Consonance and dissonance in students' learning experience

Learning and Instruction 15 (2005) 201e223 www.elsevier.com/locate/learninstruc

Consonance and dissonance in students’ learning experience Francisco Cano Facultad de Psicologı´a, University of Granada, Campus de Cartuja s/n, 18071 Granada, Spain

Abstract This investigation examined, in 1012 secondary students, both consonant and dissonant response patterns in approaches to learning (evaluated using the LPQ questionnaire; [Biggs, J. (1987). Learning process questionnaire. Melbourne: Australian Council for Educational Research]) as well as in learning conceptions and learning strategies (examined using an open task). The results of the analyses of students’ learning experience clearly support three new findings. First, the research encountered two kinds of consonance (basic and complex) and two kinds of dissonance (negative and positive) in students’ ways of linking how learning appears to them, and what strategies they use to learn. Second, it was shown that these patterns of response were significantly related to performance, better academic results being obtained by the Positive Dissonance and Complex Consonance groups. Third, these patterns and learning approach combinations (study orchestrations) were found to be associated with one another; in dissonant study orchestrations the patterns of relationships among conceptions of learning and strategies became incoherent. Ó 2005 Elsevier Ltd. All rights reserved. Keywords: Conceptions of learning; Approaches to learning; Learning strategies; Study orchestrations; Consonance; Dissonance

E-mail address: [email protected] 0959-4752/$ - see front matter Ó 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.learninstruc.2005.04.003

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1. Introduction The analysis of learning from the learner’s perspective is an expanding research specialisation characterised by ‘‘exploring what learning means to the learner, how it is experienced, understood or conceptualised by her or him’’ (Marton, Watkins, & Tang, 1997, p. 21). The great number of studies carried out in this field has enabled researchers to identify different closely-related aspects of students’ learning experience: ‘conceptions of learning and strategies’ (Marton, Dall’Alba, & Beaty, 1993; Sa¨ljo¨, 1979), ‘approaches to learning’ (Biggs, 1987; Entwistle, McCune, & Walker, 2001; Marton & Sa¨ljo¨, 1976a) and ‘learning outcomes’ (Trigwell & Prosser, 1991a, 1991b; Van Rossum & Schenk, 1984). A recent area that has attracted researchers’ attention is the complex combinations (consonant as well as dissonant) amongst conceptions of learning and strategies (Boulton-Lewis, Marton, Lewis, & Wills, 2000), and also the complex combinations (consonant as well as dissonant) amongst approaches to learning, (Entwistle, Meyer, & Tait, 1991; Meyer, 2000). While a substantial amount of investigation has been conducted into the relations between pairs of these sets of variables, very little has examined relations among all these variables, and to our knowledge no investigation has been conducted into the relationships of the above-mentioned complex combinations or patterns of response with one another. This study set out to explore secondary students’ patterns of response in conceptions of learning and strategies, as well as in approaches to learning (study orchestrations). Its main purpose was to discover different patterns of response (consonant as well as dissonant) in conceptions of learning and strategies that might be significantly related to academic performance, and which would improve our understanding of study orchestrations. 1.1. Conceptions of learning Conceptions of learning are individual constructions arising from knowledge and experience which refer to ‘‘the differing ways in which learners experience, understand and make sense of learning in general’’ (Boulton-Lewis, Wills, & Lewis, 2001, p. 154). Sa¨ljo¨ (1979), building on previous studies by Marton and Sa¨ljo¨ (1976a, 1976b), asked participants, whose ages and educational backgrounds were very different, what ‘‘learning’’ meant for them, and he came across five quite distinct conceptions of learning. Years later, Marton et al. (1993), after interviewing university students, were able to develop a more precise characterisation of each conception (e.g. they discerned a ‘‘what’’ and a ‘‘how’’ aspect), and identified a sixth one. The six categories may be described as follows: (1) increasing one’s knowledge, (2) memorising and reproducing, (3) applying, (4) understanding, (5) seeing something in a different way and (6) changing as a person. The first three focus on wording or reproducing knowledge (learning as reproducing) and tend to lead to low-level learning results. The last three centre on meaning, and on transforming information through relating it to previous knowledge and experience (learning as transforming). These three latter conceptions involve great complexity in cognitive processing, and tend to lead to high-level learning results (Martin & Ramsden, 1987;

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Marton et al., 1997; Van Rossum & Schenk, 1984). Investigations carried out in European educational contexts have, on the whole, encountered similar results to those conducted in settings outside Europe (see Boulton-Lewis et al., 2000 for an overview). For example, the latter authors, working with 22 first-year Australian indigenous university students, found three main conceptions with some subcategories: acquiring knowledge (increasing knowledge, and using knowledge), understanding (acquiring and remembering, acquiring and using, and relating) and personal growth. Researchers do not always use the personal interview technique in order to glean students’ conceptions of learning. For instance, Purdie, Douglas, and Hattie (1996), Berry and Sahlberg (1996) and Tynja¨la (1997) asked students to write their own answers to open-ended questions about learning. In this way it is possible to increase both the number of participants and that of the variables for analysis. 1.2. Conceptions of learning and strategies: consonance and dissonance Several studies have analysed learning strategies employed by students. Van Rossum and Schenk (1984) and Marton et al. (1993) showed that the more complex a student’s conception of learning, the deeper and more elaborate his/her strategies tend to be, and the higher-quality his/her learning results are. Learning strategies are, according to Weinstein and Mayer (1986), behaviours or thoughts activated by the student in order to facilitate the process of encoding information, and to improve the integration and recall of knowledge. They might be grouped, depending on the degree of cognitive effort required, into several hierarchical categories: rehearsal, organisation, elaboration, and comprehension monitoring. This effort is much smaller in rehearsal than in organisation, and in the latter it is much smaller than in elaboration. Weinstein and Mayer’s classification was used in Boulton-Lewis et al.’s (2000) above-mentioned study to describe learning strategies used by participants. These came from three Australian universities and had a higher attrition rate than the rest of the university students. The researchers proposed that these strategies were associated with conceptions of formal learning held by students. That is, learning as acquisition, understanding and personal growth may be achieved by using rehearsal, organisation, and elaboration/monitoring learning strategies, respectively. However, this typical or consonant pattern of relationships between conceptions of learning and strategies, conveniently foreseeable in theory, is not always observed in practice. This observation is in contrast to Marton et al.’s (1993) proposal that conceptions and strategies are closely related, and therefore, would seem to point to the existence of variations in the way students combine their conceptions of learning and strategies. Many of the 22 participants deployed atypical or dissonant patterns of response, that is to say, they used strategies that did not match the conceptions of learning they held. An identical observation was reported in a recent study with 15 indigenous Australian university students, and was called ‘dissonance between conceptions of learning and ways of learning’ (Boulton-Lewis, Wills, & Lewis, 2003). According to Boulton-Lewis et al. (2000, p. 410), this dissonance may lead students

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to ‘‘experience cognitive conflict and this may result in difficulties with formal learning’’. Although this study contributes to our understanding of students’ learning experience, it leaves two gaps that would merit analysis. First, as there are different types of conceptions of learning and strategies, it is likely that different patterns of consonance and dissonance exist amongst them. Second, if this is true, what effect does each of them have on performance? The answers to this question would surely go some way towards explaining the relationships among the ways students experience learning and the academic results they obtain, and also would contribute to a better understanding of the approaches to learning they deploy. 1.3. Approaches to learning Early studies of approaches to learning were carried out by Marton and Sa¨ljo¨ (1976a, 1976b) in a specific context: reading an academic article and answering questions on it afterwards. Students were interviewed and asked what they had understood and how they had gone about reading it. They generally expressed one of two major ways of experiencing and handling learning situations, called deep and surface approaches. After this first assessment of approaches to learning, focusing on a specific context, further work was done some years afterwards from another perspective, this time in a more general context. The aim was to gain insights into what students usually do while learning and studying, and to design inventories which would assess students’ readiness to adopt deep or surface approaches to learning in general (Marton et al., 1997). In Britain, Entwistle developed the Approaches to Studying Inventory (ASI, Entwistle & Ramsden, 1983), while in Australia, Biggs designed the Study Process Questionnaire (SPQ, Biggs, 1987). In theory, surface and deep approaches to learning are opposed and mutually exclusive (Biggs, 1987; Entwistle, Hanley, & Hounsell, 1979). Learners who deploy a surface approach tend to be extrinsically motivated (in order to comply with course requirements with minimal personal engagement, avoid failure), and resort to a repetitive strategy (concentrating on specific facts, memorising and reproducing them accurately). These learners focus their attention on ‘‘the sign’’, i.e., the learning material as such, rather than its overall meaning. Learners who deploy a deep approach tend to be intrinsically motivated (striving to understand the author’s intent and using the material for self-fulfilment), and resort to a meaningful strategy (searching for meaning, integrating formal knowledge with personal experience, and relating facts to conclusions). These learners focus on ‘‘the signified’’ (i.e. to which the learning material refers), on main ideas and themes. In addition to these two approaches, researchers have identified a third, called ‘strategic’ by Entwistle, and ‘achieving’ by Biggs. This third approach is presented in a slightly different way by each author, but in both cases it refers to the intention on the part of the learner of achieving the highest possible grades by using effective time management and organising study skills. Factor analyses usually associate achieving approach with deep approach, but depending on the subjects and teaching conditions, sometimes it loads on surface approach (Biggs, Kember, & Leung, 2001).

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Students’ approaches to learning are strongly related to their conceptions of learning. Surface approach is frequently associated with a conception of learning as an increase in knowledge, and deep approach is often linked to a conception of learning as a search for meaning (Sa¨ljo¨, 1982; Van Rossum & Schenk, 1984). Approaches do not describe stable traits of individuals, but rather, ways of learning which depend on students’ interactions with their learning environment. If this is perceived as containing good teaching, clear goals and freedom in learning assessment methods, it is likely that students will use a deep approach to learning (Dart et al., 1999; Entwistle & Ramsden, 1983; Trigwell & Prosser, 1991a, 1991b). Approaches, in their turn, influence learning outcomes. The deeper the approach to learning, the better these outcomes will be (Marton & Sa¨ljo¨, 1976a; Trigwell & Prosser, 1991a, 1991b; Van Rossum & Schenk, 1984). 1.4. Approaches to learning: consonance and dissonance At an individual or group level of response, students deploy contextualised patterns of engagement in learning (combinations of approaches) that are sensitive to students’ conceptions of learning as well as to their perceptions of their learning context, and are called ‘study orchestrations’ (Lindblom-Yla¨nne & Lonka, 1998; Meyer, 1991; Meyer, 2000). In some cases, orchestration displays ‘conceptual consonance’ between how the content and the context of learning are perceived, and how learning takes place. In other cases, orchestration displays ‘conceptual dissonance’, that is to say, it shows atypical or maladaptive linkages between some or all of the more common sources of explanatory variation in contextualised learning behaviour (Meyer, 2000). For instance, in terms of inventory response data, a student who at the same time combines high surface approach scores with high deep approach scores would be manifesting a dissonant orchestration. She/he would be showing ‘‘a lack of response discrimination between discrete aspects of learning engagement that are theoretically incongruent with one another’’ (Meyer, 2000, p. 6). The detection of dissonant responses requires taking the individual as the unit of analysis. Recently, Meyer (2000, p. 10) has proposed a theoretical framework for exploring dissonance using an ‘interference model,’ defined as a non-hierarchical (linear or non-linear) observed model, for example, a factor model, that ‘‘contains either (a) two or more separately distinct and conceptually consonant, but contrasting, dimensions of variation and/or (b) at least one dimension of variation constituted in terms of conceptually ‘dissonant’ sources of variation’’. This model can be complemented by performing a k-means cluster analysis of the individual factor scores. It allows us to estimate the location of individual responses within the common-factor structure. Dissonance study orchestrations are related to differences among individuals in the way they conceptualise learning, perceive the context, and consequently, approach and engage in learning tasks (Meyer, 1991). Students are not separate from their learning contexts, but relate to them and try to adapt to them. However, some of them ‘‘are unable to distinguish between contextualised approaches to learning. In other words, the relationship between their perceptions of the learning context and

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their approaches to learning disintegrates and becomes incoherent’’ (Prosser, Trigwell, Hazel, & Waterhouse, 2000, p. 61), that is, some students have problems in adapting to their learning environment, no matter what their educational setting may be (Long, 2003). In a study of high-achieving university students, LindblomYla¨nne and Lonka (2000) detected a dissonant reproductive orchestration related to the inability to self-regulate learning. These researchers interpreted this as a mismatch between the self-regulation learning strategies fostered by the learning environment and the strategies the students themselves want to use. Vermunt and Verloop (1999) have stressed the role played by the ‘friction’ or incongruence between the student’s learning strategies and the teacher’s teaching strategies. If this ‘friction’ is positive, students are stimulated to develop more mature learning conceptions and learning strategies. If it is negative, the opposite happens; for instance, students who have a constructive conception of learning may be forced to use reproductive learning strategies. According to Vermunt and Verloop (2000) negative friction might have been responsible for arousing in some university students a certain kind of dissonance linked to their views of learning. These students showed a disintegration between their learning strategies, their conceptions and their learning motives and goals. Several studies have demonstrated that university students’ dissonant orchestrations are linked to a lower-than-average academic performance. This is not only true of disadvantaged students (Cliff, 2000; Entwistle et al., 1991; Meyer, Parsons, & Dunne, 1990), who were usually the only participants in most early investigations (Meyer, 1991), but also of high achievers (Lindblom-Yla¨nne & Lonka, 1998). In both cases, it is remarkable that in general, participants were university students chosen in accordance with their academic performance, and that they were few in number. In one study at least (Lindblom-Yla¨nne & Lonka, 2000), the small sample size may have been responsible for the absence of a significant statistical association between study orchestrations and academic achievement. In view of these limitations, it might be useful to analyse contextualised patterns of engagement in learning (consonant and dissonant study orchestrations) in a larger number of participants, who have not been selected previously according to their academic performance (i.e. groups of ‘average’/‘normal’ or mixed students rather than disadvantaged students or high achievers only), and who belong to educational levels prior to university. This analysis could well fit in with patterns of linkages among conceptions of learning and strategies, suggested above and would follow a research procedure that has been common in this field up to the present time. This procedure involves analysing students’ learning conceptions and approaches, and study orchestrations, with regard to learning in general. (Boulton-Lewis et al., 2000, 2001; Cliff, 2000; Entwistle et al., 1991; Entwistle et al., 2001; Lindblom-Yla¨nne, 2003; Lindblom-Yla¨nne & Lonka, 1998, 2000; Long, 2003; Vermunt & Minnaert, 2003; Vermunt & Verloop, 2000). The aims of this study, therefore, will be threefold. First, to identify patterns of response, consonants as well as dissonant, in conceptions of learning and strategies, and in approaches to learning, deployed by secondary students. Second, to determine the effects exerted by both consonant and dissonant patterns on academic

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performance. And third, to analyse dissonances in approaches to learning in the light of dissonant response patterns in conceptions of learning and strategies.

2. Method 2.1. Participants These were 1012 European secondary students who attended 17 schools (Grades 7e12), and came from all social strata. Girls accounted for 60.77% of the sample, and boys for 39.22%, their age ranging from 12 to 18 years. Prior to the investigation, parents had given consent for students to participate. 2.2. Materials An open-ended task sheet on which students were asked to write down their answers to three questions and give examples to illustrate their answers. No time limit was set. ‘What does learning mean to you?’ ‘What do you mean when you say that you have learned something?’ ‘How do you learn?’ The Learning Process Questionnaire (LPQ), composed of 36 items, grouped into six subscales, each containing six items. Students gave responses on a Likert-type scale, from 1 (never or rarely true of me) to 5 (always or almost always true of me). The subscales measured the learning approach dimensions proposed by Biggs (1987, 1993): surface motive, surface strategy; deep motive, deep strategy; and achieving motive, achieving strategy. Only the first four subscales were used in this study. The study orchestrations were defined in a liberal fashion, since the LPQ does not include learning pathology scales. However, by using only the above-mentioned subscales, we complied scrupulously with the criterion which demands that variation dimensions or factors be completely opposite from the theoretical point of view. The subscales were subjected to two types of factorial analyses, exploratory and confirmatory. The exploratory factorial analysis, using the principal-components method followed by oblique rotation of the factor loading matrix, indicated the presence of two factors or components with eigenvalues greater than one, explaining 73% of the variance, and fitted well with Biggs’s model. Deep-strategy scale, followed by deep-motive scale loaded on Factor I (Deep) (0.85 and 0.84, respectively). Surface-motive scale, followed by surface-strategy scale loaded on Factor II (Surface) (0.86, and 0.74, respectively). Reliability, measured by means of Cronbach’s a, was 0.66 for Factor I and 0.48 for Factor II. These results satisfy the definition of an interference model, mentioned above; the structure of the model reflects two separately distinct, but constrasting dimensions of variation in students’ learning (DeepeSurface) that are associated with how they experience it and go about it. The confirmatory factorial analysis of this solution, using the covariance matrix and the unweighted least squares method (ULS), gave acceptable Goodness-of-Fit indices: c2 Z 2.74, df Z 1, p ! 0.098. Goodness-of-Fit Index (GFI) Z 1.00, Adjusted

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Goodness-of-Fit Index (AGFI) Z 0.96; Root Mean Square Residual (RMR) Z 0.01. These results are in line with those submitted by other authors (Kember and Leung, 1998). 2.3. Procedure Participants completed the questionnaires in whole-class sessions. They were each given a booklet containing information about the research, the questionnaires and instructions, as well as assurances regarding the confidentiality of all data collected. They were asked to answer the LPQ, complete the open task, and give their full name, age and sex. At the end of the academic year, students’ grades for all subjects were noted; their average mark was used as a measure of academic performance. 2.3.1. Coding of students’ responses Students’ answers to the open task were recorded on a computer as text files and printed out to facilitate coding. In order to identify the different ways in which students expressed their conceptions, the researcher read these answers several times, and grouped and regrouped them a number of times depending on their similarities, differences, and complementarities across and within participants (e.g. similarities occurred when some expressions differed at the word-level but their conceptualization was the same). After students’ answers had been placed into preliminary categories, ‘‘the critical attributes and distinguishing features of the categories thus identified were subjected to a detailed analysis. This analysis established the final categories of description’’ (Tynja¨la, 1997, p. 283). Following this procedure described by Tynja¨la (1997), Marton et al. (1993, 1997) and Boulton-Lewis et al. (2000, 2001) eight mutually exclusive learning conceptions were derived. Students were then allocated to the conceptions that were most typical for them or ‘core conceptions’ (Boulton-Lewis et al., 2001). Learning strategies were analysed and classified using the procedure outlined by Boulton-Lewis et al. (2000), and students were assigned to the category of strategy that they reported using the most.

3. Results 3.1. Conceptions of learning and strategies: qualitative analyses Eight mutually exclusive conceptions of learning were derived: (1) increase in one’s knowledge (mere accumulative acquisition); (2) increase in one’s knowledge for the purpose of using it in the distant future (in life, in work); (3) memorisation and reproduction (learning is memorising for the purpose of passing an examination or being able to reply when asked about something); (4) understanding and acquisition (understanding information and storing it by memorising it); (5) understanding, acquisition and use (understanding information, memorising it and then being able to use it); (6) understanding and interrelating (understanding and linking new information to previous information and experience); (7) seeing things in a different

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way (not only understanding and interrelating, but also seeing things from different perspectives, modifying one’s own points of view; (8) personal change (involving not only understanding and changing one’s viewpoints, but also experiencing personal growth). The first three conceptions are about acquiring information, the next four involve understanding information, and the last one concerns the growth of the individual as a person. In the first three, learning is seen primarily as reproducing knowledge (reproductive conception), while in the rest, it is seen primarily as transforming information (constructive conception). Learning strategies reported by students were classified into three categories: rehearsal (activities ranging from repeating names or other items to copying or underlining material to be learnt), organisation (activities involving arranging items in categories or extracting the main ideas, organising them and relating them to one another), and elaboration (activities involving the creation of a phrase or mental image to connect two or more items, or making connections between what the student already knows and what she/he is trying to learn. Examples of students’ answers assigned to each one of these categories are as follows: ‘‘I learn by repeating over and over again until I know it’’ (rehearsal). ‘‘In order to learn a topic first of all I underline it, then I make an outline, and finally I try to understand it’’ (organisation). ‘‘I pay attention to the teacher’s explanations and I complement them with the text book, then I reflect on the topic and I try to relate it to previous learning’’ (elaboration). While rehearsal strategies require relatively little cognitive effort, organisative and elaborative strategies demand much more involvement and cognitive effort. 3.2. Consonance and dissonance in conceptions of learning and strategies Table 1 shows the distribution of participants in accordance with conceptions of learning and strategies that were detected. It is clear from Table 1 that while in the first three categories of learning conceptions most students are assigned to rehearsal learning strategies, in the five Table 1 Frequency of conceptions of learning and strategies Learning strategies

Learning conceptions Separately

Grouped

M1

M2

M3

C1

C2

E1

E2

E3

REPR

TRAN

Rehearsal Orga/Elab

301 63

151 47

58 6

104 143

15 72

1 41

0 4

1 4

510 116

121 265

Total

364

198

64

247

87

42

4

6

626

386

Note: M1 Z increase in one’s knowledge; M2 Z increase in one’s knowledge for the purpose of using it in the distant future; M3 Z memorisation and reproduction; C1 Z understanding and acquisition; C2 Z understanding, acquisition and use; E1 Z understanding and interrelating; E2 Z seeing things in a different way; E3 Z personal change. ORGA/ELA Z organisation and elaboration. REPR Z learning as reproducing (M1 C M2 C M3); TRAN Z learning as transforming (C1 C C2 C E1 C E2 C E3).

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remaining categories of learning conceptions most students are assigned to organisative and elaborative learning strategies. This first practical finding, the theoretical reasons outlined above and for the sake of complying with statistical analyses requirements, avoiding empty cells or cells with very few participants, led us to stack conceptions and strategies into two groups each, as follows: Conceptions: learning as reproducing (M1 C M2 C M3), and learning as transforming (C1 C C2 C E1 C E2 C E3). Strategies: rehearsal and organisation/elaboration. An adequate exploratory multivariate technique to examine the interrelationships between categorical variables (that is, non-continuous or discretized ones) is factorial correspondence analysis, also known in its abbreviated form as ‘correspondence analysis’ (CA). This technique converts frequency table data into graphic displays in which rows and columns (variables) are depicted as points, and operates in the same way as factor analysis, building coordinate scores (also called factor scores or factors). These coordinates account for the association between variables as represented by the familiar chi-square statistic (c2), and are similar to the principal components in principal-components analysis which partition the total variance instead of the total c2 (Dixon, 1985). From a table of n observations on p variables, describing a p-dimensional cloud of points (if p ! n), correspondence analysis will determine the first k axis of an orthogonal system of axes that describes the most variance (i.e. inertia or c2/n) from the cloud (Benze´cri, 1992; Greenacre, 1993). By means of a correspondence analysis, the possible relationship between learning conceptions and learning strategies was examined. This was found to be significant (c2 Z 255.53, df Z 14, p ! 0.001) and was structured on a single axis, which explained 100% of the interdependence (see Fig. 1). Fig. 1 shows that the cloud of points (two learning conceptions and two learning strategies) does not extend equally in all directions, but has a definite shape due to affinities among the four variables. Conceptions of learning as transforming (Factor Z ÿ0.640) were associated with the organizing/elaborative type of learning strategies (Factor Z ÿ0.647), and conceptions of learning as reproducing (Factor Z 0.395) were associated with repetitive-type learning strategies (Factor Z 0.390). These four variables are far from the centre (0.0) and, therefore, well represented on the axis. The farther a point is from the origin, the smaller its TRAN

REPR

Or/El

Rehe

....+....+....+....+....+....+....+....+....+....+....+....+....+....+....+....+.... -.60 -.45 -.30 -.15 0.0 .15 .30 .45 .60 AXIS 1

Fig. 1. Correspondence analysis. Plot of conceptions of learning and strategies. Note: REPR Z learning as reproducing; TRAN Z learning as transforming. Rehe Z rehearsal strategies; Or/El Z organisative and elaborative strategies.

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marginal weight, and the greater its contribution to inertia. The single axis obtained opposes conceptions of learning as reproducing and rehearsal learning strategies on the right, to conceptions of learning as transforming and organisation/elaboration learning strategies on the left. After ascertaining that learning conceptions and learning strategies were significantly related, the next step was to discover what kind of patterns might exist among them. In Table 2 two typical or consonant patterns, and two atypical or dissonant ones are clearly displayed. Students who manifested consonant patterns used strategies consistent with their conceptions of learning. Consonance may be basic or complex. Consonance is basic when students conceive learning as reproducing and they use rehearsal strategies (n Z 510). Consonance is complex when students conceptualise learning as transforming and they use organisative/elaborative strategies (n Z 265). Students who showed dissonant patterns used strategies that were not in keeping with their conceptions of learning. Dissonance may be positive or negative. Dissonance is positive when students use strategies that are cognitively deeper than their conception of learning (learning as reproducing in conjunction with organisative/elaborative strategies) (n Z 116). Dissonance is negative when students use strategies that are cognitively shallower than their conception of learning (learning as transforming in conjunction with rehearsal strategies) (n Z 121).

3.3. Consonance and dissonance in conceptions of learning and strategies, and academic perfomance The relationship between patterns of response in conceptions of learning and strategies, and academic performance was found to be significant, c2 Z 41.641, df Z 3, p ! 0.001. In each pattern of conceptions and strategies the percentages of students’ overall passes and fails were, respectively, Basic Consonance: 50.8% and 49.2%; Positive Dissonance: 69.0% and 31.0%; Negative Dissonance: 48.8% and 51.2%; Complex Consonance: 72.5% and 27,5%. As may be observed, percentages were slightly different for the first and third patterns, but completely different for the second and fourth. Positive Dissonance and Complex Consonance were associated with better academic performance. Table 2 Frequency of grouped learning strategies and grouped learning conceptions Learning strategies

Learning as Reproducing

Transforming

Rehearsal Organisationeelaboration

510 116

121 265

Total

626

(Basic Consonance) (Positive Dissonance)

(Negative Dissonance) (Complex Consonance)

386

Note: Learning as reproducing Z grouping of three learning conceptions (M1 C M2 C M3). Learning as transforming Z grouping of five learning conceptions (C1 C C2 C E1 C E2 C E3).

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Table 3 Cluster analysis of factor scores in approaches to learning Approaches

Deep Surface

Cluster 1 (H) (n Z 283)

Cluster 2 (D) (n Z 217)

Cluster 3 (S) (n Z 225)

Cluster 4 (L) (n Z 287)

X

s

X

s

X

s

X

s

0.61 0.81

0.55 0.59

1.02 ÿ0.89

0.60 0.67

ÿ1.00 0.77

0.60 0.56

ÿ0.59 ÿ0.72

0.58 0.56

F

640.8* 603.7*

Note: H Z atypical highehigh deep and surface approaches. D Z deep approach (typical). S Z surface approach (typical). L Z atypical lowelow deep and surface approaches. *p ! 0.001.

3.4. Consonance and dissonance in study orchestrations In the analysis of study orchestrations, we followed Meyer’s (2000) guidelines. The two-factor model (DeepeSurface) obtained in the exploratory factor analysis of the LPQ subscales was used as a starting point. Students’ factor scores were calculated and these were submitted to a k-means cluster analysis. F ratio size allowed us to ascertain if the cluster means were equal. This test indicated the relative importance of the variables in determining clusters, but, as k-means is a non-hierarchical clustering technique, we had to try several different numbers of clusters to obtain a good and interpretable clustering. The solutions of 2, 3 and 4 clusters presented statistically significant differences in students’ factor scores. However, the four clusters solution was chosen as it exhibited a clear-cut separation among patterns of response in factor scores and also accommodated the best criteria of Meyer’s (2000) ‘interference model’ for dissonant study orchestrations (explained in Section 1). This information is set out in Table 3. Cluster 1 (H) contains students who have a dissonant orchestration, an atypical or anomalous pattern of response: high scores on both surface approach and deep approach (atypical highehigh approaches). Cluster 2 (D) groups together students who show a consonant orchestration, a typical pattern of response: deep approach to learning. Cluster 3 (S) includes students who also display a consonant orchestration: surface approach to learning. Cluster 4 (L) contains students who exhibit a dissonant orchestration, here with low scores on both surface approach and deep approach (atypical lowelow approaches). Post hoc comparisons among the means of the two approaches in the four clusters (study orchestrations) were carried out using Tukey’s test. All were significant ( p ! 0.01) except the comparison in surface approach between cluster 1 and cluster 3. 3.5. Consonance and dissonance in study orchestrations and academic performance Academic performance corresponding to students in each orchestration is shown in Table 4.

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F. Cano / Learning and Instruction 15 (2005) 201e223 Table 4 Academic performance means and standard deviations by cluster, and post hoc comparisons Clusters X s

Post hoc comparisons (Tukey HSD)

H

D

S

L

HeD

HeS

HeL

DeS

DeL

SeL

1.76 1.20

2.31 1.17

1.33 1.03

1.80 1.17

**

**

e

**

**

**

Note: H Z atypical highehigh deep and surface approaches. D Z deep approach (typical). S Z surface approach (typical). L Z atypical lowelow deep and surface approaches. **p ! 0.001; e, non-significant.

The effects of study orchestrations (cluster membership) on academic performance were explored by means of a one-way ANOVA. The dependent variable was performance and the independent one was orchestration. The main effect was significant, F(3,1008) Z 26.70, p ! 0.001. Students in cluster 3 (Consonant-Surface) performed most poorly, followed by students in clusters 4 (Dissonant-Low) and 1 (Dissonant-High). The average general academic performance score in the two dissonant clusters was 1.78. This score was lower than 2.00, which was the pass mark. The highest academic performance was achieved by cluster-2 students (Consonant-Deep). Post hoc comparisons between the means of the four clusters were made employing Tukey’s test. All were seen to be significant ( p ! 0.01) except the comparison between clusters 1 (High) and 4 (Low). Once the consonances and dissonances in study orchestrations as well as in learning conceptions and strategies have been established, we are now in a position to describe the former in terms of the latter. 3.6. Consonance and dissonance in conceptions of learning and strategies, and in study orchestrations Table 5 shows the patterns of response in conceptions of learning and strategies (grouped according to consonance/dissonance, and separately) for each Study Orchestration. Correspondence analysis between learning conceptions and strategies (grouped by consonance/dissonance) and study orchestrations brought to light the existence of a significant relationship (c2 Z 87.43, df Z 9, p ! 0.001) between them, structured around two axes. In Fig. 2 learning conceptions and strategies (grouped by consonance/dissonance) have been plotted against study orchestrations, allowing us to see the relative closeness of some points. The first axis explained 96.5% of the interdependence and showed strong links between Deep Study Orchestration (Factor Z ÿ0.496), Complex Consonance (Factor Z ÿ0.428) and Positive Dissonance (Factor Z ÿ0.216) on the one hand, and Surface Study Orchestration (Factor Z 0.360), Basic Consonance (0.216) and Negative Dissonance (Factor Z 0.235) on the other. However, caution should be used when interpreting Positive Dissonance, and especially the two dissonant study orchestrations, since these points are near the origin, and therefore not well represented by the first axis. The first axis or dimension indicates, consequently,

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Table 5 Frequency and percentages of patterns of response in conceptions of learning and strategies (grouped according to consonance/dissonance, and separately) by study orchestration Study orchestrations

Conceptions of learning and strategies Consonance/dissonance

Conceptions

Strategies

BC

CD

ÿD

CC

Repr

Tran

Rehe

Or/El

Consonant-Surface

n %

145 (64.4)

21 (9.3)

33 (14.7)

26 (11.6)

166 (73.8)

59 (26.2)

178 (79.1)

47 (20.09)

Consonant-Deep

n %

70 (32.3)

35 (16.1)

14 (6.5)

98 (45.2)

1105 (48.4)

112 (51.6)

84 (38.7)

133 (61.3)

Dissonant heh

n %

138 (48.8)

34 (12.0)

40 (14.1)

71 (25.1)

172 (60.8)

111 (39.2)

178 (62.9)

105 (37.1)

Dissonant lel

n %

157 (54.7)

26 (9.1)

34 (11.8)

70 (24.4)

183 (63.8)

104 (36.2)

191 (66.6)

96 (33.4)

Note: Consonance/dissonance in conceptions of learning and strategies: BC Z Basic Consonance (learning as reproducing and rehearsal strategies). CD Z Positive Dissonance (learning as reproducing and organisative/elaborative strategies). ÿD Z Negative Dissonance (learning as transforming and rehearsal strategies). CC Z Complex Consonance (learning as transforming and organisative/elaborative strategies). Study orchestrations: Cons_Deep Z consonant deep; Cons_Surf Z consonant surface; Diss_heh Z dissonant highehigh deep and surface approaches; Diss_lel Z dissonant lowelow deep and surface approaches.

a clear distinction between Complex Consonance and Deep Study Orchestration, on the left side, and Basic Consonance and Surface Study Orchestration, on the right side. The second axis explained only 2.6% of the interdependence, for which reason it represented only a tenuous link between Dissonant HigheHigh Study Orchestration (0.553), Negative Dissonance (Factor Z 0.464) and Positive Dissonance (0.300) on the one hand, and Dissonant LoweLow Study Orchestration (Factor Z 0.440) and Basic Consonance (0.183) on the other. Table 5 offers additional information which helps to bolster the correspondence analysis results. To do this we analysed each Study Orchestration taking into account the two types of data it afforded: consonance/dissonance between conceptions and strategies; and conceptions and strategies separately. In consonant study orchestrations (deep and surface), the distribution of consonance/dissonance among conceptions and strategies was found to be different, and theoretically consistent: a higher percentage of Complex Consonance (45.2) in the former, and a higher percentage of Basic Consonance in the latter. It is important to point out that in Deep Study Orchestration the percentage of ‘Positive Dissonances’ was greater (16.12%) and the percentage of ‘Negative Dissonances’ was much smaller (6.5%) than in the rest of the orchestrations. These data accurately reflect the correspondence analysis, for they clearly differentiate Deep and Surface Study Orchestrations. If we observe the conceptions of learning and strategies percentages separately, we see that they are also different, and theoretically consistent, for each orchestration. In Consonant Surface Orchestration the highest percentages were for rehearsal

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l l

h h

Fig. 2. Correspondence analysis. Plot of conceptions of learning and strategies (grouped by consonance/ dissonance) and study orchestrations. Note: Consonance/dissonance in conceptions of learning and strategies: BC Z Basic Consonance; CD Z Positive Dissonance; ÿD Z Negative Dissonance; CC Z Complex Consonance. Study orchestrations: Cons_Deep Z consonant deep; Cons_Surf Z consonant surface; Diss_heh Z dissonant highehigh deep and surface approaches; Diss_lel Z dissonant lowelow deep and surface approaches.

strategies (79.1%) and for conceptions of learning as reproducing (73.8%). In Consonant Deep Orchestration the highest percentages were for conceptions of learning as transforming and to organising/elaborating strategies. In the two dissonant study orchestrations, (highehigh and lowelow deep and surface approaches), the distribution of consonance/dissonance between conceptions and strategies was similar, showing conflicting tendencies in both cases. Half the students (48.8% and 54.7%) had conceptions of learning as reproducing and used

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rehearsal strategies (Basic Consonance). However, considerable proportions (25.1% and 24.4%) expressed conceptions of learning as transforming and used organising/ elaborating strategies (Complex Consonance). In addition to this tension or conflict between opposing tendencies, we find two clear facts in both dissonant study orchestrations. First, at the right side of Table 5, we observe that these orchestrations manifested similar percentages in the two conceptions of learning as well as in the two learning strategies. Second, if we observe the Negative Dissonances (at the left side of the table), we see that they showed percentages that were very similar to one another, and at the same time, similar to those encountered in the surface consonant study orchestration. It is not surprising, therefore, that the correspondence analysis did not detect a close interrelationship between consonance/ dissonance in conceptions of learning and strategies and dissonant study orchestrations. In a nutshell, we can reach two conclusions. First, consonant study orchestrations were clearly differentiated, both with regard to one another and to dissonant study orchestrations. Second, in the latter, the opposite occurred. They showed contradictory tendencies which seemed to point towards a conception of learning as memorisation and use of repetitive-type learning strategies, as well as towards a display of Negative Dissonances.

4. Discussion The results of this investigation bring to light three hitherto undetected findings. First, two kinds of consonance (basic and complex) were revealed, and two kinds of dissonance (negative and positive) emerged in students’ ways of integrating how learning appears to them, with the strategies they utilise in order to learn. Second, these patterns of response were shown to be significantly related to academic performance: Positive Dissonance and Complex Consonance were associated with better results. Third, these patterns and learning approach combinations (study orchestrations) were associated with one another; in dissonant study orchestrations the patterns of relationships among conceptions of learning and strategies became incoherent. 4.1. Conceptions of learning and strategies Qualitative analysis of data gathered by means of open-ended questions, a technique which is not strictly phenomenographic, found eight conceptions of learning, susceptible to grouping in two main categories: learning as reproducing and learning as transforming. By and large, this confirms results obtained by Purdie et al. (1996) and Marton et al. (1997) with secondary students, and by Tynja¨la (1997) and Boulton-Lewis et al. (2000) with university students. What is more, it demonstrates that the technique used was suitable for analysing large samples, thus consolidating and extending findings by Berry and Sahlberg (1996), Purdie et al. (1996) and Tynja¨la (1997), whose studies on conceptions of learning involved smaller samples.

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It also confirms that learning strategies reported by students were similar to those identified by Weinstein and Mayer (1986), who grouped strategies into two categories, rehearsal and organisation/elaboration. Students’ conceptions of learning and learning strategies were linked in a significant way. Reproductive and constructive conceptions were connected to rehearsal and organisation/elaboration strategies, respectively. In this way, by means of a technique that was not strictly phenomenographic, involving a numerous sample, our study corroborated results achieved by various authors (Boulton-Lewis et al., 2000; Van Rossum & Schenk, 1984) who used rigorously phenomenographic procedures and very small samples. In our investigation students were found to combine conceptions of learning and strategies in consonant as well as dissonant patterns, in line with recent findings by other authors (Boulton-Lewis et al., 2000) and in contrast to the view proposed by Marton et al. (1993). This would seem to attest to the existence of a dimension of variation in students’ ways of linking how learning appears to them and what strategies they use. The new contribution of this study is the clear identification of four patterns of response in these combinations and the bringing to light their relationship with academic performance. In consonant patterns (basic or complex), students held a conception of learning which was theoretically consistent with the strategies they reported using in order to learn. In dissonant patterns (positive or negative), students expressed conceptions of learning that did not match their reported strategies. It was ascertained that the percentage of students’ passes was greater than the percentage of fails in the Complex Consonant pattern (learning as transforming-organisative/elaborative strategies), and in the Positive Dissonant pattern (learning as reproducing-organisative/elaborative strategies). A conception of learning is a focus of awareness that constitutes part of a student’s experience of learning (Morgan & Beaty, 1997). However, this experience does not appear to be so conscious and congruent as might be expected, as otherwise we would not have encountered so many Negative and Positive Dissonances. Negative Dissonances would seem to bear a relation to the ‘cognitive conflict’ proposed by Boulton-Lewis et al. (2000), and to the ‘negative friction’ postulated by Vermunt and Verloop (1999). Negatively dissonant students would seem to be experiencing conflicts or negative frictions among their learning conceptions, strategies, and learning environments. This tentative conclusion is supported by the fact that, in general, the academic performance of these students was poor, as seen above. Positive Dissonances, on the other hand, appear to be linked to ‘positive friction,’ as suggested by Vermunt and Verloop (1999) and to ‘cognitive conflict,’ as put forward by Boulton-Lewis et al. (2000), although in contrast to these authors’ meanings, here conflict has positive connotations. It seems plausible that positively dissonant students are experiencing a positive incongruence or friction between themselves and their teachingelearning environment. Their conception of learning is reproductive, but their teachingelearning environment is challenging them to live their learning as a more purposeful and structured activity, demanding that they think and reflect, and therefore use constructive learning strategies. In general, the consequence of this Positive Dissonance is evident e better learning outcomes.

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4.2. Study orchestrations Four contextualised patterns of engagement in learning were encountered. Two exhibited coherence or conceptual consonance, and two exhibited incoherence or conceptual dissonance. These findings have served to underline the following: First, they have confirmed the usefulness of the ‘interference model’ (two-factor, deep and surface approaches) in detecting study orchestrations. Second, they have enabled us to extend results documented by other researchers (Cliff, 2000; Lindblom-Yla¨nne & Lonka, 1998, 2000; Meyer, 1991; Meyer, 2000) to a much larger sample who were not selected on the basis of previous levels of academic achievement and who were in secondary education. The groups of students corresponding to each of these four study orchestrations differed from one another as regards academic performance. Those who performed better displayed deeper and more consonant orchestrations. Students who achieved most poorly in achievement were those who manifested consonant, but superficial orchestrations, and those who deployed dissonant orchestrations. This is in line with previous research (Cliff, 2000; Entwistle et al., 1991; Lindblom-Yla¨nne & Lonka, 1998; Meyer et al., 1990) who documented the negative effects of dissonant orchestrations on academic achievement. Furthermore, these results may be generalised to instruments for measuring approaches, such as the LPQ, and what seems more important, to secondary students at any level of achievement. As some authors point out (Entwistle et al., 1991; Lindblom-Yla¨nne & Lonka, 2000; Meyer, 2000; Prosser et al., 2000) dissonant study orchestrations probably originate in difficulties of interaction between the learner and his/her learning environment. Our analyses in themselves do not offer any definite explanation, but they may bring forth some valuable information if we take together the results of the different variations under examination: study orchestrations and conceptions of learning and strategies. 4.3. Study orchestrations and conceptions of learning and strategies The design of the study enabled us to examine together, within the same sample, some of the most outstanding aspects of students’ learning experience. The exploration of variations in study orchestrations in the light of the patterns of linkages among conceptions of learning and strategies, has led to the discovery of significant relationships among them. These patterns appear in all study orchestrations. Now, if we take into account Meyer’s (2000) submission that study orchestrations are sensitive to both students’ conceptions of learning and perceptions of their learning context, the distribution of students in each of the four patterns should be different for each orchestration. The results obtained confirm this relationship. In consonant study orchestrations, consonance between perception of the learning context and ways of learning is clearly reflected in the coherence observed in the combinations of learning conceptions and strategies. The majority of students expressed conceptions of learning that were in keeping with their learning strategies.

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In other words, these students were able to match in a compatible way how learning appears to them and how they usually engage in learning situations. However, some students seemingly had problems in their interaction with learning which came to the surface in the shape of what has been referred to throughout this study as ‘dissonances in conceptions of learning and strategies’ a concept that is akin to Vermunt and Verloop’s (1999) ‘friction’ and Boulton-Lewis et al.’s (2000) ‘conflict’. One of the central contributions of this research is that it highlights the fact that these frictions or dissonances are differentially distributed in each study orchestration. In the surface consonant orchestration, there is a predominance of Negative Dissonances. In the deep consonant orchestration, on the other hand, very few Negative Dissonances are observed, while quite a few Positive Dissonances are seen. This appears to confirm that students in the latter category probably perceive their teachingelearning environment as focusing on processes of knowledge construction and utilisation, or as ‘process-oriented,’ in Vermunt and Verloop’s (1999) terms. As a consequence, they react to the demands of this environment efficiently, deploying learning strategies which demand great cognitive effort in the organisative and elaborative sense. As is shown in the corresponding analysis, this goes hand in hand with greater academic success. In Dissonant Study Orchestrations, incongruent combinations of approaches to learning were reflected in the patterns of linkage between conceptions of learning and strategies. The relationship between the latter is seen to break up and become incoherent. This tentative conclusion is supported by two facts which, in the shape of opposing tendencies, emerged in these kinds of orchestrations. In the first place, while a certain number of students experienced learning as a constructive process, a greater number experienced it as a reproductive process. Second, while some students manifested Positive Dissonances, slightly more showed Negative Dissonances. In all cases, this led to achievement difficulties. These results are in line with those obtained by Lindblom-Yla¨nne and Lonka (1998) and Vermunt and Verloop (2000). It may be that tensions felt by students in their interaction with their teachingelearning context took the form of mostly Negative Dissonances (negative friction) between conceptions of learning and strategies. Feeling no motivation to experience learning in a more mature way, they orchestrated their study in atypical or disintegrative patterns, as other authors have proposed (Lindblom-Yla¨nne & Lonka, 2000; Prosser et al., 2000; Vermunt & Verloop, 1999). 4.4. Implications All in all, this research has led to a greater understanding of students’ ways of experiencing and handling learning situations, and a number of implications spring from it. If teachers wish to improve their students’ learning outcomes, first they must be able to identify in time those students whose orchestrations and dissonances are negative, and second, they must be ready to implement any necessary changes in their teaching. Students must be encouraged to experience learning in keeping with the strategies they use in order to learn, and motivated to deploy a deep and consonant study orchestration in response to information received, in particular, and

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to contextual demands, in general. This is not easy to achieve, but it would be highly desirable to attempt it, especially in secondary education, as it is here that students are unwittingly forging the study orchestrations they will in all probability go on to use in higher education. Aligning objectives, teaching and evaluation (Biggs, 2001); helping students to experience learning as an interpretative and structured process aimed at the understanding and transforming of both information and oneself; fostering students’ conceptual knowledge of the subject matter (Prosser et al., 2000) and heightening their awareness about learning and themselves as learners; all of these would be first steps in the right direction. 4.5. Limitations and future research In spite of reaching its stated objectives in general, this research has several limitations. The ex-post facto design and the instruments employed meant that a great amount of information was gathered, but allowed only an analysis of students’ experience of learning from an abstract point of view (learning in a general sense). Moreover, the study orchestrations were defined in a liberal fashion, using an instrument with a small number of subscales. Finally, although some variations in student dissonant learning patterns were detected, more evidence is needed to explain them, to examine their stability or change through secondary school, and in short, from one learning context to another. Future research could inquire further into both Negative Dissonances and Positive Dissonances as well as negative orchestrations, taking into account three points. First, they could be examined in a more concrete fashion, not merely through the administration of inventories to identify ‘atypical’ cases or by asking students to talk about learning as an abstract phenomenon. It might be more profitable to conduct a naturalistic experiment, giving students a specific learning task, as Marton and Sa¨ljo¨ (1976a, 1976b) and Prosser et al. (2000) have done, or even to examine and compare different learning environments, as suggested by Lindblom-Yla¨nne and Lonka (2000). In a follow-up interview students could be asked about what the object of learning is for them and how they relate to it (approach to learning), with regard to their perception of the teachingelearning context in which the task is set, and also to the meaning that learning, in general, has for them (conception of learning) (see Marton et al., 1997 and Boulton-Lewis et al., 2001, for an interview schedule which includes a wide variety of questions). In any case, if the researcher decided to use learning approach inventories, it would be desirable to select those which would best reflect the most fine-grained image of students’ perceptions of their learning context (inventories including a rich variety of subscales or even designed for specific subjects or tasks). Second, besides paying special attention to students with Negative Dissonances it might be worthwhile to analyse more closely the variables that influence students’ change towards Positive Dissonances, and that challenge and stimulate them ‘‘to develop skill in the use of learning and thinking activities they are not inclined to use on their own.[and to promote].student-regulation of learning processes (Vermunt & Verloop, 1999, p. 270), one of the principal goals of teaching. Clearly,

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the grade (or academic year) itself should not be the main variable to be analysed. Recent studies have stressed the importance of students’ perceptions of their teachingelearning environment and even the manner in which teachers experience their teaching. Vermunt and Minnaert (2003) demonstrated that in response to an innovative student-oriented learning environment project, students’ learning patterns in the third term were far more ‘dissonant’ than in the first one. Recently, Prosser, Ramsden, Trigwell, and Martin (2003) reported that in ‘units of study’ in which teachers’ experiences of teaching were congruent and consonant, students enjoyed a higher-quality learning experience. Third, researchers would have to bear in mind that students’ learning experience makes up a whole (Marton et al., 1997; Morgan & Beaty, 1997), for which reason, only an all-involving and integrative analysis of information about how students are living and experiencing learning will be likely to explain satisfactorily the phenomenon of atypical response patterns in learning approaches as well as in conceptions of learning and strategies, and facilitate the design of preventative measures aimed at bridging the gap between teaching and learning.

Acknowledgements I am very grateful to the Editor, Professor Dr. Wolfgang Schnotz (University of Koblenz-Landau, Germany), and to the anonymous referees, for their detailed written comments of the study, and to Jean Stephenson (University of Granada, Spain), for her constant support and help in drafting the manuscript in English.

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