Profiling individual differences in student motivation: A longitudinal cluster-analytic study in different academic contexts

Contemporary Educational Psychology 30 (2005) 359–396 www.elsevier.com/locate/cedpsych

ProWling individual diVerences in student motivation: A longitudinal cluster-analytic study in diVerent academic contexts Ivar Bråten ¤,1, Bodil S. Olaussen 1 Institute for Educational Research, University of Oslo, Box 1092, Blindern, N-0317 Oslo, Norway Available online 24 March 2005

Abstract This research examined whether distinct student proWles emerged from measures of interest, mastery goals, task value, and self-eYcacy in samples of Norwegian student nurses and business administration students. Additionally, proWle diVerences in self-reported strategy use and epistemological beliefs were examined, as well as changes in student proWles over one academic year. Distinct groups of participants were identiWed in both samples, with considerable consistency in student proWles across the two academic contexts. In both contexts, more positively motivated participants consistently reported more use of deeper-level strategies and expressed more sophisticated beliefs about the nature of knowledge and knowledge acquisition. The longitudinal analysis showed that despite overall decreases in adaptive motivation in both contexts, many participants were able to maintain relatively high levels of motivation across the academic year, and, especially among the business administration students, quite a few developed more adaptive motivation over time. Yet, a great many participants in both samples lost some of their enthusiasm and engagement.  2005 Elsevier Inc. All rights reserved. Keywords: Academic motivation; Strategic processing; Epistemological beliefs; Cluster analysis; Learner proWles

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Corresponding author. Fax: +47 22 85 42 50. E-mail address: [email protected] (I. Bråten). These authors contributed equally to this article.

0361-476X/$ - see front matter  2005 Elsevier Inc. All rights reserved. doi:10.1016/j.cedpsych.2005.01.003

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1. Introduction Our purpose was to examine whether distinct student proWles would emerge from diVerent motivational variables in samples of student nurses and business administration students. Additionally, proWle diVerences in self-reported strategy use and epistemological beliefs were examined, as well as changes in student proWles over one academic year. 1.1. A person-centered longitudinal approach Within any community of learners, there probably exist subgroups that share similar motivational patterns. Uncovering such subgroups within the same college classroom and understanding what characterize them with respect to other aspects of learning may give us knowledge that is important not only for theory building but also for educational practice. According to Pintrich (2003a), motivational science could at this point beneWt more from examining motivational patterns than from trying to prove or falsify the importance of single motivational constructs in relation to other constructs. One potential beneWt of such examination could be greater understanding of how diVerent current motivational constructs relate to one another (cf. Pintrich, 2000a). One additional reason why we decided to use cluster analysis to form subgroups based on diVerent proWles of motivational beliefs was that a variable-centered analysis (e.g., multiple regression analysis) would not really help us understand individuals who might show distinct patterns of motivation (Linnenbrink & Pintrich, 2001). According to Alexander and Murphy (1999), “the key to reaching competence or proWciency in any demanding academic domain lies in the motivations that students bring into the instructional environment” (p. 428). However, persons move through instructional environments, not variables, with this implying that an examination of personal motivation in context makes it appropriate to measure motivational constructs at a person-centered level (cf. Magnusson, 1998; Magnusson & Stattin, 1998). Moreover, Magnusson (1998; Magnusson & Stattin, 1998) has suggested that questions concerning individual development over time are best addressed through a person-centered approach such as cluster analysis. Hopefully, a longitudinal cluster-analytic study of patterns of motivation in diVerent academic contexts may have the potential to inform educators about the motivational subcommunities that may exist and develop in their classrooms over time, with such information making them more able to adapt their instruction to or try to change individual trajectories of motivation. 1.2. An expectancy-value framework Previous work (e.g., Bembenutty, 1999; Meece & Holt, 1993) examining how diVerent motivational variables proWle individual diVerences has mainly focused on students’ achievement goal orientations, that is, their reasons or purposes for engaging in academic tasks (Pintrich, 2003b). However, we wanted to proWle individual diVerences on the basis of a wider range of motivational beliefs. Guided by an expec-

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tancy-value model of academic motivation (WigWeld & Eccles, 2000, 2002), we therefore decided to use personal interest, mastery goal orientation, task value, and perceived self-eYcacy as clustering variables. Broadly speaking, the expectancy-value perspective assumes that how well students expect to do on upcoming tasks and how valuable they consider those tasks to be directly inXuence performance, eVort, and persistence in achievement tasks, as well as choices of which tasks to pursue. Personal interest, mastery goal orientation, and task value are all related to the value component of this model, essentially concerning the question: Why am I doing this task? Personal interest refers to a relatively stable motivational disposition to be engaged in, enjoy, and like certain domains, topics, and activities (Krapp, 1999; Schiefele, 1999). A mastery goal orientation is apparent when an individual’s reason for approaching or doing a task is to master the task according to self-set standards, to progress in learning, and to gain more understanding (Pintrich, 2000a). Task value is understood as beliefs about how important it is to do well on given tasks, how useful those tasks are in relation to future plans, and how intrinsically valuable they are to the individual (WigWeld & Eccles, 2000). Pintrich (1989; Pintrich, Smith, Garcia, & McKeachie, 1991) also broadly construed the value component of expectancy-value models as not only task value beliefs but also goal orientation. In addition, personal interest could be deWned as a value-related construct (Schiefele, 1999). However, perceived self-eYcacy, referring to “beliefs in one’s capabilities to organize and execute the courses of action required to produce given attainments” (Bandura, 1997, p. 3), is related to the expectancy component of the model, essentially concerning the question: Can I do this task? Pintrich (1989; Pintrich et al., 1991) also deWned self-eYcacy as an expectancy component within the expectancy-value framework, and WigWeld and Eccles (2000) recently noted the similarity between their own expectancy construct and Bandura’s (1997) construct of perceived self-eYcacy. It is suggested by the expectancy-value framework that some students may display consistent patterns of motivation, for example, by reporting high, moderate, or low levels on both value and expectancy components. However, the expectancy-value framework also suggests that some students may display inconsistent patterns of motivation in terms of the value and expectancy components involved (cf. Pintrich, 1989). Simply stated: Some students may believe they can do study tasks but see little reason for doing them, whereas others may see good reasons for doing study tasks but do not believe they can handle them. 1.3. Motivational beliefs and self-regulated learning Additionally, all four motivation constructs play prominent roles in current models of self-regulated learning (e.g., Pintrich, 2000b; Pintrich & Zusho, 2002; Zimmerman, 1998, 2000). Self-regulation models describe how students Wrst set goals and plan their approach to the task, and then monitor, regulate, and evaluate their strategic eVort in the service of those goals and plans (Pintrich, 2000b; Zimmerman, 2000). The cyclical nature of self-regulation is well captured in Zimmerman’s (1998, 2000) three-phase self-regulation model, describing the phases of forethought, performance control, and self-reXection, where the forethought phase precedes actual task perfor-

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mance and refers to processes that set the stage for action (Schunk & Zimmerman, 2003). The forethought phase typically involves the activation of personal interest in the task, an orientation towards task mastery (i.e., mastery goal orientation), beliefs about the importance, utility, and relevance of the task (i.e., task value beliefs), and beliefs about being capable to learn and perform eVectively (i.e., self-eYcacy beliefs). Moreover, such motivational beliefs are seen as related to the planning and selection of self-regulatory strategies directed at acquiring and displaying skill (Zimmerman, 2000). Much research in academic motivation has conWrmed that interest in the subject matter (Hidi, 2001; Krapp, 1999; Schiefele, 1999), as well as mastery goal orientation (Dweck, 1999; Pintrich, 2000b), task value beliefs (Eccles, WigWeld, & Schiefele, 1998), and self-eYcacy (Bandura, 1986, 1997) positively predict academic performance. To some extent, the positive relationship between these forms of motivation and performance seems to be mediated by students’ deeper processing of information and more adaptive self-regulation (for review, see Pintrich & Schunk, 2002). In turn, adaptive strategic processing and increased competence probably aVect students’ motivational beliefs positively (Alexander, Graham, & Harris, 1998). 1.4. Epistemological beliefs and academic motivation Whereas much research has linked motivation to strategic processing (e.g., Pintrich & Schunk, 2002), little attention has so far been directed to how motivation relates to personal epistemology (Buehl, 2003). Personal epistemology concerns individuals’ beliefs about “how knowing occurs, what counts as knowledge and where it resides, and how knowledge is constructed and evaluated (Hofer, 2004, p. 1). There are several diVerent paradigmatic approaches to the conceptualization and investigation of personal epistemology (Hofer & Pintrich, 1997, 2002), including the developmental approach trying to identify stages in students’ epistemological thinking, mostly through the use of interviewing methodology (e.g., Baxter Magolda, 1992; King & Kitchener, 1994), and the epistemological belief system approach of Schommer (1990), using quantitative assessments to examine how beliefs about knowledge and learning are related to academic cognition and performance. According to Schommer (1990), personal epistemology may be described as a system of more or less independent beliefs, and she developed a 63-item questionnaire to examine this system. Factor analyses reported by Schommer and associates (e.g., Schommer, 1990; Schommer, Crouse, & Rhodes, 1992) have consistently yielded four factors, which, stated from a naïve perspective, are: Simple Knowledge, Certain Knowledge, Fixed Ability, and Quick Learning. While the two Wrst dimensions (i.e., beliefs about the simplicity and certainty of knowledge) fall under the more generally accepted deWnition of personal epistemology as beliefs about the nature of knowledge and knowing (Hofer & Pintrich, 1997), the two other dimensions mainly concern beliefs about learning (Schommer-Aikins, 2004). Schommer-Aikins (2004) has suggested that beliefs about knowledge and knowing and beliefs about learning could be seen as reciprocally interacting systems of epistemological beliefs, both inXuencing important aspects of cognition and performance.

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Recently, it has been proposed (Buehl, 2003; Pintrich, 2002; Pintrich & Zusho, 2002) that epistemological beliefs may also be linked to academic motivation. In particular, it has been suggested that epistemological beliefs may give rise to personal goals (e.g., mastery goals), which then foster more or less adaptive strategic processing (Pintrich, 2002; Pintrich & Zusho, 2002). In line with this, Dweck (1986; Dweck & Leggett, 1988) proposed that the belief that intelligence is malleable, which could be considered to be an epistemological stance (Schommer-Aikins, 2004), may orient students towards mastery goals, in turn leading to a mastery-oriented behavior pattern where students seek challenges and respond to diYculties with adaptive strategy use. In contrast, students who believe that intelligence is Wxed are claimed to orient themselves towards performance goals and less adaptive (or even helpless) patterns where they may avoid challenging tasks and react to diYculties with less eVective strategies. In Buehl’s (2003) model of the relationships between students’ personal epistemology, academic motivation, and task performance, epistemological beliefs are seen as inXuencing not only goal orientations, but also task value beliefs and perceived self-eYcacy. To date, these suggestions of a linkage between epistemological beliefs and academic motivation have not been backed by much empirical research. Still, some research has indicated that students’ epistemological beliefs may be linked to their personal interest (Bråten & Strømsø, in press-a, 2004; Buehl, Murphy, & Monoi, 2003; Rozendaal, de Brabander, & Minnaert, 2001), achievement goal orientations (Bråten & Strømsø, in press-a, 2004; Buehl et al., 2003; Garrett-Ingram, 1997; Neber & Schommer-Aikins, 2002; Schutz, Pintrich, & Young, 1993), task value beliefs (Garrett-Ingram, 1997), and perceived selfeYcacy (Bråten & Strømsø, in press-a; Buehl et al., 2003; Garrett-Ingram, 1997; Hofer, 1994; Neber & Schommer-Aikins, 2002), essentially showing that more sophisticated epistemological beliefs are associated with more adaptive motivations. However, much additional work is needed to understand the relationships between motivational constructs and dimensions of personal epistemology more fully (Buehl, 2003; Pintrich, 2002). In the present research, we tried to address this issue by proWling diVerences in Norwegian students’ academic motivation and examining what characterize subgroups regarding not only strategic processing but also dimensions of epistemological beliefs. To the extent that epistemological beliefs are important for motivation, we would expect the proWles to diVer regarding such “implicit theories” (Hofer & Pintrich, 1997) about epistemology. 1.5. Previous cluster-analytic research While some previous studies of learner proWles have used only motivational variables as clustering measures (e.g., Bembenutty, 1999; Meece & Holt, 1993), others have used both motivational and cognitive variables (e.g., Alexander, Jetton, & Kulikowich, 1995; Alexander & Murphy, 1998; Pintrich, 1989; Turner et al., 1998). For example, cluster analyses with diVerent goal orientation variables showed that the patterning of goals within individuals can explain perceived ability and selfreports of strategy use (Meece & Holt, 1993), academic delay of gratiWcation and reported use of motivational regulation strategies (Bembenutty, 1999), and epistemological beliefs (Buehl et al., 2003).

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In a study of college students, also grounded in expectancy-value theory, Pintrich (1989) proWled individual diVerences on the basis of motivational variables (intrinsic goal orientation, task value, control beliefs, and expectancy for success) and strategy variables. Pintrich’s study demonstrated that expectancy and value constructs could be used to identify subgroups of college students, and, moreover, that some subgroups could be characterized by consistent patterns (i.e., high, moderate, or low) in terms of expectancy and value constructs and others by more inconsistent patterns (e.g., low expectancy and high value). Alexander (Alexander et al., 1995; Alexander & Murphy, 1998) used knowledge, personal interest, and strategic processing as clustering variables. Alexander et al. (1995) identiWed high, mixed, and low proWles among university students learning in the Weld of human biology–immunology. While the high proWles were characterized by high levels of knowledge, personal interest, and strategic processing, the low proWles were characterized by limited knowledge and low levels of personal interest and strategic processing. The mixed student proWles evidenced high levels of knowledge in combination with lower levels of interest and strategic processing. In Alexander and Murphy’s (1998) longitudinal study targeting the domain of educational psychology (see below), high, mixed, and low proWles were again identiWed, with the mixed proWles characterized by several diVerent combinations of high, moderate, and low levels of knowledge, interest, and strategies. Overall, the learner proWles reported in previous studies encompassing not only diVerent clustering variables but also varied student populations and domains seem to indicate at least some measure of consistency, in that they contrast proWles characterized by adaptive motivational beliefs and cognitions with proWles characterized by low levels of motivations and cognitions. Additionally, mixed proWles typically emerged in those studies, with adaptive aspects of learning (motivational or cognitive) co-existing with less adaptive aspects. However, there is still a great need to explore whether learner proWles are consistent across Welds of study and academic contexts (Alexander & Murphy, 1999). This is important because both motivational and cognitive characteristics, as well as interrelationships between them, can be moderated or shaped by contextual factors (Pintrich, 2003a; Pintrich & Zusho, 2002). In addition, research on learner proWles should move beyond one-time point designs. This is because administering the clustering measures at diVerent points in time makes is possible to examine the malleability or stability of learner proWles over time. In their longitudinal study of undergraduates enrolled in an educational psychology course, Alexander and Murphy (1998) found that the three clusters that emerged from knowledge, interest, and strategy measures in the beginning of the semester, gave way to four clusters at the end of the semester. At the beginning of the semester, they identiWed a learning-oriented cluster, characterized by moderate levels of knowledge and high levels of interest and strategic processing, a strong-knowledge cluster, characterized by high levels of knowledge, low levels of interest, and moderate levels of strategy use, and a low-proWle cluster, characterized by relatively low levels on all clustering measures. At the end of the semester, learning-oriented and strong-knowledge clusters were again identiWed, but the low-proWle cluster was replaced with an eVortful-processor cluster, characterized by low levels of knowledge, moderate levels of interest, and

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high levels of strategy use, and a nonstrategic-reader cluster, characterized by moderate levels of knowledge and interest and by low levels of strategic processing. When Alexander and Murphy (1998) tracked the movement of all participants across the 15week semester in terms of their cluster membership, they found that more than half of the students characterized as learning oriented or strong knowledge at the outset found their way to the eVortful-processor or the nonstrategic-reader cluster at the conclusion of the semester. On the more positive side, a large portion of the students characterized as low proWle at the outset found their way to clusters characterized by more adaptive motivation and cognitions at the conclusion of the semester, with 25% of those students migrating to the learning oriented cluster. Still, the nonstrategic-reader cluster contained the largest number of students at the conclusion of the semester. However, little is still known about the nature of academic development, and more longitudinal explorations of student proWles are certainly needed (Alexander & Murphy, 1999). 1.6. Extending previous research Because two important limitations with previous research on learner proWles are that (a) few studies have compared motivational proWles across diVerent academic contexts, and, at least to our knowledge, (b) only one study (Alexander & Murphy, 1998) has studied the stability of student proWles over time, we tried to extend prior work in those two directions. First, we extended prior work by targeting two diVerent academic contexts. Thus, the two samples that participated, consisting of student nurses and business administration students, respectively, were selected to represent two Welds of study characterized by diVerent contexts in terms of both instructional approach and evaluation practice. In terms of instructional approach, the student nurses experienced more student-activating methods and group work whereas the business administration students experienced more use of large teacher-regulated lectures. In terms of evaluation practice, the student nurses experienced few individual graded examinations whereas the business administration students experienced a very strong evaluation focus with many examinations as well as much emphasis on the importance of grades and public knowledge of performance levels. Given such diVerences, we were able to explore the contextual generalizability of emerging student proWles based on motivational variables. Despite the diVerences between the participating student nurses and business administration students with respect to academic practice (see also Participants and academic contexts below), prior research demonstrating a relatively good measure of consistency in learner proWles led us to expect a high degree of similarity in the types of clusters to emerge from the motivational data from the two student groups. Essentially, self-regulation models (Pintrich, 2000b; Zimmerman, 1998, 2000) argue that more or less successful students are separated by the same set of motivational beliefs across academic contexts. In accordance with this view, successful students in both contexts may be characterized by generally adaptive beliefs and less successful students by generally less adaptive beliefs, with other groups of students possibly falling somewhere in-between with respect to both performance and adaptive motivation.

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Second, the present research built on and extended the Alexander and Murphy (1998) study by exploring the malleability or stability of student proWles across one entire year of profession-oriented education. Generally, research within the expectancy-value framework has shown that students’ expectancy to do well in many activities as well as their valuing of those activities decline over the course of the school years (for reviews, see Pintrich & Schunk, 2002; WigWeld & Eccles, 2000). According to Pintrich (2003a), this decline seems to be characteristic of most adaptive motivational beliefs. At the college level, a decline in adaptive motivation has also been observed over time (Kowalski & Bray, 1998). A contextual explanation for this decline in adaptive motivation emphasizes that academic contexts where evaluation is made salient and competition between students is likely contribute to the lowering of students’ adaptive motivational beliefs (Stipek, 1996; WigWeld & Eccles, 2000). Accordingly, in the present research, a general decline in adaptive motivational beliefs could be expected that would be most pronounced for the students who experienced the strongest evaluation focus and most competition (i.e., the business administration students). With respect to movement in terms of cluster membership over time, it could be expected that the majority of students would move into clusters characterized by less adaptive motivation. However, it is also possible that greater diversity among the students in terms of their academic motivation may develop over one academic year, in accordance with the statistical notion of a fan-spread eVect (Bryk & Weisberg, 1977) and as evidenced by Alexander and Murphy (1998). Of course, compared to Alexander’s (Alexander et al., 1995; Alexander & Murphy, 1998) broad multidimensional approach to learner proWles, our choice of clustering variables could not capture the same multidimensionality of learning but rather restricted us to an exploration of changes in multimotivational clusters. 1.7. Research questions and hypotheses Given this theoretical orientation, we set out to explore whether cluster analysis using motivational variables grounded in expectancy-value theory and included in current models of self-regulated learning would result in distinct student proWles. We also wanted to validate the resulting cluster solutions against diVerent strategy variables, as well as to examine whether the clusters could be diVerentiated with respect to epistemological beliefs. Further, we wanted to see whether the emerging student proWles were consistent across two academic contexts, nursing and business administration. Finally, we explored potential changes in student proWles in each of these contexts over time. SpeciWcally, three research questions guided our investigation: First, what distinct student proWles emerge from measures of personal interest, mastery goal orientation, task value beliefs, and self-eYcacy, and how are those proWles characterized in regard to strategic processing and epistemological beliefs? Based on the expectancy-value framework (WigWeld & Eccles, 2000, 2002) and previous cluster-analytic research within this framework (e.g., Pintrich, 1989), we hypothesized that subgroups characterized by consistent patterns (i.e., high, moderate, or low) in terms of value and expectancy components would be identiWed, as well as subgroups characterized by inconsistent patterns (i.e., low value and high expectancy or vice

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versa). In accordance with self-regulation models and research related to those models (Pintrich, 2000b; Zimmerman, 2000), we also hypothesized that subgroups characterized by adaptive motivational beliefs would use adaptive strategies more than subgroups characterized by less adaptive motivational beliefs. Additionally, theoretical assumptions and preliminary Wndings concerning the relationship between epistemological beliefs and academic motivation (e.g., Buehl, 2003; Buehl et al., 2003; Pintrich, 2002) led to the hypothesis that subgroups characterized by adaptive motivational beliefs would also display the most sophisticated epistemological beliefs. Second, are student proWles based on these motivational variables consistent across the contexts of nursing and business administration? On the basis of general models of self-regulated learning (Pintrich, 2000b; Zimmerman, 1998, 2000) and previous research encompassing varied student populations, domains, and academic contexts (for review, see Alexander & Murphy, 1999), we hypothesized that a high degree of contextual generalizability of emerging student proWles based on motivational variables would be documented. Third, what changes are there in student proWles across one year of profession-oriented education? Based on previous research within the expectancy-value framework showing general decline in adaptive motivation over time (for reviews, see Pintrich & Schunk, 2002; WigWeld & Eccles, 2000), we hypothesized that changes in student proWles would reXect such a general decline, with the majority of students moving into clusters characterized by less adaptive motivational beliefs. Consistent with a contextual explanation emphasizing the roles played by the salience of evaluation and the likelihood of competition in motivational decline (Stipek, 1996; WigWeld & Eccles, 2000), we expected that the negative development in adaptive motivational beliefs might be most pronounced for the business administration students. Even though the motivational subcommunities in college classrooms might be similar across academic contexts, contextual diVerences could well inXuence the distribution of students to those subcommunities. Thus, given the same set of motivational clusters, the proportion of students migrating to clusters characterized by less adaptive motivational beliefs might still diVer as a result of contextual factors.

2. Method 2.1. Participants and academic contexts We used two independent samples representing diVerent Welds of study and academic contexts for this research. The Wrst sample consisted of 99 students at the Faculty of Nursing at a college in southeast Norway, who were in the Wrst year of a bachelor program in nursing when the study started. The sample included 86 females and 13 males, ranging in age from 18 to 47 years, with an overall mean age of 28.0 years (SD D 8.4) at the outset of the study. The nursing program could be completed in full time mode over three years. Fifty-six females and seven males were full time students, with these students having an overall mean age of 23.9 years (SD D 5.5). Thirty females and six males were part time students who completed the nursing pro-

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gram over four years, with most of these students having previous work experience and working part time during the time they were studying. The overall mean age of the part time nursing students were 35.3 years (SD D 7.7). The second sample was made up of 105 full time students at the Norwegian School of Management in Oslo, who were in their Wrst year of a four-year master program in business administration when the study started. The second sample consisted of 41 females and 64 males, aged between 18 and 32 years, with an overall mean age of 20.8 years (SD D 2.3) at the outset of the study. The student nurses attended a state college with no school fees. There was little competition for entrance to the nursing program, and most of the students who applied for entrance were admitted. During the Wrst year, the student nurses studied medical subjects such as anatomy and physiology, as well as social science subjects such as sociology and social anthropology. In courses concerning nursing as a profession, students were required to integrate and apply information from the diVerent subjects. Moreover, the nursing students had 10 weeks of practical training in community health-care services during the Wrst year. The instruction during the Wrst year emphasized student-activating methods and consisted of varied group work in groups of 10–15 students. In combination with this, more traditional lectures were also used. During the second year, the students studied medical subjects such as pathology and diseases and social science subjects such as psychology and education in addition to the overarching subject of nursing. The instruction was similar to that oVered during the Wrst year, and the training period consisted of 20 weeks of practice in the Weld of specialized health care (e.g., hospital wards). The focus for the last year of the nursing program was nursing science and 20 weeks of practice in community health-care services. In regard to evaluation, the student nurses had to take only three individual, graded examinations during the Wrst year. During the second year, the students had to sit for three individual examinations at the college and individually write an examination paper at home within a four-day time limit. Evaluation in the last year consisted of one oral and two written individual examinations at the college, in addition to a 9000 word paper that could be written individually or by a group of students. The student nurses’ chances of Wnding employment in public health services after Wnishing college were very good no matter what grades they received in their studies, and their salary would also be independent of those grades. In Norway, nurses’ salary is generally regarded as relatively low. The Norwegian School of Management is a private college where the students are charged $2900 each year. The entrance requirements for the business administration program were severe, and only students who had received very good grades in their previous studies had been admitted. In Norway, the study of business administration is generally regarded as a potential passport to much coveted rewards in terms of elite positions in society and economic life (Birkelund, Gooderham, & Nordhaug, 2000). During the Wrst year, the business administration students studied several compulsory subjects (e.g., mathematics, accounting, and macroeconomics), with most of the instruction based on traditional transmission-oriented pedagogy and, thus, consisting of large teacher-regulated lectures. During the second year, when the students attended courses in subjects such as statistics, marketing, accounting, and intercultural

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communication, large lectures were still the main form of instruction. During the third year, the business administration students studied subjects such as Wnancial strategy, international marketing, and organizational psychology, and in the last year they specialized in a topic of their own choice (e.g., marketing) and wrote a master thesis, either individually or with a group of other students. In regard to evaluation, the business administration students had to take 11 diVerent examinations during the Wrst year, most of them quite traditional graded tests on which they had to perform individually at the college. During the second year, the students had to sit for 16 diVerent examinations, with most of these also in the form of individual, graded tests. During the third and fourth years, evaluation was based on reports, term papers, case presentations, and class contributions in addition to individual written examinations. The importance of getting good grades for future job prospects was much emphasized in the business administration program. In connection with every evaluation, the college published grade ranking lists and marked out the 10% best grades. Twice a year, several attractive companies visited the college and presented themselves to the students, especially inviting those who were among the “top ten” on the grade ranking lists. It should be noted that assessments for the present research were made in the autumn terms (November) of the Wrst and second years. Thus, the participants had experienced only parts of the programs described above at the time of testing. 2.2. Clustering measures 2.2.1. Personal interest We used a Norwegian version of the Study Interest Questionnaire (SIQ) (Schiefele, Krapp, Wild, & Winteler, 1993) to measure students’ personal interest in their Weld of study. This measure was composed of 18 items focused on students’ enjoyment and valuing of study activities and subjects for their own sake (sample item: After a long weekend or vacation I look forward to starting on the study again). The students rated each item on a 4-point scale ranging from not at all true of me (1) to very true of me (4). Schiefele et al. (1993) reported several types of validity data for the SIQ, indicating that this is a suYciently valid instrument for measuring study interest. In the present research, the reliability estimate (Cronbach’s
) for the SIQ was .84 for both samples. 2.2.2. Mastery goal orientation The measure of mastery goal orientation was adapted from Midgley et al. (1998). The measure consisted of six items pertaining to learning, self-improvement, and the mastery of challenging tasks (sample item: An important reason why I do my work in school is because I want to get better at it). The students rated each item on a 5-point anchored scale (1 D not at all true, 5 D very true). The reliabilities (Cronbach’s
) for this scale were .82 for the student nurses and .77 for the business administration students. 2.2.3. Task value The measure of task value was adapted from the Motivated Strategies for Learning Questionnaire (MLSQ) (Pintrich et al., 1991). The six items of this measure

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focused on how important, useful, and interesting students considered their study tasks to be (sample item: It is important for me to learn the material in this study). Students rated each item on a 7-point Likert scale ranging from not at all true of me (1) to very true of me (7). The reliability estimates (Cronbach’s
) for the task value measure were .90 for the student nurses and .82 for the business administration students.2 2.2.4. Self-eYcacy The measure of self-eYcacy was also adapted from the MSLQ (Pintrich et al., 1991). The eight items of this measure focused on students’ judgments about their capability to accomplish study tasks as well as on their conWdence in their skills to perform those tasks (sample item: I am conWdent I can do an excellent job on the assignments and tests in this study). Students rated themselves on a 7-point Likert scale ranging from not at all true of me (1) to very true of me (7). The reliability estimates (Cronbach’s
) for the self-eYcacy measure were .83 for the student nurses and .90 for the business administration students. 2.3. Criterion measures We used three diVerent strategy measures adapted from the Pintrich et al. (1991) MSLQ as external criterion variables to validate diVerences among resulting proWles (cf. Milligan & Cooper, 1987). External criterion variables should be theoretically meaningful. As noted earlier, strategic processing Wgures prominently in theoretical models of self-regulated learning together with students’ adaptive motivational beliefs (e.g., Pintrich, 2000b; Zimmerman, 2000). In addition, models of self-regulated learning have been the focus of recent research demonstrating that adaptive motivational beliefs are associated with reports of cognitive and metacognitive strategy use (for reviews, see Pintrich, 2000b; Pintrich & Schunk, 2002). Because so much recent theory and research have highlighted relationships between students’ motivational beliefs and their strategic processing, we considered the use of strategy measures as external criteria both theoretically and empirically justiWable. The four items measuring rehearsal focused on strategies involving the reciting or naming of material to be learned (sample item: I make lists of important terms and memorize the lists). The six-item measure of elaboration focused on strategies involving the building of connections between information from multiple sources (e.g., readings and lectures) and between new information and prior knowledge (sample 2 Because two of the six items of the task value measure focus on the interest an individual has for the academic content itself, there may seem to be some overlap between this measure and the personal interest measure (SIQ). However, following the expectancy-value framework (e.g., WigWeld & Eccles, 2000, 2002), the four other items of the task value measure focus on the importance and utility of the content and activities for the individual, so as used here task value is a much broader construct than just personal interest. While uncritical use of highly correlated variables as clustering variables may be problematic (Aldenderfer & BlashWeld, 1984), there was only moderate positive correlation (r D .51) between the task value measure and the SIQ in the present study, with this also suggesting that task value beliefs represent a separate construct than personal interest.

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item: When reading I try to relate the material to what I already know), and the 12item measure of metacognitive strategies focused on the planning, monitoring, and regulation of cognition (sample item: I try to think through a topic and decide what I am supposed to learn from it rather than just reading it over when studying). Our criterion measures thus included both surface processing strategies (i.e., rehearsal) and deeper processing strategies (i.e., elaboration and metacognitive strategies). On all three strategy measures, students rated themselves on a 7-point Likert scale ranging from not at all true of me (1) to very true of me (7). The reliability estimates (Cronbach’s
) were .72 (student nurses) and .75 (business administration students) for rehearsal, .78 (student nurses) and .71 (business administration students) for elaboration, and .78 (student nurses) and .79 (business administration students) for metacognitive strategies. 2.4. Epistemological belief measures Our measures of epistemological beliefs were adapted from the Schommer Epistemological Questionnaire (SEQ) (Schommer, 1990, 1998). The SEQ is composed of 63 statements about knowledge and knowledge acquisition that students are asked to rate on a 5-point Likert scale (1 D strongly disagree, 5 D strongly agree). We decided to use the SEQ because it is a much researched and widely used instrument for assessing epistemological beliefs, also employed in several recent investigations of non-American students (e.g., Bråten & Strømsø, in press-b; Huet, Escribe, & Marine, 2001; Qian & Pan, 2002; Rozendaal et al., 2001). Moreover, the dimensions of epistemological beliefs identiWed by the SEQ have been found to be related to several important aspects of learning (for review, see Schommer-Aikins, 2002). In the present research, we measured two dimensions of epistemological beliefs that have previously been identiWed in factor analysis of the Norwegian version of the SEQ (Bråten & Strømsø, in press-a). The measure of students’ beliefs about the speed of knowledge acquisition included nine items focused on the time it takes for learning to occur. High scores on this measure are supposed to represent the belief that learning occurs quickly or not at all, while low scores represent the belief that learning is a gradual process requiring both time and eVort (sample item: Almost all the information you can learn from a textbook you will get during the Wrst reading). The measure of students’ beliefs about knowledge construction and modiWcation consisted of seven items dealing with the idea that knowledge is constructed and modiWed through the identiWcation of new ideas, the use of learning-to-learn skills, the integration of information from multiple sources, critical processing, and the recognition that existing knowledge is only tentative. High scores on this measure are supposed to represent the view that knowledge is given and stable, while low scores represent the view that knowledge is actively constructed and constantly evolving (sample item: Today’s facts may be tomorrow’s Wction). While our measure of beliefs about speed of knowledge acquisition corresponded to a dimension of personal epistemology also identiWed in Schommer’s (1990; Schommer et al., 1992) factor-analytic research, our measure of beliefs about knowl-

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edge construction and modiWcation corresponded to a dimension identiWed by Wood and Kardash (2002) in a recent factor-analytic study of the SEQ. Essentially, the two dimensions of personal epistemology that we measured also corresponded to the dimensions identiWed and measured by Buehl and her colleagues concerning the need for eVort and the integration of knowledge, respectively (Buehl, Alexander, & Murphy, 2002; Buehl et al., 2003). In the present research, reliability estimates (Cronbach’s
) for the measure of beliefs about the speed of knowledge acquisition were .66 (student nurses) and .67 (business administration students). For the measure of beliefs about knowledge construction and modiWcation, the reliabilities (Cronbach’s
) were .46 (student nurses) and .63 (business administration students). Given the weaker reliability for the measure of beliefs about knowledge construction and modiWcation for the student nurses, the results concerning this measure for the student nurses should be interpreted with caution. 2.5. Procedure All the measures were group administrated to the participants approximately two months into the Wrst year of their profession-oriented education (November/Year 1) and then re-administrated approximately two months into the second year (November/Year 2). The second measurement occurred in the autumn term of the second year rather than at the end of the Wrst year because the study was part of a larger longitudinal project, also including students from other academic Welds, where the intention was to survey students’ beliefs, motivations, and cognitions each year over the course of their entire college careers. The measures were counterbalanced in their administration, with the exception that the clustering measures adapted from the MSLQ (i.e., task value and self-eYcacy) were always administered before the strategy measures adapted from the same questionnaire (i.e., rehearsal, elaboration, and metacognitive strategies). The reason why the task value and self-eYcacy measures were always administered prior to the strategy measures was that we retained the original format of the MSLQ, where the motivation section appears before the learning strategy section. (In fact, the entire MSLQ was included in the survey, with the other MSLQ subscales administered for other research purposes.) For both samples, the data were collected during large regular lectures intended for all student nurses and business administration students, respectively, who were at the same level of study. The students were informed that their participation was entirely voluntary and assured that the information they provided would be conWdential. All tasks were Wnished in the course of a 45-min session. The original scales in English and German (SIQ) were translated into Norwegian by a group of Wve educational psychology researchers who were all proWcient in English and German in addition to Norwegian. All the items were translated so that their essential meanings were retained and so that they were easy to understand in the context of Norwegian students. Rather than using back translating as a way to ensure that essentially the same meaning was maintained across translations, the Wve researchers worked together on the translations with disagreements concerning the

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comparability of the versions solved through group discussion. First, one researcher worked independently on the translation of the items of a measure. Second, this preliminary translation was carefully reviewed by each of the four other researchers. Whenever one or more of the researchers considered an item to be problematic in terms of comparability with the original, diVerent nuances of expression were thoroughly discussed by the entire group until a unanimous decision was reached with respect to every item. The content of the items on our scales represented very few translation problems for the research group or interpretation problems for our participants. The conclusion that there were very few interpretation problems for the participants is based on a small-scale try-out of the measures, where six students responded to all the questionnaires and assessed whether any of the items were ambiguous or diYcult to understand. In addition, even though the participants were allowed to ask questions concerning the meaning of items during testing, very few of them took the opportunity to do so. The items of the motivation and strategic processing measures were not worded to have students focus on particular classes or subjects. Rather, students were asked about their beliefs and experiences in their profession-oriented education (i.e., nursing or business administration) in general, for example, by using phrases such as “in this study” or “program” as part of the item stem. In addition, the SEQ (Schommer, 1990, 1998) was used as a domain-general measure of students’ epistemological beliefs.

3. Results 3.1. Descriptive data The univariate distributions of all variables were found to be approximately normal in both samples. Table 1 shows the means and standard deviations for these variables. We used paired sample t tests to assess diVerences from the Wrst to the second year. Table 1 also shows which diVerences were signiWcant at p < .01 by the Bonferroni adjustment. It can be seen that the student nurses’ scores on the mastery goal measure decreased signiWcantly from the Wrst to the second year, while the business administration students’ scores decreased signiWcantly not only on this measure but on the interest and task value measures as well. Thus, these recorded decreases from the Wrst to the second year suggest that students were reportedly less positively motivated after one year than at the outset of their studies, with this negative trend seemingly most pronounced for the business administration students. To further examine whether the business administration students evidenced more of a decline in motivation over time than did the student nurses, we performed a repeated measures multivariate analysis of variance (MANOVA) with group (student nurses and business administration students) as the between-subject factor and time (Year 1 and Year 2) as the repeated measures factor. Dependent variables were personal interest, mastery goal orientation, task value, and self-eYcacy. This analysis resulted in a signiWcant group by time interaction, F (4, 182) D 3.30, p D .01, partial 2 D .07. The signiWcant interaction for the MANOVA was followed by univariate analyses (ANOVAs) for the four dependent

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Table 1 Means and standard deviations of scores on measures for the two samples in the Wrst and second year Variable

Motivation Personal interest Task value Mastery goal orientation Self-eYcacy Strategies Elaboration Metacognition Rehearsal Epistemological beliefs Speed of knowledge acquisition Knowledge construction

Student nurses (n D 98)

Business administration students (n D 105)

Max

First year

Second year

First year

Second year

M

SD

M

SD

M

SD

M

SD

3.25 6.39 4.40

(.40)a (.78)a (.56)

3.19 6.16 4.17

(.43)a (.79)a (.56)

2.85 5.73 3.92

(.41) (.70) (.53)

2.68 5.25 3.70

(.47) (.87) (.56)

4 7 5

4.96

(.81)a

4.85

(.84)a

4.76

(.90)b

4.74

(.90)b

7

5.08 4.71 4.26

(.99)a (.81) (1.28)a

4.86 4.36 4.21

(.92)a (.66) (1.31)a

4.62 4.38 3.52

(.80)b (.73)b (1.03)b

4.51 4.21 3.43

(.89)b (.62)b (1.13)b

7 7 7

1.92

(.48)a

1.97

(.45)a

1.95

(.47)

2.09

(.47)

5

2.45

(.45)a

2.57

(.45)a

2.35

(.49)b

2.47

(.44)b

5

Note. DiVerences in performance from Wrst to second year, as assessed by paired samples t tests, were signiWcant at p < .01 by the Bonferroni adjustment if not marked with the same superscript letter.

variables, showing a signiWcant group by time interaction for task value, F (1, 185) D 6.81, p D .01, partial 2 D .04. Thus, with respect to task value, the business administration students seemed to decline more over time than did the student nurses. In regard to reported use of strategies and epistemological beliefs, the only signiWcant changes were that the student nurses were reportedly less likely to use metacognitive strategies the second than the Wrst year, and that the business administration students were more likely to believe in quick learning the second than the Wrst year. 3.2. Cluster analysis We used Ward’s minimum-variance hierarchical clustering technique (Everitt, Landau, & Leese, 2001) to proWle students on the basis of their motivational beliefs and then examined the characteristics of the resulting clusters in regard to both strategic processing and personal epistemology. We decided to use Ward’s hierarchical clustering technique because it has been considered to be especially eVective in recovering the underlying structure of a given data set (Alexander & Murphy, 1999; Atlas & Overall, 1994; BlashWeld, 1996), and because it has been used in several other recent investigations of learner proWles (Alexander et al., 1995; Alexander & Murphy, 1998; Buehl et al., 2003; Meece & Holt, 1993; Vermetten, Vermunt, & Lodewijks, 2002). Ward’s technique is an agglomerative method. This means that it starts with every individual representing a unique cluster and continues to examine distances between clusters in a stepwise fashion until all individuals have been grouped into one cluster (Norusis, 1994).

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Following Aldenderfer and BlashWeld (1984) and Everitt et al. (2001), we used several methods to aid us in the selection of the appropriate number of clusters. First, we examined the dendrograms, that is, the graphical representations of the clustering procedure, with the largest gaps or distances between clusters in the dendrograms helping us to determine the number of meaningful clusters. Second, we extracted potential cluster solutions and then looked for signiWcant diVerences among these clusters with respect to the clustering variables (i.e., personal interest, mastery goal orientation, task value, and self-eYcacy beliefs). Third, we performed discriminant function analyses to validate the presumed multimotivational character of the clusters. We did that when cluster membership was identiWed and used the clustering variables to predict group membership. For all the identiWed cluster solutions, cluster membership was correctly predicted for at least 91% of the data. Finally, we used the external criterion strategy variables (i.e., reports of rehearsal, elaboration, and metacognitive strategies) to validate the diVerences among the resulting proWles, with the relationships between cluster proWles and the strategy variables operating as measures of convergent validity. We conducted separate cluster analyses on the motivational data from the student nurses and the business administration students, with this allowing us to examine the consistency of emerging clusters across diVerent Welds of study and academic contexts. Additionally, we conducted separate cluster analyses on the Wrst- and secondyear data, with this providing us with the opportunity to examine changes in student proWles across one academic year. In the following, we Wrst describe the Wrst-year clusters for the student nurses and the business administration students, respectively. We then report on the second-year clusters for the two samples separately and, Wnally, explore changes in cluster proWles for the two samples from the Wrst to the second year. 3.3. Descriptions of the Wrst-year clusters 3.3.1. Student nurses A three-cluster solution appeared to be most appropriate for the Wrst-year motivation data from the student nurses. The means and standard deviations for the clustering variables, as well as for the criterion strategy variables and the epistemological belief measures, are presented in Table 2. Based on their motivational characteristics, we labeled these clusters the positive motivation, moderate motivation, and low proWle clusters. The students in the positive motivation cluster (n D 41) reported high levels of personal interest in their Weld of study, orientation towards task mastery, conWdence in their ability to accomplish study tasks, and a positive valuing of those tasks. The students in the moderate motivation cluster (n D 46) reported more moderate interest in their Weld of study, and they also scored below the positive motivation cluster on the mastery orientation, task value, and self-eYcacy measures. Finally, students in the low proWle cluster (n D 11) were only modestly motivated for academic tasks, not showing much interest in their study or being much oriented towards task mastery. Moreover, students in the low proWle cluster did not seem conWdent that they could perform their study tasks; nor did they value those tasks very much. The

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Table 2 Means and standard deviations of scores on measures for the clusters based on Wrst-year data from the student nurses Dependent variables

Positive motivation (n D 41)

Moderate motivation (n D 46)

Low proWle (n D 11)

Max

M

SD

M

SD

M

SD

3.51 6.78 4.85

(.25)a (.27)a (.15)a

3.17 6.35 4.24

(.27)b (.70)b (.36)b

2.57 5.05 3.36

(.34)c (.93)c (.52)c

4 7 5

5.51

(.52)a

4.75

(.72)b

3.92

(.49)c

7

5.53 5.11 4.66

(.88)a (.78)a (1.38)a

4.91 4.53 4.09

(.85)b (.68)b (1.08)b

4.00 3.91 3.61

(1.00)c (.58)c (1.35)b

7 7 7

1.73

(.48)c

1.98

(.43)b

2.37

(.29)a

5

2.27

(.42)c

2.51

(.39)b

2.84

(.46)a

5

1

Motivation Personal interest Task value Mastery goal orientation Self eYcacy Strategies2 Elaboration Metacognition Rehearsal Epistemological beliefs3 Speed of knowledge acquisition Knowledge construction

Note. Superscript numbers indicate separate analyses. Superscript letters that diVer in the same row denote means that are signiWcantly diVerent from one another at p < .05.

number of students in the three clusters totals 98 due to missing data for one of the students on the SIQ. To understand the diVerences among these clusters, we Wrst conducted a MANOVA with cluster group as the independent variable and personal interest, mastery goal orientation, task value, and self-eYcacy as the dependent variables. Results indicated a signiWcant overall diVerence between clusters, F (8, 184) D 31.33, p < .001, and follow-up analyses of variance (ANOVAs) showed univariate eVects for all the dependent measures, Fs (2, 95) > 33.59, ps < .001, MSEs < 3.57. The eVect sizes in the form of partial 2 were .54 (personal interest), .70 (mastery goal orientation), .44 (task value), and .41 (self-eYcacy). We also conducted a series of multiple comparisons using Fisher’s least signiWcant diVerence (LSD) procedure to obtain a better understanding of the univariate eVects. The results of these between-cluster comparisons are presented in Table 2 as superscript letters. As can be seen, the three clusters were signiWcantly diVerent from one another with respect to all four motivational variables. That is, students in the positive motivation cluster had signiWcantly higher scores on all the motivational measures than students in the two other clusters, while students in the moderate motivation cluster signiWcantly outperformed students in the low proWle cluster on all the measures. The discriminant function analysis that we performed to validate the presumed multimotivational character of the clusters showed that overall group membership was accurately predicted for 93.9% of the cases. While the prediction accuracy for the positive motivation group was 100%, the prediction accuracy for the moderate motivation and the low proWle cluster, respectively, was 89.1 and 90.9%.

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We then used the strategy measures as external criterion variables to validate the diVerences among the three clusters. When we conducted a MANOVA with cluster membership as the independent variable and rehearsal, elaboration, and metacognitive strategies as the dependent variables, a multivariate eVect was identiWed, F (6, 186) D 5.82, p < .001, and follow-up ANOVAs indicated signiWcant univariate eVects for both elaboration and metacognitive strategies, Fs (2, 95) > 14.53, ps < .05, MSEs < 24.76. Partial 2 for elaboration was .23; for metacognitive strategies it was .24. Post hoc multiple comparisons by Fisher’s LSD procedure showed that all cluster groups were signiWcantly diVerent from one another with respect to both elaboration and metacognition. While students in the positive motivation cluster reportedly used rehearsal signiWcantly more than students in the two other clusters, there was no signiWcant diVerence between students in the moderate motivation and the low proWle cluster on this measure (see Table 2). Thus, the strategy measures used as external criterion variables in the present research seemed to validate the diVerences among the proWles, showing in line with several recent models of self-regulated learning (e.g., Pintrich & Zusho, 2002; Zimmerman, 2000) that more motivated students are also more likely to be strategic learners. Finally, we explored the three multimotivational clusters with regard to their epistemological beliefs, that is, their beliefs about the speed of knowledge acquisition and their beliefs about knowledge construction and modiWcation. A MANOVA with the two epistemological measures as the dependent variables and cluster membership as the independent variable showed a signiWcant multivariate eVect, F (4, 186) D 8.52, p < .001. This omnibus test was followed by univariate analyses (ANOVAs) for the two dependent variables, indicating that the cluster groups diVered signiWcantly on both, Fs (2, 94) > 9.22, p < .001, MSEs > 8.32. The eVect sizes (partial 2) were .18 (speed of knowledge acquisition) and .16 (knowledge construction and modiWcation). Multiple comparisons with Fisher’s LSD procedure indicated that all cluster groups were signiWcantly diVerent from one another with respect to both beliefs about the speed of knowledge acquisition and beliefs about knowledge construction and modiWcation (see Table 2). SpeciWcally, the students in the positive motivation cluster were least likely to believe that learning occurs quickly or not at all while the students in the low proWle cluster were most likely to believe so. Likewise, the students in the positive motivation cluster were least likely to believe that knowledge is stable and given while the students in the low proWle cluster were most likely to hold such naive beliefs about the nature of knowledge. 3.3.2. Business administration students A three-cluster solution also emerged for the data from the business administration students. The means and standard deviations for all variables for the three Wrstyear business administration clusters, which we correspondingly labeled the positive motivation, the moderate motivation, and the low proWle clusters, are displayed in Table 3. To understand the diVerences among these clusters, we again started conducting a MANOVA with the clustering measures of personal interest, mastery goal orienta-

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Table 3 Means and standard deviations of scores on measures for the clusters based on Wrst-year data from the business students Dependent variables

Positive motivation (n D 24)

Moderate motivation (n D 64)

Low proWle (n D 17)

Max

M

SD

M

SD

M

SD

3.29 6.45 4.33

(.25)a (.41)a (.39)a

2.84 5.73 3.93

(.26)b (.46)b (.43)b

2.29 4.67 3.30

(.31)c (.42)c (.50)c

4 7 5

5.80

(.51)a

4.60

(.69)b

3.87

(.69)c

7

5.17 4.89 3.46

(.69)a (.90)a (1.11)ab

4.58 4.39 3.66

(.70)b (.51)b (.99)a

3.90 3.62 2.85

(.74)c (.53)c (.76)b

7 7 7

1.89

(.47)

1.93

(.45)

2.17

(.54)

5

2.06

(.38)b

2.42

(.48)a

2.56

(.49)a

5

1

Motivation Personal interest Task value Mastery goal orientation Self-eYcacy Strategies2 Elaboration Metacognition Rehearsal Epistemological beliefs3 Speed of knowledge acquisition Knowledge construction

Note. Superscript numbers indicate separate analyses. Superscript letters that diVer in the same row denote means that are signiWcantly diVerent form one another at p < .05.

tion, task value, and self-eYcacy as the dependent variables and cluster membership as the independent variable. This analysis indicated a signiWcant multivariate eVect, F (8, 198) D 32.45, p < .001, and follow-up ANOVAs showed that there were signiWcant univariate eVects for all the dependent variables, Fs (2, 102) > 27.81, ps < .001, MSEs < 6.75. The eVect sizes (partial 2) were .58 (personal interest), .35 (mastery goal orientation), .62 (task value), and .49 (self-eYcacy). When we used Fisher’s LSD procedure to conduct multiple comparisons, we found that all three clusters diVered signiWcantly from one another with respect to all four motivational variables (Table 3). The positive motivation cluster (n D 24) reported the highest interest, as well as the highest mastery goal orientation, task value, and self-eYcacy. The moderate motivation cluster (n D 64) scored signiWcantly lower on all motivational measures than the positive motivation cluster but signiWcantly higher than the low proWle cluster (n D 17). The discriminant function analysis showed a prediction accuracy of 100% for each of the three cluster groups. When we conducted a MANOVA with the external criterion measures (i.e., reported use of rehearsal, elaboration, and metacognitive strategies) as the dependent variables and cluster membership as the independent variable, we again identiWed a signiWcant multivariate eVect, F (6, 196) D 8.36, p < .001, and follow-up with ANOVAs showed univariate eVects for all strategy measures, Fs (2, 100) > 4.03, ps < .05, MSEs < 15.76. EVect sizes (partial 2) for the strategy measures were .08 (rehearsal), .23 (elaboration), and .28 (metacognitive strategies). Multiple comparisons with Fisher’s LSD indicated that all three motivational clusters diVered signiWcantly from one another with respect to elaboration and metacognitive strategies. In regard to

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rehearsal, students in the moderate motivation cluster reportedly used rehearsal signiWcantly more than students in the low proWle cluster but did not diVer signiWcantly from students in the positive motivation cluster (see Table 3). When we Wnally explored cluster diVerences with respect to epistemological beliefs, a MANOVA with the two epistemological belief measures as the dependent variables and cluster membership as the independent variable indicated a signiWcant overall diVerence between the clusters, F (4, 196) D 4.00, p < .01. The follow-up ANOVAs indicated that there was a signiWcant univariate eVect for the measure of beliefs about knowledge construction and modiWcation, F (2, 99) D 7.03, p < .001, partial 2 D .12, MSE D 10.33, and, using Fisher’s LSD procedure, we determined that there was a signiWcant diVerence between the positive motivation cluster and the other two clusters on this measure, with the positive motivation cluster seemingly holding the most sophisticated beliefs about the nature of knowledge (see Table 3). In summary, our analysis of the Wrst-year data indicated consistency in student proWles across diVerent Welds of study. Similar three-cluster solutions captured the data well for both the student nurses and the business administration students. In both samples, we identiWed a positive motivation cluster with students characterized by high levels of personal interest, orientation towards task mastery, conWdence in the ability to accomplish study tasks, and a positive valuing of those tasks, a moderate motivation cluster with students more moderately motivated for academic tasks, and a low proWle cluster with students characterized by relatively low motivation for their chosen Weld of study and their study tasks. Additionally, the relationships between the cluster proWles based on motivational variables and the criterion strategy variables indicated good convergent validity, in that the positive motivation cluster in both samples reportedly used elaboration and metacognitive strategies most and the low proWle cluster in both samples reportedly used those strategies least. However, rehearsal was reportedly used most by the students in the positive motivation cluster in the sample of student nurses, while there was no signiWcant diVerence between students in the positive motivation cluster and students in the two other clusters with respect to reported use of rehearsal in the sample of business administration students. Finally, the Wrst-year data suggested that students in the positive motivation cluster held the most sophisticated epistemological beliefs about the nature of knowledge (i.e., knowledge construction and modiWcation) in both samples. In addition, students in the positive motivation cluster were least likely to believe that learning occurs quickly or not at all in the sample of student nurses, while the cluster diVerences with respect to this variable did not reach statistical signiWcance in the sample of business administration students. 3.4. Descriptions of the second-year clusters 3.4.1. Student nurses After one year of profession-oriented education, the participating student nurses again fell into three distinct clusters, which were similar to those emerging from the Wrst-year data and, therefore, labeled identically. The means and standard deviations for the clustering variables for the second-year clusters are shown in Table 4. Even

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though the means of the clustering variables for the second-year clusters were comparable to those for the Wrst-year clusters, the number of students associated with each cluster had changed considerably. In particular, there was a noticeable decrease in the number of students associated with the positive motivation cluster from the Wrst to the second year (41 vs. 20, respectively) and a noticeable increase in the number of students associated with the low proWle cluster (11 vs. 21, respectively), while the number of students associated with the moderate motivation cluster was more stable from the Wrst to the second year (46 vs. 52, respectively). Because six of the students had missing data on one or more of the second-year measures, only 93 students are represented in the second-year clusters. A MANOVA with cluster group as the independent variable and the four motivational clustering variables as the dependent variables indicated an overall diVerence among the clusters, F (8, 174) D 30.03, p < .001, and univariate ANOVAs showed that the three clusters varied signiWcantly with regard to all four clustering variables, Fs (2, 90) > 31.18, ps < .001, MSEs < 4.47. The eVect sizes assessed with partial 2 were .73 for personal interest, .61 for mastery goal orientation, .54 for task value, and .41 for self-eYcacy. Fisher’s LSD multiple-comparison procedure indicated that all clusters were signiWcantly diVerent from one another on all motivational measures (see superscript letters in Table 4), with these data describing subgroups of students who were, respectively, positively, moderately, and modestly motivated for their Weld of study and their academic tasks. The discriminant function analysis showed that overall group membership was accurately predicted for 92.5% of the cases. The prediction accuracy for the moderate motivation group was 86.5%; for the two other groups it was 100%. Table 4 Means and standard deviations of scores on measures for the clusters based on second-year data from the student nurses Dependent variables

Positive motivation (n D 20)

Moderate motivation (n D 52)

Low proWle (n D 21)

Max

M

SD

M

SD

M

SD

3.67 6.87 4.78

(.14)a (.18)a (.21)a

3.23 6.29 4.24

(.21)b (.40)b (.36)b

2.58 5.22 3.48

(.31)c (.90)c (.44)c

4 7 5

5.55

(.45)a

4.94

(.73)b

3.95

(.64)c

7

5.61 4.71 4.61

(.86)a (.66)a (1.34)a

4.85 4.43 4.38

(.78)b (.59)a (1.18)a

4.10 3.88 3.40

(.71)c (.61)b (1.33)b

7 7 7

1.71

(.43)b

1.89

(.40)b

2.29

(.43)a

5

2.40

(.55)b

2.52

(.45)b

2.80

(.30)a

5

1

Motivation Personal interest Task value Mastery goal orientation Self-eYcacy Strategies2 Elaboration Metacognition Rehearsal Epistemological beliefs3 Speed of knowledge acquisition Knowledge construction

Note. Superscript numbers indicate separate analyses. Superscript letters that diVer in the same row denote means that are signiWcantly diVerent from one another at p < .05.

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A MANOVA with cluster membership as the independent variable and the three criterion strategy variables as the dependent variables showed an overall diVerence between the clusters, F (6, 168) D 6.33, p < .001. The univariate ANOVAs indicated that the three clusters diVered signiWcantly with regard to their reported use of rehearsal, elaboration, and metacognitve strategies, Fs (2, 86) > 5.58, ps < .01, MSEs < 21.90. EVect sizes (partial 2) for the strategy measures were .12 (rehearsal), .30 (elaboration), and .19 (metacognitive strategies). Using Fisher’s LSD, we found that all clusters diVered signiWcantly from one another on the elaboration variable, with students in the positive motivation cluster obtaining the highest and the students in the low proWle cluster obtaining the lowest scores (see Table 4). With respect to both rehearsal and metacognitive strategies, the students in the positive and moderate motivation clusters reportedly used such strategies more than students in the low proWle cluster. When we Wnally conducted a MANOVA with cluster group as the independent variable and the two epistemological belief measures as the dependent variables, we found a signiWcant multivariate eVect, F (4, 164) D 6.27, p < .001. Conducting followup ANOVAs, we found that the clusters were signiWcantly diVerent on both epistemological measures, Fs (2, 83) D 4.31, ps < .05, MSEs < 9.39. Partial 2 for speed of knowledge acquisition was .20; for knowledge construction and modiWcation it was .09. Between-cluster comparisons with Fisher’s LSD revealed that students in the low proWle cluster were more likely to believe in both quick learning and given and stable knowledge than were students in the two other clusters (see Table 4). 3.4.2. Business administration students Using the same methods to aid us in the selection of the appropriate number of clusters as before, we found that the business administration students in the second year fell into four distinct clusters. The means and standard deviations for all variables for these four clusters are shown in Table 5. Ninety-Wve students had valid data on all measures the second year. Results of the MANOVA with cluster group as the independent variable and the four motivational measures as the dependent variables showed an overall diVerence among the clusters, F (12, 233) D 27.95, p D .001. In addition, signiWcant univariate eVects were demonstrated for all four clustering variables, Fs (3, 91) > 34.80, ps < .001, MSEs < 4.85. The eVect sizes (partial 2) were .71 (personal interest), .56 (mastery goal orientation), .67 (task value), and .53 (self-eYcacy). The results of the multiple comparisons with Fisher’s LSD are represented as superscript letters in Table 5, with these data used to generate descriptions of the motivational characteristics of the resulting clusters, which we labeled the positive motivation, moderate motivation with high self-eYcacy, low self-eYcacy, and low proWle clusters. The students in the positive motivation cluster (n D 12) retained high interest in their Weld of study, were still oriented towards mastery of their academic tasks, valued those tasks positively, and felt competent that they could perform them successfully. There was, however, a noticeable decrease in the number of students associated with the positive motivation cluster from the Wrst to the second year (24 vs. 12, respectively).

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Table 5 Means and standard deviations of scores on measures for the clusters based on second-year data from the business students Dependent variables

Positive motivation (n D 12)

Moderate motivation/ high eYcacy (n D 34)

Low selfeYcacy (n D 29)

Low proWle (n D 20)

Max

M

SD

M

SD

M

SD

M

SD

3.35 6.49 4.33

(.18)a (.28)a (.30)a

2.83 5.71 3.92

(.29)b (.40)b (.35)b

2.66 4.87 3.67

(.19)c (.47)c (.30)c

2.00 4.25 3.03

(.33)d (.80)d (.50)d

4 7 5

5.79

(.74)a

5.26

(.50)b

4.00

(.41)c

4.33

(.96)c

7

5.61 4.78 3.88

(.47)a (.50)a (1.39)

4.71 4.32 3.54

(.61)b (.52)b (.95)

4.28 4.02 3.23

(.88)c (.66)bc (1.07)

3.71 3.95 3.13

(.67)d (.54)c (1.27)

7 7 7

1.74

(.47)b

2.06

(.43)a

2.17

(.46)a

2.17

(.49)a

5

2.08

(.44)c

2.36

(.33)b

2.64

(.43)a

2.73

(.40)a

5

1

Motivation Personal interest Task value Mastery goal orientation Self-eYcacy Strategies2 Elaboration Metacognition Rehearsal Epistemological beliefs3 Speed of knowledge acquisition Knowledge construction

Note. Superscript numbers indicate separate analyses. Superscript letters that diVer in the same row denote means that are signiWcantly diVerent from one another at p < .05.

The students in the moderate motivation with high self-eYcacy cluster (n D 34) had lower scores on all motivational measures than the students in the positive motivation cluster. Because the cluster means were comparable to those of the Wrst-year moderate motivation cluster on most variables, we retained the label moderate motivation. However, because the students in this cluster seemed to consider themselves more eYcacious than the students in the corresponding Wrst-year cluster, we also described the cluster in terms of high self-eYcacy. The students in the low self-eYcacy cluster (n D 29) also seemed to be moderately interested in their Weld of study, and, to a certain extent, they claimed to orient themselves towards task mastery and to value their study tasks. However, while the students in this cluster recorded lower means than the students in the moderate motivation with high self-eYcacy cluster and higher means than the students in the low proWle cluster with respect to personal interest, mastery goal orientation, and task value, they recorded one of the lowest cluster means on the self-eYcacy measure. This places them on a par with the low proWle cluster as far as self-eYcacy beliefs are concerned. The fourth cluster, by comparison, consisted of 20 students who scored signiWcantly below the students in the three other clusters on the personal interest, mastery goal orientation, and task value measures, and who also recorded one of the lowest cluster means on the self-eYcacy measure. Thus, the students in this cluster seemed to be only modestly motivated for their studies, and we therefore retained the label low proWle for this cluster. The number of students in the low proWle cluster was rather consistent from the Wrst to the second year.

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The discriminant function analysis showed that overall group membership was correctly predicted for 95.8% of the cases. For the positive motivation cluster, 100% of the cases were correctly predicted. For the moderate motivation with high selfeYcacy, low self-eYcacy, and low proWle clusters, the prediction accuracy was 97.1, 93.1, and 95.0%, respectively. Results of a MANOVA with cluster group as the independent variable and the three strategy measures as the dependent variables showed signiWcant diVerences between cluster groups, F (9, 212) D 6.08, p < .001, and signiWcant univariate eVects were subsequently demonstrated for reported use of elaboration and metacognitive strategies, Fs (3, 89) > 6.84, ps < .001, MSEs < 17.63, but not for reported use of rehearsal, F D 1.49, p > .22. The eVect sizes (partial 2) were .41 for elaboration and .19 for metacognitive strategies. Fishers’s LSD procedure further indicated that all cluster groups were signiWcantly diVerent from one another with respect to elaboration. Students in the positive motivation cluster reportedly used metacognitive strategies more than students in the three other clusters used them. In addition, students in the moderate motivation with high self-eYcacy cluster reported more use of such strategies than students in the low proWle cluster did (see Table 5). When we conducted a MANOVA to explore the four clusters with respect to their epistemological beliefs, a multivariate eVect was found, F (6, 176) D 4.95, p < .001, and the clusters were also signiWcantly diVerent on both univariate epistemological measures when we conducted follow-up ANOVAs, Fs (3, 89) > 2.91, ps < .05, MSEs < 7.75. The eVect sizes (partial 2) were .09 for speed of knowledge acquisition and .24 for knowledge construction and modiWcation. Fisher’s LSD indicated that the students in the positive motivation cluster were more likely to hold sophisticated epistemological beliefs about the speed of knowledge acquisition than students in the three other clusters, who did not diVer signiWcantly from one another. With respect to beliefs about knowledge construction and modiWcation, students in the positive motivation cluster were more likely to hold sophisticated epistemological beliefs than were students in the three other clusters, and, additionally, students in the moderate motivation with high self-eYcacy cluster were more likely to hold sophisticated epistemological beliefs than were students in the low self-eYcacy and low proWle clusters (see Table 5). In summary, our analysis of the second-year data indicated that there was less consistency in student proWles across diVerent Welds of study in the second than in the Wrst year. While the data from the student nurses were still captured by the threecluster solution that emerged from the data from both samples in the Wrst year, we found that the business administration students in the second year fell into four diVerent clusters. In both samples, we identiWed clusters of positively motivated and low proWle students. However, for the second-year business administration students, the moderate motivation cluster gave way to two diVerent clusters, with the contrast between these clusters most evident with respect to self-eYcacy beliefs. Again, the relationships between the cluster proWles and the criterion variables indicated good convergent validity. In both samples, all clusters diVered signiWcantly from one another with respect to reported use of elaboration. In addition, the positive motivation cluster in both samples reportedly used metacognitive strategies most while the

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low proWle cluster reportedly used such strategies least. However, only in the sample of student nurses were higher levels of motivation also associated with more selfreported use of rehearsal. Finally, the second-year data from both samples suggested that students with higher levels of motivation were more likely to hold sophisticated epistemological beliefs about the nature of knowledge and knowledge acquisition than were students with lower levels of motivation. 3.5. Student changes from Wrst to second year To examine the changes that occurred in individual students in the two academic contexts across one year of profession-oriented education, we tracked the movement of the student nurses and business administration students, respectively, from the Wrst to the second year in terms of their cluster membership (cf. Alexander & Murphy, 1998). In the following, we report on the results of this tracking separately for the two samples. 3.5.1. Student nurses Fig. 1 displays the shifts that occurred for the individual students who constituted the various clusters in the Wrst and second year. Due to missing data the Wrst or the second year, the movement of only 92 students could be tracked. For the same reason, the percentages of students moving from a speciWc Wrst-year cluster do not necessarily total 100%, with the percentages reported referring to the total number of students in that particular Wrst-year cluster.3 It can be seen that the students characterized as positively motivated in the Wrst year were least stable with respect to cluster membership. While 37% of these students retained that label, more than half of them (54%) lost some of their motivation and found their way into the moderate motivation cluster. In fact, some of these students (7%) lost so much of their motivation over the year that they ended up in the low proWle cluster the second year. By comparison, the students characterized as moderately motivated in the Wrst year seemed to be more stable cluster members, with about two thirds of this group (65%) retaining that label. In addition, 15% of these students lost some of their motivation and found their way into the cluster distinguished by low motivation for studies and academic tasks. Only 9% increased their motivation across the academic year. Finally, all the student nurses we described as low proWle in the Wst year were still among the modestly motivated students one year later. By and large, our tracking of the movement of the student nurses over one academic year in terms of cluster membership painted a somewhat discouraging picture. Especially, it may seem alarming that so many students who were positively motivated in the Wrst year, in greater or less degree, lost their enthusiasm and engagement over one year of profession-oriented education. In addition, none of the students who were modestly motivated in the Wrst year seemed to be inspired by their school-based 3 The reason why the second-year positive motivation cluster contains 20 students (see Table 4) whereas Fig. 1 displays only 19 students moving into this cluster, is that the one student who had missing data the Wrst year had valid data and was associated with the positive motivation cluster the second year.

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Fig. 1. Representation of changes in cluster membership from Wrst to second year among the student nurses, with number (and relative proportion) of students moving from a speciWc Wrst-year cluster to a particular second-year cluster. Dot-and-dash lines indicate that the proportion of students moving from a speciWc Wrst-year cluster to a particular second-year cluster is less than .20, thin solid lines indicate that the proportion is between .20 and .30, and thick solid lines indicate that it is larger than .30.

experiences in the following year. Finally, the low proWle cluster almost doubled its number from the Wrst to the second year, with students migrating to this cluster from both the positive and the moderate motivation cluster. 3.5.2. Business administration students Fig. 2 displays the results of the tracking of the movement of the business administration students from the Wrst to the second year in terms of their cluster membership. Due to missing data the second year, the movement of only 95 students could be tracked. As with the student nurses, the percentages of students moving from a speciWc Wrst-year cluster do not necessarily sum to 100% because the percentages reported refer to the total number of students in that particular Wrst-year cluster. As can be seen, more than half of the students described as positively motivated the Wrst year (54%) lost some of their motivation and found their way into the moderate

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Fig. 2. Representation of changes in cluster membership from Wrst to second year among the business administration students, with number (and relative proportion) of students moving from a speciWc Wrst-year cluster to a particular second-year cluster. Dot-and-dash lines indicate that the proportion of students moving from a speciWc Wrst-year cluster to a particular second-year cluster is less than .20, thin solid lines indicate that the proportion is between .20 and .30, and thick solid lines indicate that it is larger than .30

motivation with high self-eYcacy cluster the second year, and three of them (13%) even ended up in the low self-eYcacy cluster. Only 25% of those Wrst-year positivemotivation students retained that label the second year. From the Wrst-year moderate motivation cluster, students found their way into all four clusters that emerged from the second-year data. While most students who were moderately motivated the Wrst year found their way into either the moderate motivation with high self-eYcacy group (28%) or the low self-eYcacy group (38%), 9% found their way to the positive motivation cluster, and 14% ended up in the low proWle cluster. Finally, the students described as low proWle the Wrst year were relatively stable cluster members, with as many as 65% of them retaining that label the second year. Still, some of these low proWle students increased their motivation and found their way into either the low self-eYcacy cluster (12%) or the moderate motivation with high self-eYcacy cluster (18%).

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All in all, the results of the tracking of the business administration students gave us a more mixed impression than the data from the student nurses. On the positive side, many members of the Wrst-year moderate motivation cluster increased their selfeYcacy the second year, while some of them even increased their motivation more generally. In addition, some of the members of the Wrst-year low proWle cluster managed to leave this cluster and migrated to clusters characterized by higher interest and task motivation. On the negative side, many students who were described as positively motivated in the Wrst year lost some of their enthusiasm and engagement the second year. Moreover, many moderately motivated students decreased their selfeYcacy across the academic year, and some even lost so much of their initial motivation that they ended up in the low proWle group.

4. Discussion The results of this study contribute to the understanding of the multimotivational structure of subcommunities within college classrooms, how such subcommunities are characterized on related aspects of learning, the extent to which they are consistent across academic contexts, and how they may change over time. 4.1. ProWles, strategies, and personal epistemology We identiWed distinct clusters of students based on their personal interest, mastery goal orientation, task value, and self-eYcacy beliefs. In accordance with much prior research on the relationship between academic motivation and strategic processing (Pintrich & Schunk, 2002), students with the highest levels of motivation reported most use of deeper processing strategies (i.e., elaboration and metacognitive strategies), while students with the lowest levels of motivation reportedly used such strategies least. The external strategy variables thus validated the diVerences among the motivational cluster proWles. Moreover, the resulting cluster proWles and the strategic eVort characteristic of those proWles seem to correspond to learner proWles observed in other research using diVerent clustering variables. In particular, our results are consistent with research indicating that classrooms may contain a clustering of students who report adaptive motivation and the use of deeper-level strategies, as well as a clustering containing students with low academic motivation and limited strategic capability (e.g., Alexander et al., 1995; Alexander & Murphy, 1998; Meece & Holt, 1993; Pintrich, 1989; Turner et al., 1998). In addition, our Wndings corroborate previous cluster-analytic research identifying mixed proWles, that is, the co-existence of adaptive and less adaptive aspects of learning (Alexander et al., 1995; Alexander & Murphy, 1998; Meece & Holt, 1993; Pintrich, 1989; Turner et al., 1998). SpeciWcally, student nurses with higher levels of motivation also reported most use of surface processing strategies, and a group of business administration students with moderate levels of motivation reported particularly low levels of self-eYcacy (see below). Our Wndings also indicated that there were diVerences among the clusters with respect to students’ beliefs about knowledge and knowledge acquisition. SpeciWcally,

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students with higher levels of motivation seemed more likely to believe that knowledge acquisition occurs gradually and requires both time and eVort, and to have recognized that knowledge is actively constructed and constantly evolving. In contrast, students lower in academic motivation seemed more likely to believe that learning occurs quickly or not at all, and that knowledge is something given and stable. This indicates that more positive motivation may relate not only to more use of deeperlevel strategies but also to more sophisticated epistemological beliefs. Even though our data cannot address the issue of causality, our Wndings at least seem consistent with the theoretical notion that students’ epistemological beliefs may be important for their academic motivation (Buehl, 2003; Dweck, 1986; Pintrich, 2002). In addition, our Wndings regarding personal epistemology and academic motivation accord well with those reported by Buehl et al. (2003). Essentially assessing the same dimensions of personal epistemology as we did in this study, Buehl et al. showed that students who were more likely to believe in eVort and knowledge integration were also more positively motivated for academic tasks. In future research, it is necessary to further examine the relation between dimensions of personal epistemology and forms of academic motivation, preferably with measures that can provide a broader and more reliable perspective on personal epistemology than those used in the present study. 4.2. Degree of consistency across contexts Our Wndings provided evidence for some measure of consistency in student proWles across diVerent academic contexts. Even though there were great diVerences between the nursing and business administration programs with respect to entrance requirements, instruction, and evaluation practices, the multimotivational structure of subgroups was comparable in the two contexts. Further, in both contexts the observed cluster proWles diVered in similar ways with respect to strategic processing and epistemological beliefs, with highly motivated students consistently reporting more use of deeper-level strategies and holding more sophisticated beliefs about the nature of knowledge and knowledge acquisition than other students. This picture of considerable consistency in individual diVerence proWles across two very diVerent profession-oriented academic contexts gives credence to the observed patterns. However, there were also some notable diVerences between the two samples. In particular, the four-cluster solution that emerged for the business administration students the second year diVered somewhat from the other solutions. At that time, only about 10% of the business administration students could still be characterized as positively motivated. The scores of this group on the motivational measures were comparable to those of the Wrst-year positive motivation cluster, but given the fact that the business administration students generally decreased in motivation from the Wrst to the second year, this second-year cluster can be described as outstanding in terms of academic motivation. As mentioned earlier, much attention is given to the 10% of the students who receive the highest grades in this particular master program. Even though we cannot tell whether the second-year positive motivation cluster consisted of the students who received the 10% best grades, it is interesting that, already after

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the Wrst year, a “top ten” cluster of students based on motivational characteristics could be identiWed. Presumably, the students in the “top ten” cluster had clearly understood the institutional message that the most attractive jobs within the Weld of business administration require student interest, task mastery and valuing, conWdence, and active engagement. The four-cluster solution emerging from the second-year business administration data also involved that the moderate motivation cluster gave way to two diVerent clusters that contrasted sharply with respect to self-eYcacy beliefs. Within expectancy-value perspectives (e.g., Eccles et al., 1998), it is certainly possible that expectancy (i.e., self-eYcacy) and value (i.e., interest, mastery orientation, and task value) components of motivation are not working in perfect harmony. In line with this, previous research has identiWed subgroups of students low in self-eYcacy and high in task value (Pintrich, 1989) or vice versa (Roeser, Strobel, & Quihuis, 2002). Also, Dweck’s (1986) early model described the possible combination of high selfeYcacy and low levels of mastery goals, as well as the combination of low selfeYcacy and high levels of mastery goals. However, only among the business administration students did perceived self-eYcacy diverge substantially from other forms of motivation, with this resulting in subgroups of students essentially characterized by diVerences in their self-eYcacy beliefs. The suggestion that perceived self-eYcacy may become more salient and interact more with other forms of student motivation in a competitive academic context is also consistent with results from a recent study comparing the motivational beliefs of business administration and teacher education students (Bråten, Samuelstuen, & Strømsø, 2004). In that study, perceived self-eYcacy was found to interact with students’ achievement goals in the competitive context of business administration but not in the cooperative context of teacher education. It is also possible that the large diVerences in selfeYcacy that we observed among the business administration students the second year were related to gender diVerences. In general, female students have been found to display less adaptive patterns of motivation than male students, even though such diVerences tend to vary by domain or task (Eccles et al., 1998; Pintrich & Zusho, 2002). In particular, female students may have lower perceptions of eYcacy than male students in subjects such as mathematics and science, even when they perform as well as, or even better than, males (Eccles et al., 1998). We therefore examined whether female business administration students were more likely to be associated with clusters characterized by low levels of self-eYcacy the second year, whereas male students were more likely to populate clusters characterized by high self-eYcacy. The chi-square, 2 (3, 95) D 10.82, p D .012, was signiWcant. SpeciWcally, we found that female students were overrepresented (62.1%) in the low self-eYcacy cluster (R D 2.1), whereas they tended to be underrepresented (23.5%) in the moderate motivation with high self-eYcacy cluster (R D ¡1.4). This suggests that female students may be particularly vulnerable to develop low self-eYcacy in the very competitive, predominantly masculine context of business administration. Additionally, future research needs to explore potential relationships between motivational cluster proWles and other demographic variables (e.g., socioeconomic or sociocultural background variables). Because we did not assess student ability in

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the present study, it can also not be ruled out that the diVerent cluster subgroups that emerged represented ability diVerences, and this possibility also needs to be examined in future research. Finally, a notable diVerence between the two samples concerned the reported use of rehearsal, with higher levels of motivation associated with more self-reported use of rehearsal only in the sample of student nurses. Possibly, highly motivated student nurses may strategically use rehearsal because they believe it will help them to perform better, while highly motivated business administration students may think rehearsal strategies are of limited value because they do not aid in deeper learning or understanding. Such diVerences may be related to diVerences in subject content, with, for example, medical subjects such as anatomy and physiology viewed as particularly suitable for learning through the use of simple memorization. However, the observed diVerences in reported use of rehearsal might also be related to gender diVerences between the two samples. This is because previous research with Norwegian post-secondary students has suggested that female students tend to be more in line with a “reproduction oriented learning style” (Vermunt, 1996) than male students (Bråten & Olaussen, 2000). 4.3. Changes across one academic year Because there are few longitudinal studies examining the nature of academic development, our most important contribution probably concerns the changes in cluster membership observed over one year of profession-oriented education. Even though overall decreases in adaptive motivation were reported in both samples, our longitudinal cluster-analytic approach allowed us to impart some nuances to this picture. In both samples, many students were able to maintain relatively high levels of motivation across the academic year, and, especially among the business administration students, quite a few developed more adaptive motivation over time. Somewhat paradoxical, more encouraging trends with respect to change in cluster membership were detected among the business administration students even though the business administration students displayed most overall decrease in motivation when considered as a collective. This seems to be related to the fact that the diversities that the business administration students initially displayed with respect to academic motivation magniWed over time, with the three proWles that emerged the Wrst year giving way to four diVerent motivational conWgurations the second year. In any event, the changes in motivation that we observed across one academic year raise concerns about single-assessments of motivation. Often, components of academic motivation are measured at one time point only, that is, as relatively enduring attributes of a person that predict future behavior. However, even personal interest, which is usually described as a stable characteristic of a person, seems to change considerably for many students over an academic year. This highlights the need for multiple assessments of academic motivation over time. In line with this, Winne and Perry (2000) have emphasized the need for methods that measure components of self-regulated learning as events rather than aptitudes, that is, as “temporally unfolding patterns of engagement with tasks” (p. 563).

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The present study adds to our understanding about the relationships between dimensions of personal epistemology and adaptive motivation also by indicating that such relationships are relatively stable over time. However, it is an intriguing but untested possibility that students who, against the general trend, increase in adaptive motivation over time, may be aided in this “upward” movement by more sophisticated beliefs about the nature of knowledge and knowledge acquisition. Still, it cannot be denied that a great many students in both samples lost some of their enthusiasm and engagement over the academic year. This Wnding parallels the consistent Wndings from much cross-sectional and longitudinal research showing that over the course of the school years, student motivation on the average becomes less adaptive (for reviews, see Eccles et al., 1998; Pintrich & Schunk, 2002; WigWeld & Eccles, 2000). Moreover, this general decline in adaptive motivational beliefs over time seems to be characteristic of both expectancy and value components of academic motivation (Eccles et al., 1998). The decline in motivation that we observed might be seen as an undesirable but inevitable eVect of profession-oriented education. Many individuals may start their studies with a somewhat groundless enthusiasm or optimism because they do not fully understand what is required of them in higher education. Over time, they may naturally cool down as they adjust their motivational beliefs to the realities of the classroom, in particular to the feedback and social comparison information provided to them. And the most eVective cooling systems will probably be classroom contexts with a strong focus on evaluation and competition, providing much evaluative and social comparative information (Eccles et al., 1998). In accordance with this, Church, Elliot, and Gable (2001) recently found, in a study of college students, that a strong emphasis on evaluation in the classroom seemed to orient students away from mastery goals and towards performance goals. The idea that the salience of evaluation and the likelihood of competition play important roles for students’ change in motivation over time (e.g., see Stipek, 1996; WigWeld & Eccles, 2000), is consistent with the observation that the business administration students as a group showed most overall decline in adaptive motivation. Presumably, the great number of individual graded examinations and the very strong emphasis on good grades in the program, as well as the constant publication of the results of the grade competition, made many students who did not feel they could excel in the study compared with other students expect to do less well in many activities and value those activities less. It is also conceivable that female students, even though they had been able to satisfy the severe entrance requirements for the business administration program, thrived less than male students in this challenging, competitive context and were particularly vulnerable to decline in self-eYcacy during the academic year (cf. Dweck, 1986). Whereas these explanations may seem plausible, it should also be acknowledged that our current data cannot form the basis of deWnite statements about the contextual and personal factors that may produce a decline in motivation over time; nor about the contextual and personal factors that may generate individual growth trajectories that counter the general motivational decline. The observed decline in adaptive motivation over time may also be seen as a great challenge to institutions and instructors in higher education. As pointed out by Brophy (1999), “motivationally eVective teachers make school learning experiences

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meaningful for students not only in the cognitive but also in the motivational sense” (p. 78). Brophy (1999) introduced the concept of “the motivational zone of proximal development” to characterize a match between learning activities and the learner’s current characteristics that may stimulate interest in pursuing the learning. To create motivationally optimal matches for their students, it seems important that instructors in higher education are made aware of the motivational subcommunities that may exist in their classroom. Presumably, diVerent groups of students may beneWt from diVerent motivational interventions aimed at maintaining or developing enthusiasm for and appreciation of study work. Because students’ beliefs about the nature of knowledge and knowledge acquisition may inXuence the way they interpret or reconstruct the learning environment (Vermetten et al., 2002), instructional measures targeting student motivation should probably also help students reXect on and, if necessary, change such beliefs. According to theory and preliminary research Wndings, such changes in epistemological beliefs may also inXuence students’ academic motivation directly. 4.4. Limitations and suggestions for further research Several limitations with the current study need to be addressed in future research. First, our Wndings are limited by the motivation constructs we selected and the way we measured them, with other constructs or alternative measures potentially resulting in diVerent groupings. Still, the motivation constructs we selected are all among the basic constructs that have been the focus of most recent research on student motivation in classroom contexts (Pintrich, 2003a). However, the fact that all our measures, like in much other current motivation research, were in the form of self-report questionnaires might be a potential shortcoming, as on-line measures (e.g., thinkalouds or behavioral measures) may be better indicators of these constructs (Pintrich, 2003a). Future work on motivational patterns using on-line measures is therefore needed. Second, the fact that the academic contexts were not directly assessed in this study is also a limitation, in that such assessment would be needed to make more deWnite statements about the inXuence of contextual variables on changes in students’ motivational proWles over time. While academic context is often assessed through students’ perceptions of classroom variables (i.e., the psychological context), this approach should probably be combined with observation data of what is actually going on in the classroom context (Church et al., 2001). Third, we are not able to conclude that the diVerences we observed in motivation proWles between student nurses and business administration students are due to contextual diVerences alone because of a potential confound between academic Weld and instructional context. For example, there might be diVerences in the content or subject matter of the Welds of nursing and business administration that are reXected in diVerent learner proWles. Moreover, the present research is limited by a lack of adequate control over demographic variables such as gender, age, and sociocultural background, as well as over other individual diVerence variables such as ability and academic achievement, with all of these variables presenting potential confounds in

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this study. With this in mind, we can only suggest that instructional context makes a diVerence but not really decide the extent to which diVerences in proWles are attributable to diVerences between instructional contexts and Welds of study, respectively. One possible approach to disentangling the roles played by instructional practice and academic Weld in future research might be to study the motivational patterns of students within the same Weld who are taught in diVerent ways, also controlling for relevant demographic and individual diVerence variables. Finally, there is still a great need for longitudinal examination of academic development at the post-secondary level. As even one year of data aVord us only a glimpse of students’ motivational development over time, we need further longitudinal examination that encompasses several years of instructional engagement (Alexander & Murphy, 1999). It cannot be ruled out, for example, that many students might experience a motivational revival as they approach the conclusion of their profession-oriented education. Hopefully, further longitudinal research in this area may help us understand patterns of change in individual learner proWles over the entire course of students’ college career. In turn, such understanding may provide a better basis for helping all students develop as motivated learners.

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