profile membership were also expected to be found, both maladaptive and adaptive, and most often to a neighbouring profile (Lo et al., 2017; Tuominen; Niemivirta et al., 2020; Tuominen-Soini et al., 2011, 2012). Based on evidence of the importance of the broader context on the achievement goals that students adopt (Urdan & Schoenfelder, 2006), it was expected that group-dependent differences would be found.

(3) How do students’ goal orientation profile changes or stability predict academic achievement at the end of lower secondary school?

Drawing on previous research, we hypothesised that success or mastery-oriented students would display the highest achievement, while avoidance-oriented students would achieve lower grades at the end of lower secondary school (Niemivirta et al., 2020). Furthermore, we expected stable success or mastery-oriented students and students displaying an adaptive change (i.e., moving to a more favourable achievement goal profile) to demonstrate higher achievement than those showing a maladaptive change (Tuominen, Niemivirta, et al., 2020).

2. Method 2.1. Context

In Finland, comprehensive education is a continuum including primary (1–6) and lower secondary (7–9) grades, even if students are often allocated to new classrooms or even schools after the primary grades.

This means that most students find themselves with new classmates at the beginning of 7th grade. After that, students usually remain in the same class until the end of lower secondary school. In grades 7–9, a specific subject teacher teaches each subject, emphasising the importance of peer effect over teacher impact as the same teachers teach all or several of the other classes of the school, depending on the school size.

At the end of the lower secondary school, students make their choice for the dual upper secondary education (general vs. vocational) based mainly on their 9th grade GPA. This means that lower secondary school achievement plays an important role in the forming of the future of most students.

Within the Finnish comprehensive school, there is no official streaming or tracking, but in addition to the cumulative effects of subject choices (e.g., additional foreign languages), the so called ‘classes with a special emphasis’ provide implicit streaming within many schools (Berisha & Sepp¨anen, 2017). Between-class variance seems to cause cumulative effects in the Finnish comprehensive school, especially to- ward the end of lower secondary level (Ketonen & Hotulainen, 2019;

Hansen, Gustafsson, & Ros´en, 2014; Koivuhovi, Vainikainen, & Kala- lahti, 2022; Thuneberg, Hautam¨aki, & Hotulainen, 2015), a phenome- non hidden within the small Finnish between-school differences in the OECD’s PISA studies due to their sampling, which bypasses the class level (e.g., Leino et al., 2019). Whereas these compositional effects have mainly been found in school achievement, cognitive competence and test-performance (see e.g., Ketonen & Hotulainen, 2019; Hansen et al., 2014; Thuneberg et al., 2015), the question of whether similar class-level differences can be detected in student motivation has been examined less often. For instance, a similar multilevel approach revealed both student and classroom differences in the development of mathematics and literacy test scores across lower secondary school (Ketonen & Hotulainen, 2019). This development was associated with a range of class-level factors, some classrooms having a stronger effect on students’ test scores. In the present study we wanted to broaden this scope beyond test performance and achievement by focusing particularly on class compositions of students’ achievement goal orientations and their development.

2.2. Participants

The present study used data from the Metropolitan Longitudinal study in Finland, conducted in lower-secondary schools in 14 munici- palities in the Helsinki metropolitan area in Southern Finland. Research based on the same data with different foci has been published formerly, the emphases being on the effect of class composition on cross-curricular competences and test-performance (e.g., Hienonen et al., 2018; Ketonen

& Hotulainen, 2019; Hienonen et al., 2021) and students’ health and

educational aspirations (e.g., Dobewall et al., 2019). The digital ques- tionnaire data and adjacent cognitive tasks (not used in the present study) were collected from all comprehensive schools in the area with five schools omitted due to their refusal to participate or because of technical issues. Permission to conduct data collections in schools was obtained from the Education Department of each municipality. At the student level, typical reasons for non-response included students’ absence from school on the survey day. This missing data cannot be identified in the dataset but should be borne in mind when interpreting the results.

The data collections were conducted in autumn 2011 (7th graders, 12–13-year-olds) and in spring 2014 (9th graders, 15–16-year-olds) by teachers during an ordinary school day. Classes with fewer than five students present at the time of the study were excluded from the analyses (16 students at time 1 and 126 students at time 2) following the recommendation on ensuring unbiased estimates and their standard errors at level 2 (Clarke, 2008; see also Maas & Hox, 2005). The final data comprised 9825 students (50.8% girls) in 614 classrooms and 128 schools in 2011, and 9120 students (49.6% girls) in 606 classrooms and 127 schools in 2014. Additionally, only the students who stayed in the

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same class during their three years of lower secondary schooling and who participated at both time points (N =6,376, 50.7% girls) were included in the longitudinal analyses (MLTA). They represented 494 classrooms in 120 schools, with classes with fewer than five students (totalling 180 students) again excluded from the analyses. Ethical approval for the study was obtained from the Ethical Committee of the National Institute of Health and Welfare.

2.3. Materials

The questionnaires contained identical achievement goal orientation scales at both time points (grades 7 and 9, respectively). We used scales from a well-established achievement goal orientation instrument (Nie- mivirta, 2002) validated in a multitude of empirical studies (e.g., Tuo- minen, Niemivirta, et al., 2020) with all items phrased in terms of schoolwork in general (for similar approach, see Grant & Grant and Dweck, 2003; Skaalvik, 1997). Of the five dimensions of the original scale (see Niemivirta, 2002), in the present study we used the following three, focusing on non-normative motivational orientations. The scale for the mastery approach comprised three items assessing students’ focus on learning, understanding and gaining competence: ‘‘To acquire new knowledge is an important goal for me in school”, “I study in order to learn new things”, “An important goal for me in my studies is to learn as much as possible”. The scale for the success approach²comprised three items measuring students’ aspiration for good grades and success at school: ‘‘It is important to me that I get good grades”, “An important goal for me is to do well in my studies”, “My goal is to succeed in school”. The scale for avoidance orientation included three items reflecting students’ tendency to avoid achievement situations and to minimise the effort and time spent on studying: ‘‘I try to get away with as little effort as possible in my school work”, “I am particularly satisfied if I don’t have to work much for my studies”, “I always try to do nothing more than just the required schoolwork”. Students rated all items using a 7-point Likert-type scale ranging from 1 (Not true at all) to 7 (Very true). The scales and items have been used in several prior studies conforming their construct validity and measurement invariance over time (see e.g., Tuominen, Niemivirta, et al., 2020; Tuominen-Soini et al., 2012). Before composite mean scores were computed for each of the three orientations, their measurement invariance was confirmed with the present data through longitudinal confirmatory factor analysis³(LCFA).

Students’ comprehensive school leaving GPA was calculated as a mean of all theoretical subjects derived from the National Joint Appli- cation Register (comprehensive school diploma) for grade 9. Due to the lack of comparable official data for earlier grades, students’ time 1 GPA was calculated from their self-reported end of primary school (6th grade) grades. Finnish grades are given on a scale of 4 (=fail) through to 10 (=excellent). In addition to prior achievement, the data included students’ self-reported gender and socioeconomic status (SES), the latter taken as being indicated by the mothers’ standard of education as an ordered categorical variable (1 =basic or vocational education, 2 = general upper secondary education, 3 = lower tertiary education/

bachelor’s degree), 4 =upper tertiary education/master’s degree). As previous studies have indicated, the role of gender, SES and school achievement in relation to students’ achievement goal orientations (e.g., M¨adamürk et al., 2021), time 1 GPA, SES and gender were examined both in relation to goal orientation patterns and used as covariates in the model predicting academic achievement at the end of lower secondary school.

2.4. Methodological approach

A person-oriented approach (also referred to as a mixture model approach) is a technique that enables the study of person-specific configurations of assessed dimensions (see Bergman & Andersson, 2010) such as achievement goal orientations. Person-oriented methods repre- sent a cluster analytical approach in which students with a similar profile in a set of variables can be classified as one type (Vermunt &

Magidson, 2002). Amongst the person-oriented methods, multilevel latent profile analysis (MLPA) has the advantage that it allows for a nested data structure and as a model-based approach, it also allows the evaluation of model fit and comparisons of different models with distinct numbers of profiles at all levels (see Henry & Muth´en, 2010). For instance, as student-level profiles may vary across classrooms, this variation can be modelled at the higher level with class-level latent profiles (i.e., patterns of classrooms). Previously, multilevel mixture models have been applied in educational research to identify patterns of students’ cognitive achievement (Ketonen & Hotulainen, 2019) and homework learning types (Flunger et al., 2020), considering simultaneously the dependence of the found student profiles on between-classroom differences.

Longitudinal extensions of mixture models offer an opportunity for exploring the consistency of profile solutions over time. With latent transition analysis (LTA), the stability and change of certain profiles can be investigated in longitudinal data, testing whether the same profiles are identified over time and whether students belong consistently to the same profile over time (i.e., within-sample temporal stability and within-sample stability, respectively) (Kam, Morin, Meyer, & Top- olnytsky, 2013). The non-independence of the measures across time is considered explicitly by classifying students simultaneously across all time points (two in this study). Thus, comparisons across measurement points as well as findings concerning developmental trends can be made.

In the present study, the multilevel extension of latent transition analysis (MLTA) has the potential to provide additional information on whether there is class-level variability in the within-person developmental patterns.

All analyses were conducted with the maximum likelihood with robust standard errors estimator in Mplus 8.2, which corrects for non- normality in the measures (Muth´en & Muth´en, 1998 – 2018). Due to the nested structure of the data, either “type is mixture twolevel”

(cross-sectional MLPAs and two-wave MLTA) or “type is complex” procedure (the association of profile changes/stability with covariates and time 2 achievement) was conducted in Mplus. MLPAs and MLTA were conducted using the composite mean scores of the three achievement goal orientation scales (see Appendix 1 for detailed model-building process).

3. Results

3.1. Descriptive results

In the preliminary analyses, we explored the intraclass correlation coefficients (ICCs) of the three achievement goal orientation constructs with all available data for students in the 7th (N =9825) and 9th (N = 9120) grades at both the class and the school levels. Since the ICCs for school level (the percentage of how much of the variation is explained by differences between schools) were 3.2% or lower, even almost non- existent in grade 9 (in 7th grade 0.030, 0.032, 0.016 and in 9th grade

2In the original instrument, the scale is named achievement orientation (Nie- mivirta, 2002) and later as mastery-extrinsic orientation (e.g., Tuominen, Nie- mivirta, et al., 2020).

3 Following indices was used to evaluate overall model fit: Comparative Fit Index (CFI) with a cutoff value of > 0.95, the root mean square error of approximation (RMSEA) with a cutoff value of <0.06, and the standardized root mean square residual (SRMR) with a cutoff value of <0.08 (Kline, 2005).

In order to take into account the slight non-normality of the sample data, maximum likelihood parameter estimates with robust standard errors was used.

Error covariance between two similarly worded items was freed at Time 2.The fit indices suggested a sufficient model− data fit for scalar invariant factor structure over time after freeing two pairs of intercepts (χ²=2222.884, df = 132, p <.001, CFI =0.951, RMSEA =0.050, SRMR =0.054), thus meeting the requirements for longitudinal analysis.

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0.018, 0.019, 0.006 in mastery, success and avoidance orientation, respectively) and since the cases per level-2 unit (classes) nested within level-3 unit (schools) would remain small regarding some of the schools (on average 4.8 classes per school), we continued with a two-level model (students nested within classrooms). When considering only the two levels of student and class, the higher level explained 7.0% of the variation in mastery orientation at 7th grade, 6.7% in success orientation and 5.5% in avoidance orientation effects (reflecting not only class but partly school effect as well). At the 9th grade, the respective proportions of explanation were 5.5% for mastery, 6.2% for success and 2.5% for avoidance orientation. As expected, most of the variance in achievement goal orientations was explained by individual factors, but even if relatively small, the ICC for group effect was >5% for all orientations except for avoidance orientation at 9th grade. The descriptive statistics (Means, SDs, ICCs), internal consistencies and correlations of the measures are summarised in Table 1.

3.2. Cross-sectional MLPAs

To identify achievement goal profiles at time 1 and 2, two multilevel LPAs were conducted separately for 7th and 9th grade datasets using all available data. We started with a series of single-level LPAs. Models with latent structures with up to seven latent student profiles at both time points were fitted. Figs. 1 and 2 present the elbow plots of the information criteria (AIC and BICs) for the different profile solutions of the single-level LPAs. The point after which the slope flattens out indicates the optimal number of profiles in the data (M¨akikangas et al., 2018). As shown, all information criteria decreased from the one-profile to the three-profile solutions, after which the slope clearly flattens out at both

time points. Furthermore, although the criteria values of the solutions with more than three profiles suggested better fit (for detailed values, see Table A1 in Appendix 2), all additional profiles represented only slightly differing variations of profiles that had already been identified in the three-profile solution (exclusion criterion suggested by Meeus, Van de Schoot, Klimstra, & Branje, 2011). In the next step, the MLPA results showed that adding a multilevel structure greatly improved model fits (lower AICs and BICs), indicating a considerable within-class dependency of observations at both 7th and 9th grade. While the AIC supported a more complex model (four latent class-level profiles), the BICs identified a model with just two latent class-level profiles with the already-defined three latent student-level profiles as optimal for both time points (for detailed values, see Table A2 in Appendix 2). Hence, the model with two latent class-level and three latent student-level profiles was chosen as the final model for both time points.

At Level 1, the models with three student profiles and two class-level profiles had a clear interpretation, contained profiles with large enough memberships, with similar profiles repeatedly found in previous studies (see Niemivirta et al., 2020). The solution with three student-level profiles also held across the 7th and 9th grade datasets, indicating that similar achievement goal profiles could be identified over time. The student-level profiles based on their achievement goal orientations were: S1: success-oriented (7th grade: 59.9%, 9th grade: 42.4%), S2:

moderate multiple goals (7th grade: 33.7%, 9th grade: 53.1%) and S3:

avoidance-oriented (7th grade: 6.4%, 9th grade: 4.6%) (for profile-specific variable means, see Table 2). Success-oriented students expressed high levels of both mastery and success orientation, and clearly lower levels of avoidance orientation. Students with multiple Table 1

Descriptive statistics, internal consistencies, intraclass correlation coefficients and correlations for achievement goal orientation variables at Time 1 and Time 2 and GPA at Time 2.

Variable M SD α _ICC ₁ ₂ ₃ ₄ ₅ ₆

7th grade

1 Mastery approach 5.34 1.06 .82 0.070

2 Success approach 5.86 1.07 .86 0.067 .70**

3 Avoidance 4.17 1.38 .72 0.055 −.37** −.36**

9th grade

4 Mastery approach 4.99 1.20 .85 0.055 .37** .31** −.24**

5 Success approach 5.42 1.25 .90 0.062 .30** .40** −.25** .69**

6 Avoidance 4.33 1.34 .79 0.025 −.18 −.14** .35** − .17** −.12**

9th grade GPA 7.98 1.09 .23** .35** −.26** .29** .45** − .14**

Note. All achievement goal orientation items were rated on a scale from 1 to 7. α =Coefficient alpha. ICC =intraclass correlation coefficient. GPA =grade point average (on a scale of 4 (=fail) through to 10 (=excellent). **p <.01.

Fig. 1. Elbow plot of information criteria values for different Level 1 profile solutions at Time 1.

Fig. 2.Elbow plot of information criteria values for different Level 1 profile solutions at Time 2.

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goals seemed to hold moderate levels of mastery and success orientation, but also of avoidance orientation. Avoidance-oriented students were also characterised by moderate level of avoidance orientation but combined with clearly lower mastery and success orientations than the other two profiles (see also Fig. 3 illustrating the time-invariant three-profile solution).

At Level 2, the proportional distribution of the student profiles varied significantly between classrooms, suggesting that the typicality of the different achievement goal profiles varied from one classroom to the next (for detailed student profile probabilities in each latent classroom profile, see Table 3). Again, the class-level profile solution was corresponding across 7th and 9th grade datasets, indicating that similar classroom profiles could be identified over time. The more common classroom profile C1 (N7th =365, N9th =319) included students from all three achievement goal profiles but had a relatively higher proportion of students from the avoidance-oriented goal profile (87.5% in 7th and 66.0% in 9th grade) and from the moderate multiple goal profile (74.6%

and 61.4%) compared to the other classroom profile.⁴In the other

classroom profile C2 (N7th =249, N9th =287) the majority of students were characterised by the success-oriented profile (77.1% in 7th and 67.1% in 9th grade) with a smaller proportion of students from the other two achievement goal profiles. Accordingly, we labelled the two class- level profiles as mixed orientation classrooms (C1) and success-oriented classrooms (C2).

3.3. Two-wave MLTA

Based on the two MLPAs, a model with three student-level profiles (Level 1) and two class-level profiles (Level 2) was imposed for the subsequent MLTA. The entropy of the MLTA model was 0.806, indicating a clear classification. The resulting profile solution at Level 1 with time-invariant means can be seen in Fig. 3 and with standard errors in Table 4. The three profiles extracted at student-level – success-oriented, moderate multiple goals, and avoidance-oriented – were highly identical with the profiles found in the two preceding MLPAs. Table 4 also shows the cross-classification and transition probabilities of the student profile memberships across time for both classroom profiles. Also, the class- level profile solution was similar in terms of composition to the profiles found in the preceding MLPAs (the ratio of different student profiles distributed in the two classroom types) but the number of classrooms in the success-oriented class-level profile was lower in MLTA and avoidance-oriented students were almost non-existent in these classrooms.

In total, nine configurations of stabilities and transitions were possible at the student level for both classroom types. Next, the configurations of stabilities and transitions with a reasonably high transition probability (>0.10) and adequate cell size (N >30) (see criterion suggested by Meeus et al., 2011) were considered in more detail, separately for mixed orientation classrooms and success-oriented classrooms (see Table 4). Following this specification, a total of four patterns described the success-oriented classrooms and eight patterns the mixed orientation classrooms, as shown in Fig. 4. In the success-oriented classrooms (N =74), student-level transition probabilities indicated a systematic pattern of transition to a neighbouring orientation profile. A transition from the success-oriented profile to the moderate multiple goals profile was especially common (25.2%) even if the reverse was also true (5.6%). Actually, in the success-oriented classrooms, the number of students transiting from a moderate multiple goals profile to a success-oriented profile was higher than the number of students staying in this profile (3.2%). Other transitions (e.g., from a success-oriented or moderate multiple goals profile to the avoidance-oriented profile or reverse) were unlikely in these classrooms and the most common pattern was maintaining the success-orientation (64.0%). In the mixed Table 2

Results of cross-sectional MLPAs on Level 1: means and standard errors for achievement goal orientations in different student profiles at Time 1 and Time 2.

7th grade Success-oriented

N =5889, 60% Moderate multiple goals N =3312, 34%

Avoidance- oriented N =624, 6%

Variable M SE M SE M SE

Mastery approach 5.93 0.03 4.64 0.05 3.27 0.14

Success approach 6.48 0.02 5.15 0.08 3.24 0.14

Avoidance 3.77 0.03 4.74 0.03 5.36 0.08

9th grade Success-oriented

N =3863, 42% Moderate multiple goals N =4842, 53%

Avoidance- oriented N =415,

Variable M SE M SE 5% M SE

Avoidance 4.14 0.03 4.60 0.02 4.54 0.15

Fig. 3. Three-profile solution based on raw scores.

Table 3

Results of cross-sectional MLPAs on Level 2: student profile probabilities in two of the latent class-level profiles at Time 1 and Time 2.

Student profile probability 7th grade Classroom profile Success-

oriented Moderate

multiple goals Avoidance- oriented Mixed achievement goal

orientation (N =365, 59%) .48 .43 .09

Success-oriented (N =249,

41%) .77 .21 .02

9th grade Classroom profile Success-

oriented Moderate

multiple goals Avoidance- oriented Mixed achievement goal

orientation (N =319, 53%) .36 .57 .07

Success-oriented (N =287,

47%) .67 .30 .03

Note. The latent profiles at the class-level were estimated on the basis of the relative frequency of students’ profiles across different classrooms.

4According to Pearson’s chi-squared test avoidance-oriented (adjusted residual =14.9 in 7th grade and 8.9 in 9th grade) and students with moderate multiple goals (adjusted residual =22.2 in 7th grade and 25.8 in 9th grade) were more likely to belong to classroom profile C1 and success-oriented students less likely (adjusted residual = −28.9 in 7th grade and − 29.2 in 9th grade) both in 7th (χ²(2) =869.44, p <.001) and 9th grade (χ²(2) =857.36, p

<.001).

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orientation classrooms (N = 420), staying in the success-orientation profile was less common and likely transitions were found between almost all profiles and directions: for success-oriented to both moderate multiple goals (24.6%) and avoidance-oriented (1.7%), for students with multiple moderate goals to both success-oriented (4.1%) and avoidance-oriented (3.9%) and for avoidance-oriented to moderate multiple goals (2.0%). Only the most adaptive transition for avoidance-oriented to success-oriented was unlikely. Compared to the

success-oriented classrooms, it was also more common to maintain the moderate multiple goals profile (25.3%) than to adopt a success-orientation (4.1%).

Overall, the transition probabilities that were found confirmed the within-sample student profile stability with two thirds of students adhering to the same achievement goal profile across the two time points in both classroom types (67.5% in success-oriented classrooms, 63.4% in mixed orientation classrooms). This was true for all three Table 4

Results of MLTA: time-invariant means, cross-classification of achievement goal orientation profile membership (N), and transition probabilities from Time 1 (rows) to Time 2 (columns) for both classroom types.

Time-invariant means

Success-oriented N7th/9th =4281/2892 Moderate multiple goals N7th/9th =1884/3079 Avoidance-oriented N7th/9th =211/405

Variable M SE M SE M SE

Avoidance 3.86 0.03 4.67 0.02 4.99 0.08

Cross-classification of profile membership (N), transition probabilities in parentheses 9th grade

Success-oriented Moderate multiple goals Avoidance-oriented

7th grade C1 C2 C1 C2 C1 C2

Success-oriented 1968 (.82) 633 (.73) 1323 (.74) 249 (.64) 94 (.79) 14 (.64)

Moderate multiple goals 220 (.63) 55 (.64) 1365 (.83) 32 (.51) 209 (.83) 3 (.41)

Avoidance-oriented 16 (.81) 0 (.00) 110 (.80) 0 (.00) 82 (.87) 3 (.65)

Note. C1 =Mixed achievement goal orientation classroom (N =420). C2 =Success-oriented classroom (N =74).

Fig. 4.Transitions of students’ achievement goal orientation profiles in mixed achievement goal orientation (above) and success-oriented (below) classrooms. Black lines and percentages indicate the transitions with reasonably high transition probabilities (>0.10) and cell size (N >30) for classification as the same or different profile type over time.

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profiles (stable avoidance-oriented students being existent only in the mixed orientation classrooms). However, somewhat high transition probabilities were also yielded for changes from one achievement goal profile to another in time. Across both classroom types the number of success-oriented profile students decreased from 7th to 9th grade (from 67.2% to 45.4%), whereas students with moderate multiple goals and avoidance-oriented profiles increased (from 26.5% to 48.3% and from 3.3% to 6.3%, respectively). Furthermore, maladaptive transitions (i.e., from success-oriented to either moderate multiple goals or avoidance- oriented, or from moderate multiple goals to avoidance-oriented) occurred more often (in total 29.7%) than adaptive changes (in total 6.3%; i.e., from avoidance-oriented to moderate multiple goals or from multiple goals to success-oriented). When comparing the two classroom types with Pearson’s chi-squared test, in the success-oriented classrooms it was more common to maintain the same achievement goal profile over time (i.e., stable success- or moderate multiple goals orientation;

adjusted residual = 2.5), whereas students in the mixed orientation classrooms, showed a maladaptive change (adjusted residual = 2.1) more often than in the success-oriented classrooms (χ²(2) =6.278, p = .043), including the dramatic drop from a success orientation to an avoidance orientation profile. No statistically significant difference between the two classroom profiles was found in adaptive transitions (adjusted residual =1.0).

3.4. The associations of profile changes/stability with academic achievement

Before the final stage of analyses, students were classified into the achievement goal orientation patterns for which their probability of profile membership obtained in the MLTA was the highest (the three patterns of stable profiles and five patterns of likely transitions). First, a categorical variable representing the three patterns of stable profiles and five patterns of likely transitions was regressed on all covariates (gender, SES, prior achievement) jointly. The logistic regression coefficients are presented in Table A3 (in Appendix 2). In summary, girls and students with higher prior achievement more often presented the adaptive profiles, especially those staying in success-oriented profile throughout the lower secondary school. Boys seemed to be at particular risk of degrading from success-oriented to avoidance-oriented and both gen- ders with lower SES and prior achievement from multiple goals to avoidance-oriented. On the other hand, higher SES particularly predicted adaptive transitions (over the gender or prior achievement), especially for those upgrading from multiple goals to success-oriented.

Next, GPA at time 2 was regressed on dummy variables representing each found profile pattern in a model where gender, SES and prior achievement were considered simultaneously as covariates. To deter- mine all comparisons, the same model was ran seven times. Each discovered profile pattern was considered once as the reference group in order to report how students’ GPA at time 2 was associated with a specific profile pattern as compared to other patterns including all combinations (and considering the effects of the covariates). The regression coefficients for grade 9 academic achievement by different profile patterns are shown in Table 5.

In addition to profile membership, higher prior achievement (β = 0.88; p =<.001), higher SES (β =0.09; p =<.001) and being a girl (β = – 0.23; p =<.001) all predicted higher GPA at the end of 9th grade (standardised residual variance 0.359). While controlling for the effects of covariates, the students who were stable (began and stayed) in the success-oriented profile showed higher achievement at the end of lower secondary school than students from any other profile pattern, except for the students changing to this profile from the moderate multiple goals group (no significant difference). Stable multiple goals orientation students had lower achievement than the two groups above but higher than students who moved to avoidance orientation from either success or moderate multiple goals orientation. The GPA of stable avoidance-

orientation students was as high as that of time 2 moderate multiple Table 5 Comparisons of achievement goal patterns regarding 9th grade GPA (with gender, SES and prior achievement as covariates in the model). Profile Stable success-oriented Stable multiple goals Stable avoidance- Success-oriented → Success-oriented → Multiple goals → success- Multiple goals → oriented multiple goals avoidance-oriented oriented avoidance-oriented Reference profile Est SE p Est SE p Est SE p Est SE p Est SE p Est SE p Est SE p Stable multiple goals −0.32 0.03 0.00 Stable avoidance-oriented −0.38 0.08 0.00 −0.07 0.08 0.40 Success-oriented → multiple goals −0.34 0.02 0.00 −0.02 0.03 0.49 0.05 0.08 0.55 Success-oriented → avoidance-oriented −0.60 0.08 0.00 −0.28 0.08 0.00 −0.22 0.11 0.05 −0.26 0.08 0.00 Multiple goals → success-oriented −0.03 0.04 0.49 0.29 0.05 0.00 0.35 0.09 0.00 0.31 0.05 0.00 0.57 0.09 0.00 Multiple goals → avoidance-oriented −0.55 0.05 0.00 −0.24 0.05 0.00 −0.17 0.09 0.06 −0.22 0.06 0.00 0.05 0.09 0.61 −0.52 0.07 0.00 Avoidance-oriented → multiple goals −0.35 0.08 0.00 −0.03 0.08 0.67 0.03 0.11 0.75 −0.01 0.08 0.86 0.25 0.11 0.02 −0.32 0.09 0.00 0.20 0.09 0.03 Note. Est =Regression coefficient for 9th grade academic achievement. These coefficients have to be interpreted as partial regression coefficients, adjusted for the effects of all covariates in the model, and have to be interpreted in comparison with the reference profile pattern. SE =Standard error.