The epistemological beliefs, learning approaches and study orchestrations of university students

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The epistemological beliefs, learningapproaches and study orchestrations ofuniversity studentsLourdes Rodríguez a & Francisco Cano aa University of Granada , SpainPublished online: 24 Jan 2007.

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Studies in Higher EducationVol. 31, No. 5, October 2006, pp. 617–636

ISSN 0307-5079 (print)/ISSN 1470-174X (online)/06/050617–20© 2006 Society for Research into Higher EducationDOI: 10.1080/03075070600923442

The epistemological beliefs, learning approaches and study orchestrations of university studentsLourdes Rodríguez and Francisco Cano*University of Granada, SpainTaylor and Francis LtdCSHE_A_192261.sgm10.1080/03075070600923442Studies in Higher Education0307-5079 (print)/1470-174X (online)Original Article2006Society for Research into Higher Education315000000October [email protected]

This study examined the learning experience (learning approaches, study orchestrations andepistemological beliefs) of 388 university students. Data analysis revealed two main results. First,the different aspects of students’ learning experience were related: learning approaches and episte-mological beliefs (two pairs of canonical variates accounted for the significant relationships), andepistemological beliefs and study orchestrations (sophisticated beliefs emerged mainly in thoseparticipants using deep study orchestrations). Second, study orchestrations, as well as the canonicalvariates from epistemological beliefs and learning approaches, predicted students’ academicperformance. Results suggest that higher education institutions should provide scaffolding to fosterthe development of a mature learning experience amongst their students.

Introduction

Those researchers who have examined students’ learning experience have usuallyfocused on ‘approaches to learning’ and ‘study orchestrations’ (e.g. Meyer, 2000;Biggs, 2001; Entwistle et al., 2001), and seldom integrated these constructs withothers like ‘students’ beliefs about learning and knowing’ (e.g. Schommer, 1993,1994). This lack of attention is surprising given that the origin of these constructs issomewhat connected with the pioneering work of Perry (1970). The central tenet ofthe present study is that these three constructs are related and linked to academicperformance. What follows is a brief review, which includes the definition of these keyconstructs and an exploration of the relationships referred to above.

First accounts of students’ experiences

Initial research on how students interpreted their educational experiences at universitywas conducted by Perry (1970). His longitudinal, phenomenological (using interviews)

*Corresponding author. Departamento de Psicologia Evolutiva y de la Educacion, Universidad deGranada, Campus Universitario de Cartuja, 18071 Granada, Spain. Email: [email protected]

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618 L. Rodríguez and F. Cano

researches revealed that Harvard and Radcliffe college students’ ways of viewingthe world (epistemological development) evolved during their studies at university interms of complexity and ways of knowing. Students moved from absolutist views ofknowledge (i.e. things are either right or wrong) to a more relativistic stance (i.e.knowledge is complex and flexible, being correct only in specific contexts).

Originating from this pioneering work, two research perspectives on students’learning emerged: the metacognitive and the phenomenographic (Purdie et al.,1996). In the former, researchers investigated students’ epistemological conceptionsor beliefs about knowledge and learning, using a quantitative methodology (Ryan,1984; Schommer, 1990, 1994). In the latter, researchers analysed the variety ofmeanings that learning has for people and the different ways in which they learn,using a qualitative methodology (Marton & Säljö, 1984; Marton et al., 1993). Afterthese initial qualitative studies of students’ learning experience, a substantial body ofresearch was carried out in order to both gain insights and assess, this time in a quan-titative way (by means of inventories), how students approach and orchestrate theirlearning, that is, what they usually do while learning and studying (Marton et al.,1997). In comparison with the pioneering work carried out in the USA, the studiescarried out within this research perspective have a more ‘relational’ emphasis, whichrecognises that learning approaches are influenced by students’ perceptions of theirlearning environment (Prosser & Trigwell, 1999).

The assessment of students’ learning experience is a central feature of Harvey andGreen’s (1993) conceptualisations of quality in teaching and learning as ‘fit for thepurpose’ (e.g. to get students to learn effectively) and as ‘transforming’ (e.g. to changestudents’ views of their world, and also academic staff’s conceptions of the teaching–learning process). For higher education institutions, which over recent decades havebeen subject to increasing demands for quality assurance in teaching and learning(Biggs, 2001), one way of assessing the impact of their current practices for reviewingand enhancing teaching and learning involves knowing both how students approachand orchestrate their learning, and what beliefs they hold about knowledge andlearning (i.e. whether students are constructing meaning from their educationalexperiences and transforming their ways of viewing the world, and of perceivingknowledge and learning). This assessment is particularly crucial for teacher educationstudents, who are at the heart of the teaching–learning system, and might reflect thesepractices in classroom teaching and learning (Richardson et al., 1991; Pajares, 1992;Chan, 2003).

Epistemological beliefs

Schommer (1990, 1994) challenged Perry’s theoretical stance, submitting that beliefsare too complex to be captured in a single dimension, and proposed a multidimen-sional construct which she called ‘epistemological beliefs’. These are part of theunderlying mechanism of metacognition, and comprise a system of four more or lessindependent beliefs or assumptions about the nature of learning and knowledge.Schommer (1993) developed the Epistemological Questionnaire (EQ) consisting of

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Epistemological beliefs of university students 619

12 subsets of items, and, through factor analysis, identified four dimensions reflectingbeliefs (expressed from a naive perspective) in innate ability, quick learning, simpleknowledge and certain knowledge.

Schommer’s model (1994) suggests that epistemological beliefs are not inborn,unchanging characteristics of an individual, but evolve over time, education beingone factor influencing this development, mainly with regard to beliefs aboutknowledge. The higher the educational level achieved by adults, the more likely theyare to believe that knowledge is constantly evolving and highly complex (Schommer,1998). On the other hand, beliefs about learning, the speed and control of learning,‘which seem to intimately involve “the self”, were predicted by the adults’ home life’(Schommer, 1994, p. 314).

Beyond identifying these four dimensions in personal epistemology, Schommeralso demonstrated ‘how these beliefs may influence comprehension and cognition ofacademic tasks, and her work has been the most concerned with classroom learning’(Hofer & Pintrich, 1997, p. 90). Several studies have examined the influence ofepistemological beliefs on academic performance, and results indicate that the formerpredict the latter. The less high school students believed in quick learning(Schommer, 1993; Schommer et al., 1997; Cano, 2005), fixed ability, simple knowl-edge and certain knowledge (Schommer, 1993; Cano, 2005), the higher theiracademic performance.

Although an extensive body of research exists on the epistemological beliefs of highschool students, there is a dearth of research examining these beliefs and, likewise, thelearning approaches and study orchestrations of student teachers. These last twoconstructs are central to the research perspective developed in the field of studentlearning in higher education, which focuses on describing and assessing students’ways of experiencing and handling learning situations (Entwistle et al., 2001).

Learning approaches

These are strongly related to both students’ ideas or conceptions of learning andperceptions of their teaching–learning context, and refer to how students go aboutlearning, to their learning intentions (motives) and their methods (strategies) (Biggs,2001). This construct plays a central role as a process between the input (e.g. teachingcontext, student factors) and the output (e.g. quality of cognitive learning outcomes)(Säljö, 1982; Marton & Säljö, 1984, Van Rossum & Schenk, 1984; Biggs & Moore,1993). Researchers have identified two contrasting and theoretically opposed learningapproaches: deep and surface (Biggs, 1987a; Entwistle et al., 2001). Students whodeploy a deep approach to learning tend to conceive of learning as transforming infor-mation, to be intrinsically motivated and to use strategies focusing on the meaning ofthe material to be learned. Students who deploy a surface approach tend, on thecontrary, to conceive of learning as reproducing knowledge, to be extrinsicallymotivated and to use strategies focusing on the reproduction of those materials.

The original work on learning approaches came from interviews with students, ina micro-context (naturalistic experiment), while carrying out a specific task (students

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had to read academic articles and describe their intentions and strategies whenreading the text) (Marton & Säljö, 1984). This implies that an approach to learningis context-specific. However, some years later, further work was carried out in a moregeneral context to gain insights into what students usually do while learning andstudying, and to design inventories which would assess students’ readiness to adoptdeep or surface approaches to learning in general (Marton et al., 1997). A number ofquestionnaires have been devised to measure learning approaches. In Australia Biggs(1987b, c) developed the Study Process Questionnaire (SPQ) for university students,and its secondary school version, the Learning Process Questionnaire (LPQ). InBritain, Entwistle and Ramsden (1983) developed the Approaches to StudyingInventory (ASI).

These preferences for adopting particular approaches were referred to by laterauthors as ‘predispositions’ or ‘orientations’. The apparent contradiction betweenpredispositions, which are relatively consistent, and approaches that vary accordingto the learning task and content, has no straightforward solution (Case & Gunstone,2002). However, the stability of the former does not imply fixity, because, asRamsden (1988, p. 175) emphasises, they are ‘changeable and responsive to thecontext of teaching, evaluations, and curriculum’. The term ‘orientation’ did notcatch on (Biggs, 2001), and researchers generally use the term ‘approach’ to refer tothe manner in which students go about their learning tasks, as assessed by means ofquestionnaires such as the SPQ (Watkins, 2001).

An important aspect of learning approaches is its relationships with metacognitionas conceptualised by Baird (1990, p. 184): ‘the knowledge, awareness and control ofone’s own learning’. When students learn they play an active role in determining whatthey will learn (intention), how they will learn it (strategy), and in allocating mentalresources; indeed learning approaches and metacognition are linked constructs(Biggs, 1985; Vermunt, 1996; Case & Gunstone, 2002).

It is well documented that students’ learning approaches are related to a numberof factors, some of which are categorised as personological factors (e.g. a student’sperceived self-ability, prior knowledge) and others as contextual (e.g. teaching–learning activities, climate). Generally, deep learning approaches are likely to beencouraged by interest in and background knowledge of the material to be learned,a well-planned and well-resourced learning environment, an appropriate workloadand a warm classroom climate (Biggs & Watkins, 1995; Trigwell & Prosser, 1991a,b; Biggs, 1999; Dart et al., 2000; Watkins, 2001; Lizzio et al., 2002). Since learningapproaches are meaningful in the context of the teaching–learning system, they‘give the barometer readings that tell how the general system is working’ (Biggs,2001, p. 99).

An important topic of debate amongst researchers is whether students’ learningapproaches change as a result of their formal educational experiences as they progressin their studies. Much research has been undertaken on this issue, but the evidenceis inconclusive. While some studies found a decline in the students’ (secondary anduniversity) scores with the deep approach to learning (Biggs, 1987b; Gow & Kember,1990; Biggs & Moore, 1993; Volet et al., 1994; Eklund-Myrskog & Wenestam,

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1999), others found the opposite (Watkins & Hattie, 1981; Watkins et al., 1986;Richardson, 1994; Davis & Sales, 1996). Generally, authors comment that studentsprobably tend to use less desirable approaches (surface), mainly in tertiary studies, asthey are adapting to the new institutional demands (e.g. heavy curriculum, work pres-sures, assessment procedures) (Gow & Kember, 1990; Kember, 2000). When thesedemands are mitigated, students’ learning approaches seem to shift towards meaningand conceptual understanding. Case and Gunstone (2002) restructured an engineer-ing course with the specific aim of promoting metacognitive development (i.e. the useof deep approaches), and although it was not easily achieved, the authors pointed outthe supportive role of certain factors such as course design, journal tasks and testswithout time limits. Apparently, only an innovative teaching–learning environmentwhere educational objectives, teaching methods and assessment procedures arealigned would maximise the deep approach, as Biggs (2001) hypothesised, or at leastminimise the surface approach and problems with motivation, as Honkimäki et al.(2004) asserted.

The relationship between students’ learning approaches and academic grades‘shows inconsistent results, though it is generally believed that a deep approach/meaning orientation will contribute positively to learning outcomes’ (Zeegers, 2001,p. 118). In a recent meta-analysis based on 55 independent samples with 27,078respondents from 15 countries, Watkins found average correlations of -.11 and .16for surface and deep approaches, respectively. However, he recognised that ‘theserelationships assume that higher quality learning outcomes are rewarded by theassessment system’ (Watkins, 2001, p. 174).

Study orchestrations

The ‘individual combinations of approaches or orientations may be called orchestra-tions’ (Lindblom-Ylänne & Lonka, 1998, p. 4). These are contextualised patterns ofengagement in learning, adopted by individual students or by a group of students, aresensitive to students’ perceptions of their learning context as well as to their learningconceptions, and can display ‘conceptual consonance’ or ‘conceptual dissonance’(Meyer, 1991; Lindblom-Ylänne & Lonka, 1998; Meyer, 2000). While in the formerthere is a theoretically interpretable (typical or congruent) relationship between howthe context and the content of learning are perceived and how learning takes place(e.g. in terms of inventory response data, a student who combines high surfaceapproach scores with low deep approach scores), in the latter this relationship is theo-retically uninterpretable (atypical or maladaptive) (e.g. a student who combines highdeep approach scores with high surface approach scores). The phenomenon ofdissonance ‘exists in small groups of students in a range of contexts’ (Meyer &Boulton-Lewis, 2003), and is the focus of increasing interest from researchers andeducational institutions, as is manifested in the special edition of Studies in HigherEducation, vol. 28(1), 2003, devoted to this topic.

Dissonant study orchestrations are associated with a number of factors such as inabil-ity to self-regulate learning (Lindblom-Ylänne & Lonka, 2000), low metacognitive

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awareness (Cliff, 2000), and disintegration of relationships, both among learningapproaches and perceptions of learning context (Prosser et al., 2000). While there isa dearth of research examining the effects of tertiary experience on study orchestrations,there is a growing body of research identifying significant links between academicachievement and study orchestrations. Generally, students’ dissonant orchestrationsare linked to a lower-than-average academic performance (Meyer et al., 1990; Entwistleet al., 1991; Lindblom-Ylänne & Lonka, 1998; Cliff, 2000).

Interrelationships between epistemological beliefs, learning approaches and study orchestrations

Although epistemological beliefs and learning approaches are to some degreeconnected with Perry’s works, very little is known about their interrelationships. In1981 Perry speculated about the links between epistemological beliefs and ways ofstudying, but did not explore them.

Some studies have indicated a relationship between conceptions of knowledge andconceptions of learning. Davis (1997) found that while students’ absolutist views ofknowledge corresponded to reproductive learning conceptions, students’ relativisticknowledge conceptions were associated with meaning-orientated learning concep-tions. Buelens et al. (2002) obtained similar results using teaching assistants as partic-ipants. Recently, Cano and Cardelle-Elawar (2004) demonstrated that secondarystudents’ epistemological beliefs were significantly associated with their conceptionsof learning: the higher students’ scores on complex and sophisticated beliefs, themore elaborate and meaning-orientated their conceptions of learning.

If, as has been demonstrated, epistemological beliefs are linked to learning concep-tions, and the latter associated with learning approaches (Säljö, 1982), it would seemplausible that epistemological beliefs and learning approaches are also linked. Of rele-vance is that both constructs refer to learning experience in general, and to metacog-nitive activities in particular (Schommer, 1993; Case & Gunstone, 2002). However,only a few authors have focused on trying to integrate these constructs.

Two recent studies, by Chan (2003) and Cano (2005), found empirical evidenceof the interrelationships between epistemological beliefs and learning approaches.Chan (2003), using the EQ and the SPQ, investigated the epistemological beliefs andlearning approaches of 292 teacher education students. Factor analyses revealed atwo-factor structure for the SPQ (surface and deep approaches), similar to that foundby Biggs (1991, 1993), and a four-factor structure for the EQ, where two factors weresimilar to those of Schommer (1993)—innate/fixed ability and certain knowledge—and two were different—authority/expert knowledge and learning effort/process.Correlation analysis revealed that deep approach was negatively associated withauthority/expert knowledge, and positively related to learning effort/process, and thatsurface approach was positively correlated with certain knowledge and authority/expert knowledge. A possible deficiency in this research, that would merit further anal-ysis, is the statistical technique used, which is perhaps too simple to allow the possiblecomplexity of the relationship between learning approaches and epistemological

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beliefs to emerge. This complexity surely merits exploration using a multivariatetechnique (e.g. canonical correlation analysis).

Cano (2005) administered the LPQ and the EQ to 1,600 secondary students andexamined its dimensionality by carrying out exploratory and confirmatory factor anal-yses. Two factors emerged for the LPQ (deep and surface approaches) and three factorsfor the EQ (quick, effortless learning; simple knowledge and certain knowledge). Inorder to analyse the relationship between students’ academic achievement and theirepistemological beliefs and learning approaches, the author proposed a path model,and this achieved a noteworthy goodness-of-fit to the data. Epistemological beliefs andlearning approaches predicted academic achievement, and were significantly interre-lated, because the influence of epistemological beliefs on academic performance wasmediated by learning approaches. The effect was statistically significant for the factor‘quick effortless learning’: the more a student believed that learning occurred quicklyand without effort, the more s/he was likely to use a surface approach, which in turnwas linked negatively to performance. The latter was associated positively with deepapproach, and negatively with naive epistemological beliefs (the more simplistic astudent’s epistemological beliefs, the poorer his/her academic performance).

In spite of offering an insight into students’ learning experience, this study omitssome areas worthy of further analysis. Considering that study orchestrations areassociated with learning approaches, as previously indicated, and that a structuralrelationship between epistemological beliefs and learning approaches was detected inthe latter study, it can be inferred that study orchestrations are also likely to be relatedto epistemological beliefs. Moreoever, if this is true, a question arises: what effect doeseach construct have on academic performance?

The aims of the study

The main purpose of the present study was to provide an in-depth analysis of the linksbetween epistemological beliefs, learning approaches and study orchestrations, and toexamine how these constructs are related to academic performance. More specifically,the present research addressed four questions:

1–2. Do any relationships (canonical variates) exist (a) between the set of epistemologicalbeliefs variables and the set of learning approaches, and (b) between the former and studyorchestrations?

3–4. To what extent could academic performance be predicted and explained by the possi-ble canonical variates from each set of variables (epistemological beliefs, and learningapproaches), on the one hand, and by the study orchestrations, on the other hand?

Method

Participants

A total of 388 university students participated in the study, of whom 29.6% were male,and 70.4% female, their ages ranging from 17 to 40 (M = 21.11; SD = 2.78), with

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the overwhelming majority (94.3%) between 18 and 25 years of age. All were enrolledon all of the study modules in either the first or the last year of studies at a teachereducation college, and made up 90% of the total number of students for these coursesand years.

The curriculum of this institution can be described as constructive in terms of itsperspective. Staff stress the importance of designing course objectives explicitly basedon defined levels of understanding (constructive alignment) (Biggs, 2001), and of plan-ning a coherent teaching–learning environment which allows students to constructactive knowledge related to real-life situations (Entwistle et al., 2001). Furthermore,teaching resources and forms of assessment reflect this perspective. The former includea wide variety of interactive sessions, cooperative learning (e.g. group assignments)and essay writing. The latter focus on conceptual understanding, and include diverseforms of assignment: written (e.g. journals and proposals) as well as oral and audio-visual (e.g. group discussions and collaborative presentations and projects).

Materials

The Epistemological Questionnaire (EQ) comprised 12 groups of items consisting ofstatements about learning and knowledge that students rated on a Likert-type scale,from 1 (strongly disagree) to 5 (strongly agree). To ensure the applicability of thisquestionnaire to our sample, two types of factor analyses were carried out, exploratoryand confirmatory. The exploratory factor analysis, using the principal-componentsmethod and varimax rotation, revealed the presence of four factors with eigenvaluesgreater than one and explained 50.19% of the variance:

● Factor I: Belief in Quick learning or not at all (Quick learning);● Factor II: Belief that Knowledge is Unambiguous and Handed down by authority

(Simple Knowledge);● Factor III: Belief that the Ability to learn is unchangeable (Fixed ability); and● Factor IV: Belief in Certain Knowledge (Certain Knowledge).

Inter-item reliabilities for items composing each factor, measured by means of Cron-bach’s Alpha, were .60 for Factor I, .52 for Factor II, .57 for Factor III, and .55 forFactor IV. The structure largely resembles that obtained by Schommer (1993, 1998);our second and fourth factors are similar, the only difference being that factors I andIII correspond to factors III and I, respectively, in Schommer’s results. It is importantto point out that the higher a student’s scores on these factors, the more naive will behis/her epistemological beliefs. So, for example, a student who obtains a high scoreon fixed ability will believe, in a naive way, that the ability to learn cannot beimproved. A confirmatory factor analysis was also carried out, which providedreasonably acceptable goodness-of-fit indices: χ2 = 112.93, df = 48, p < .001. Good-ness-of-Fit Index (GFI) = .95; Adjusted Goodness-of-Fit (AGFI) = .92; Root MeanSquare Residual (RMR) = .02.

The Study Process Questionnaire (SPQ) was composed of six subscales with sevenitems in each. The students rated these on a five-point Likert-type scale, from 1

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(never or rarely true of me) to 5 (always or almost always true of me). The subscalesmeasured the learning approach dimensions proposed by Biggs (1987c, 1993):surface motive, surface strategy; deep motive, deep strategy; and achieving motive,achieving strategy. Only the first four subscales were used in this study since studyorchestrations were being investigated, and it was important to comply with Meyer’s(2000) criterion demanding that variation dimensions or factors be theoreticalopposed.

The subscales were subjected to two types of factorial analyses, exploratory andconfirmatory. Exploratory factor analysis, using the principal-components method,followed by oblique rotation of the factor loading matrix, indicated the presence oftwo factors or components with eigenvalues greater than 1, explaining 73% of the vari-ance. Deep motive subscale and deep strategy subscale loaded on Factor I (Deep).Surface motive subscale and surface strategy subscale loaded on Factor II (Surface).Reliability, measured by means of Cronbach’s Alpha, was .57 for Factor I and .47 forFactor II. The two-factor structure is in line with the results submitted by other authors(Kember & Leung, 1998) and it satisfies the definition of an interference modelmentioned above: two distinct but contrasting dimensions of variation in students’learning (deep-surface). Confirmatory factor analysis of this solution gave acceptableGoodness-of-Fit indices: χ2 = 12.36, df = 1, p < .001. Goodness-of-Fit Index (GFI)= .98., Adjusted Goodness-of-Fit Index (AGFI) = .85.; Root Mean Square Residual(RMR) = .05. These results are in line with those submitted by other authors.

Procedure

Participants were made up of those attending courses when the questionnaires, inwhole-class sessions, were administered. All the students agreed to participate volun-tarily. They were each given a pack containing information about the research, ques-tionnaires and instructions, as well as assurances regarding the confidentiality of alldata collected. They were asked to answer the EQ and the SPQ , to give their fullname, age and sex, and indicate their agreement by signing a consent form. At theend of the academic year, students’ grades for all subjects were noted; their averagemark was used as a measure of academic performance.

Results

Epistemological beliefs, learning approaches, and study orchestrations

The exploratory factor analyses of the EQ and SPQ, mentioned above, were taken asa starting point to obtain a measure of the three main constructs of this study.

Epistemological beliefs and learning approaches were measured by means of factorscores (obtained by multiplying the standard scores for the original variables by thefactor score coefficients).

Study orchestrations were identified following Meyer’s (2000) guidelines. A K-means cluster analysis of students’ factor scores on the two-factor model (deep-surface,

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arising from the exploratory factor analysis of the SPQ subscales) was carried out. Asthe 2, 3 and 4 cluster requested solutions showed no notable statistical differences fromeach other, for theoretical reasons a 4-cluster solution was chosen. This indicated clear-cut differences between the patterns of response in factor scores, F(3, 384) = 230.03,p <.001, and F(3, 384) = 231.23, p <.001, for deep and surface learning approachesrespectively, as set out in Figure 1.Figure 1. Plot of mean factor scores for each of the four clusters (Study orchestrations)On the one hand, clusters 1 and 4 included participants showing dissonant orches-trations, an atypical pattern of response: high scores or low scores on both surface anddeep approaches, that is atypical high-high, and low-low approaches, respectively.The number of students in the two clusters was 141, this being 36.34% of the totalsample. On the other hand, clusters 2 and 3 included participants presenting conso-nant orchestrations, a typical pattern of response: deep or surface learningapproaches, respectively. Post hoc comparisons between the means of the twoapproaches in the four clusters were carried out using Tukey’s test, and all weresignificant at p <.01.

Relationship between epistemological beliefs and learning approaches

This relationship was examined using a canonical correlation analysis in which oneset of variables consisted of epistemological beliefs and the other of learningapproaches.

The first canonical correlation was .43 (19% of variance), the second was .31 (10%of variance). With both canonical correlations included, χ2

(8) = 123.13, p < .001, andwith the first canonical correlation removed, χ2

(3) = 40.88, p < .001. The two pairsof canonical variates, therefore, accounted for the significant relationships between

-1.6-1.4-1.2-1

-0.8-0.6-0.4-0.200.20.40.60.811.21.4

Deep Surface

Learning approaches

Factor scores Atypical h-h

Deep

Surface

Atypical l-l

Figure 1. Plot of mean factor scores for each of the four clusters (Study orchestrations)

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the two sets of variables, and were saved for later use (F1 is the name assigned to thefirst canonical variable in the first set, and F2 to the second; S1 is the name assignedto the first canonical variable in the second set, and S2 to the second). Data on thesetwo pairs of canonical variates appears in Table 1.

The total percentage of variance and total redundancy indicate that the first pair ofcanonical variates (F1 and S1), as well as the second (F2 and S2), were related to somedegree. Those variables in the beliefs set that correlated closely with the first canonicalvariate were Quick learning and Simple knowledge, and to a lesser extent, Certainknowledge, and in the approaches set, Deep approach, with negative Surface approachcorrelating to a lesser extent. Taken as a pair, these variables suggest that those withimmature epistemological beliefs (.39, .74, .24) also tended to deploy a Surface learn-ing approach (.95) and negative Deep approach (-.28). The second canonical variatein the beliefs set was composed of negative Simple knowledge (-.91) and to a lesserextent, negative Quick learning (-.27), while the corresponding canonical variate fromthe approaches set was composed of Deep approach (.96) and to a lesser extent, Surfaceapproach (.28). Taken as a pair, these variables indicate that sophisticated beliefs (inrelative knowledge and gradual learning) correspond to a deep learning approach.

Together the two canonical variates account for 58% of variance (31 plus 27) inthe beliefs set, and 99% of variance in the approaches set (50 plus 49). Together

Table 1. Correlations, standardised canonical coefficients, canonical correlations, percentages of variance, and redundancies between epistemological beliefs and approaches to learning variables

and their corresponding canonical variates

Canonical variates

First Second

Cor Coef Cor Coef

Beliefs set (F1) (F2)Quick learning .57 .39 −.34 −.27Simple knowledge −.22 −.16 −.94 −.91Fixed ability .87 .74 −.11 −.09Certain knowledge .32 .24 .29 .10

% of variance .31 .27 Total = .58Redundancy .06 .02 Total = .08

Approaches set (S1) (S2)Deep −.28 −.27 .95 .96Surface .96 .95 .27 .28

% of variance .50 .49 Total = .99Redundancy .09 .05 Total = .14

Can. Correlation .43 .31

Note. Cor = Correlations of canonical variables with original variables. Coef = Standardised canonical variate coefficients. Can = canonical. F1 and F2 = canonical variates for first set of variables (CNVRF1 and CNVRF2). S1 and S2 = canonical variates for second set of variables (CNVRS1 and CNVRS2).

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the two beliefs variates ‘explain’ 8% of the variance (6 plus 2) in the approachesset, while the two approaches variates, taken together, overlap the variance in thebeliefs set by 14%.

Relationship between epistemological beliefs and study orchestrations

This was examined by means of a between-subjects multivariate analysis of variance(MANOVA) in which study orchestrations were used as the independent variable (IV)and epistemological beliefs as dependent variables (DVs). The results indicated thatthe combined DVs were significantly affected by study orchestrations, F(12, 1008) =7.28, p <.001, but reflected only a modest association (Wilks = .071). Table 2 showsthe descriptive statistics for the four DVs, their univariate F and their effect sizes.

Participants who deployed a Surface study orchestration scored positively in allepistemological beliefs. As these were scored inversely, those with higher epistemo-logical beliefs scores showed more naive beliefs than those with lower scores.Conversely, participants who orchestrated their study in a Deep way scored nega-tively in the majority of the epistemological beliefs, so demonstrating more mature orsophisticated beliefs about knowledge and learning.

After conducting these analyses, our next step was to ascertain possible relation-ships among the above-mentioned constructs (as IVs) and academic performance (asDV). Because these constructs were assessed on different scales (approaches/beliefson a quantitative scale, and study orchestrations on a nominal scale), two standard,but separate, multiple regression analyses were carried out to this end, one forapproaches/beliefs, and the other for study orchestrations.

Approaches/beliefs and academic performance

Research question 3 was whether academic performance could be predicted by thefour canonical variates (F1, F2, S1 and S2) that emerged in the analysis of the twosets of variables: epistemological beliefs and learning approaches. Using the latter as

Table 2. Epistemological beliefs means and standard deviations by study orchestrations, and results of Univariate F Test

Study orchestrations

Atypical h-h Deep Surface Atypical l-l Anova

Epistemological beliefs M SD M SD M SD M SD F(3,384) η2

Quick learning .05 .76 −.15 .72 .25 .85 −.09 .59 6.01* .04Simple knowledge −.39 .70 .02 .78 .17 .72 .20 .72 11.16* .08Fixed ability .13 .70 −.17 .68 .29 .59 −.20 .57 13.30* .09Certain knowledge .19 .69 −.07 .68 .01 .62 −.11 .44 4.04* .03

* p <.001.

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IVs and the former as DV, a standard multiple regression was conducted. Table 3shows the results of this analysis.

All the canonical variates except S2 contributed significantly to the regressionequation. The latter did not correlate with academic performance to a significant level(r = .05, p = .25). Effect size measured by means of the Herzberg formula (Herzberg,1969) was 0.7824.

Study orchestrations and academic performance

Research question 4 was whether academic performance could be predicted andexplained by participants’ study orchestrations. Using the latter as a dummy IV andthe former as DV, a standard multiple dummy-variable regression was conducted.As study orchestrations is a nominal variable with 4 levels, only 3 (g-1) dummyvariables (Cohen & Cohen, 1983) were created. The results of this analysis aregiven in Table 4.

Table 3. Standard multiple regression of canonical variates on academic performance

Correlation matrix Major and additional analyses

Canonical variables DV 1 2 3 b β t p sr2

1. F1 −.20 −.05 −.12 −2.19 .03 .0112. F2 .16 .00 .08 .17 3.27 .00 .0253. S1 −.24 .43 .00 −.09 −.19 −3.56 .00 .0294. S2 .05 .00 .31 .00 .00 .00 .09 .93 .000

R = .31524. R2=.09938. Adj R2= .8997. p < .001

Note. F1 and F2 = canonical variates for first set of variables (CNVRF1 and CNVRF2); S1 and S2 = canonical variates for second set of variables (CNVRS1 and CNVRS2); β = estimated standardised regression coefficient; sr2 = squared semipartial correlation; unique variability was .06500 and shared variability was .25024.

Table 4. Standard multiple regression of study orchestrations on academic performance

Correlation matrix Major and additional analyses

DV 1 2 b β t p sr2

1. Atypical h-h .04 .06 .08 1.37 .17 .0042. Deep .14 .31 .12 .18 3.40 .00 .0283. Surface −.13 .38 .28 −.17 −.22 −4.03 .00 .039

(Atypical l-l) 2.06 78.65 .00

R = .2440. R2 = .0595. Adj R2 = .05218. p <.001

Note. β = estimated standardised regression coefficient; sr2 = squared semipartial correlation; unique variability was .0710 and shared variability was .1730.

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Study orchestrations predicted academic performance in a statistically significantway with the proportion of variance of the latter being 5.2%, accounted for in thesample via R2. Effect size measured by means of the Herzberg formula (Herzberg,1969) was 0.4238.

The sample mean for the reference group (atypical low-low) was 2.0624 (the inter-cept) and 2.1249 for the atypical high-high study orchestration. As each group addedto the intercept only its own Bi, Deep study and Surface study orchestrations’academic performance means were 2.1922 (.1298 + 2.0624), and 1.8847 (-.1777 +2.0624), respectively. While students using Deep study orchestration achieved thehighest academic performance, students deploying Surface study orchestrationachieved the lowest. Moreover, there was a difference between them in that academicperformance was positively related to the former and negatively to the latter.

The t values of Table 4 provide significance tests of the difference between thereference group mean (Atypical low-low = 2.0624) and the means of each of the othergroups. All these differences except Atypical high-high were statistically significant.

Discussion

The findings of this research lend support to a dual conclusion. First, analysis of thedifferent components of participants’ learning experience showed that learningapproaches and epistemological beliefs (two pairs of canonical variates accounted forthe significant relationships) were interrelated, as were epistemological beliefs andstudy orchestrations. Second, study orchestrations, as well as the canonical variatesof epistemological beliefs and learning approaches, predicted student teachers’academic performance. Each of these conclusions is discussed in turn.

Relationship between constructs

Epistemological beliefs and learning approaches were not independent but generallyconsistent and logically interrelated constructs; the more simplistic and naive theformer, the more superficial and reproduction-oriented the latter, and the moremature and sophisticated the former, the deeper and more meaning-oriented thelatter. This relationship might be due to the underlying metacognition shared by thetwo constructs. Although these results seem to be generally congruent with thoseobtained in other research (Chan, 2003; Cano & Cardelle-Elawar, 2004), theyprovide further information, showing that not only are the two sets of measures corre-lated, but also that little overlap exists between them. This could be interpreted asdemonstrating that epistemological beliefs and learning approaches are associated butdistinct elements defining students’ learning experience, elements for which research-ers have identified a number of different sources. While epistemological beliefs areinfluenced by home, formal education and age (Schommer, 1998), learningapproaches seem to be more ‘relational’, since they depend on students’ perceptionsof their teaching–learning environments (Entwistle et al., 2001). Therefore, althoughstudents’ experience of learning makes up a whole (Morgan & Beaty, 1997, Marton

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et al., 1997), it seems important that any attempt at understanding and/or interveningin this experience should include reference to its complementary components.

Participants’ study orchestrations were the third component of learning experienceto be explored. Analyses of these data showed not only consonant or typical patternsof response, but also dissonant or atypical ones. That around 36% of student teach-ers, who are receiving formal educational experiences in a potentially constructivistlearning environment, showed dissonant patterns of study orchestration, might beindicating, as ‘barometer readings’, that the institution should insist on promotingquality assurance as ‘transforming’ (Harvey & Green, 1993). The advice of experts inthis regard is to proceed in a dually proactive manner: firstly, to continue providinghigh-quality teaching–learning environments that can be perceived by students ascontexts encouraging the use of deeper learning approaches (Richardson, 1994; Trig-well et al., 1999); and secondly, by guaranteeing the necessary scaffolding to facilitatestudents (and teachers) in developing sophisticated beliefs about learning andknowing (Brownlee et al., 2001), which is generally in agreement with Biggs’s (2001)criterion that the institution itself needs to be reflective.

As expected, epistemological beliefs and study orchestrations were related to someextent, though this relationship was clearer in consonant than in dissonant studyorchestrations. In the former, those orchestrating their study in a deep way demon-strated sophisticated epistemological beliefs, while those using superficial orchestra-tions demonstrated naive beliefs. In the latter, an amalgam of immature andsophisticated beliefs was observed, which in a way conforms with Meyer’s (2000)assertion that dissonant study orchestrations manifest interferences or violations ofthe typical model of contextualised learning. It is interesting to note that these find-ings extend previous results in the sense that students’ patterns of engagement inlearning are sensitive not only to their perceptions of their learning context (Meyer,1991; Lindblom-Ylänne & Lonka, 1998; Meyer, 2000) but to their beliefs aboutknowledge and learning.

Two limitations of this study were, however, the exclusive reliance on self-reporting measures and the restricted origin of participants (coming from only oneuniversity). There is scope for future research to investigate the above-mentionedrelationships, collecting data from different universities and using additional types ofmeasures (e.g. structured interviews). In spite of these limitations, taken together,these results show how students’ ways of going about learning on courses orprogrammes are interrelated with the beliefs they hold about learning and knowl-edge. As these constructs are, in turn, connected to students’ awareness of theirlearning environment (Trigwell et al., 1999; Watkins, 2001), such interrelationshipsmight be of particular interest to teacher education institutions for one main reason:it seems plausible that student teachers will reflect in their future academic practicethe different teaching–learning conceptions and practices they are experiencing inthe present (Pajares, 1992). For this reason, various experts insist on working withacademic staff to arouse their sensitivity and alertness towards sophisticated andhigh-quality conceptions and styles of teaching and learning (Trigwell et al., 1999;Entwistle et al., 2001).

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Learning experience and academic performance

One issue to emerge clearly from this study concerns the links between the constructsreflecting students’ learning experience and their learning outcomes. Results wereconsistent with those of previous research which suggests that students’ learningapproaches and epistemological beliefs are predictors of their academic performance(Schommer, 1993; Schommer et al., 1997; Watkins, 2001; Cano, 2005). Further-more, they provide additional evidence that the variates accounting for the interrela-tionships between these two sets of variables, although exhibiting small effect sizes,were significant predictors of academic performance, the only exception being S2.This could be explained by this latter variate appearing to be poorly defined. Itshowed a notable loading in only one of the epistemological beliefs factors (negativeSimple knowledge), being minimal in the other three.

In addition, participants’ study orchestrations were associated with their academicperformance, in line with previous research (Meyer et al., 1990; Entwistle et al., 1991;Lindblom-Ylänne & Lonka, 1998; Cliff, 2000). Although the effect size was small, itwas evident that those participants using Deep study orchestrations received the high-est scores, those using Surface study orchestrations the lowest, and those demonstrat-ing dissonant study orchestrations scoring between these two extremes. As someauthors point out (Entwistle et al., 1991; Lindblom-Ylänne & Lonka, 2000; Meyer,2000; Prosser et al., 2000), dissonant study orchestrations are linked to disintegratedrelationships between learners’ perceptions of their learning environment and theirlearning approaches, and to poor learning outcomes. An issue that might meritconsideration for further research would be to analyse how these relationships inparticular, and epistemological beliefs and learning approaches in general, are relatedto students’ formal educational experiences as they progress in their studies.

It was somewhat worrying to detect dissonant study orchestrations amongst futureteachers, as shown by this study, and less desirable (surface) approaches amongstundergraduate students at tertiary level, as inferred from the review of the literaturein the introduction. The question is: how can we explain that, while higher educationinstitutions seem to be focused on quality assurance by means of innovative teaching–learning environments, some students are apparently failing to construct meaningfrom their educational experiences in a desirable (deep) way? Some recent researchhas indicated that the impact of the university educational experience on students’epistemological beliefs, on the one hand, and on learning approaches, on the otherhand, seems to be more limited than expected. For example, Brownlee et al. (2001)recognise that to transform epistemological beliefs may be a slow and difficultprocess. These authors observed only slight changes in the belief development of pre-service teachers after applying a programme focused on supporting such change.

Zeegers (2001, p. 130) noted that in Australia, despite much rhetoric to thecontrary, ‘tertiary students are not encouraged to engage in favourable, that is, deeplearning as a result of the tertiary experience’. As the author suggested, one possibleexplanation for this result was related to the teaching–learning environment, whichdid not require students to go about their learning in a meaningful way. However,

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context itself could not explain why a university educational research project carriedout in the Netherlands, which focused on powerful learning environments, ‘failed toinfluence reported learning strategies in the direction of more deep and self-regulatedlearning’ (Vermetten et al., 2002). Seeking an explanation for this result, theseauthors designed a second study in which student groups with different learning char-acteristics were identified (by means of cluster analysis), and thus proved thatstudents from these groups vary in their perceptions, tending to use instructionalmeasures in different ways, to suit their own particular way of learning. Finally, theysuggest that ‘to improve the quality of student learning, instructional measures shouldaddress the conceptual domain of learning conceptions and beliefs, of which studentshave to become aware’ (p. 282).

Taking into account that student learning in higher education relates not only tothe contextual domain (e.g. teaching and assessment) but to the personologicaldomain (e.g. perceptions of the context, motivation, habitual ways of learning)(Biggs, 2001), to hold constant some variables (e.g. context and students) couldpossibly be helpful in addressing this issue, and might shed light on the debate onconsistency versus variability in learning experience. With regard to this, a longitudi-nal study (i.e. the same students) in the current powerfully innovative learningenvironment (i.e. the same context) would allow us to gain an insight into students’diverse perceptions of context, and to discover possible changes: (a) in these percep-tions of the context, which seems relevant for learning approaches, study orchestra-tions and beliefs about learning, and (b) over time, which seems important for beliefsabout knowledge, as Schommer (1994) suggested.

To summarise, student teachers’ learning experience included different but relatedelements (learning approaches, study orchestrations and epistemological beliefs),which were linked to academic performance. Follow-up studies of these students todiscover how the university teaching–learning environment impacts on their learningexperience would give us a better understanding, enabling academic staff to providegreater scaffolding support, particularly in teacher education institutions and possiblyalso in higher education institutions in general.

Acknowledgements

The authors acknowledge the helpful comments of both the editor and two anony-mous referees on a draft of this article, and the support and assistance of BarbaraLamplugh in reviewing the manuscript in English.

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The epistemological beliefs, learning approaches and study orchestrations of university students