The relationship between approaches to teaching and approaches to studying: a two-level structural equation model for biology achievement in high school

The relationship between approaches to teachingand approaches to studying: a two-level structuralequation model for biology achievement in high school

Pedro Rosário & José Carlos Núñez & Pere J. Ferrando &

Maria Olímpia Paiva & Abílio Lourenço &

Rebeca Cerezo & Antonio Valle

Received: 1 May 2012 /Accepted: 28 January 2013 /Published online: 10 February 2013# Springer Science+Business Media New York 2013

Abstract Since the 1970s, a large body of research has reported on the differences betweendeep and surface approaches to student learning. More recently, however, this metaphor forstudents’ approaches to learning has been applied to the practice of teaching. Studies at theuniversity level have identified two approaches to teaching: the information transmission/teacher-focused approach and the conceptual change/student-focused approach. The presentstudy analyzes the relationship between teachers’ approaches to teaching and high schoolstudents’ approaches to learning. The data were analyzed by fitting a two-level structuralequation model based on the hypothesis that student academic achievement is significantlydetermined by the way they study and that the way they study is partially determined by theway teachers teach. The participants were high school students (778 twelfth graders)enrolled in biology courses and their teachers (40 total). The same model was proposed atboth levels (i.e., within and between levels) and fit the data quite well. As expected, withinlevel, the effects of the ‘approaches to learning’ on ‘biology achievement’ regression werefar larger than the corresponding effects at between level. The central findings suggestworthy directions for future research.

Keywords Approaches to learning . Approaches to teaching . High school . Two-levelstructural equation model . Academic success

Metacognition Learning (2013) 8:47–77DOI 10.1007/s11409-013-9095-6

P. Rosário (*) :M. O. Paiva :A. LourençoSchool of Psychology, University of Minho, Campus de Gualtar, 4710 Braga, Portugale-mail: [email protected]

J. C. Núñez : R. CerezoUniversity of Oviedo, Oviedo, Spain

P. J. FerrandoUniversity Rovira i Virgili of Tarragona, Tarragona, Spain

A. ValleUniversity of A Coruña, A Coruña, Spain

Introduction

In the past few decades, a large body of research has been developed on the relationshipbetween students’ approaches to learning (deep and surface approaches, DAs and SAs,respectively) and their academic results (Cano and Berbén 2009; Entwistle et al. 2001;Marton and Säljö 1976a, 1976b; Prosser and Trigwell 1999; Rosário et al. 2010a, 2007;Struyven et al. 2006). Students can approach school tasks using a DA, in which they try tounderstand the meaning of the content, or they can approach tasks with extrinsic motivation,thus adopting an SA (Biggs 2003).

Towards the end of the last century, the works of Prosser and Trigwell and colleagues(Prosser and Trigwell 1991, 1999; Prosser et al. 1994) initiated a new line of research thatanalyzed teachers' approaches to teaching using methods similar to those used to studystudents’ approaches to learning. These authors investigated whether teachers approachedteaching in qualitatively different ways and found two approaches to teaching. One approachfocused on the student’s conceptual changes, and the other focused on the transmission ofinformation to the student.

Although it is very intuitive to merge approaches to teaching and approaches to learning andthat their instructional implications are clear, there are very few studies that analyze students’approaches to learning and teachers’ approaches to teaching (Trigwell et al. 1999). Perhaps forthis reason, Ramsden and colleagues (Ramsden et al. 2007) have recently challenged research-ers in this field by asking, “Might the outcomes of these different approaches to teaching thenbe reflected in students’ approaches to learning?” (p. 141). In our present study, which wasconducted with high school students and included an analysis with a two-level structuralequation model, we attempt to answer the following questions. Are teachers’ approaches toteaching associated with students’ approaches to studying? Do approaches to studying mediatethe relationship between the approaches to teaching and academic achievement? To whatextent do some of the relationships depend on the chosen level of analysis?

Students’ approaches to learning

Over the past three decades, research findings have suggested that students’ approaches tolearning are an important explanatory construct in the learning process with clear implicationsfor academic success (Biggs 1993; Entwistle 2000). Marton and Säljö (1976a, 1976b) describetwo ways that students may approach an academic text. In an SA to learning, the content islearned without any comprehensive or integrative demands because the student is focused onmeeting goals that are extrinsic to the material. This approach contrasts with a DA to learning,which is characterized by intrinsic interest, whereby the student tries to understand thematerialby relating it to prior knowledge and to the world in general (Entwistle 2009).

The metaphor of the “surface” approach versus the “deep” approach is a conceptual toolthat is quickly understood in class and in other educational contexts, and it has been shownto be qualitatively and quantitatively powerful for parents, teachers, and students whenconceptualizing academic tasks (Biggs 1993; Entwistle 1991). The core of students’approaches to learning is the connection between the intention behind focusing on a taskand the corresponding strategy (Rosário et al. 2010a).

Understanding approaches to learning is an open activity that guides the research onprocesses that can improve the quality of education. In this sense, research on students’approaches to learning should be viewed as an attempt to strengthen the student’s dynamicrole in the learning process, thereby increasing his or her comprehension of the real world(Entwistle 1991; Ramsden 2003).

48 P. Rosário et al.

Teachers’ approaches to teaching

Prosser and Trigwell (1999, 2006), Prosser and colleagues (1994), and Ramsden (2003),among other authors, applied the metaphor used for students’ approaches to learning to thepractice of teaching. Studies at the university level have identified two approaches toteaching: the information transmission/teacher-focused (ITTF) approach and the conceptualchange/student-focused (CCSF) approach.

Teachers who adopt an ITTF approach to teaching base their activities on their role asteachers, focusing on the transmission of information related to the course content. Theyattend to the technical issues related to the teaching process. For example, they use note-books with classes carefully planned out, they provide the students with notes or summariesof the material, and they use behavior management strategies, assuming that the organizationof the classroom and their mastery of teaching play a decisive role in their students’ results(Ramsden et al. 2007; Trigwell et al. 1999). This approach is mainly focused on thetransmission of knowledge.

For the CCSF teachers, the teaching process is oriented towards the student’s involve-ment in an active process of understanding. These teachers take into consideration the priorknowledge of their students, and they organize teaching strategies to help students build onthat knowledge (e.g., asking questions that involve complex cognitive processes such asinference and through presentations and discussions of projects). Although these teachersbelieve their own actions are important, the teachers who adopt this approach consider thefinal learning results to be much more dependent on the student’s active role within thespecific learning context (Trigwell and Prosser 2003). Therefore, they organize their teach-ing around promoting the development of the student’s knowledge. Findings suggest thatthere is a relationship between teachers’ intentions when approaching their teaching and thestrategy they use to teach (Prosser et al. 1994). Nevertheless, the feeling of autonomy andcontrol over the material, the teaching methods, the number of students in the class, and theteachers’ beliefs about how their students should study the content have a great impact onchoosing a certain approach to teaching (Prosser and Trigwell 1997).

The data provided by several studies indicate that both approaches to teaching (ITTF andCCSF) and approaches to learning (SA and DA) are not mutually exclusive; they represent acontinuum rather than a dichotomy and are responsive to personal and contextual variables.With respect to approaches to teaching, Lindblom-Ylänne et al. (2006) reported that theCCSF approach is affected by variables in the learning context, such as the number ofstudents in the classroom, the teaching methods, and the students’ study methods. In otherinvestigations, however, no support was found for this hypothesis. For example, in a studyperformed by Stes et al. (2008), who investigated 50 teachers from the University ofAntwerp, the data revealed that it was not possible to associate the CCSF approach toteaching with the variables examined in their study (e.g., the student’s level of expertise, thediscipline of teaching, and the number of students in the classroom). These data demonstratethe importance of investigating the variables that may affect the way teachers teach.

The relationship between approaches to teaching and approaches to learning

In recent decades, researchers have stressed the importance of examining the relationshipbetween classroom learning environments (e.g., teacher practices in class) and students’learning (Ames 1992; Pintrich 2003; Urdan and Schoenfelder 2006).

A considerable corpus of research has established links between the environment, goalsand students’ motivational outcomes. Classroom practices and the messages teachers

The relationship between approaches to teaching and approaches... 49

communicate to their students can influence students’ behavior and achievement (Ames1992; Covington and Omelich 1984; Epstein 1988).

In the contemporary literature on achievement goals, important studies havefocused on the relationships among achievement goals, classroom goal structures,and achievement outcomes (Ciani et al. 2010; Murayama and Elliot 2009; Shim etal. 2013).

Classroom goal structures represent “goal-related messages that are made salient in theachievement setting (i.e., laboratory, classrooms, schools)” (Kaplan et al. 2002, p. 24) andhave been associated with important educational outcomes. Ciani et al. (2008), forexample, found that high school teachers in performance-oriented schools reported moreperformance-oriented teaching practices in the classroom. Meece and colleagues (2006)examined the literature on the influence of classroom and school settings on students’academic motivation and achievement and concluded that elementary and secondarystudents in school settings emphasizing mastery, understanding, and improving skills weremore likely to display adaptive motivation, learning engagement and positive trends inschool grades. These same authors also suggested that school settings that value compe-tition for grades and displays of high ability may diminish young students’ motivation andenthusiasm for learning. “Several studies indicate that classroom goal structures influencestudent behavior and learning by shaping the type of personal goals that students adopt”.(Meece et al. 2006, p. 495)

The study of classroom discourse is important to answer educational questions.Teacher questioning, for example, is an important and widespread instructional strategyused in class (Graesser and Person 1994) and a key component of classroom discourse.In classroom settings, teacher questions are important instructional tools that conveyelements of content to students, but they are not universally effective (Zhang et al.2010). Nystrand and colleagues (2003) identified some important features of teacherquestioning that were likely to improve the quality of learning among middle schoolstudents. Their findings emphasized the importance of teachers asking authentic questionsand building on student ideas. For example, the questions asked by teachers in classshould not have a predetermined answer, and they should incorporate students’ previouscontributions in class.

In the current study, while we investigated the implications of students’ approaches tolearning and academic achievement, we also investigated whether teachers’ approaches toteaching are related to students’ approaches to learning.

Although there is a large body of literature that investigates approaches to learning (e.g.,Biggs 1993; De la Fuente et al. 2008; Entwistle 1991; Rosário et al. 2007, 2010b; Struyvenet al. 2006) and approaches to teaching (e.g., Meyer and Eley 2006; Prosser and Trigwell2006; Ramsden et al. 2007; Stes et al. 2010; Trigwell and Prosser 2004), there are very fewinvestigations that relate teachers’ approaches to teaching and students’ approaches tolearning.

One of the first empirical studies to elucidate the specific relationship betweenapproaches to teaching and approaches to learning was performed by Trigwell et al.(1999) using an early version of the Approaches to Teaching Inventory (ATI). Based onexploratory factor analyses and cluster analyses, their data revealed a relationship betweenthe ITTF approach and students’ SA to learning but not between the teachers’ CCSFapproach and the students’ DA to learning. Nevertheless, these investigators warned thatthese statements should be interpreted cautiously because the sample of teachers was smalland came from only one subject area. They also noted that the size of the relationship wassmall, although it tended to support the proposed hypothesis. Therefore, the authors


ultimately recommended studying the link between approaches to teaching and approachesto learning.

Six years later, Richardson (2005) reviewed publications from the past 25 years on thistopic and concluded that the relationships between teachers’ approaches to teaching, theirconceptions of teaching, and their perceptions of the teaching environment are important. Healso recognized that “future research needs to aim at illuminating the interplay between theview of student learning [approaches to studying] and the view of teaching [approaches toteaching]” (p. 679).

Goals of the present study

Advancing our knowledge about the relationship between teaching and learning is crucialnot only to design and improve teacher training but also to promote student academicsuccess. As mentioned previously, the results of previous studies that connect approachesto learning and approaches to teaching are inconclusive. Moreover, the scarce informationregarding the relationship between approaches to teaching and approaches to learning wasobtained by studying university students; therefore, there is insufficient information abouthigh school students (Campbell et al. 2001).

In the present investigation, we accept the challenge of Ramsden and colleagues(2007) and propose to analyze the relationships between approaches to teaching,approaches to learning and academic achievement of high school students in biologyusing a two-level structural equation model. Furthermore, we performed analyses todetermine whether the relationship depends on the chosen level of analysis (between orwithin levels).

To meet the goals of the study, because there are no valid instruments to assess theseapproaches in the Portuguese educational context, we created two new biology-specificquestionnaires: one to assess students’ approaches to learning and the other to assessteachers’ approaches to teaching. Approaches to teaching are usually assessed with theApproaches to Teaching Inventory (ATI, Trigwell and Prosser 1996). Because the resultsindicate that the ATI is a context-dependent instrument (Meyer and Eley 2006; Prosser andTrigwell 2006; Stes et al. 2010) for the university context, we created a questionnaire thatwas adapted to the Portuguese high school context using a theoretical framework ofteachers’ approaches to teaching known as the Teachers’ Approaches to Teaching Inventory(TATI). The Study Process Questionnaire (SPQ) and the Learning Process Questionnaire(LPQ) were developed by Biggs (1987a, 1987b) and have been used in numerous inves-tigations to assess university and secondary students’ approaches to learning. In his theo-retical framework, Biggs (1985, 1993) proposed three approaches to learning and studying(surface, deep, and achieving), each one with a motivation and an associated strategy;however, in recent works, Biggs and colleagues have concluded that a two-factor solution(deep and surface) is more effective for describing the learning process (Biggs et al. 2001;Kember et al. 2004). Regarding the approaches to learning we developed a questionnairethat assesses high school students’ approaches to learning, the Students’ Approaches toLearning Inventory (SALI).

In the current study, we began by analyzing the measurement models for each of theinstruments (TATI and SALI) with a confirmatory factor analysis using large samples ofstudents and teachers. We then analyzed the relationship between the teachers’ approaches toteaching and their biology students’ approaches to learning using a two-level structuralequation model with latent variables.


Method

Samples

School characteristics and procedures

In Portugal, high school is organized into three grade levels (tenth, eleventh, and twelfthgrades) or 15-, 16-, and 17-year-old students, respectively. The eleven high schools enrolledin this research are located in an urban school district in the northern part of the country andwere randomly chosen from a pool of forty-five eligible high schools. The families of thestudents may be considered middle class based on the percentage of students receiving freeor reduced-price lunches (14.3 %, according to the data collected from the school offices).All of the high school students and teachers from the schools were invited to participate inthis investigation. The participating students (approximately 38 % of the high schoolpopulation) received permission from their parents, and the teachers sent e-mails expressingtheir willingness to participate.

In the validation studies of the instruments, the participants comprised students from thethree high school grade levels and high school teachers from the science departments of the11 randomly selected schools. We included forty dyads (twelfth graders from 40 biologyclasses and their biology teachers, or approximately 60 % of the dyads available in thoseschools) in the structural equation model analysis.

Except for the final academic grades, all of the data were collected from biology classesduring the second term of the academic year (between January and April). Because of thecontextual nature of approaches to learning and approaches to teaching, the biology teachersand their students completed the questionnaires focusing on the specific context of thebiology class. The questionnaires were administered to the teachers and the students at thesame time, although the teachers completed their questionnaires outside of the classroom.

Description of the samples used in the validation studies of the SALI and TATI

For the validation of the SALI, 1504 students from 11 public high schools participated (tenthgrade: n=497, 33.1 %; eleventh grade, n=426, 28.3 %; twelfth grade: n=581, 38.6 %). Ofthe participants, 506 (33.6 %) were males, and 998 (66.4 %) were females. The ages of thestudents ranged between 15 and 18 years (M=16.2, SD=.88). For the validation of the TATI,611 high school biology teachers from 11 public high schools participated. Of the partic-ipants, 149 (24.4 %) were males, and 462 (75.6 %) were females. Their ages ranged between24 and 64 years (M=47.2, SD=9.80). Approximately 15 % of the teachers had at least10 years of teaching experience, and 64 % had more than 20 years of teaching experience atthe high school level.

Description of the samples used in the two-level structural equation model analysis (two-levelSEM analysis)

The sample used for the two-level SEM analysis comprised 40 biology teachers and their 778twelfth-grade students from the aforementioned 11 public schools. In this sample of students,322 (41.4 %) were males, and 456 (58.5 %) were females, with ages ranging between 16 and18 years (M=17.2, SD=.54). Of the biology teachers enrolled in the study, 35 (87.5 %) werefemales, and 5 (12.5 %) were males. All of the teachers were between 28 and 61 years of age(M=44.9, SD=8.23), and 59 % had more than 20 years of professional experience.


Instruments and measures

Students’ Approaches to Learning Inventory (SALI, High School)

The SALI consists of 12 items (see Table 6), with 3 items for each of the 4 dimensions:Surface Motivation (SM; e.g., “I believe teachers should tell me exactly what material willbe on the exam because I am only going to study that material”), Deep Motivation (DM; e.g.,“I feel pleased with my studying when I understand the answers to the ‘why’ questions”),Surface Strategy (SS; e.g., “I only study what I think is enough to pass”), and Deep Strategy(DS; e.g., “After class, I reread my notes to make sure they are clear and that I understandthem”). The participants responded to the statements on a 5-point Likert scale ranging from1 (strongly disagree) to 5 (strongly agree). Cronbach’s alpha values for the four dimensionswere satisfactory if we take into account that each dimension was assessed with only 3 items(αSM=.75, αSS=.84, αDM=.82, αDS=.78).

Teachers’ Approaches to Teaching Inventory (TATI, High School)

The TATI also consists of 12 items (see Table 7), with 3 items for each of the 4 fourdimensions: Information Transmission/Teacher-Focused Strategy (ITTF-S; e.g., “I onlyprovide texts/materials/exercises on the information students need to prepare for theirassessments”), Conceptual Change/Student-Focused Strategy (CCSF-S; e.g., “I encouragethe students to investigate and read extra material so they can construct personal responses tothe tasks assigned”), Information Transmission/Teacher-Focused Intention (ITTF-I; e.g., “Ithink that the learning concepts and their connections should be explicitly transmitted by theteachers and not acquired by the students as a result of personal discovery or investigation”),and Conceptual Change/Student-Focused Strategy Intention (CCSF-I; e.g., “In my disci-pline, it is important to offer time and opportunities for the students to interact and learn withtheir classmates”). The teachers responded on a 5-point Likert-type scale ranging from 1(strongly disagree) to 5 (strongly agree). The estimated reliability coefficients (Cronbach’salpha values) were satisfactory for the four dimensions, especially considering that therewere only 3 items per dimension (αITTF-S=.87, αITTF-I=.87, αCCSF-S=.88, αCCSF-I=.86).

Academic achievement

Portuguese students who wish to pursue undergraduate programs in sciences (e.g., chemis-try, medicine, biology, psychology) must complete a national exam that covers materialpertaining to biology. To prepare for this exam, twelfth graders complete standardized testsfrom the assessment department of the Ministry of Education each term. These out-of-schooltests include different types of questions (i.e., multiple choice, open-ended, and closed-ended questions) that are focused either on understanding or on short-term reproduction. Forour purposes, the average of the three marks obtained on the standardized tests wascalculated and used as a measure of academic achievement. The scores were obtained fromthe school secretary at the end of the school year (at the end of June). In Portugal, highschool scores range from 0 to 20, with a passing grade of 10.

Data analysis

Several statistical analyses were performed to assess different facets of the validity of theinstruments (content, construct, and predictive validity). Content validity was taken into


account when designing the inventories, in the sense that the items were based onsolid theoretical frameworks. To assess construct validity, we analyzed the factorstructures of both the SALI and the TATI by fitting confirmatory factor analysis(CFA) models based on the initial theory. Next, we assessed the relationships betweenapproaches to teaching, approaches to learning, and student academic achievement inbiology using a two-level SEM analysis. The assessment of the relationship betweenapproaches to learning and academic achievement also served to analyze the predictivevalidity of the inventories. A series of statistics and indices were used to assess themodel-data fit in all the proposed models (i.e., CFA and SEM). In addition to the chi-square test (χ2) and its associated probability (p), we used the following tests: (a) twoabsolute indices, the goodness-of-fit-index (GFI) and the adjusted goodness-of-fit-index(AGFI) (Jöreskog and Sörbom 1983); (b) two relative indices, the comparative fitindex (CFI) (Bentler 1990) and the Tucker-Lewis index TLI, e.g., Hu and Bentler(1999); and (c) a close-fit parsimony-based index and the root mean square error ofapproximation (RMSEA), including 90 % confidence intervals (Browne and Cudeck1993; MacCallum and Austin 2000). According to these authors, the model-data fitmay be considered acceptable when the GFI and AGFI values are>.90, CFI is>.95,TLI is>.95, and RMSEA is <.07.

Analyses and results

Initial data screening

Tables 1 and 2 show descriptive data and the two Pearson correlation matricescorresponding to the two measurement models of the TATI and SALI. Before conductingthe statistical analyses corresponding to the first goal of this study, we examined thematrices for missing data, the presence of outliers, and the linearity and normality of thedata. We examined the data to determine whether any of the variables or subjects had asignificant amount of missing data. Thirty-four students were eliminated from the SALImatrix, and 12 teachers were eliminated from the TATI matrix. Following the recommen-dations of Kline (2010), we determined that none of the variables had a significant numberof missing values. The final SALI sample, therefore, comprised 1504 students, and theTATI sample comprised 611 teachers. With regard to the two-level structural equationmodel, nine students and no teachers were eliminated. The final samples consisted of 778students and 40 teachers.

The maximum likelihood (ML) estimation can produce distorted model-data fit resultsand incorrect standard errors when the assumption of normality is violated (West et al.1995). Thus, we first examined the distribution of all the variables for kurtosis and skewness.We referenced the guidelines by Curran et al. (1997), who concluded that skewness valueshigher than 3 and kurtosis values higher than 10 indicate severe non-normality. We furtherreferenced Finney and DiStefano (2006), for whom 2 and 7, respectively, are the maximumallowable values for skewness and kurtosis (if the values are greater, the ML should not beused). None of the above-mentioned variables observed in the samples had values approach-ing these criteria (see Tables 1 and 2). Therefore, it was deemed appropriate to estimate thefit of the model using the ML. Finally, another important step in the initial analysis of thedata matrix is to verify that the variables are significantly correlated, although the correla-tions should not be excessively high (r>.85). As observed in Tables 1 and 2, none of thecorrelations exceeds .85.


Tab

le1

Correlatio

nmatrixfortheTeachers’Approachesto

Teaching

Inventory(TATI)anddescriptivestatistics(m

ean,

standard

deviation,

skew

ness

andkurtosis)

12

34

56

78

910

1112

1–

2−.04

8–

3.349

**

−.08

4*–

4−.05

8.369

**

−.08

2*–

5−.13

0**

.333

**

−.15

4**

.705

**

–

6.752

**

−.13

8**

.322

**

−.09

2*−.12

1**

–

7.375

**

−.15

0**

.664

**

−.08

8*−.17

6**

.305

**

–

8−.07

0.650

**

–.08

9*.370

**

.377

**

−.10

0*−.15

6**

–

9−.10

7**

.653

**

−.16

0**

.344

**

.374

**

−.14

3**

−.23

0**

.708

**

–

10.346

**

−.10

7**

.723

**

−.06

3−.12

6**

.317

**

.659

**

−.07

6−.17

6**

–

11−.05

3.315

**

−.113*

*.708

**

.739

**

−.08

2*−.06

6.388

**

.388

**

−.08

2*–

12.657

**

−.15

4**

.330

**

−.13

7**

−.18

4**

.667

**

.319

**

−.12

1**

−.14

7**

.312

**

−.10

8**

M3.63

4.10

3.51

4.07

3.94

3.52

3.31

4.04

3.88

3.65

4.05

3.63

SD

1.40

1.14

1.34

1.13

1.12

1.38

1.30

1.15

1.10

1.40

1.12

1.41

Skewness

−.71

−1.29

−.65

−1.31

−1.17

−.71

−.37

−1.21

−1.07

−.65

−1.25

−.75

Kurtosis

−.75

.93

−.82

1.05

.69

−.79

−.97

.67

.76

.89

.97

−.80

*p<.01

**p<.001


Tab

le2

Correlatio

nmatrixfortheitemson

theStudents’Approachesto

LearningInventory(SALI)anddescriptivestatistics(m

ean,

standard

deviation,

skew

ness

andkurtosis)

12

34

56

78

910

1112

1–

2−.110*

*–

3.226

**

−.14

8**

–

4−.08

0**

.251

**

.138

**

–

5.535

**

−.02

7.189

**

−.01

7–

6−.14

6**

.605

**

−.18

7**

.266

**

−.06

0*–

7.217

**

.095

**

.580

**

−.08

2**

.149

**

−.116*

*–

8−.12

6**

.292

**

−.15

8**

.619

**

−.01

2.336

**

−.12

4**

–

9.513

**

−.05

6*.230

**

−.03

0.442

**

−.09

9**

.184

**

−.07

4**

–

10−.13

9**

.567

**

−.09

9**

.258

**

−.02

8.625

**

−.08

3**

.297

**

−.09

3**

–

11.183

**

−.12

9**

.833

**

−.13

4**

.186

**

−.17

3**

.529

**

−.14

3**

.196

**

−.10

0**

–

12−.10

8**

.231

**

−.16

2**

.451

**

−.08

1**

.262

**

−.17

6**

.550

**

−.12

3**

.230

**

−.14

2**

M3.25

3.41

2.51

3.42

2.83

3.33

2.69

3.24

2.67

3.06

2.77

3.73

SD

1.28

1.11

1.26

1.09

1.25

1.19

1.33

1.31

1.30

1.21

1.28

1.16

Skewness

−.31

−.13

.64

−.07

−.01

−.16

.26

−.08

.25

.18

.17

−.55

Kurtosis

−.94

−.81

−.59

−.58

.97

−.86

−1.07

−1.16

−1.04

−.90

−.96

−.55

*p<.01

**p<.001


Analysis of the structural characteristics of the TATI and SALI

TATI, high school

Because the TATI has the same structure as the ATI (Approach to Teaching Inventory;Trigwell and Prosser 1996), we compared the fit of two nested measurement models to theempirical data provided by the sample of 611 teachers (see Fig. 1a and b). In Model 1 ofthe TATI (Fig. 1a), there was only a first-order factor structure (with four interrelatedfactors, each one explaining 3 items out of the 12 total items on the TATI inventory). InModel 2 of the TATI (Fig. 1b), the four first-order factors were explained by two second-order factors (also interrelated). In Model 1 of the TATI, the four first-order factors includeinformation transmission/teacher-focused intention (ITTF-I), information transmission/teacher-focused strategy (ITTF-S), conceptual change/student-focused intention (CCSF-I),and conceptual change/student-focused strategy (CCSF-S). In Model 2 of the TATI, asecond factor level with two factors (the ITTF approach and the CCSF approach) isproposed in accordance with approaches to teaching theory. Moreover, we assumed thatthere was no relationship between the measurement errors of the observed variables (i.e.,the items of the inventory). The two models were fitted with Mplus 6.11 (Muthén andMuthén 2011).

The results suggest that both models provided a satisfactory fit. For Model 1, χ2(48)=

101.724, p<.001, GFI=.973, AGFI=.956, CFI=.986, TLI=.981, and RMSEA=.043(.030–.054); for Model 2, χ2

(49)=101.924, p<.001, GFI=.973, AGFI=.957, CFI=.987,TLI=.982, and RMSEA=.042 (.030–.054). Because the models were nested, the differ-ence in fit was assessed using the difference between the chi-square statistics; thedifference was not statistically significant (Δ χ2

M1,M2(1)=.200, p=.65). According tothese results and our initial theoretical assumptions, we chose the hierarchical model(Model 2, Fig. 1b), which incorporates two approaches to teaching. For reasons ofparsimony, the estimated parameters are provided in Fig. 1b (all parameters are statisticallysignificant at p<.001).

SALI, high school

Following the theoretical framework formulated by Biggs and his colleagues, as in the caseof the analysis of the factor structure of the TATI, we formulated two nested models toexplain the factor structure of the SALI (see Fig. 2a and b). In the first model of the SALI(Model 1, Fig. 2a), the students’ responses to the SALI can be explained by four factors(surface motivation, surface strategy, deep motivation and deep strategy), but each item isonly explained by one factor. In accordance with the basic theory of the inventory, the fourfactors are inter-correlated. The second model of the SALI (Model 2, Fig. 2b) is a nestedmodel of the previous model, with the four first-order factors (surface motivation, surfacestrategy, deep motivation and deep strategy) explained by two second-order factors (SA tolearning, DA to learning). We postulated that the DA and SA are inter-correlated. Finally, weassumed that there is no inter-correlation among the measurement errors associated with theitems of the inventory.

The fit of the models indicated that both represent equally well the structure of theinventory as a function of the correlations of the empirical data matrix and that the global fitof the models is acceptable. For Model 1, χ2

(48)=115.661, p<.001, GFI=.987, AGFI=.980,CFI=.990, TLI=.986, and RMSEA=.031 (.023–.037); for Model 2, (χ2

(49)=116.645,p<.001, GFI=.987, AGFI=.980, CFI=.990, TLI=.987, and RMSEA=.030 (.023–.037).


Because both the TATI and SALI are nested models, we compared the differences betweenthe fits of the models and observed that the differences were not statistically significant (Δχ2

M1,M2(1)=.984, p=.30). After considering these data and Biggs’ model of approaches tolearning, we chose the hierarchical model (Model 2, Fig. 2b), in which the two approaches tolearning subsume the congruent motivation and strategy. Furthermore, the estimated param-eters for the SALI are provided in this model, as shown in Fig. 2b (all parameters arestatistically significant at p<.001).

Fig. 1 a. Graphic depicting the Teachers’ Approaches to Teaching Inventory (TATI) (Model 1). ITTF =Information Transmission/Teacher-Focused. CCSF = Conceptual Change/Student-Focused. b. Graphic depict-ing the Teachers’ Approaches to Teaching Inventory (TATI) (Model 2). ITTF = Information Transmission/Teacher-Focused. CCSF = Conceptual Change/Student-Focused


Analysis of the relationships between approaches to teaching, approaches to learning,and academic achievement

In our study, students were nested in classes with the same teacher. Thus, to determinethe relationship between approaches to teaching and approaches to learning as well asthe relationship between approaches to learning and the academic achievement ofbiology students, a structural equation model was analyzed at two levels (betweenlevels and within levels). The conceptual basis for this type of modeling is that studentsin the same class are more likely to share educational characteristics with each otherthan with other students. For example, in the current study, there was shared varianceamong the twelfth-grade students with a common teacher and common classroom

Fig. 1 (continued)


shared variance according to their common teacher and common classroom. Further-more, we assumed that the relationships between teachers’ approaches to teaching,students’ approaches to learning, and academic achievement in biology were the samebetween levels and within levels. Assuming the invariance of the model at both levels,it can be concluded that the predictions were equal in spite of the different units ofanalysis (the student or the class).

Assuming that the aforementioned two assumptions are correct, and considering thefindings of previous research (e.g., Biggs 1993; Entwistle 2009; Entwistle et al. 2002;

Fig. 2 a. Graphic depiction of the Students’ Approaches to Learning Inventory (SALI) (Model 1). b. Graphicdepiction of the Students’ Approaches to Learning Inventory (SALI) (Model 2)


Prosser et al. 1994; Rosário et al. 2010b; Struyven et al. 2006; Trigwell and Prosser 1996),we established the following predictions for the model:

1. Teachers’ ITTF approaches are significantly related to the students’ approaches. Weexpect a positive relationship with SAs and a negative relationship with DAs.

2. Teachers’ CCSF approaches are significantly related to the students’ approaches. Weexpect a positive relationship with DAs and a negative relationship with SAs.

3. There is a significant negative association between the use of an SA and students’academic achievement in biology. Alternately, there is a significant positive associationbetween the use of a DA and achievement.

We also assumed that neither the measurement errors nor the structural errors arecorrelated. By taking into account all the predictions and restrictions discussed, we estab-lished the SEM that is depicted in Fig. 3.

Fig. 2 (continued)


As discussed above, to specify this model in detail, estimate its parameters and assessthe model-data fit, one must remember that the data have been obtained through acomplex cluster design in which the individual observations (students, n=778) are nestedin clusters (classrooms/teachers, n=40). More specifically, the variables inside the box inFig. 3 potentially have both within- and between-cluster variability, whereas the varia-bles outside the box have zero within-cluster variability and can only vary at the clusterlevel. If this complex sampling structure is ignored and the model is simply fit using astandard analysis, it is likely that biased parameter estimates, incorrect standard errorsand distorted goodness-of-fit measures will be obtained (e.g., Muthén 1991). Related tothe first point above, the approach we took to address the hierarchical nature of the datawas to evaluate the SEM as a two-level (between levels and within levels) model (see,e.g., Heck and Thomas 2009, Mehta and Neale 2005). This two-level model requiresthat the SEM be specified both at the first (lower) individual level and at the secondcluster level.

In our study, we assessed the two-level model by following a three-step procedure. Thefirst two steps can be considered preliminary and assess the appropriateness of the data forthis type of modeling, and the SEM is specified and fitted in the third step.

DeepApproach

Surface Approach

SS SM

ITTF Approach

CCSF Approach

DM

1

ITTF-S ITTF-I

CCSF-S CCSF-I

1

11

Biology Achievement

DS

Fig. 3 Proposed structural model. Small circles represent disturbances. Variables with variations solely at thecluster level are shown outside the box, and variables that can vary at both levels are inside the box. SS =Surface Strategy; DS = Deep Strategy; SM = Surface Motivation; DM = Deep Motivation; ITTF-I =Information Transmission/Teacher-Focused Intention; CCSF-I = Conceptual Change/Student-Focused Inten-tion; ITTF-S = Information Transmission/Teacher-Focused Strategy; CCSF-S = Conceptual Change/Student-Focused Strategy


In the first step, the descriptive statistics of the variables are assessed to determine themost appropriate estimation procedure. In the second step, for those variables that have bothbetween-level and within-level variation, we assess whether the variability between clusters(i.e., the between-level variability) is large enough to warrant an analysis at the cluster level.This information can be obtained by inspecting the intra-class correlations, which estimatethe ratio of the between-cluster variance to the total variance for each variable (e.g., Muthén1991). If the proportion is near zero for most variables, then there is not enough between-level variation to justify the second level analysis. A common cut-off value for assessing thisissue is .05 (Heck and Thomas 2009).

We turn now to the two-level specification. As discussed above, the full SEM derivedfrom predictions 1 to 3, which is depicted in Fig. 3, can only be assessed at the second level.The interpretation of the structural relationships at this level is direct, and one only needs torecognize that the units of analysis are the clusters rather than the individual students. Thus,the general interpretation is that the teachers’ approaches to teaching are linked to the waythat the class (as a unit) tends to study; therefore, the approach to learning that the class tendsto take is related to the academic performance of the class in biology.

The most complex specification relates to the part of the model inside the box inFig. 3, which must be specified at both levels. Considering our second point mentionedabove (see Figs. 3 and 4), we assumed that the structure of relationships is the same atboth levels. Thus, the common sub-model is an oblique bi-dimensional measurementmodel with four indicators (two per factor) and a criterion or dependent variable (biologyachievement) that is partly a function of both factors. This common specification followsfrom prediction 3 above and can be interpreted as follows at the first individual level:students (i.e., the first-level units) who use an SA tend to display worse academicachievement in biology, whereas those who use a DA tend to experience better academicachievement. Because the sub-model has the same structure at both levels, it displays across-level configurational invariance. Even when the assumed structure is the same, however, notein Fig. 4 that the modeling of the interfactor correlation varies within levels and betweenlevels. At the within-cluster level, SA and DA are exogenous factors; thus, the correlation isdirectly modeled at the factor level. At the between-cluster level, however, SA and DA areendogenous factors that depend on the exogenous ITTF and CCSF factors. Therefore, therelationship at this level is modeled as a correlation between the disturbances or residualscorresponding to these factors.

Two stronger across-level invariance restrictions that simplify estimation and/or improveinterpretability may be considered beyond configurational invariance (Mehta and Neale2005). First, if each loading is constrained to have the same value at both levels (i.e.,across-level strong invariance), then a common scale of measurement is established at bothlevels, and this scale makes the factor variances at each level directly comparable (Methaand Neale 2005). As we discuss below, this comparison provides useful information for ourstudy. The plausibility of the strong invariance restriction, however, must be assessed asexplained below. As for the second constraint, a typical result in two-level measurementmodels is that the residual variances of the indicators are very small at the between-clusterlevel (the reliability of the indicators is very high at this level). If these variances approachzero, then they are likely to give rise to estimation problems, such as convergence problemsand unstable or implausible estimates (see Preacher et al. 2010). In this case, the recom-mended procedure is to fix them at zero (Heck and Thomas 2009; Metha and Neale 2005;Muthén and Muthén 2011). The appropriateness of this constraint can be assessed byinspecting the residual variance estimates when they are obtained freely.


DeepApproach

Surface Approach

ITTF Approach

CCSF Approach

DeepApproach

Surface Approach

BiologyAchievement

BetweenLevel

Within Level

-.14

.42

.80 .82

.84 .86

.52

.29

-.40

-.47 -.28

.38

.88.80

.91.90

ITTF-S ITTF-I

CCSF-S CCSF-I

SS SM

DS DM

1 1

1 1

Biology Achievement

-.57

-.72

-.73

SS SM

DS DM

Fig. 4 Standardized parameter estimates for the between-level and within-level sub-models. In the between-level,ITTF approach→ Surface Approach; Surface Approach→ Biology Achievement and Deep Approach→ BiologyAchievement are not statistically significant


We shall now provide methodological details about the description, identification andfitting of the model. For further general information, please refer to more technical papers,such as Metha and Neale (2005) and Preacher et al. (2010), or to manuals, such as Heck andThomas (2009) and Muthén and Muthén (2011).

The two-level model fitted in our study uses a varying parameter formulation (Muthén1991). The first-level model is formulated for individual-level variation, and the second-level model is formulated for the variation in the parameters of the individual-level modelacross the clusters. More specifically, we assumed a fixed-slope, random-intercept formula-tion. Thus, at the within level, the loadings relating the indicators to the factors and theweights relating the factors to biology achievement were assumed not to vary across clusters(fixed slopes). However, the expected values of the indicators and the expected value ofbiology achievement were assumed to vary across clusters. This variation is modeled byusing random intercepts, which are shown in the within-level part of Fig. 4 as filled circles atthe ends of the arrows. At the between-level, the random intercepts of the indicators becomecontinuous latent variables that vary across clusters and act as indicators of the between-cluster factors. Similarly, the random intercept of biology achievement becomes a continu-ous latent variable that reflects the differences in achievement at the classroom level. For thisreason, the indicators and biology achievement are shown in circles in the between-level partof the model in Fig. 4.

Identifying the model requires a metric for defining the provided factors. The basic,common form of identification that we used in our study entails fixing all of the factormeans at zero and each first factor loading at one at both levels (Muthén and Muthén2011). Furthermore, and for the measurement of the sub-model, the two constraintsdiscussed above (strong invariance and zero residual variances at the between level) areto be specified. Finally, the assessment of the model-data fit is standard; we used thesame statistics and indices that we used in assessing the measurement models, asdiscussed above.

Two-level SEM analysis and results

Table 3 shows the descriptive statistics corresponding to the first step above: thecorrelation coefficients among the variables and the means, standard deviations, skewnessvalues and kurtosis values. The univariate results show that none of the variables have anextreme distribution or excessive kurtosis, suggesting that the data are suitable for MLestimation. We fitted the two-level SEM by using an ML estimation with robust standarderrors and an adjusted chi-square statistic, implemented in Mplus 6.11 (Muthén andMuthén 2011).

The intraclass correlation estimates obtained in the second preliminary step above were asfollows: .13 (BA), .29 (DS), .30 (SM and SS), and .28 (DM). These estimates suggest thatwithin-level variability is generally far larger than between-level variability (as expected).However, they are well above the .05 cut-off value and clearly indicate that there is sufficientbetween-cluster variation to proceed with the two-level analysis (e.g., Muthén 1991),especially if we take consider that these estimates are generally attenuated (biased down-ward) because of measurement error.

The SEM was specified as discussed above, and the appropriateness of the addi-tional specifications for the sub-model inside the box was also assessed. As expected,the inspection of the residual variances of the measurement indicators at the between-cluster level suggested that they were all very small. Therefore, they were fixed atzero. As for the appropriateness of the across-level configurational invariance


restriction, we followed the strategy used by Metha and Neale (2005). First, we fittedthe model with and without invariant across-level loadings and compared the fit inboth cases. Although the robust chi-square statistic cannot be used for differencetesting, it was clear that the fit was virtually the same in both conditions. Thus, therestriction of invariant across-level loadings did not noticeably worsen the fit. Evenmore, the RMSEA, which favors parsimony, was better for the simpler invariantmodel (.016 vs. .020). Second, we compared the parameter estimates obtained underthe invariance restriction to those obtained from the unconstrained model, and theywere all very similar. Thus, both results indicate that the invariance restriction isappropriate. Overall, the simple model with zero residual variances and invariantacross-level loadings that we proposed fit the data very well: χ2

(29)=34.561,p=.25, GFI=.990, AGFI=.984, CFI=.995, TLI=.992. These results clearly supportits appropriateness.

Figure 4 shows the diagrams and the standardized parameter estimates for both thebetween-cluster (panel a) and within-cluster (panel b) levels. The non-standardized estimatesand their standard errors are shown in Table 4.

If the common elements in both levels are compared, it is clear that the SA andDA factors are well-measured at both levels, although the measurement indicators areperfectly reliable at the between-cluster level. Because of the strong invariancerestriction, the proportions of the common factor variance at the between-cluster andwithin-cluster levels are directly comparable, and the estimated ‘true’ intraclass corre-lations were .354 for the SA factor and .255 for the DA factor. Thus, approximately35 % of the variance in the SA factor and 25 % of the variance in the DA factor liesbetween the teachers. Furthermore, at both levels, a significant proportion of the

Table 3 Correlation matrix for the variables included in the structural model and descriptive statistics (mean,standard deviation, skewness and kurtosis)

BA SM DM DS SS ITTF-I ITTF-S CCSF-S CCSF-I

SM −.393** –

DM .472** −.609** –

DS .453** −.608** .798** –

SS −.396** .760** −.603** −.595** –

ITTF-I −.133** .260** −.348** −.326** .286** –

ITTF-S −.119** .253** −.313** −.284** .285** .720** –

CCSF-S .109** −.340** .371** .373** −.313** −.455** −.470** –

CCSF-I .103** −.323** .375** .382** −.335** −.478** −.487** .842** –

M 13.29 8.07 9.46 10.08 7.96 9.33 11.55 11.38 11.70

SD 3.24 4.03 3.74 3.70 3.98 3.63 2.95 3.28 3.27

Skew −.01 .34 −.23 −.49 .46 .15 −1.12 −1.26 −1.47Kurt −.85 −1.04 −1.09 −.81 −1.02 −.96 1.04 .91 1.48

BA biology achievement; SM surface motivation; DM deep motivation; DS deep strategy; SS surface strategy;ITTF-I transmission/teacher-focused intention; ITTF-S transmission/teacher-focused strategy; CCSF-Sconceptual change/student-focused strategy; CCSF-I conceptual change/student-focused intention. Skewskewness; Kurt kurtosis

* p<.01

**p<.001


variance in biology achievement (BA) was explained by the model (see the coeffi-cients of determination in Table 5).

Regarding the structural relationship, we first note that the standardized point estimatesof the biology predictions are similar at both levels (medium effect sizes). They onlyattain statistical significance at the within-cluster level because the corresponding standarderrors at this level are far smaller. Thus, the problem may be due to a lack of powercaused by the relatively low number of sampling units (40 clusters) at the second level(see Snijders 2005). This apparent limitation, however, must be qualified because thenumbers of units in both levels of our study (778 and 40) compare favorably with thoseusually found in educational studies (Maas and Hox 2004). Above all, this potential lackof power does not invalidate the use of the two-level model. Given the sample sizes used

Table 4 Unstandardized parameter estimates for the two-level model

Estimate SE1 Est/SE2 P<3

Within-cluster level

Surface approach → SM 1.000 – – –

Surface approach → SS .968 .035 27.711 .000

Deep approach → DM 1.000 – – –

Deep approach → DS .972 .048 20.384 .000

Surface approach → BA −.147 .086 −1.702 .089

Deep approach → BA .467 .099 4.698 .000

Surface approach ↔ Deep approach −5.430 .910 −5.966 .000

Between-cluster level

Surface approach → SM 1.000 – – –

Surface approach → SS .968 .035 27.711 .000

Deep approach → DM 1.000 – – –

Deep approach → DS .972 .048 20.384 .000

ITTF approach → ITTF-I 1.000 – – –

ITTF approach → ITTF-S .753 .160 4.713 .000

CCSF approach → CCSF-I 1.000 – – –

CCSF approach → CCSF-S .988 .131 7.553 .000

ITTF approach → Surface approach .204 .167 1.228 .220

ITTF approach → Deep approach −.253 .113 −2.248 .025

CCSF approach → Surface approach −.372 .140 −2.661 .008

CCSF approach → Deep approach .370 .123 3.012 .003

Surface approach → BA −.145 .151 −.964 .335

Deep approach → BA .217 .192 1.131 .258

Surface approach ↔ Deep approach −1.415 .573 −2.470 .014

ITTF approach ↔ CCSF approach −5.004 2.153 −2.325 .020

ITTF information transmission/teacher-focused approach; ITTF-S ITTF strategy, ITTF-I ITTF intention.CCSF conceptual change/student-focused approach; CCSF-I CCSF intention, CCSF-S CCSF strategy. SSsurface strategy, SM surface motivation, DM deep motivation, DS deep strategy. BA biology achievement1 Standard Error2 Estimate/Standard Error3 Associated probability


and the simple model that was tested, the parameter estimates from the two-level modelcan be expected to be unbiased Maas and Hox (2004, 2005). Furthermore, given that arobust estimation procedure was used, the standard errors are also expected to be correct.Overall, the results are expected to be interpretable and as discussed below, many ofthem are of interest.

As for the remaining relationships, the results suggest that at the within-clusterlevel, academic achievement in biology was significantly and positively related to thereported use of an DA to learning (β=.42) and significantly and negatively related tothe reported use of an SA (β=–.14). At the between-cluster level, in accordance withour prediction, the more student-focused the teachers are (CCSF approach), the morethe class as a unit tends to report using the DA to learning (β=.52), and the moreteacher-focused the teachers report they are (ITTF approach), the less the class as aunit tends to report using a DA to learning (β=–.40). The results also show that theless student-focused the teachers report they are (CCSF approach), the more the classtends to report using the SA to learning (β=–.47), and the more teacher-focused theteachers report they are (ITTF approach), the more the class tends to report using theSA to learning (β=.29), but this relationship was not statistically significant. Please

Table 5 R-squared estimates for the two-level model

Estimate SE1 Est/SE2 P<3

Within-cluster level

Observed variable

Biology achievement .274 .059 4.627 .000

Surface motivation .679 .060 11.386 .000

Deep motivation .737 .057 12.924 .000

Deep strategy .701 .063 11.179 .000

Surface strategy .641 .061 10.574 .000

Between-cluster level

Observed variable

ITTF-intention .768 .130 5.930 .000

ITTF-strategy .633 .140 4.532 .000

CCSF-strategy .806 .115 7.029 .000

CCSF-intention .821 .146 5.603 .000

Biology achievement .406 .154 2.645 .008

Surface motivation 1.000 – – –

Deep motivation 1.000 – – –

Deep strategy 1.000 – – –

Surface strategy 1.000 – – –

Latent variable

Surface approach .453 .154 2.941 .003

Deep approach .657 .116 5.662 .000

ITTF information transmission/teacher-focused approach; CCSF conceptual change/student-focused approach1 Standard Error2 Estimate/Standard Error3 Probability


note that at the between-cluster level, the reported approaches to teaching are significant-ly associated with the reported approaches to learning (as hypothesized), although the R-square values (see Table 5) suggest that the relationship is stronger with the reported DA(65.7 % of the variance) than with the reported SA (45.3 % of the variance).

Discussion

As shown in the theoretical review, the results of prior investigations connecting approachesto learning and approaches to teaching are inconclusive, both at the university level and atthe high school level (Campbell et al. 2001). Because of the educational implications of thistopic, we expected to expand the understanding of the relationship between teachers’approaches to teaching and students’ academic achievement and to analyze the way thisrelationship is mediated by students’ approaches to learning. One of the novel aspects of thepresent research is the use of the recently developed and sophisticated two-level SEMmethodology to approach these questions.

With regard to our first question (Are teachers’ approaches to teaching related to students’approaches to studying?), and as demonstrated in previous studies (e.g., Jackling 2005;Leung et al. 2007; Prosser and Trigwell 1998; Struyven et al. 2006; Trigwell and Prosser1991a, 1991b; Trigwell et al. 1999; Valle et al. 2003), the results derived from our researchgenerally support the prediction that the students’ reported approaches to studying arepartially associated with their teachers’ reported approaches to teaching. This relationshipdoes appear weaker than expected; however, as stated by Entwistle (2009), it is obvious thatthe student’s choice of a certain approach to learning depends on many subject-relatedfactors (e.g., desire to learn, prior knowledge, previous strategies developed by studentsabout how to cope with teacher instruction, student autonomy in studying behavior), and onthe features of the learning/teaching environment (e.g., assessment type, teacher questioning,quality of the learning environment) (Diseth 2007; Entwistle and Entwistle 1991; Ramsdenet al. 2007; Struvyen et al. 2006; Valle et al. 2003).

As we predicted, the more student-focused the teachers report they are, the more studentstend to report using a DA and the less students tend to report using an SA. On the other hand,the more teacher-focused the teachers report they are (ITTF approach), the less students tendto report using a deep approach to learning.

Considering the direction and statistical significance of these results, we conclude thatthe adoption of a comprehensive approach to teaching would lead students to adopt adeeper and less superficial approach to learning. Conversely, adopting an approach toteaching oriented towards the transmission of information would lead the students to adopta less deep approach to learning. However, considering the strength of the relationshipsfound in this study, the conclusions should be qualified. We should acknowledge that thereis a link but that it is weak. Therefore, future research should carefully assess why this linkis not notably stronger (as it logically should be).

Concerning the second question raised (Do approaches to studying mediate therelationship between the approaches to teaching and academic achievement?), the dataprovided by this study offer evidence that students’ reported approaches to studyingmediate the relationship between the teachers’ reported approaches to teaching andstudent academic achievement. On the one hand, the model fits without including thedirect effects of the reported approaches to teaching on academic achievement. On theother hand, the reported approaches to studying are significantly related to academic


achievement in biology (at least at the individual level). Furthermore, the relationshipswere oriented in the expected direction: a reported DA was positively and significantlyrelated to academic achievement, whereas a reported SA was negatively associated withacademic achievement. We also discovered that the relationship found between thereported use of a DA and academic achievement is much stronger than that observedbetween the reported use of an SA and academic achievement. These results areinteresting because they suggest a clear association between the reported use of a DAand academic achievement; a more comprehensive learning process is associated withhigher academic achievement and vice versa.

With respect to the third question (To what extent do some of the relationships dependon the chosen level of analysis?), we first note that the most parsimonious level weconsidered fit the data very well, which suggests that the same structural model holds atboth levels for variables that have within-cluster and between-cluster sources of variation.Despite this finding, we also found that the regression weights of the reported approachesto learning on biology achievement were significant at the within-cluster level but not atthe between-cluster level. More specifically, the point estimates were similar in both cases.However, the standard errors were far larger at the between-cluster level. As discussedabove, a simple explanation for this result is lack of power in the between-cluster part ofthe model, which is predictable given the relatively low number of clusters. In support ofthis interpretation, we note that although none of the weights attain significance at thebetween-cluster level, the proportion of variance in biology achievement explained by themodel is significant at both levels.

Snijders (2005) reported that if an estimate of the effect size is available, then theminimal number of second-level units required to attain significance can be estimated byusing a Monte Carlo simulation. Information about effect sizes (i.e., the magnitude of thestructural coefficients) was not available before this study but it is now; thus, it can beused in the design of further studies. These studies would hopefully clarify the ‘true’impact that the reported approaches to learning have on the criterion variable at thebetween-cluster level.

A relevant result is that most of the variability in academic achievement, which isaccounted for by the model, is related to variables that were not the main targets of ourstudy. In addition to reported approaches to teaching, another sphere requiring urgentinvestigation is why achievement appears to depend so little on the reported approach tostudying (which includes motivation and strategy).

Our findings showed that teachers may exert an influence on the students’ reportedapproaches to learning, but their influence was actually weaker than expected. This resultcould also be explained by the fact that by the end of high school, students have alreadydeveloped their own strategies of how to cope with teacher instruction, regardless of thenature of the instruction. The finding that students’ reported approaches to studyingmediate the relationship between the reported approaches to teaching and achievementcould suggest that the students’ decisions about how to make the best use of instruction,regardless of the type of instruction, could play a critical role in the relationship betweenteaching and learning. Student autonomy and self-regulated behaviors should beaddressed in future studies to deepen our understanding of the complex processes relatedto teaching and learning. For example, it is important to investigate the process by whichstudents detect teachers’ approaches to teaching, how they interpret this information, andhow they change their study behaviors.

Because of the responsive nature of students’ approaches to learning, these resultssuggest the need to further analyze the characteristics of the micro-processes of teaching


(e.g., the nature and frequency of assessments, the type of questions in the classroom,workload, the nature and quality of notes, the frequency and type of feedback provided)because there may be other effects at the classroom level that have not been accountedfor and that could provide a better explanation than the one considered herein. Accord-ingly, studying the instruction process in depth could help to clarify the complexrelationship between teaching and learning. These data offer important guidelines fororganizing teachers’ school-based trainings, especially those linked to promoting thequality of the teaching process.

Study limitations

Although the present study has produced interesting results at both theoretical andpractical levels, we acknowledge the following its implications should be taken cau-tiously because of some certain theoretical and methodological limitations. Althoughthere may be others, we mention the following three problematic aspects below.

First, the structural relationships have low explanatory power with regard to some ofthe constructs of this study. These results do not compromise the validity of the model,which fits very well. However, they do represent an important limitation in light of theexplanatory capacity of the relationships among the variables. Some non-explainedvariability may be determined by other variables not included in the model (both atthe individual and the class level). If so, it would be necessary to extend and perhapsre-specify the model by including other variables potentially involved in the micro-processes of teaching (e.g., the nature and frequency of assessments, the type andworkload of homework, the feedback provided, the value and instrumentality of thesubject for the students, the depth and inclusiveness of prior knowledge, previousstrategies developed by the students for coping with teacher instruction, the skills andautonomy of students for self-regulating their learning behavior), which would increasethe explained variance of the endogenous variables. By increasing the explained vari-ance of the model, our understanding of high school students’ complex learningprocesses would also increase.

Second, although our findings strongly corroborate those from previous studies, thecontext-bound nature of the studies related to SAL and TAT theories should not beneglected, which suggests that generalizing to other disciplines (other than biology) andto other cultural contexts should be performed carefully (Prosser and Trigwell 2006; Steset al. 2010). Third, the design of this study was cross-sectional, which does not allow forcausal inferences, even using the two-level SEM perspective. To establish strongerevidence of causal relationships and to gain information regarding other reciprocalrelationships among the variables in this model, data should be collected at two or moretemporal moments (i.e., using a non-experimental design with repeated measures) andshould include more teachers and their students (see Maas and Hox 2005). As explainedherein, some of the discrepancies observed between the two levels may be due to a lackof power. Therefore, future studies should follow multilevel designs and include enoughunits at the between-cluster level to assure stability and accuracy at that level. It wouldalso be desirable to dispose of several samples to assess the invariance of the structuralsolution via cross-validation.

Finally, although the results derived from this investigation suggest a connectionbetween the teachers’ reported approaches to teaching and the students’ reportedapproaches to learning, which is an important finding for these two frameworks, themagnitude of the effects suggests the need for further consideration. One aspect to consider


is that the approaches to teaching and the approaches to learning were assessed throughself-report, which allowed for the gathering of information from many students andteachers about their perceptions of the learning and teaching processes but not their actualbehaviors. Moreover, students and teachers were the only source of information about theirown perceptions of learning or teaching, and one should not neglect the possible biasassociated with this method.

In the present study, the two self-report questionnaires designed to measure teachers’approaches to teaching and students’ approaches to learning showed good reliability andvalidity. Nevertheless, the 12 items may be insufficient for capturing the complexity of thelearning and teaching processes. For these reasons, future studies should consider combin-ing different instruments that assess the same information in different ways to build morerobust measures of the constructs. For example, when referring to self-regulation assess-ment, Zimmerman (2008) stresses the importance of using event measures that are focusedon the analysis of behaviors with a well-defined beginning and end, which would help tocapture the dynamics of changes in real time. In summary, an important implication of thiswork is that complementary measures of teaching and learning are needed to capture thecomplexity of the relationship between them, especially when sophisticated statisticalmodeling is used.

It is also important to note that given the responsive nature of the two constructs tothe learning environment, it is important to consider the possibility that teachers mayintend to follow a certain teaching methodology and approach to teaching, but they maynot be able to carry it out in practice (e.g., due to administrative issues, such as thenumber of students in class and specific guidelines in the assessment system, or personallimitations, such as an inability to analyze their own behavior in class and inefficientfeedback mechanisms to control their teaching). Moreover, students must be able toefficiently perceive what their teachers require of them in class. If they do not identifyor if they misinterpret the tasks assigned in class or the type of assessment that will beused, this mismatch will hinder the connection between the teacher’s approach to teachingand the student’s approach to learning. In other cases, students may understand what theteachers are asking but may not be able to perceive the utility of these tasks and strategiesof assessment (Rosário et al. 2010a).

Other aspects of our study concern the measures of academic achievement. Althoughthe chosen measure was derived from standardized tests with different types of questions,only one indicator was used. Because examination scores could assess surface recall moreeasily than deep understanding, future studies should use a variety of outcome measures(e.g., concept maps or portfolio diaries) (Hazel et al. 2002).

These possible explanations reinforce that future studies must include not only othervariables (related to the quality of the instructional process) that may increase theirexplanatory capacity (e.g., type of homework, nature and type of feedback, type ofquestions in class) but also event measurements that capture the procedural nature ofboth constructs.

Conclusions

Despite the limitations of our study, three main conclusions can be noted. First, theTATI and SALI questionnaires demonstrated suitable psychometric qualities (e.g., reli-ability and construct validity). Second, combining two levels suggests the followingrecursive structure: on the one hand, the more teachers report using a CCSF approach


to teaching, the more likely students (as a class unit) are to report adopting a DA tolearning. In turn, the more that students (within-cluster level) and groups (between-cluster level) report adopting a DA, the better they perform in biology. On the otherhand, the more that teachers report using the ITTF approach, the more their students (asa group) tend to report using an SA to learning. In turn, the more that individuals andgroups report using an SA, the lower their academic achievement in biology will be.Third, the strength of the association between the reported approach to learning andbiology achievement may vary according to the level of analysis used (i.e., withinlevels or between levels). The discrepancy may also be due to a lack of power at thesecond level.

Overall, the present two-level SEM study supports the prediction that the teacher’sreported approach to teaching is related to his or her students’ reported approaches tostudying. Similarly, the teacher’s reported approach to teaching is indirectly related to thestudents’ academic achievement. Because this study found a small effect for the relationshipbetween reported approaches to teaching and reported approaches to learning, futureresearch should try to deepen these relationships using multilevel designs.

Acknowledgments We gratefully acknowledge Professor Noel Entwistle’s comments and suggestions on theprevious version of this manuscript and the insightful suggestions provided by the editor and four anonymousreviewers.

Appendix 1

Table 6 SALI items

Translated items

I should memorize and repeat the ideas and sentences the teacher says in class to get good grades.

I find it important to spend time and effort relating new topics to what I already know about that issue.

I only study what I think is enough to pass.

After class, I reread my notes to make sure they are clear and that I understand them.

I find that adding extra information to my class notes is a waste of time. My aim is to study what is specificallyrequired of me.

I findmost of the new content interesting, and I try to understand the topics and summarize them inmy ownwords.

I only study on the day before the test, and I read my class notes only one or two times.

I study every day throughout the term to fully understand the topics discussed in class.

I do not see the point of studying topics that are not likely to be on the test.

I feel pleased with my studying when I understand the answers to the “why” questions.

I believe teachers should tell me exactly what material will be on the exam because I am only going to studythat material.

When I receive my graded tests/homework, I read the corrections carefully and try to understand the reasonsbehind my mistakes.

Students responded on a scale from 1 (strongly disagree) to 5 (strongly agree).

Scoring is in the following cyclical order:

1. SM, 2. DM, 3. SS, 4. DS, 5. etc.

Deep Approach Score: Σ All DM scores+all DS scores

Surface Approach Score: Σ All SM scores + all SS scores


Appendix 2

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Translated items

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ITTF: Σ All ITTF-I scores + all ITTF-S scores

CCSF Score: Σ All CCSF-I scores + all CCSF-S scores


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The relationship between approaches to teaching and approaches to studying: a two-level structural equation model for biology achievement in high school