Leen Haerens Ghent University, Belgium. David Kirk University of Bedfordshire, UK. Greet Cardon Ghent University, Belgium

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1 Motivational profiles for secondary school physical education and its relationship to the adoption of a physically active lifestyle among university students European Physical Education Review 16(2) ª The Author(s) 2010 Reprints and permissions: sagepub.co.uk/journalspermissions.nav DOI: / X epe.sagepub.com Leen Haerens Ghent University, Belgium David Kirk University of Bedfordshire, UK Greet Cardon Ghent University, Belgium Ilse De Bourdeaudhuij Ghent University, Belgium and Maarten Vansteenkiste Ghent University, Belgium Abstract The promotion of an active lifestyle is one of the central aims of physical education (PE). The present study aimed at investigating the relation between students motivation for PE and activity levels using self-determination theory as a guiding framework. A retrospective design was used involving 2617 university students, of which 878 (33.5 percent) were male (age ). Validated questionnaires were used to measure motivation for PE, self-reported concurrent (activity levels at secondary school) and delayed transfer (activity levels at university). Corresponding author: Leen Haerens, Department of Movement and Sports Sciences, Ghent University, Ghent Leen.Haerens@UGent.be

2 118 European Physical Education Review 16(2) A combination of hierarchical and non-hierarchical cluster analyses was used to generate motivational profiles for PE. Differences in activity levels were analysed using MANCOVAS. The results revealed that students with more optimal (i.e. more autonomous) motivational profiles reported more transfer and reported being more active at secondary school and in early adulthood. Implementing strategies that enhance self-determined motivation might result in more students adopting an active lifestyle. Keywords motivation, physical education and sport, self-determination theory, transfer Introduction It has been generally recognized that physical education (PE) should play an important role in the promotion of an active and healthy lifestyle with an emphasis on students preparation for lifelong physical activity (Corbin, 2002; Fairclough, 2003). In several European countries the emphasis on improving health is translated into the promotion of health-related attainment goals for PE. For PE to reach these goals, transfer of learning needs to take place. Transfer of learning requires a relation between what students learn in school and the knowledge, skills and attitudes needed to lead a successful and productive life beyond school and has generally been a problematic issue for educational researchers and policy-makers (Lave, 1997). It is argued that what is learned during PE classes should stimulate pupils to engage in physical activities beyond the curriculum and the school. Such transfer can occur when students are still in school (concurrent transfer), but also when they have left compulsory school education (delayed transfer). Indeed, since PE aims at promoting lifelong physical activity, it is important to consider delayed transfer into adult life next to concurrent transfer or transfer into leisure time when at school. Concurrent transfer of learning occurs while students are still at school, involving transfer between young students school experience and their experiences outside school, in their everyday lives, interactions with peers and family, and what they do in leisure time. Green et al. (2005a), for example, claim that the multi-activity curriculum of PE that has been developed in the UK and elsewhere since the 1950s has been instrumental in increasing sports participation among youth. There is some support for this view in recent surveys in the UK (Department of Culture, Media and Sport (DCMS), 2009) and Belgium (Lefevre et al., 2002). Data from the Taking Part (DCMS, 2009) study of young people aged in England show that 95 percent had participated in an active sport during the previous four weeks. Data from a Belgian study of young people aged years old (Lefevre et al., 2002) similarly show that 89.4 percent of boys and 82.6 percent of girls participate in sports on a weekly basis. While these data do not support claims for the concurrent transfer of the specific activities of the school PE programme in themselves, it does offer some support for Green et al. s (2005a) claim that sport occupies a significant place in the leisure culture of many young people in this age group. Delayed transfer into adulthood refers to children going to school to learn the skills, knowledge and values they need to hold a meaningful job, raise a family, and make a positive contribution to society. While this preparation for adult life view seems to be common sense, it has been widely criticized by scholars of education for well over half a century (e.g. Chanan and Gilchrist, 1971; Peddiwell, 1937). Rather paradoxically, one of the strongest criticisms of this view is that there is actually very little delayed transfer of learning from the school to adult life. Much criticism of

3 Haerens et al 119 traditional mathematics teaching, for example, has questioned the assumption that mathematical learning should be abstract and decontextualized to be educationally valuable. Empirical research on shoppers and dieters showed that the practices people use for solving problems of calculation bear little resemblance to the ways in which children are taught mathematics in school (Lave, 1997). Delayed transfer of learning in PE seems to conform to the situation with mathematics described by Lave (1997: 18). Despite the internationally proclaimed idea that PE should promote an active and healthy lifestyle (Corbin, 2002; Fairclough, 2003), most studies show that only a very small minority of adults who are regularly physically active continue to play the games and sports that were part of school PE programmes (e.g. DCMS, 2009). It would appear then that there is little delayed transfer of learning between what is learned in school PE and adults physical activity behaviours in later life. Related to this issue of delayed transfer of learning, Bailey et al. (2008) argued that there is insufficient theoretical and empirical evidence for the long-term educational benefits claimed for PE and suggest that more research is needed. In summary, the literature suggests that lifelong participation in physical activity remains an ubiquitous and widely supported aspiration of school PE programmes, and the issues of concurrent and delayed transfer of learning are of central importance to its realization (Kirk, 2010). This situation raises the question how PE might possibly improve the transfer of what is learned in school to life beyond the school and to adult life. One way of approaching this question is to investigate students motivation for secondary school PE and how this may be related to levels of physical activity outside school, thereby providing some insights into the processes underlying transfer of learning. In the present study, self-determination theory (SDT) (Deci and Ryan, 2000; Vansteenkiste et al., 2010) is used as a theoretical framework to investigate students motivation in a school PE context and whether there is evidence of concurrent and delayed transfer of learning. Self-determination theory (SDT) SDT is a macro-theory of human motivation, emotion and personality, developed during the past four decades, that provides an understanding of why people initiate and persist in behaviours. SDT has been successfully applied in a broad variety of contexts, including ecology, psychotherapy, work and, more central to the present paper, education, exercise and sports (see Deci and Ryan, 2000). Currently, the theory constitutes of five mini-theories (Vansteenkiste et al., 2010): a) Cognitive Evaluation Theory, b) Organismic Integration Theory, c) Causality Orientations Theory, d) Basic Needs Theory and e) Goal Content Theory. In this paper, we primarily draw on Cognitive Evaluation Theory and Organismic Integration Theory. Within Cognitive Evaluation Theory, the distinction between extrinsic and intrinsic motivation is central. Intrinsically motivated behaviours are considered the most autonomous form of motivation because intrinsic motivation relates to the engagement in an activity for its own sake, out of interest or enjoyment. Extrinsic motivation refers to the engagement in an activity to attain an outcome separable from the activity itself and was considered by some theorists (e.g. Harter, 1981) as the antipode of intrinsic motivation. However, empirical work (e.g. Ryan and Connell, 1989) within Organismic Integration Theory indicated that people can engage in non-enjoyable behaviours relatively autonomously, given that they fully endorse the reason for enacting the behaviours. As a result, different types of extrinsic motivation were differentiated, some of them being more controlled and some being more autonomous in nature.

4 120 European Physical Education Review 16(2) Introjected and external motivation are considered to be two forms of controlled motivation. External regulation represents the classical type of extrinsic motivation and is the most pressurizing form, since people then participate in an activity to avoid punishments, to obtain rewards, or to meet external expectations. For example, students put effort into PE to obtain good grades. Introjected motivation occurs when people feel pressurized to engage in an activity, but this pressure originates from themselves, for instance, if pupils engage in PE to avoid feelings of guilt, shame and anxiety or to enhance feelings of pride and ego-enhancement. Identified and integrated motivations are two types of autonomous motivation. Identified motivation occurs when people participate volitionally and with a sense of freedom because they perceive the personal relevance of the activities offered, for example, if activities correspond to a personally endorsed goal (e.g. participating in PE because it improves health). After identified motivation, integrated regulation is distinguished. Integrated motivation occurs when various personally meaningful behaviours are brought in full alignment with other personally endorsed values and norms (e.g. putting a lot of effort into PE because one values sports, being in a good shape, social interaction and so on). SDT was built around the central idea of distinguishing between different types of motivation that vary according to their level of self-determination or to the extent to which behaviours are fully endorsed by the individual. More specifically, Deci and Ryan (1991) proposed that the regulation of behaviour can be viewed as being autonomous, controlled or amotivated. Previous research in the context of school PE has shown that more autonomous forms of motivation predict (a) greater engagement during PE classes, as indexed by higher concentration (Ntoumanis et al., 2005) and more effort (Ntoumanis, 2001, 2005; Ferrer-Caja, 2000), (b) higher physical activity levels during school PE lessons (Cox et al., 2008) and (c) more participation in optional PE activities (Ntoumanis, et al., 2005). Within Organismic Integration Theory autonomous and controlled motivation are furthermore contrasted with amotivation. Amotivation represents absence of intentionality and motivation due to (a) lack of self-confidence to enact required behaviours, (b) a lack of perceived contingency between the enacted behaviours and their consequences and (c) a lack of valuation of the activity (Ryan et al., in press). When amotivated, people feel futile and discouraged to enact the activity and, as a result, amotivation has been found to predict maladaptive behaviours, including low effort (e.g. Ntoumanis et al., 2001) and low intention to be physically active in leisure time (Ntoumanis et al., 2001). The current study Unlike most previous SDT studies in school PE (e.g. Cox et al., 2008; Ferrer-Caja, 2000; Ntoumanis, 2005), in the present study we adopted a person-centred rather than variable-centred approach. Whereas the primary aim of a variable-centred approach is to investigate the effect of the different motivational dimensions on outcomes, the aim of the person-centred approach is to examine how different motivational dimensions (i.e. autonomous motivation, controlled motivation and amotivation) get combined within different groups of individuals, each characterized by a particular motivational profile. It is possible that some students might combine motives in a relatively unique manner so that they, for instance, participate in PE both to meet external demands and because they find PE classes inherently enjoyable at the same time.

5 Haerens et al 121 Another innovative aspect of the current study involved the focus on transfer of learning, an issue that has received little attention within SDT. Specifically, we focused on the extent to which different motivational profiles for PE are associated with greater transfer into leisure time when participants are still at school as adolescents (concurrent transfer), or have left school as young adults (delayed transfer). Transfer needs to be distinguished from pupils persistence, which refers to the continued engagement in an optional activity once the socializing figure (i.e. PE teacher) who introduced the activity is no longer watching (Vansteenkiste et al., 2008). The issue of persistence has been more extensively studied within SDT (e.g. Deci et al., 1999) but far less attention has been paid to the issue of transfer, that is, the generalization of newly acquired activities to either new contexts or new activities. A couple of studies have shown that more autonomous forms of motivation are associated with concurrent transfer, that is, with a stronger intention to participate (e.g. Ntoumanis, 2001; Standage et al., 2003) and the effective participation (Vansteenkiste et al., 2004a) in leisure time physical activity outside school while students are still at school. Additionally, the work of Hagger et al. (2003) on the trans-contextual model is premised on the assumptions that underpin the notion of transfer and the ubiquitous aspiration that PE can play a decisive part in promoting leisure-time physical activity participation through promoting autonomous motivation. The current study aimed to build on this small body of work by examining delayed transfer, in addition to concurrent transfer, as a function of participants motivational profile. University students were chosen as the focus of this study not because they represent a sample of convenience for university researchers, but because they are an important group in which to study post-school physical activity patterns. Most university students have entered a phase of relatively independent living where they can make genuine though circumscribed choices about lifestyle, including physical activity. Level of education has been shown to be a decisive factor in predicting physical activity levels (e.g. Cerin et al., 2009) or inactivity levels (e.g. Tammelin et al., 2003) in adulthood, and so we might expect to find a relatively physically active community among university students. Moreover, since they have only recently left school, we propose that engagement with physical activity during the first year of university might reflect a similar pattern to their engagement while at school and thus may provide some information on the extent of delayed transfer. This information, in turn, may provide us with some insights into the possibilities and challenges of achieving the ubiquitous goals of school PE for the promotion of lifelong physical activity and active lifestyles. The following three specific aims were pursued. First, we sought to investigate university students reports of any transfer that may have occurred from school PE into their lives as university students. Second, we aimed to map out the different motivational profiles of university students on participation in secondary school PE, thereby making use of three different clustering dimensions (i.e. autonomous motivation, controlled motivation and amotivation). In the current study, we choose to focus on the composite scores of autonomous and controlled motivation rather than on their subdimensions for parsimony. Specifically, we believe it is critical to gradually refine the cluster results by adding more motivational clustering dimensions into the analyses. Given the paucity of previous research on students motivational profiles for PE (see Boiché et al., 2008, for an exception), we began by using the composite scores for autonomous and controlled motivation. In line with previous research in the general classroom (e.g. Vansteenkiste et al., 2009) making use of these two dimensions, we expected that four different groups would emerge, with two of them being characterized by high and low levels of autonomous and controlled motivation, respectively. The other two groups would be characterized by an opposing profile of autonomous and controlled

6 122 European Physical Education Review 16(2) motivation. In contrast to the study by Vansteenkiste et al. (2009), we added amotivation as a third clustering dimension. In so doing, we intended to help refine the four retained clusters, possibly leading to the extraction of a fifth cluster, characterized by high scores on amotivation. Third, we investigated whether groups with different motivational profiles would differ in terms of (a) self-reported transfer, (b) activity levels at secondary school outside PE classes (concurrent transfer) and (c) activity levels during young adulthood (delayed transfer). Different predictions can be made based on SDT compared to currently popular motivation theories, including self-efficacy theory (Bandura, 1989) and expectancy-value theory (e.g. Eccles and Wigfield, 2002). The latter theories consider motivation as a unitary, quantitative construct and suggest that a higher quantity of motivation should yield more optimal outcomes. As a result, based on such a quantitative viewpoint, the clusters of students that combine different types of motivation (e.g. autonomous and controlled) should display the most optimal set of outcomes as they are most strongly motivated. SDT, in contrast, suggests that higher quantities of motivation do not necessarily yield more desirable outcomes if the motivation is controlled rather than autonomous in nature (Ryan and Deci, 2000; Vansteenkiste et al., 2006, 2009). As a result, the cluster scoring relatively high on autonomous versus controlled types of motivation would display the strongest transfer effect. This is because in order to carry over learned activities from one s PE class to a new environment (i.e. leisure time) and a later period in life (i.e. adult life), one needs to feel volitional in carrying out the required PE activities. If students felt that they had no choice but to engage in the PE activities, it is unlikely that they would spontaneously generalize their acquired behaviours once the socialization figure is no longer watching their behaviour. In addition, clusters in which higher levels of amotivation are present are assumed to display the lowest scores on transfer of learning. This is because, if pupils perceive PE as useless, redundant or worthless, they are also hypothesized to report lower levels of transfer of learning. In addition to these main objectives, we investigated two additional issues. In a first set of ancillary analyses, we examined whether the hypothesized differences in self-reported, concurrent and delayed transfer between the motivational clusters would remain significant after controlling for students PE grades, in order to examine whether the quality of students motivation would matter in predicting transfer above and beyond their obtained grades. This would help to counteract the possibility that the most optimally motivated students would exclusively display the strongest transfer because of their higher grades. Second, we examined whether the associations between university students motivation for PE and transfer of learning would remain significant after controlling for their concurrent levels of autonomous and controlled motivation for physical activity more generally. This is important because it might well be the case that optimal motivation for PE is only associated with transfer of learning through its association with optimal motivation for physical activity. Methods Participants and procedure Participants were 2617 Ghent University students, of whom 878 (33.5 percent) were male and 1734 were female (66.5 percent), and whose mean age was All students gave informed consent for their participation in the study. Students were contacted by to fill in an online questionnaire. Participation was voluntary and anonymity was guaranteed. The study protocol was approved by the Ethical Committee of Ghent University.

7 Haerens et al 123 Measures An adapted version of the Behavioral Regulation in Exercise Questionnaire (BREQ2, Markland and Tobin, 2004) was used to retrospectively measure motivation for school PE. This tool assessed the multifaceted motivational regulations proposed by SDT by means of 16 items. After an introduction in which it was explained that the questionnaire aims at gaining insight into motives for participating in secondary school PE, students were presented with a stem: I put effort in physical education classes followed by items representing an autonomous motivational style (identified or intrinsic motivation) or a controlling motivational style (external or introjected regulation). An example of intrinsic motivation was because I enjoyed physical education classes. An example of identified regulation was: because I thought it was important to be regularly physically active. An example of external regulation was because others felt I had to, an example of introjected regulation was because I would have been ashamed of myself if I did not. Exploratory factor analyses (principal component analyses) suggested that three different composite scores should be created, one for autonomous motivation, one for controlled motivation, and one for amotivation. Internal consistencies, as indexed by Cronbach s alpha, were satisfactory for autonomous (a ¼ 0.90), controlled motivation (a ¼ 0.78) and amotivation (a ¼ 0.90). Together the three components explained 63.8 percent of the variance in the motivation items. After Varimax rotation, all autonomous motivation items had loadings of at least 0.62 on the first component, all controlled motivation items had loadings of at least 0.58 on the second component and all amotivation items had loadings of at least 0.79 on the third component. One cross-loading was found for the item I put effort into physical education classes because teacher forced me to (loading of 0.58 for the controlled component and 0.40 for the amotivated component). As can be noticed in Table 1, the three motivation scores displayed a simplex pattern, with autonomous motivation being less strongly negatively associated with controlled motivation relative to amotivation and amotivation and controlled motivaton being positively correlated. Hence, the factor-analytical results further provided an empirical argument to use the composite scores of autonomous and controlled motivation, along with amotivation, as the clustering dimensions. The Behavioral Regulation in Exercise Questionnaire (BREQ2) was also employed using the stem I participate in physical activities to measure motivation to be physically active. Similar to previous research (e.g. Vansteenkiste et al., 2004b), composite scores for autonomous and controlled motivation were created by averaging the subscales of intrinsic and identified and introjected and external regulation, respectively. Internal consistencies, as indexed by Cronbach s alpha, were satisfactory for the two subscales (intrinsic motivation, a ¼ 0.95; identified regulation, a ¼ 0.80) used to create the composite scores for autonomous motivation (a ¼ 0.91). A measure of self-reported grades was created by adding the question: I usually had good grades for physical education, rated on a five-point Likert scale from totally not true for me (0), to totally true for me (4). Students views on whether transfer had occurred were assessed by the following question: Physical education classes stimulated me to also engage in sport activities outside school and was rated on a five-point Likert scale from totally not true for me (0), to totally true for me (4). Physical activity levels were determined using the validated Flemish Physical Activity Questionnaire (Philippaerts et al., 2006) that was also used in previous studies investigating the

8 Table 1. Correlations between study variables (n ¼ 2617) Variable Autonomous motivation PE 2. Controlled motivation PE 0.21* 3. Amotivation 0.62* 0.37* Concurrent transfer 4. Total PA at secondary school 0.26* 0.14* 0.16* 5. Sports 0.30* 0.16* 0.18* 0.80* 6. Active transportation 0.09* 0.06* 0.07* 0.76* 0.21* Delayed transfer 7. Total PA at University 0.18* 0.09* 0.08* 0.37* 0.38* 0.18* 8. Sports 0.20* 0.08* 0.08* 0.38* 0.49* 0.09* 0.67* 9. Active transportation 0.07* 0.08* * 0.13* 0.23* 0.48* 0.20* 10. Self-reported transfer 0.49* 0.07* 0.31* 0.09* 0.09* 0.04*** 0.04*** 0.05** Grades for physical education 0.58* 0.34* 0.42* 0.28* 0.33* 0.10* 0.22* 0.24* 0.12* 0.28* 12. Autonomous motivation for PA 0.58* 0.14* 0.30* 0.27* 0.34* 0.08* 0.35* 0.46* 0.14* 0.32* 0.43* *p 0.001, **, p < 0.01***p<0.05; PA ¼ physical activity

9 Haerens et al 125 effects of a school-based physical activity intervention among adolescents (e.g. Haerens et al., 2007). Philippaerts et al. (2006) investigated reliability and validity of the FPAQ and reported moderate to high reliability of the FPAQ for the different indices used in the current study. The test retest intra-class correlation coefficients exceeded To obtain validity measures Philippaerts and colleagues (2006) correlated questionnaire data with accelerometer data (model 7164, Computer Science Application, Inc., Shalimar, FL); Pearson correlations were significant for all activity measures and ranged between 0.43 and 0.79, indicating acceptable validity of the instrument. Data on demographic factors like sex and age were collected in the first part of the questionnaire. The second part evaluated the physical activity levels of the participants. Total physical activity level was determined on the basis of three components: (a) active transportation, such as walking or cycling, (b) leisure-time sports, and (c) physical activities as part of a job or education. The active transportation index was constructed adding up daily minutes spent in walking and cycling during the week and on weekends. The sports index was compiled using questions addressing weekly minutes spent in sports participation. An additional question was added to assess weekly minutes spent in moderate to vigorous activities as part of a job or education. Finally, two questions were added to retrospectively assess time spent in active transportation and sports participation at secondary school. One question was When you were in 12th grade, how many hours and minutes per week did you spent in active transportation? The same question was repeated for participation in sports activities. Statistical analyses Cluster analyses were used to generate motivational profiles. The analysis required two steps, thereby using a combination of hierarchical and nonhierarchical clustering methods (Gore, 2000). In the first step, a hierarchical cluster analysis was carried out using Ward s method based on squared Euclidean distances. To reduce the impact of univariate outliers (values of more than 3SD above or below the mean) and multivariate (individuals with high Mahalanobis values) outliers, these were removed. The hierarchical method was used as a preliminary step in identifying the cluster solutions, which then provided the input for the nonhierarchical procedures. In the second step, the extracted initial cluster centres based on Ward s hierarchical method were used as non-random starting points in an iterative, non-hierarchical k-means clustering procedure. To examine stability of cluster solutions, the sample was randomly split into halves and the full two-step procedure (Ward, followed by k-means) was then applied to each half. The participants in each half of the sample were assigned to new clusters on the basis of their Euclidean distances to the cluster centres of the other half of the sample. These new clusters were then compared for agreement with the originals by means of Cohen s kappa (K). The two resulting kappas were averaged, an agreement of at least 0.60 was considered acceptable (Asendorpf et al., 2001). To investigate differences in self-reported concurrent and delayed transfer of learning according to motivational profile, MANCOVA analyses and post-hoc comparisons were conducted. Finally, the latter analyses were repeated twice, once controlling for grades for school PE and once controlling for autonomous and controlled motivation for physical activity.

10 126 European Physical Education Review 16(2) Z-score 2,5 2 1,5 1 0,5 0 0,5 1 1,5-2 AM (n=191) C-AM (n=329) 0,74 LOW (n=568) A-C (n=517) A (n=1012) Autonomous (A) 1,54 0,74 0,65 0,59 0,74 Controlled (C) 0,34 1,44 0,53 0,88 0,74 Amo va on (AM) 2,41 0,81 0,19 0,42 0,55 Figure 1. Cluster solution based on scores for autonomous motivation, controlled motivation and amotivation for physical education Results Correlations Correlations between the study variables can be found in Table 1. Cluster analyses Prior to conducting cluster analyses, we removed 21 univariate and 23 multivariate outliers. Five clusters were retained using Ward s cluster analyses, explaining 63.2 percent of the variance in autonomous motivation, 71.0 percent of the variance in controlled motivation and 68.1 percent of the variance in amotivation (all values surpassed the threshold of 50 percent). Figure 1 presents the final cluster solution. The Y-axis represents the z-scores. The z-scores for autonomous and controlled motivation and amotivation are reported in Table 2. Five clusters emerged: 1) a pronounced amotivated group (AM) (n ¼ 191), 2) a combined controlled-amotivated cluster (C-AM) (n ¼ 329), 3) a relatively lowly motivated group (LOW) (n ¼ 568), 4) a combined autonomous-controlled group (A-C) (n ¼ 517) and an autonomous cluster (A) (n ¼ 1012). The double-split cross-validation procedure (described earlier) resulted in an average kappa value across two randomly chosen subsamples of 0.67, providing significant evidence for the stability of the four cluster solution. We then evaluated whether male and female participants were equally distributed across the five clusters. Chi-square testing revealed a significant cluster assignment by gender effect w 2 (4, n ¼ 2617) ¼ 21.81, p <.001. Both sexes were equally distributed across the highly amotivated group, the lowly motivated group and the combined controlled -autonomous group. However, 43 percent of men as opposed to 36 percent of women were categorized in the autonomous group, whereas 9 percent of men versus 14 percent of women were assigned to the combined controlled-amotivated group. All following analyses were controlled for sex and age.

11 Haerens et al 127 Table 2. Z-scores of the constituting dimensions and composite validity scores and means for the dependent variables for the four extracted clusters together with F-values and effect sizes AM C-AM LOW C-A A N ¼ 329 N ¼ 568 N ¼ 517 (12.6%) (21.7%) (19.8%) N ¼ 191 (7.3%) N ¼ 1012 (38.7%) F-value Eta 2 Constituting Dimensions Autonomous 1.55A 0.74B 0.65C 0.60D 0.74E ***.69 motivation Controlled 0.34A 1.44B 0.53C 0.88D 0.74E ***.70 motivation Amotivation 2.40A 0.81B 0.19C 0.42D 0.55E ***.77 Outcome variables Concurrent transfer Total amount 54.51A 56.37A 64.32B 71.88C 77.29D 26.56***.04 Sports 24.55A 25.98A 30.64B 36.71C 41.71D 36.30***.05 Active 30.00A 30.39A 33.68A, B 35.17B 35.58B 3.79**.01 transportation Self-reported 0.77A 0.37B 0.41B 0.36C 0.38C 141***.18 Transfer earning Delayed transfer Total amount 44.87A 48.81A, B 47.45A 55.45B 66.00C 15.41***.02 Sports 18.75A 19.77A 18.90A 24.92B 29.44C 15.10***.02 Active transportation 21.32A,B 17.88C 20.59B 21.59A,B 22.52A 3.96**.01 A cluster mean is significantly different from another mean if they have different capitals. AM ¼ Pronounced amotivated group, AM-C ¼ Combined amotivated controlled group, LOW ¼ Lowly motivated group, A-C ¼ Combined autonomous controlled group and A ¼ relatively autonomously motivated group. **p 0.01, ***p ANCOVA-analyses A MANCOVA was conducted with cluster membership as the independent variable, including all outcome variables as dependent variables and sex and age as covariates. The Wilk s Lambda was significant (F ¼ 28.76, p < 0.001, Z 2 ¼ 0.07), indicating that significant multivariate cluster differences were found. Follow-up univariate F-values, Z 2 and pairwise comparisons (using Tukey s Honestly Significant Difference test) are shown in Table 2. Self-reported transfer. Average scores for self-reported transfer were generally low. An average score of 1.26 (+ 1.22) on a five point Likert scale ranging from 0 to 4 was found for the total sample. More specifically, examination of the data showed that 61.9 percent of the participants indicated that school PE had not stimulated them to engage in sports activities outside school (scores of 0 and 1). Twenty percent of the students had an average score (score 2). Only 18 percent of the students scored high (score of 3 or 4) on to the question about transfer, indicating that only a minority of the students felt that school PE stimulated them to engage in sports activities outside school. Of that minority most students were categorized in the autonomous group (56.0 percent) and the

12 128 European Physical Education Review 16(2) 0,6 0,4 0,2 Z-score 0 0,2 0,4 0,6 AM AM-C LOW A-C A 0,8 Total PA secondary school Sport secundary school AT secondary school Total PA adult Sports adult AT adult Transfer Figure 2. Between cluster differences in concurrent transfer (when at secondary school), delayed transfer (into adulthood) and self-reported transfer of learning The Y-axis represents the Z-scores, which give an indication of the relative position of the cluster average in the total sample for each of the dependent variable listed on the X-axis. PA ¼ physical activity, Sec ¼ secondary school, AT ¼ active transportation AM ¼ Pronounced amotivated group, AM-C ¼ Combined amotivated controlled group, LOW ¼ Lowly motivated group, A-C ¼ Combined autonomous controlled group and A ¼ relatively autonomously motivated group. combined autonomous-controlled group (26.9 percent) group. These data are confirmed by the significant between-cluster differences found for self-reported transfer of learning. In line with expectations, students in the autonomous group and the combined autonomous-controlled group displayed the highest scores on self-reported transfer, followed by students in the lowly motivated group and the combined controlled amotivated group. In line with expectations, students in the pronounced amotivated group displayed the lowest scores on self-reported transfer. Concurrent transfer: physical activity levels at secondary school. In line with expectations, total engagement in physical activity during secondary school was significantly higher in the autonomous group compared to the four other groups. Also, the combined autonomous-controlled group displayed significantly higher activity levels when compared to the three other groups. Finally, the lowly motivated group scored higher when compared to the combined controlled-amotivated group and the pronounced amotivated group. A similar pattern was found for engagement in sports activities during secondary school (see Table 2, Figure 2). For time spent in active transportation when at secondary school, the two groups characterized by some degree of autonomous motivation (autonomous, controlled-autonomous) displaying significantly better scores than the two groups with negative z-scores for autonomous motivation (controlled-amotivated, pronounced amotivated).

13 Haerens et al 129 Table 3. F-values and effect sizes for the differences between clusters controlled for self-reported grades (model 1) and for general motivation to be physically active (model 2) Model 1: Cluster effects controlled for grades Model 2: Cluster effects controlled for physical activity motivation F-value Eta 2 F-value Eta 2 Concurrent transfer Total amount 2.81** ***.01 Sports 3.79* ***.01 Active transportation Self-reported transfer 86.8*** ***.11 Delayed transfer Total amount 3.40** Sports 3.16** ***.01 Active transportation *p 0.05, **p 0.01, ***p Delayed transfer: physical activity levels in early adulthood. Total engagement in physical activity when at university was significantly higher in autonomous group when compared to the four other groups. Also the combined autonomous-controlled group displayed significantly higher activity levels when compared to the lowly motivated group and the pronounced amotivated group. For the specific outcome of sports participation in early adulthood, a significantly better score was found among students in the two groups characterized by autonomous motivation when compared to the three other groups with negative z-scores on autonomous motivation for PE. In addition, the autonomous group participated significantly more in sports as an adult when compared to the combined autonomous-controlled group. For time spent in active transportation at university, betweengroup differences were less pronounced. Only the combined controlled-amotivated group spent significantly less time in active transportation when compared to the four other groups. Ancillary analyses Grades. A second MANCOVA (see Table 3, model 1) was conducted with cluster membership as the independent variable, including all outcome variables as dependent variables and sex, age and grades for school PE as covariates. The Wilk s Lambda was significant (F ¼ 15.10, p < 0.001, Z 2 ¼ 0.03), indicating that significant multivariate cluster differences remained after controlling for selfreported grades for PE. Follow-up univariate F-values, and Z 2 are shown in Table 3, model 1. Although effect sizes decreased, most of the differences between groups remained significant (except for active transportation). These findings indicate that the motivational profile for school PE matters for predicting participants likelihood to be physically active, and that it matters independently of students perceptions of their grade levels for PE. Concurrent motivation for PA. A third MANCOVA (see Table 3, model 2) was conducted with cluster membership as the independent variable, including all outcome variables as dependent variables and sex, age and autonomous and controlled motivation for physical activity as covariates. The Wilk s

14 130 European Physical Education Review 16(2) Lambda remained significant (F ¼ 15.22, p < 0.001, Z 2 ¼ 0.03), indicating that significant multivariate cluster differences remained after controlling for concurrent motivation for physical activity. Follow-up univariate F-values, and Z 2 are shown in Table 3, model 2. Univariate results showed significant differences between clusters for self-reported, concurrent and delayed transfer. These results indicate that motivation for school PE plays an important role in predicting the likelihood that participants will engage in physical activity at secondary school and in early adulthood, independent of students self-determined motivation to be physically active, that is, their levels of autonomous motivation to engage in physical activities as university students. Discussion A number of researchers have argued that PE teachers should aim to promote an active and healthy lifestyle so that students are prepared for lifelong physical activity (Corbin, 2002; Fairclough, 2003). This aim requires that concurrent and delayed transfer of learning takes place, in other words, that participation in activities offered during school PE prepares students to engage in physical activity in their life beyond school both as a secondary school student and as an adult, by providing them with appropriate skills and knowledge and with the motivation to use and apply what they have learned. Self-reported transfer In light of these considerations the first purpose of this study was to investigate the extent to which university students felt that experiences during school PE had prepared them to participate in physical activities in early adulthood. If the results from research on school mathematical learning (Lave, 1997) are comparable to PE, one would expect little relationship between engagement in school PE and engagement in physical activities beyond the school context. And indeed, almost two-thirds of the participants in this study reported that school PE had not prepared them to engage in sport activities outside school. Since, as we have seen, the promotion of an active and healthy lifestyle is an important aim of school PE, these findings raise the question for PE of how to stimulate young people to be physically active outside school, in other words, how the curriculum and learning environment can be (re)organized to realize this important educational aim. We might question whether the currently dominant and almost universal practice of organizing the PE curriculum around short taught blocks of traditional sport activities (e.g. basketball, soccer, gymnastics, etc.) is the best way to stimulate lifelong physical activity (Bailey et al., 2008), since typical lifetime physical activities (e.g. fitness activities, walking, cycling) are less often offered in schools. To sharpen this point, Fairclough et al. (2002) proposed that, in order to promote the PE goal of preparing students for lifetime participation in physical activity, physical educators must offer lifetime activities since these activities have the greatest transfer value into adult life. However, we suspect that this proposal is not so straightforward. First of all, there are as yet no data to support the claim that lifelong physically activity will occur only if specific lifetime activities are offered in PE. At the same time, it seems reasonable to assume some transfer of learning may occur in the case of lifetime activities, at least in terms of cross-domain generalization (i.e. from PE class to leisure time), if not cross-activity generalization (e.g. from basketball in PE to fitness in leisure time). Second, it is possible that it is not the curriculum content by itself but also the broader learning environment (including teaching styles, intended learning outcomes and motivational climate) that is influencing the occurrence of transfer. Some studies have shown that,

15 Haerens et al 131 independent of subject matter, students are more likely to want to continue their involvement in an outside school activity if school PE allows them to experience a sense of volition and autonomy (Cox and Williams, 2008; Cox et al., 2008), an insight which brings us to the second aim of the present study. Motivational clusters A second aim of the present study was to extend the research on students motivation for school PE. Literature revealed that most previously conducted studies, using self-determination theory as a framework, used a variable-centred approach (e.g. Cox et al., 2008; Ferrer-Caja, 2000; Ntoumanis, 2005) as opposed to a person-centred approach, to investigate how students are motivated for PE. Additionally, in the present study previous research was extended by investigating outcomes such as self-reported concurrent and delayed transfer of learning. In the current study, five clusters needed to be retained to parsimoniously account for the interpersonal differences in students motivation for PE. Three of the five clusters directly (i.e. autonomous, high motivation, low motivation group) resemble the clusters obtained by Vansteenkiste et al. (2009) in school education generally. By adding amotivation as an additional clustering dimension next to autonomous and controlled motivation, the present study both extended and refined the four-cluster solution obtained by Vansteenkiste et al. (2009). Specifically, the results indicate that the controlled group is not only characterized by high controlled motivation but also amotivation (refinement) and that a fifth group, characterized by extremely high scores on amotivation, needed to be retained (extension). Notably, the five-cluster solution obtained in the present study differs from the three-cluster solution obtained by Boiché et al. (2008). These authors found evidence for the presence of a self-determined cluster (comparable to the autonomous group), a non-self-determined cluster (comparable to the controlled-amotivated group), and a moderate self-determined cluster (comparable to the autonomous-controlled group) in two samples of French high school students (aged years old). Boiché et al. (2008) suggested that a combination of positive scores on external and internal forms of motivation can only exist if the levels of those types of motivations are moderate. In contrast, our results suggest that both types of motivation can also combine, if levels are relatively low (e.g. low motivated group). However, results are hard to compare for various reasons: in the study of Boiché et al. (2008) (a) the motivational regulations were measured with a different questionnaire (i.e. adaption of the Sport and Academic Motivation scale), (b) clusters were formed on the subtypes instead of composite scores of autonomous and controlled motivation, (c) cluster analyses were performed exclusively using Ward s hierarchical cluster analyses instead of the recommended two-step procedure involving a hierarchical and non-hierarchical cluster analyses (Gore, 2000), and (d) the amount of variance in some of the clustering dimensions (i.e. external and introjected regulation) explained by the three-cluster solution did not surpass the critical threshold of.50. The latter suggests that the extraction of additional clusters might have been necessary to surpass this cutoff. Clearly, more research is needed to determine the number and types of motivational subgroups that may be present in a school PE context and the use of the subdimensions of autonomous and controlled motivation represents a useful next step in this regard. For PE practice, it is important to note that, although each group was represented by a substantial number of students, almost 40 percent of students were categorized in the most preferable motivational group (autonomous group) and an additional 20 percent of the students were classified in the

16 132 European Physical Education Review 16(2) autonomous-controlled group. These findings suggest that school PE as experienced by these students is already providing an environment in which self-determined motivational profiles are evident. On the other hand, it should be notified that the present study sample of university students is not representative for the general population. Drawing on the work of Roberts (2001), Green et al. (2005b) show clearly that social class is an important factor in determining participation in sport and other physical activities in young people s leisure time beyond the school, with the more privileged young people most likely to be physically active beyond the school. Similarly, previous research has found that more highly educated individuals are more autonomously motivated (e.g. De Bilde et al., in press). Interestingly, consistent with reported differences in physical activity according to sex (Pate et al., 2002; Sallis et al., 2000), female students were slightly overrepresented in the group with controlled-amotivated group, which is a group with a less optimal motivation profile. Based on SDT, emphasizing the important influence of the social context for self-determined motivation, we could hypothesize that differences in teacher behaviours towards girls might result in some girls being less autonomously motivated. Previous studies show, for example, that teacher student interactions in PE are often characterized by more negative statements towards girls when compared to boys and PE teachers more often use boys for demonstrations (Macdonald, 1990; Wright, 1997). Motivational clusters and transfer Although only a small proportion of our sample reported a certain degree of transfer, there were meaningful differences between the different motivational profiles. Specifically, students with a more optimal motivational profile for PE (high scores on autonomous motivation) were more likely to report that they were stimulated by PE to engage in physical activity outside school, whereas students with the least optimal profile for PE (pronounced amotivated group) were least likely to report transfer of learning. Differences between groups were confirmed by the differences for concurrent transfer as measured by students sports participation during secondary school. Students with high scores on autonomous motivation for PE were more likely to engage in sport activities when at secondary school, whereas students with high scores on amotivation were less likely to engage in sports activities when at secondary school. The addition of controlled motivation to autonomous motivation clearly has no benefits; it even tends to forestall transfer into leisure time as the combined autonomous-controlled group scored lower than the autonomous group, which scored exclusively high on autonomous motivation. The presence of controlled motivation was furthermore most detrimental when students failed to develop autonomous forms of motivation. This is because high scores on controlled motivation and low scores on autonomous motivation appeared to go together with higher scores on amotivation. Apparently, if students are solely participating in PE to comply with external or internal pressures, they are also more likely to be amotivated for PE. In general, this means that transfer of learning is more likely to occur among students who find PE classes inherently enjoyable or meaningful, while feeling pressured to engage in PE in itself is not associated with a continuation of activity outside PE. These findings are in line with previous studies (e.g. Ntoumanis, 2005) which relied on a dimensional rather than person-centred approach. Extending previous work on transfer of learning, the current study also examined students delayed as well as their concurrent transfer. The findings were similar with students scoring high on autonomous types of motivation engaging significantly more in sports activities, although explained variances were greater for concurrent (during secondary school) versus delayed transfer (at university). These findings suggest that motivation for PE is more closely related to activity