University Students' Perceptions of the Learning ...reforma.fen.uchile.cl/Papers/Learning...

Studies in Higher Education Volume 27 No 1 2002

University Studentsrsquo Perceptions ofthe Learning Environment andAcademic Outcomes implicationsfor theory and practiceALF LIZZIO KEITHIA WILSON amp ROLAND SIMONSSchool of Applied Psychology Grif th University Australia

ABSTRACT The relationship between university studentsrsquo perceptions of their academic environmenttheir approaches to study and academic outcomes was investigated at both university and facultylevels The responses of a large cross-disciplinary sample of undergraduate students were analysedusing higher order path and regression analyses and the results con rmed studentsrsquo perceptions asin uencing both lsquohardrsquo (academic achievement) and lsquosoftrsquo (satisfaction development of key skills)learning outcomes both directly and mediated through their approaches to study Perceptions ofheavy workload and inappropriate assessment in uenced students towards surface and perceptions ofgood teaching towards deep approaches to study Studentsrsquo perceptions of their current learningenvironment were a stronger predictor of learning outcomes at university than prior achievement atschool Protocols are proposed to guide more ne-grained analysis of studentsrsquo perceptions

This article seeks to make both a theoretical and practical contribution to the literatureregarding the nature and impact of university studentsrsquo perceptions of an academic environ-ment on their learning approaches and outcomes We will argue that the clarity andgeneralisability of previous research investigating the association between presage factors ina university learning environment and studentsrsquo approaches to learning in that environmentare limited by a number of methodological issues in the areas of measurement of constructssampling and statistical analyses employed The present study seeks rstly to address theseand to test further the relationships between characteristics of the academic environmentstudentsrsquo approaches to learning and the outcomes achieved both in the context of speci cfaculties and at the level of the individual student Secondly we will report on the practicalapplication of ndings to teaching practice and the development of speci c protocols toelaborate the speci c nature of studentsrsquo perceptions of aspects of their academic environ-ments

Does the type of academic environment within which students are asked to learn (egworkload teaching quality) have any real impact on how they approach their learning and thequality of the outcomes they are able to achieve Or will students lsquodo wellrsquo and lsquonot so wellrsquoirrespective of their environments Such questions are not just theoretically interesting butalso practically signi cant for university educators seeking to understand the impact of theircourse design decisions on studentsrsquo perceptions and behaviour

Research efforts addressing the impact of studentsrsquo perceptions can be readily framedwithin Biggsrsquos (1989) 3P model which conceptualises the learning process as an interacting

ISSN 0307-5079 print ISSN 1470-174X online02010027-26 Oacute 2002 Society for Research into Higher EducationDOI 10108003075070120099359

28 A Lizzio et al

system of three sets of variables the learning environment and student characteristics(presage) studentsrsquo approach to learning (process) and learning outcomes (product) In basicterms the model proposes that rstly personal and situational factors in uence a student toadopt a particular approach to learning which in turn mediates or in uences the types ofoutcomes achieved and secondly that presage factors (eg perceptions of the learningenvironment) can also directly in uence learning outcomes

Presage factors are as the term suggests those which exist prior to the time of learningand comprise two broad types the enduring personal characteristics brought to the learningsituation by the student (eg prior knowledge academic ability personality) and situationalcharacteristics which de ne the learning environment (eg teaching methods workloadcourse structure) As Biggs (1985) and others (Ramsden 1991) have argued the key elementin the practical application of this model is the proposition that it is studentsrsquo perceptions oftheir learning environment in light of their motivations and expectations which determinehow situational factors in uence approaches to learning and learning outcomes

Process factors describe how students approach their learning While there is somediversity in the terms used there is a fair degree of empirical evidence that students adopt twobasic orientations or approaches A lsquodeeprsquo approach to learning is described as striving forimproved understanding by applying and comparing ideas Conversely lsquosurfacersquo learninginvolves reproductive strategies with little attempt to integrate information (Marton amp Saljo1976 Thomas amp Bain 1984)

Product factors describe the learning outcomes (cognitive affective or behavioural)which students derive from the learning process Traditionally depth or accuracy oflearning has been summarily described through assessment scores (grade point averages)Affective or lsquofelt outcomesrsquo in a climate emphasising student evaluations of education havebecome increasingly discussed These are generally of two typesmdashglobal evaluations ofaccomplishment or expressed satisfaction with a course and speci c perceptions of particularskill development (eg key or transferable skills)

It is important to note that while the present study focuses on a primarily linear orunidirectional understanding of component relationships (ie student characteristics andperceptions in uence student approaches which in uence student outcomes) the relation-ships between these elements are best conceived as an interactive system (Biggs 1993 1999)A more complex analysis would consider rstly the relationships between all systemcomponents and secondly that such in uence can be reciprocal or mediated in nature(Meyer 1998) Trigwell amp Prosser (1997) further argue that the interpretation of interactionsin the model should include the concept of lsquotemporalityrsquo and propose that the modelrsquoselements are not a causal chain of independently constituted components over time but aresimultaneously present in studentsrsquo awareness

The present study is not concerned with systematically operationalising and testing allpossible elements of the 3P model The focus is somewhat more constrained We seek toaddress three questions Firstly do studentsrsquo perceptions of their learning environmentsystematically in uence learning approaches and outcomes Secondly if so which aspects ofthe learning environment have moreless in uence in this regard Finally which in uentialaspects of the learning environment appear to be most amenable to change towards the goalsof enhancing learning outcomes

Ramsden amp Entwistle (1981) were the rst to empirically establish a relationshipbetween approaches to learning and perceived characteristics of the academic environmentThis association was explored through a concurrent factor analysis of the scales of theApproaches to Studying Inventory (ASI Entwistle et al 1979) and the Course PerceptionsQuestionnaire (CPQ Entwistle amp Ramsden 1983) based on the responses of a large

Perceptions of the Learning Environment 29

cross-disciplinary sample of English higher education students The resultant combinedfactor structure evidenced a strong association between studentsrsquo perceptions of a heavyacademic workload and their adoption of a reproducing or surface approach to studyHowever no association was evident between studentsrsquo perceptions of other aspects of thelearning environment (eg teaching practices) and the adoption of a meaning orientation ora deep approach to learning

Despite the clear theoretical importance and practical relevance of this work and theunderlying problem of a lack of expected association between academic context and thelearning approach at the level of the individual student subsequent independent studies wereslow to follow Meyer amp Parsons (1989) in an effort to directly test the generalisability ofRamsden amp Entwistlersquos ndings replicated their methodology on a large cross-disciplinarysample of South African students Once again apart from an association between courseworkload and a reproducing study orientation no associations were found between anyaspects of the learning environment as measured by the CPQ and the two main studyorientations of reproducing and meaning as measured by the ASI

The important contribution made by Meyer amp Parsons was an empirical and conceptualanalysis of the instruments typically used to measure studentsrsquo perceptions of academicenvironments and approaches to learning From a comparison of differences in the respectivefactor structures obtained from English (Entwistle amp Ramsden 1983) and South Africanstudents (Meyer amp Parsons 1989) Meyer amp Parsons proposed that a number of theconstructs measured by the ASI may be culturally or contextually de ned (eg syllabusboundedness strategic approach extrinsic motivation) with the consequence that themeaning and therefore the structure of some subscales may vary across different studentpopulations de ned either in terms of cultural or institutional contexts With regard to theCPQ Meyer amp Parsons reported dif culty in replicating the scale structure and argued thatthe CPQ measures a restricted or at least parsimonious model of the learning environmentwhich does not permit an adequate exploration of potential relationships between contextualfactors and the approaches to study of individual students Entwistle (1989) in response toMeyer amp Parsons agreed that there may be measurement problems with the CPQ andsuggested that the underlying concepts were relatively untried at the time of the originalstudy He also argued for the development of a measure more sensitive to individualperceptions of the academic environment (Entwistle amp Tait 1990)

Subsequently research has followed Entwistlersquos (1989) suggestion that explorations ofthe relationship between approaches to studying and perceptions of the learning environmentmay be more productive at the faculty or departmental level given that ndings at this levelmay be more likely to lead directly to the review and modi cation of course design andteaching practices A small number of studies using a mixture of populations and method-ologies have addressed this task Entwistle amp Tait (1990) using a shortened version of theASI and a theoretically derived course evaluation item pool found a strong associationbetween a demanding workload and a reproducing study orientation for rst year Scottishengineering students Eley (1992) using a modi ed version of the CPQ with a small sampleof Australian second year students found students reported more surface approaches whencourses emphasised formal achievement and more deep approaches when courses wereperceived as high on supportive teaching independent learning and clear structure Trigwellamp Prosser (1991a 1991b) concurrently factor analysed the scores of a small sample ofnursing students on the ASI and the Course Experience Questionnaire (CEQ Ramsden1991) and derived a two-factor solution associating perceived heavy workloads and surfaceapproaches to study However perceptions of good teaching were associated with bothsurface and deep approaches to study

30 A Lizzio et al

Trigwell amp Prosser (1991a) distinguished between the types of learning outcomesstudents may derive from a course and found that a deep approach to study was morestrongly related to qualitative learning outcomes (the complexity of studentsrsquo understandingof the aims of a course of study) than quantitative outcomes (assessment results) In asubsequent study (Crawford et al 1998) a modi ed version of the CEQ was used to t thecontext of a rst-year mathematics subject A deep approach to learning was found toassociate with the good teaching clear goals and independence scales and a surface approachwith the inappropriate assessment and workload scales Students who reported a deepapproach to learning the subject also reported more integrated conceptions of the subjectcontent both prior and subsequent to enrolment in the subject Prosser amp Trigwell (1999)report an unpublished study (Ramsden et al 1997) of a large cross-disciplinary sample of rst-year students using a subject speci c version of the CEQ and a short form of the StudyProcess Questionnaire (SPQ Biggs 1979) Using factor analytic techniques once again thesame pattern of associations between studentsrsquo perceptions of their subjects on the scales ofthe SPQ and approaches to learning was revealed both at a general level across subjects andalso within the same subject A consistent pattern was also obtained when students weregrouped according to their scores on the questionnaires using cluster analysis

In parallel to these more general analyses other researchers have investigated speci caspects of studentsrsquo perceptions of the learning environment in more depth For exampleKember and his associates (Kember et al 1996 Kember amp Leung 1998) have conducteda more ne-grained set of studies with engineering students focusing speci cally on theimpact of workload on student learning These studies have not only con rmed but alsoextended our understanding of the factors in uencing studentsrsquo perceptions Kember ampLeung (1998) using path analysis found a positive and reciprocal link between a surfaceapproach and perceived heavy workloadmdashthat is not only do perceptions affect approachesbut approaches also affect perceptions Importantly actual workload was not in itself a goodmeasure of perceived workload with the latter found to be a complex function of a range offactors Kember et al (1996) modelled the relationship between perceived workload studybehaviours and academic outcomes using both case study and path analysis and found howstudents perceive workload to be a function of individual characteristics approaches to andperceptions of the learning context Kember et al caution against interpreting questionnaire ndings lsquoas purely a measure of the burden imposed by the curriculum nor as an indicatorof hours workedrsquo (p 356)

What can research to date tell us about the relationship between approaches to learningand academic outcomes The key point as we have discussed earlier is to specify the typeof outcome under considerationmdashunderstanding achievement or satisfaction In addition topreviously reported studies there appears to be an established pattern of ndings that theadoption of a deep approach results in students being better able to demonstrate theirunderstanding of material (Whelan 1988 Balla et al 1990 Trigwell amp Sleet 1990) ordevelop their conceptions of material (Van Rossum amp Schenk 1984 Prosser amp Millar 1989)There is also evidence indicating that a deep approach results in students achieving highergrades (Entwistle amp Ramsden 1983 Ramsden et al 1986 Eley 1992 Drew amp Watkins1998) Importantly however the relationship between study approaches and grades isnecessarily moderated by the extent to which the assessment used to measure studentsrsquoachievement itself emphasises understanding or reproduction of knowledge Finally there issome limited evidence (Ramsden 1992) to suggest that students adopting a deep approachreport higher levels of overall satisfaction with a course of study While it is important toestablish the impact of learning environments and approaches on studentsrsquo affective experi-ences such associations should be interpreted cautiously since student satisfaction and


associated processes such as interest in a task or subject may in uence as much as resultfrom a studentrsquos approach to study

A further contribution of this article is a more extensive analysis of the associationbetween studentsrsquo perceptions and types of academic outcomes In addition to traditionalconcerns of academic achievement and student satisfaction there is also a pressing need tobetter understand how learning environments in uence the acquisition of generic skills orcompetenciesmdashthat is the process skills which help students to effectively apply the contentor subject skills learnt in higher education to work environments (MacNair 1990) Theseskills (variously termed key skills core competencies or generic attributes) include problem-solving analytic skills teamwork and the ability to plan work While there has beenconsiderable recent interest in how such skills may be best provided (Bennett et al 1998) orincorporated into course designs (Arnold et al 1999) there is little direct empirical evidenceof the impact of the learning environment and student approaches on the development ofgeneric capabilities

Overall the evidence to date would seem to indicate that there are clear relationshipsbetween studentsrsquo perceptions approaches and outcomes The most consistent nding wouldappear to be that perceptions of heavy workloads in uence a surface or reproducing approachto study Much less clear is whether a deep approach is facilitated by perceptions of theacademic environment and whether in fact this consistently leads to superior academicoutcomes Importantly the commonly used procedure of concurrently factor analysingstudentsrsquo responses on different measures to derive associative factors does not permit directtesting of the relative paths of in uence of variables Thus the extent to which studentsrsquoperceptions of their learning environment directly impact on their academic outcomes orwhether the process involves a more indirect or mediated path of perceptions in uencingapproaches which in turn in uence academic outcomes remains unclear

Studies to date have also not included a direct test of the relative in uence of personaland perceived situational presage factors that is a direct comparative test of the questionswill students who have lsquodone well at schoolrsquo also do well at university irrespective of theteaching environment they perceive themselves to be in or will their perceptions of theirlearning environment (eg teaching quality workloads) be a stronger predictor of successGiven that school leaving performance operationalised in a variety of ways (eg tertiaryentrance score [Hong 1982] Scholastic Achievement Test results (Maugher amp Kolmodin1975 Bean amp Bradley 1986) has been found to be a signi cant predictor of achievement atuniversity one would expect a relationship between studentsrsquo high school and universityperformance However the association is modest at best and some studies have foundsecondary school grades not to predict academic achievement (McManus et al 1998) Thedirection of ndings may also be in uenced by the choice of statistical procedure Forexample Crawford et al (1998) found mathematics studentsrsquo tertiary entrance rankings notto be associated with a deep approach using factor analytic procedures but found arelationship using hierarchical cluster analysis As Lindblom-Ylanne et al (1999) argue thepicture remains unclear as to the relative contribution of secondary school achievement andother factors to studentsrsquo future learning potential A goal of the present study therefore isto clarify the relative contribution of the studentsrsquo previous patterns of success and theteachersrsquo current learning environment

While studies to date have been useful in elucidating and strengthening the case for anassociation between perceptions of context approaches and outcomes the con dence withwhich this can be asserted as a generalisable or universal proposition is however limited bya number of factors the restriction of samples to de ned populations (primarily rst years ordiscipline-speci c groupings) small sample sizes the use of untested modi cations of

32 A Lizzio et al

instruments to measure in particular aspects of the academic environment and statisticalprocedures which do not permit direct testing of paths of in uence Given these concernsfurther work in this area is suggested as important and timely In practical terms the task isto establish whether irrespective of studentsrsquo predispositions towards a deep or surfaceapproach to learning and the context of particular courses of study good teaching andeffective course design can positively in uence or lsquodeepenrsquo studentsrsquo approaches to thecurriculum and academic outcomes The present study sought to address this task and inparticular sought to respond to some of the potential methodological limitations of previousstudies

With regard to the measurement of approaches to studying three considerations weresalient in constructing a measure appropriate for associations between approaches and thelearning environment at the level of the individual student Firstly there is a consistentpattern of ndings which identify two primary study approachesmdashmeaning and reproducingorientationsmdashacross the four primary groupings of ASI subscales (Clark 1986 Entwistle ampWaterston 1988 Harper amp Kember 1989) Secondly and relatedly the balance of con-structs measured by the ASI (eg syllabus boundedness strategic approach extrinsic motiv-ation) appear to vary somewhat in their structures and associations with primary orientationsacross institutional and cultural contexts and in this sense have been proposed as contextu-ally dependent Thirdly there is a need to develop reliable and robust short forms of currentapproaches to study instruments (Richardson 1990 1994) Brevity is not only useful tofacilitate use of instruments in classroom contexts but also critical to ensuring reasonableresponse rates in large cross-disciplinary surveys as in the present study

Accordingly an attempt was made in the light of previous research to test two brief(six-item) generic approaches to study scales one representing meaning or deep and thesecond reproducing or surface orientations which speci cally avoided items or subscalesidenti ed by previous research as potentially context-dependent Thus for the deep ormeaning orientation scale items were selected from the Deep Relating Ideas and Use ofEvidence and Logic subscales and for the surface or reproducing scale from the Surface andImprovidence subscales A similar short form has been previously analysed using theresponses of a small sample (N 5 122) of Australian nursing students and found to yield tworeliable factorsmdashthe deep and relating ideas forming a rst and the reproducingsurfaceitems forming a second factor (Trigwell amp Prosser 1991b)

With regard to measurement of the academic environment most previous researchinvestigating the relationship between environment and approaches to study at the cross-fac-ulty or individual student level has used the Course Perceptions Questionnaire (CPQEntwistle amp Ramsden 1983) The psychometric limitations of the instrument may beparticularly relevant to understanding why there may have been some dif culties in establish-ing associations between context and approach This is reinforced by a clearer pattern of ndings from more recent studies using modi ed versions of the CEQ (Prosser amp Trigwell1999) Thus in the present study it was proposed to use the most recent form of the CPQnamely the Course Experience Questionnaire (CEQ Ramsden 1991 Wilson et al 1997) tomeasure aspects of the learning environment The CEQ has been speci cally designed as aperformance indicator of teaching effectiveness at the level of the whole course or degreeThus all items have been designed to measure aspects of the learning environment acrossdisciplines and institutions It is therefore currently the most suitable instrument formeasuring aspects of the learning environment at the general or individual level Theconceptual framework underlying the scales of the CEQ is presented in Appendix 1 Thestructure includes 36 items with ve scales measuring key aspects of the learning contextnamely Good Teaching Clear Goals and Standards Appropriate Assessment Appropriate


Workload and an emphasis on Independence and a sixth scale (Generic Skills) measuringthe reported acquisition of transferable skills More recently the instrument has undergoneextensive and rigorous cross-validation of its structure across several large multidisciplinarysamples using a combination of exploratory and con rmatory factor analysis as well aspredictive and discriminatory validity procedures (Wilson et al 1997) Findings con rmedthe stability and validity of the instrument as a measure of the learning environment at thedegree level

Finally with regard to considerations of sampling and analysis the present study soughtto extend on previous work by rstly sampling students in three contrasting disciplines(humanities science commerce) across all years of undergraduate study in a systematicattempt to establish both general and discipline-speci c patterns If the proposed relation-ships function at the lsquolevel of the individual studentrsquo we would expect to nd themconsistently across disciplines and years of study Secondly the present study sought to applymore rigorous and precise tests of relationships than has previously been the case with factoranalytic and correlational procedures through the use of higher order path analysis Thisapproach to analysis will allow direct tests of both direct and indirect paths of in uencebetween presage characteristics studentsrsquo approaches and academic outcomes at both gen-eral and discipline-speci c levels

Method

Sample

A broad-based survey of students from the same university was conducted with 5000students being randomly selected from within the university to represent approximately equalnumbers of males and females across 14 faculties (including humanities business commerceenvironmental sciences computing sciences social sciences law education health sciencesand visual and performing arts) and all years of study Questionnaires were mailed toindividual studentsrsquo home addresses approximately 3 months after the completion of theacademic year to be returned directly to the research project base within the university Upto three follow-up letters were sent in order to achieve a minimum response rate of 30within each faculty and 50 overall for the university A total of 2130 usable responses(response rate 54) was obtained comprising 1043 female (49) and 1087 male (51)students From this total sample faculties with a sample size large enough to produce validand reliable results using path analysis (n 150) were selected for inclusion in the analysisThis nal university sample of 646 comprised the disciplines of commerce (n 5 249)humanities (n 5 210) and science (n 5 187)

Measures

Presage variables Two personal (gender prior academic ability) and two environmental(studentsrsquo perceptions of teaching quality and workload) variables were selected to measurethe presage domain

Prior Academic Abilitymdashprior academic ability was operationalised by studentsrsquo TertiaryEntrance (TE) scores Based on their performance in years 11 and 12 of their secondaryeducation students earn a rating from 1 (low) to 999 (high achievement) which is used todetermine order of entrance to tertiary courses TE scores were available from of cialuniversity records for 614 students in the present sample The range of studentsrsquo TE scores

34 A Lizzio et al

in the present sample was 717 to 999 with a mean of 78302 a standard deviation of 4719a modal score of 758 and a median score of 7705

Teaching Environmentmdashperceptions of the teaching environment were measured bystudentsrsquo ratings on the CEQ (Ramsden 1991) The CEQ was designed as a performanceindicator of teaching effectiveness at the level of whole course or degree in those aspects ofteaching about which students have direct experience and are therefore validly able tocomment (namely quality of teaching clear goals and standards workload assessment andemphasis on independence) The higher order structure of the CEQ has been establishedusing con rmatory factor analysis as comprising two factorsmdasha teaching quality factor(de ned by the good teaching clear goals and standards appropriate assessment andemphasis on independence scales) and a level of workload factor (de ned by the appropriateworkload scale alone) (Wilson et al 1997) Based on this structure perceptions of theteaching environment were measured by separately summing studentsrsquo course ratings acrossthe scales related to teaching quality and workload While the two-factor higher orderstructure of the CEQ will be used in the path analysis supplementary regression analysesusing the ve scales as separate predictors will also be conducted to enable a more preciseexamination of relationships between perceptions and both approaches and outcomes

Process variables Two approaches to learning (deep approach intrinsic motivation andemphasis on meaning and surface approach extrinsic motivation and emphasis on repro-duction) were selected to measure the process domain

Approaches to learningmdashtwo short scales (six items each) were constructed using itemsfrom the ASI (Entwistle et al 1979) to represent deep and surface approaches Analysis ofstudent responses from the present sample evidenced moderate levels of internal consistency(Cronbachrsquos alpha coef cients of 067 for deep approach and 069 for surface approach) inexcess of those typically reported for these subscales (Entwistle et al 1979 Entwistle ampKozeki 1985) and close to the limit of 071 suggested by Comrey (1973) as indicating highinternal consistency Construct validity was con rmed by means of a principal componentsfactor analysis with a varimax rotation on the 12-item set Two distinct factors accountingfor 40 of the variance (Factor 1 5 24 Factor 2 5 16) were clearly identi able with allsix deep items and all six surface items loading in excess of 030 on the rst and secondfactors respectively A signi cant negative correlation ( 2 021 p 0001) between the twoscales further supports their construct validity as measures of deep and surface approaches tolearning

Product variables The outcome domain was represented by three variables academicachievement course satisfaction and generic skills development

Academic Achievementmdashacademic achievement was represented by calculating usinguniversity academic records studentsrsquo grade point average (GPA) measured on a scale from1 (low) to 7 (high) from the commencement of their degree to the point at which the surveywas conducted

Course Satisfactionmdashcourse satisfaction was measured by studentsrsquo responses to the itemlsquoOverall I am satis ed with the quality of this coursersquo rated on a 5-point Likert scale from1 disagree to 5 agree

Generic Skillsmdashgeneric skills development was measured by averaging studentsrsquo re-sponses to the six items of the generic skills scale of the CEQ This scale measures processskills relevant to employablity and lifelong learning such as written communication prob-lem-solving analytic skills teamwork ability to plan onersquos own work and con dence intackling new situations


Results and Discussion

The relationship between the presage process and product aspects of the learning environ-ment were investigated in a three stage process using multiple regression and structuralequation modelling techniques (EQS Bentler 1989) In the rst step a global model ofhypothesised relationships using the higher order structure of the CEQ was tested at thelevel of the whole university or individual student Secondly the generalisability of this modelwas tested at the faculty level for the disciplines of science commerce and humanitiesFinally the general relationships identi ed through the path analysis were further elaboratedusing the individual scale structure of the CEQ to more speci cally assess components of thelearning environment

Prior to the reporting of results it is important to outline the assumptions underpinningour interpretation of the path analysis Higher order path analysis permits the direct testingof the theoretically proposed 3P model through the calculation of a measure of t betweenthe proposed model and the optimal model derived from estimates of the various relation-ships All signi cant relationships indicate an alpha level equal to or greater than 005 WhileEQS provides several goodness-of- t indices as a guide to interpretation current researchrecommends the use of a two-index presentation strategy to evaluate model t (Hu ampBentler 1999) We have chosen the comparative t index (CFI the agreement between theempirical results and the results expected by the model) and the standardised root meansquared residual (SRMR an index of the degree to which the initial correlation matrix is notreproduced by the estimated factor model) A good t is generally indicated by a CFI closeto 095 (the maximum value is 10) and an SRMR less than 008 (Hu amp Bentler 1999) Thecorrelation matrix means and standard deviations for each variable were used as input for thestructural analysis

University Level Analysis

Initially data were analysed separately for men and women to test for gender differences butnone were found Thus the sample was combined for all analyses The results revealed agood overall t of the data to the model at the university level (CFI 5 096 based on 5 degreesof freedom SRMR 5 0007) The results for the university-level path analysis are presentedin Fig 1

Prior academic achievement Studentsrsquo tertiary entrance (TE) score was positively butweakly associated with a surface approach to university study That is students who overallappeared to lsquodo betterrsquo at school reported themselves as being slightly more likely to employsurface or reproductive study strategies at university Consistently school-based achievementwas not associated with a deep approach to university study

Studentsrsquo TE score was overall also positively but weakly associated with studentsrsquoachievement at university as measured by GPA That is how well students achieved at highschool was a positive but weak predictor of their lsquoacademic successrsquo at university This nding should perhaps be interpreted cautiously given the inherent restriction of the rangeof TE scores for students entering university compared to the wider population of schoolleavers In a broader sense this relationship may be stronger than present ndings indicate

Importantly prior academic achievement did not systematically in uence studentsrsquoperceptions of their university learning environment That is how well a student performedat school (as evidenced by their TE score) did not predict their evaluations of the quality ofuniversity teaching or the level of workload This would seem to some extent to run contrary

36 A Lizzio et al

FIG 1 The relationship between the academic environment approaches to study and academic outcomes atthe level of the whole university

to the proposition that studentsrsquo evaluations are confounded by academic ability speci callythat so termed lsquobetter studentsrsquo would evaluate university teaching more critically

Academic environment Workload studentsrsquo perceptions of the appropriateness of theworkload in their course of study were associated with their reported levels of satisfaction anddevelopment of generic skills or metacompetencies and their level of academic achievementThat perceptions of workload would be related to an attitudinal outcome variable such assatisfaction is not surprising Students are also however linking their perceptions of courseworkload with their actual learning outcomes While the association is small students areindicating that they are developing higher levels of generic and transferable skills in courseswhich are lsquoless packedrsquo and which presumably therefore allow greater opportunities andtime for the use of analytic problem-solving and interactive learning processes that facilitatethe synthesis integration and application of knowledge The effect of high workload onlearning outcomes also appears to be mediated by its encouragement of surface processing ofthe curriculum which in turn is negatively associated with generic skill development Boththe direct and mediated paths between workload and generic skills support the course designprinciple of appropriate balancing of the breadth and depth of curriculum to allow opportu-nities for students to engage in higher order learning activities The direct association ofworkload and academic achievement (GPA) should not be surprising Students who lsquodobetterrsquo have on average probably managed the workload more effectively and consequentlylsquoperceive itrsquo to be at more appropriate levels This proposition is not entirely straightforwardhowever Kember amp Leung (1998) in a study of engineering students found GPA to havelittle in uence on perception of workload Clearly further investigation of a potentiallyreciprocal relationship would be useful in this regard

Overall studentsrsquo perceptions of workload in the present study function consistently


with previously reported patterns which link higher workloads to increased surface process-ing and generally poorer learning outcomes and satisfaction with the learning environment

Good Teaching the higher order construct of good teaching (an aggregation of the goodteaching appropriate assessment clear goals and standards and independence in learningscales of the CEQ) was positively associated with studentsrsquo reporting a deep approach to theirstudy That is students who perceive their learning environments to evidence good teachingreport themselves as more likely to adopt lsquomeaning-basedrsquo and less likely to adopt repro-ductive learning strategies Such deep approaches are in turn clearly associated withstudentsrsquo self-reported development of generic skills and have a small association withacademic achievement as measured by GPA Thus this path describes a process wherebygood teaching has a positive effect on academic outcomes which is mediated by studentsrsquoapproaches to learning In addition to contributing to the adoption of deep approaches bystudents good teaching was also directly and strongly associated with all three academicoutcome measures That is irrespective of their approaches to study students in suchacademic environments report greater satisfaction and generic skill development and achievehigher grades Clearly a teaching environment which students perceive as effective consist-ent with Biggsrsquos (1985) notion of dual in uence makes both direct and indirect (throughapproaches) contributions to learning outcomes

It is important to note the comparative contributions of the presage factors of prioracademic achievement and perceptions of the academic environment While consistent withprevious research TE score (prior academic ability) was a signi cant predictor of GPA itwas a relatively weak in uence when compared to the composite environmental factor ofperceived good teaching Clearly teaching perceived as lsquoinvolvingrsquo can and does contributelsquoadded valuersquo to both studentsrsquo achievement and learning beyond their prior academicability This may be particularly reassuring to university teachers who are wondering if theyare lsquomaking a differencersquo

Learning approaches What is the effect of studentsrsquo learning approaches on academicoutcomes While both approaches were associated with higher grades this relationship wasclearly stronger for surface strategies The speci c relationship between learning approachesand academic outcomes depends squarely on the nature of the outcome in question Thepresent ndings suggest that in the present context course grades are more likely to be basedon assessment procedures which are related to the memorisation of declarative or proceduralknowledge and a factual more than a conceptual mastery of the curriculum Subsequentfaculty-level analyses will clarify this relationship The use of either approach is not related tostudentsrsquo overall satisfaction with a course of study but not surprisingly a deep approach isclearly associated with the reported development of generic metacompetencies As predictedstudents across discipline and year of study reported adopting a reproducing or surfaceapproach to study in learning environments that they negatively evaluated on all scales of theCEQ Consistently students reported that they adopted a meaning or deep approach inpositively evaluated environments

Such a clear pattern of associations across disciplines and years of study appears tosupport a systematic relationship between studentsrsquo perceptions of the academic environmentand their reported approaches to study The clarity of this pattern of ndings and itsconsistency with associations between approaches to study and perceptions of the learningenvironment previously found at the faculty or departmental level (eg Ramsden amp Entwistle1981 Trigwell amp Prosser 1991a) supports Meyer amp Parsons (1989) and Entwistlersquos (1989)proposals that measurement-related issues with the ASI and CPQ in particular may haveconfounded earlier research efforts

38 A Lizzio et al

FIG 2 Patterns of relationships between academic environment approaches to study and academic outcomesfor commerce science and humanities faculties

Faculty Level Analysis

In order to test the generalisability of this pattern of relationships to speci c educationalcontexts at the faculty level a further series of three structural equation analyses testingrelationships between the same set of academic presage process and outcome variables wasconducted on studentsrsquo perceptions in the faculties of science humanities and commerceResults at the level of individual faculties approximated that of the whole university in termsof goodness of t but fell a little short of the recommended CFI level of 095 for a good tof model and data humanities (CFI 5 089 based on 8 degrees of freedom SRMR 5 002)commerce (CFI 5 088 based on 9 degrees of freedom SRMR 5 003) and science(CFI 5 087 based on 7 degrees of freedom SRMR 5 002) Smaller sample sizes and inparticular ratios of subjects to variables may have contributed to the level of achieved model t at the faculty level However given the relative consistency of patterns of ndings acrossanalyses the faculty-level models were considered an adequate basis for an indicativediscussion

For economy of presentation the results of these three analyses are summarised in Fig2 For the most part the relationships identi ed at the level of the whole university orindividual students are in evidence at the faculty level In particular the direct associationsbetween good teaching and appropriate workload and positive academic outcomes and theindirect path of good teaching contributing to deeper processing which in turn contributesto generic skills development were both con rmed in a range of speci c academic contexts

Two speci c variations were evident between the general map and faculty-speci c mapsin the relationship of school achievement (TE) and university achievement (GPA) andapproaches to study While science students were the only group to show a positiveassociation (016) between their TE score and deep approach to study at university


TABLE I Summary results of multiple regression analyses with CEQ scales as predictors of deep andsurface approaches to learning

CEQ scale Deep approach Surface approach

B Beta t B Beta t

Good Teaching 010 017 514Dagger nsClear Goals 005 006 217 nsAssessment 008 011 380dagger 2 030 2 034 2 1397DaggerWorkload ns 2 038 2 036 2 1716DaggerIndependence 005 006 218 ns

p 005 dagger p 001 Dagger p 0001

commerce students in contrast were the only group to show a positive association (016)between TE and surface processing That is the better a science and commerce studentsrsquoschool results the more likely they are to report deep and surface approaches to studyrespectively Consistently commerce students showed a clear positive relationship (035)between a reported surface approach and a higher GPA This dynamic can be understood interms of the de ning characteristics of many commercebusiness courses the job-speci c andinstrumental motivations of students the more narrow vocational focus of courses theinherently factual and procedural knowledge base of some commerce curricula (Lucas1998) and the relatedly reproducing methods of assessment often employed Thus it maybe that not only do the lsquobetter studentsrsquo who enter some commerce programmes report agreater use of surface approaches but also given the nature of the curriculum and assessmentmethods this may be a logical and strategic choice for all commerce students to pursue inthe present context if they wish to lsquodo wellrsquo It may well be that the in uence of schoolachievement on learning approaches and outcomes at university may show a degree ofvariability across disciplinary contexts While humanities students in the present studyre ected Trigwell amp Prosserrsquos (1991b) nding of no relationship between these three factorsscience and commerce students showed contrasting relationships

Elaboration of Path Models

While the path analysis has identi ed a number of general associations between characteris-tics of the academic environment as summarised by the two-factor higher order structure ofthe CEQ and learning processes and outcomes a more precise understanding is afforded byanalyses of the relative contribution of individual component scales measuring more speci caspects of the environment Thus two series of multiple regression analyses were conductedto elaborate the relationship of aspects of the academic environment to rstly studentsrsquostudy approaches and secondly academic outcomes

Academic environment and approaches Two standard multiple regression analyses wereconducted using the individual scales of the CEQ as predictors to the criterion variables ofstudentsrsquo reported global deep and surface approach scores The results are presented inTable I Four of the ve aspects of the university learning environment (good teachingappropriate assessment emphasis on independence and clear goals and standards) weresigni cant positive predictors of deep approach to learning (F [5 1886] 5 2898 p 00001)

The strongest predictor of a deep approach was the good teaching scale which essen-

40 A Lizzio et al

tially describes the lsquoinvolving qualityrsquo of the basic learning transactions between staff andstudents Students appear to be saying that the learning environments that will most stronglyin uence them towards deeper processing are those which are characterised by reciprocaltransactions involving both the giving (clear and useful explanations helpful feedback) andseeking (interest in studentsrsquo opinions and dif culties) of information within an intrinsicallymotivating context (work to make subjects interesting and motivate students to do their bestwork) This is fundamentally a process of active engagement and parallels what Laurillard(1993) has characterised as the essentially conversational character of the teachingndashlearningprocess This particularly identi es aspects of a learning culture that need to be safeguardedand highlighted as design principles with the increasing use of media and technology in highereducation

Only two of the aspects of the university learning environment (appropriate workloadand assessment) were signi cant negative predictors of a surface approach to learning (F [51892] 5 19013 p 00001) with the direction of ndings indicating that a heavy workloadand inappropriate assessment strongly contribute to a surface study approach This isconsistent with the pattern of associations reported by Crawford et al (1998) Interestinglythe use of the higher order structure of the CEQ in the path analysis masked the relationshipbetween assessment and surface processing This perhaps serves as a caution to attempts toanalyse learning environments at too great a level of aggregation

Finally clear goals and standards were not a signi cant predictor of a surface approachThe argument that students tend to adopt a surface approach to studying as a lsquodefaultoptionrsquo when they are uncertain what an academic environment requires of them (Gibbs1992) was not supported by the ndings That clear goals and standards were also thestrongest predictor of academic achievement and only a relatively weak predictor of deepprocessing suggests that this aspect of the learning environment may impact more stronglyon lsquohow well students are able to achieversquo rather than on lsquowhat approach they usersquoAccordingly while there is some evidence for the intuitively appealing notion that a well-structured knowledge base (Gibbs 1992) can foster a deep approach the association is notparticularly strong

Academic environment and outcomes Three standard multiple regression analyses were con-ducted using the scales of the CEQ as predictors to the academic outcomes of studentsrsquoreported satisfaction and generic skills development and their level of academic achievementas measured by GPA The results are presented in Table II

Studentsrsquo evaluations of all aspects of the academic environment were signi cantpredictors of their reported levels of generic skill development (F [5 1984] 5 12542 p 00001) and accounted for 25 of the variance in their reported levels of learning Whilethese results appear to indicate that the development of higher order or metacompetencies isa complex and interrelated process which is affected by all aspects of the learning environ-ment the two strongest predictors were a good teaching environment and opportunities forindependence and choice in learning content or process This pattern of ndings stronglysupports the argument that independent learning processes (enhancing student perceptionsof relevance and ownership) in combination with a positive motivational context derivingfrom the relational environment (enhancing intrinsic motivation) are the dual elementscentral to the facilitation of effective higher order learning outcomes Trigwell amp Prosser1991a Gibbs 1992 Ramsden 1992) This nding is certainly consistent with and explana-tory of the clear path between good teaching deep approaches and generic skill develop-ment in the previous analyses

Present ndings provide a measure of empirical support for the recommendations of a


TA

BL

EII

S

um

mar

yre

sult

sof

mu

ltip

lere

gres

sion

anal

yses

wit

hC

EQ

scal

esas

pre

dic

tors

ofac

adem

icou

tcom

es

gen

eric

skill

sco

urs

esa

tisf

acti

on

and

acad

emic

achi

evem

ent

Aca

dem

icou

tcom

esA

cad

emic

envi

ron

men

tG

ener

icsk

ills

Cou

rse

sati

sfac

tion

Aca

dem

icac

hie

vem

ent

CE

Qsc

ale

(pre

dic

tors

)B

Bet

at

BB

eta

tB

Bet

at

Goo

dT

each

ing

019

02

78

98dagger

00

50

29

115

1Dagger

003

025

82

6DaggerC

lear

Goa

ls0

060

05

21

10

05

01

99

53Dagger

004

026

101

4DaggerA

sses

smen

t0

070

08

32

1Dagger0

01

00

52

21

001

008

315

daggerW

orkl

oad

006

00

62

66dagger

00

20

06

308

daggermdash

mdashn

sIn

dep

end

ence

022

02

18

12Dagger

00

40

15

704

Daggermdash

mdashn

s

p

005

dagger

p

001

Dagger

p

00

01

42 A Lizzio et al

TABLE III Summary results of multiple regression analyses with generic skills scaleitems as predictors of course satisfaction

Course satisfactionGeneric skills items(predictors) B Beta t

Problem-solving 017 017 1131DaggerAnalytic 017 016 1131DaggerTeam member 003 004 286daggerCon dence tackling unfamiliar problems 013 013 890DaggerWritten communication 007 007 548DaggerPlanning own work 013 013 1017Dagger


number of writers suggesting practical means by which the curriculum may be adapted toenhance the development of generic academic and work-based skills (Sadler 1989 Candy ampCrebert 1991 Marginson 1993) Studentsrsquo experiences appear to support the propositionthat course designs that are student centred will better develop adaptive and self-regulatinglearners (planning their own work) who are better equipped to manage more informal andrealistic learning situations (using problem-solving to tackle unfamiliar situations) Thedistinction between didactic (content-oriented) and facilitative (learning-oriented) ap-proaches to teaching (Kember 1997) seems particularly salient in this regard Studentsrsquoperceptions appear to reinforce the argument that capability derives from congruent pro-cessesmdashindependent thinking comes from engaging in independent learning processesmdashthemedium is the message (Stephenson amp Weil 1992)

A standard multiple regression analysis using the six items of the generic skills scale aspredictors to the outcome of studentsrsquo reported level of course satisfaction was conducted tofurther clarify studentsrsquo perceptions of the processes of generic skill development The resultsare presented in Table III While students related the development of all skills to their coursesatisfaction there were differences in this regard The strongest predictors of satisfaction(analysis and problem-solving) related to the development of higher order cognitive capabil-ities A second slightly less related set of predictors (planning own work and con dence intackling unfamiliar problems) re ected the meta-theme of enhanced self-regulation in termsof both the con dence and competence to function autonomously The weakest predictor ofstudent satisfaction was the development of team work skills

This latter nding is particularly noteworthy in light of the pre-eminence given byemployer organisations to the lsquoability to work with othersrsquo (Marginson 1993) Severalexplanations appear relevant Firstly it may be that students socialised into patterns ofindividual learning (Candy amp Crebert 1991) may not value apparently lsquosofterrsquo collaborativeskills as highly as lsquoharderrsquo skills related to strengthening independent and critical judgementSecondly it may simply be that students have had fewer opportunities to develop collabora-tive capacities given the continued prevalence of traditional classroom-based conceptions ofteaching and learning Finally it may well be the case as is regularly noted by staffimplementing cooperative learning methods (Thorley amp Gregory 1994) that they aredif cult to lsquodo wellrsquo and can often produce lsquomixed outcomesrsquo

While it is probably an overinterpretation of the present ndings to argue for a lsquovaluinghierarchyrsquo in studentsrsquo perceptions of generic skills given that some universities proposehierarchical taxonomies of generic abilities (Barrie amp Jones 1998) it is a question for futureresearch as to the match between studentsrsquo and staff perceptions of their relative importance


Studentsrsquo evaluations of all aspects of the academic environment were signi cantpredictors of their reported levels of overall satisfaction (F [5 1882] 5 33515 p 00001)and accounted for 49 of the variance in their levels of satisfaction with their courses Thestrongest predictors of satisfaction were a learning environment which was perceived asinvolving (good teaching) with clear expectations (clear goals) and allowing of a degree ofchoice to pursue individual interests (independence)

Studentsrsquo evaluations of only three aspects of the academic environment (clear goalsgood teaching and appropriate assessment) were signi cant predictors of their level ofacademic achievement (F [5 1847] 5 14185 p 00001) and accounted for 28 of thevariance in their GPA That students lsquodo betterrsquo (ie achieve higher grades) in courses wherethey feel more involved and are clearer about expected standards of work should not beparticularly surprising

Summary

In summary the ndings from the present study provide empirical support for orcon rmation of a number of related theoretical propositions

1 Perceptions of heavy workload and inappropriate assessment in uence students towardssurface approaches to study but perceptions of workload have no systematic relationshipto studentsrsquo use of deep approaches to studying

2 Perceptions of a good teaching environment in uence students towards deep approachesto studying and conversely studentsrsquo perceptions of a bad teaching environmentin uence them towards surface approaches to studying The strongest predictors ofstudents using a deep approach to studying are their perceptions of the quality of theteaching and the appropriateness of the assessment

3 Perceptions of teaching environments in uence learning outcomes both directly (percep-tions to outcomes) and indirectly (perceptions to approaches to outcomes) Thus changesin teaching environments may have an impact on studentsrsquo learning outcomes withoutnecessarily affecting their learning approaches

4 Positive perceptions of the teaching environment not only directly in uence academicachievement but also importantly qualitative learning outcomes Generic academic andworkplace skills are perceived to be best developed in learning environments characterisedby good teaching and independence

5 While a studentrsquos school achievement is a positive (but weak) predictor of their universityachievement (GPA) how they perceive their current learning environment is a strongercontributor to all types of learning outcomes at university Thus perceptions of universitylearning environments make a clear contribution to academic outcomes above the prioracademic success of a student

6 Prior academic achievement has no signi cant in uence on how students evaluate theirlearning environment

7 The above patterns are evident not only at the general level of the individual student butalso in three contrasting disciplinary contexts

Methodological Limitations

Comment should be made on the positioning of global course satisfaction as an outcomevariable in the path model While lsquooverall course satisfactionrsquo has been established as a global

44 A Lizzio et al

or summative outcome measure by lsquocommon practicersquo (eg Ainley amp Long 1994 Ashendenamp Milligan 1999) an argument can be made that affective factors such as perceived interestin course content (Kember et al 1996) or studentsrsquo satisfaction with careerprogrammechoice might equally be regarded as presage factors At the interpersonal level Robbins ampDeNisi (1994) in a study of the in uence of affect on cognitive processing in studentsrsquoperformance evaluations of teachers interpret their ndings as indicating that affect impactsthe early stages of evaluation thus lsquocolouringrsquo later appraisals Ramsden (1992) also reportsinterview evidence indicating that a deep approach and course satisfaction demonstrate areciprocal relationship Thus some aspects of student satisfaction may in uence studentsrsquolearning approaches and perceptions of the learning environment as well as be the conse-quences of these Clearly future research should seek to address this question from a moreinteractive and systemic perspective

Comment should also be made regarding the level of generality of the present study Weasked students to re ect on lsquotheir experience of university to date in their current programmeof studyrsquo Thus students were reporting perceptions of learning environments that were asummative aggregate of a range of speci c learning experiences (ie subjects) in theirprogrammes Such an inclusive frame of re ection across a number of potentially diverselearning contexts may be problematic because of the implicit assumption that relationsbetween system elements may be consistent over time (years of study) and different environ-ments

Finally the relationship between perceptions and approaches as modelled in the presentstudy using higher order path analysis may not be as coherent and consistent for all groupsof students While these relationships may hold for most students in higher education thereare a small number of atypical students where such clear patterns do not usually emerge Forexample Meyer et al (1990) found for academically weak or failing students that relation-ships between perceptions of learning context and approaches such as those reported heremay disintegrate Similarly Entwistle et al (1991) found the expected coherent patterns forpassing students and disintegrated patterns for unsuccessful students Meyer et al (1990)term these dissonant study orchestrations because they represent a violation or exception ofconceptual boundaries That is they are de ned by apparently conceptually incompatibledimensions (eg a student scoring high on both deep and surface approaches Meyer 2000)Recent research has both con rmed this process of atypical variation (Entwistle et al 2000)and identi ed presage factors such as a studentrsquos prior knowledge of a subject area which maybe associated with it (Prosser et al 2000) While we recognise the importance of modellingvariations of the lsquogeneral modelrsquo of student learning the sampling methods employed in thepresent study do not readily access the groups of students most likely to display dissonantorchestrations A broad mail survey across years of study is likely to produce very lowresponse rates from failing students and this proved to be the case in the present studyThere were insuf cient numbers of unsuccessful students in the nal sample to conductmeaningful or reliable analyses

Implications for Practice

Firstly and fundamentally the results con rm that elements of the learning environmentwhich are under teacher control can and do positively in uence both the way studentsapproach their study and the learning outcomes they may achieve Thus interventions ifappropriately conceived and implemented can and will lsquomake a differencersquo


What might be done in speci c terms The model of the academic environmentunderpinning the CEQ (see Appendix 1) provides an overview of domains for potentialre ection and intervention and the present study has demonstrated how each of theseenvironmental elements relates to studentsrsquo approaches and outcomes It is clear thatlsquoteaching qualityrsquo (as a composite of good teaching clear goals and standards appropriateassessment and emphasis on independence) has the strongest in uence both directly andindirectly on learning outcomes At a ner level of analysis it is the component elements ofgood teaching (reciprocally interactive and motivating transactions between teacher andstudent) which are the strongest single in uence

This would appear to suggest that good teaching is the lsquoplace to investrsquo if one is seekingto make a difference to studentsrsquo learning outcomes We would suggest however that thismay not necessarily be the lsquoplace to startrsquo and that there is an important distinction to bemade between the importance and feasibility of developmental goals We propose thatreviews of workload and assessment practices may be more feasible lsquopoints of departurersquo foruniversity educators who are seeking to improve the design of courses

The areas of workload and assessment are proposed as areas for initial intervention forthree reasons Firstly these are the only two aspects of the learning environment which havebeen consistently shown both in the present and previous studies to drive or lsquomotivatersquostudents toward a surface approach to learning across a wide range of student populationsand disciplinary contexts Secondly given that the size of this effect is quite substantial (eg40 of the variance related to surface processing in the present study was accounted for bythese two variables) appropriate interventions offer potentially great rewards in terms ofpositive impacts on learning Thirdly change in these aspects of the learning environmentwould appear to offer a greater likelihood of initial success than other areas by virtue of whatis required to make the change For example while improvements in studentsrsquo perceptionsof good teaching may involve changes in the skill levels relationship behaviours andavailability of academic staff improvements in workloads and assessment processes can beimplemented with comparatively fewer training or resource implications This is not tosuggest that we should only address lsquowhat is more easy to dorsquo far from it Rather as anumber of organisational development theorists have argued (Beckhard amp Harris 1987Cummings amp Huse 1989) early attempts at planned change should seek to optimise thechances of success by focusing on proven issues which offer a visible return and which arefeasible to implement Early lsquoreturns on investmentrsquo or successful improvements in onedomain of the learning environment will then hopefully create organisational momentumand staff motivation for continued efforts with increasingly challenging tasks

In the next section we outline an interactive process aimed at stimulating discussionsbetween teachers and students about aspects of the learning environment While we suggestthat the content of initial discussions may for example be workload or assessment it is infact the process of these interactions that is equally valuable Such structured dialogues arerelatively safe ways for teachers to start to build the interactive competencies and underlyingsense of mutual responsiveness that are the behavioural and attitudinal foundations of thelsquogood teachingrsquo construct In this sense concrete and procedural activities can be usedstrategically to pursue more abstract and personal developmental goals The underlyingintention of the approach to exploring studentsrsquo perceptions that we outline in the nextsection is because of its deliberate basis in interaction and dialogue not only the gatheringof information but also the development of teachersrsquo capability to attune validate andrespond to studentsrsquo perceptions In this sense the meta-goal is always the development oflsquogood teachingrsquo irrespective of the content under discussion

46 A Lizzio et al

Analysing Studentsrsquo Perceptions

It is one thing to suggest such a task but quite another to undertake this in a meaningful andsystematic fashion In the spirit of translating research ndings into practice we offer thefollowing modest procedure based on experience for assessing studentsrsquo speci c perceptionsof a subjectrsquos workload in relation to its nominated value It is important to remember herethat the focus of investigation is studentsrsquo perceptions As Kember amp Leung (1998) haveclearly demonstrated actual workload is only weakly related to perceived or experiencedworkload

While course evaluation surveys such as the CEQ and the ubiquitous subject evaluationquestionnaires are able to provide broad brush information on student perceptions suchprocesses often do not provide a suf ciently clear and speci c and motivating base for theplanning of improvements A number of academic staff development practitioners haveidenti ed the informational and motivational lsquovalue addedrsquo of lsquodirect conversationsrsquo betweenstudents and staff in the course improvement process For example Gibbs et al (1988)outline a structured interactive feedback process based on small group discussions SimilarlyLandbeck (1993) describes a system of student groups and committees to gather attitudinalinformation about classroom climate and context Such interpersonally based processesenable us to not only gain a sense of studentsrsquo perceptions of subjects but also explore theirsense of the underlying causes of problems and suggestions for change

While previous approaches have primarily utilised an open discussion process forgathering qualitative data on studentsrsquo perceptions the outcomes while practically usefulcan often be somewhat unsystematic and dif cult to relate to a theory of teaching For thisreason we have developed questioning protocols which provide more focused outcomes inmore speci c domains For example the process we have used for further explicatingstudentsrsquo perceptions of workload at a subject level involves conducting a series of brief (2hours) group discussions with a small number of students (typically 6ndash8 people) using acombination of structured and open discussion methods We have found that for subjectswith enrolments of approximately 150 students three 2-hour meetings with groups coveringa range of studentsrsquo achievement levels provide a relatively clear and convergent picture ofperceptions underlying causes and suggested improvements The structure and questioningprotocol for these discussions is outlined in Appendix 2 We rstly ask students to makecomparative judgements about their experience of the workload across subjects in terms ofboth volume and complexity of work since these are distinct but interactive factors instudentsrsquo conceptions of the lsquotoughnessrsquo or lsquofairnessrsquo of a course (Svensson 1997) This alsoallows an informal lsquobenchmarkingrsquo of studentsrsquo perceptions across subjects Secondly we askstudents to re ect on their sense of the interaction between what and how much they had tolearn and their approach to the task in particular focusing on the notion of lsquoshort cutsrsquo orcompromises to learning in the subject Finally we ask students to identify other factors(either in terms of course structure or culture or student attitudes or resources) which maybe contributing to perceptions of high workload The data from these conversations aresuf cient to make appropriate and positively received adjustments to the subject structureand curriculum focus without compromising learning outcomes A second protocol whichwe use to guide discussions with students regarding their perceptions of course assessmentis presented in Appendix 3

Two further points deserve consideration Firstly this interactive process appears to begreatly enhanced by making its motives transparent for students and where they have hadtime to re ect on the stimulus questions beforehand Not surprisingly we have had betterresults where we were able to better prepare students The underlying values that support


staffndashstudent interaction on this task are consistent with what Gibbs amp Lucas (1995) term acollaborative research-oriented approach to development Secondly and somewhat paradox-ically the protocol of questions which we have used as the basis for discussions with studentsalso appears to be a useful basis for private re ection or self-study review on workloads andassessment practices by academic staff It is always a useful exercise to compare our own andstudentsrsquo responses to the same questions

Studentsrsquo perceptions are of course only one source of information in making decisionsThese of course have to be considered in the light of disciplinary professional andemployer group expectations of curriculum content and where possible benchmarkedagainst the perceptions of students in similar programmes However other considerationsnotwithstanding given that studentsrsquo perceptions of the amount and type of work they haveto do appears to impact on the extent to which they lsquocope withrsquo rather than learn the materialthey should be afforded considerable attention in the subject design process

Acknowledgements

The data upon which this study is based come from a survey of students at Grif th Universityinitiated by Paul Ramsden The authors acknowledge the constructive comments from thereferees on the initial manuscript

Correspondence Alf Lizzio School of Applied Psychology Grif th University Mt GravattCampus Brisbane 4111 Australia

REFERENCES

AINLEY J amp LONG M (1994) The Course Experience Survey 1992 graduates (Canberra Australian Govern-ment Publishing Service)

ARNOLD J LOAN-CLARK J HARRINGTON A amp HUNT C (1999) Studentsrsquo perception of competencedevelopment in undergraduate business related degrees Studies in Higher Education 24 pp 43ndash57

ASHENDEN D amp MILLIGAN S (1999) Good Universities Guide 2000 to Australian Universities (Victoria ReedReference)

BALLA JI BIGGS JB GIBSON M amp CHANG AM (1990) The application of basic science concepts toclinical problem-solving Medical Education 24 pp 137ndash147

BARRIE S amp JONES J (1998) Integration of academic writing skills in curriculum making them stick in CRUST (Ed) Improving Student Learning Outcomes Proceedings of the 1998 6th International SymposiumImproving student learning pp 268ndash278 (Oxford Centre for Staff and Learning Development)

BEAN JP amp BRADLEY RK (1986) Untangling the satisfactionndashperformance relationship for college studentsJournal of Higher Education 57 pp 393ndash412

BECKHARD R amp HARRIS RT (1987) Organisational transitions managing complex change 2nd edn (New YorkAddison Wesley)

BENNETT N DUNNE E amp CARRE C (1998) Patterns of core and generic skills provision in higher educationHigher Education 37 pp 71ndash93

BENTLER PM (1989) EQS structural equations program manual (Los Angeles CA BMDP StatisticalSoftware)

BIGGS JB (1979) Individual differences in study process and the quality of learning outcomes HigherEducation 8 pp 381ndash394

BIGGS JB (1985) The role of metalearning in study processes British Journal of Educational Psychology 55pp 185ndash212

BIGGS JB (1989) Approaches to the enhancement of tertiary teaching Higher Education Research andDevelopment 8 pp 7ndash25

BIGGS JB (1993) What do inventories of studentsrsquo learning processes really measure A theoretical reviewand clari cation British Journal of Educational Psychology 63 pp 3ndash19

48 A Lizzio et al

BIGGS JB (1999) Teaching for Quality Learning (Buckingham Society for Research into Higher Education ampOpen University Press)

CANDY P amp CREBERT G (1991) Ivory tower to concrete jungle Journal of Higher Education 62 pp 570ndash592CLARK RM (1986) Studentsrsquo approaches to learning in an innovative medical school a cross-sectional study

British Journal of Educational Psychology 56 pp 309ndash321COMREY AL (1973) A First Course in Factor Analysis (New York Academic Press)CRAWFORD K GORDON S NICHOLAS J amp PROSSER M (1998) Qualitatively different experiences of

learning mathematics at university Learning and Instruction 8 pp 455ndash468CUMMINGS TG amp HUSE EF (1989) Organisational Development and Change (St Paul MN West Publishing

Company)DREW PY amp WATKINS D (1998) Affective variables learning approaches and academic achievement a

causal modelling investigation with Hong Kong tertiary students British Journal of Educational Psychology68 pp 173ndash188

ELEY MG (1992) Differential adoption of study approaches within individual students Higher Education 23pp 231ndash254

ENTWISTLE N (1989) Approaches to studying and course perceptions the case of the disappearingrelationship Studies in Higher Education 14 pp 156ndash157

ENTWISTLE NJ amp RAMSDEN P (1983) Understanding Student Learning (London Croom Helm)ENTWISTLE N amp TAIT H (1990) Approaches to learning evaluations of teaching and preferences for

contrasting academic environments Higher Education 19 pp 169ndash194ENTWISTLE N amp WATERSTON S (1988) Approaches to studying and levels of processing in university

students British Journal of Educational Psychology 58 pp 258ndash265ENTWISTLE NJ amp KOZEKI B (1985) Relationships between school motivation approaches to studying and

attainment among British and Hungarian adolescents British Journal of Educational Psychology 55 pp124ndash137

ENTWISTLE N HANLEY M amp HOUNSELL D (1979) Identifying distinctive approaches to studying HigherEducation 8 pp 365ndash380

ENTWISTLE N MEYER JHF amp TAIT H (1991) Student failure disintegrated perceptions of studying andthe learning environment Higher Education 21 pp 249ndash261

ENTWISTLE N TAIT H amp MCCUNE V (2000) Patterns of response to an approaches to studying inventoryacross contrasting groups and contexts European Journal of Psychology of Education 15 pp 33ndash48

GIBBS G (1992) Improving the Quality of Student Learning (Bristol Technical and Educational Services)GIBBS G amp LUCAS L (1995) Using research to improve student learning in large classes paper presented at the

3rd International Improving Student Learning Symposium ExeterGIBBS G HABESHAW T amp HABESHAW S (1988) 53 Interesting Ways to Appraise Your Teaching (Bristol

Technical and Educational Services)HARPER G amp KEMBER D (1989) Interpretation of factor analyses from the approaches to studying inventory

British Journal of Educational Psychology 59 pp 66ndash74HONG S (1982) Age and achievement the age factor in predicting academic success Australian Journal of

Adult Education 22 pp 21ndash28HU L amp BENTLER PM (1999) Cutoff criteria for t indexes in covariance structure analysis conventional

criteria versus new alternations Structural Equation Modeling 6 pp 1ndash55KEMBER D (1997) A reconceptualisation of the research in university academicsrsquo conceptions of teaching

Learning and Instruction 7 pp 255ndash275KEMBER D amp LEUNG DYP (1998) In uences upon studentsrsquo perceptions of workload Educational

Psychology 18 pp 293ndash307KEMBER D NG S POMFRET M TSE H amp WONG ETT (1996) An examination of the inter-relationships

between workload study time learning approaches and academic outcomes Studies in Higher Education21 pp 347ndash358

LANDBECK R (1993) Course Evaluation Package a resource kit for lecturers (Fiji University of the South Paci cCentre for the Enhancement of Learning and Teaching)

LAURILLARD D (1993) Rethinking University Teaching (London Routledge)LINDBLOM-YLANNE S LONKA K amp LESKINEN E (1999) On the predictive value of entry-level skills for

successful studying in medical school Higher Education 37 pp 239ndash258LUCAS U (1998) Beyond categories of description a further role for phonomenographic research in C RUST

(Ed) Improving Student Learning Outcomes Proceedings of the 6th international symposium Improvingstudent learning pp 202ndash215 (Oxford Centre for Staff and Learning Development)


MACNAIR G (1990) The British enterprise in higher education agreement Higher Education Management 2pp 60ndash71

MARGINSON S (1993) Arts Science and Work work related skills and the generalist courses in higher education(Canberra Australian Government Publishing Service)

MARTON F amp SALJO R (1976) Symposium learning processes and strategies On qualitative differences inlearning II Outcome as a function of the learnerrsquos conception of the task British Journal of EducationalPsychology 46 pp 115ndash127

MAUGHER PA amp KOLMODIN CA (1975) Long-term predictive validity of the scholastic aptitude testJournal of Educational Psychology 67 pp 847ndash851

MCMANUS IC RICHARDS P WINDER BC amp SPROSTON KA (1998) Clinical experience performance in nal examinations and learning style in medical students prospective study British Medical Journal 316pp 345ndash350

MEYER JHF (1998) Assessing outcomes in terms of the lsquohiddenrsquo observables in C RUST (Ed) ImprovingStudent Learning Outcomes Proceedings of the 6th international symposium Improving student learningpp 25ndash37 (Oxford Centre for Staff and Learning Development)

MEYER JHF (2000) The modelling of lsquodissonantrsquo study orchestration in higher education European Journalof Psychology of Education 15 pp 5ndash18

MEYER JHF amp PARSONS P (1989) Approaches to studying and course perceptions using the LancasterInventorymdasha comparative study Studies in Higher Education 14 pp 137ndash363

MEYER JHF PARSONS P amp DUNNE TT (1990) Individual study orchestrations and their association withlearning outcome Higher Education 20 pp 67ndash89

PROSSER M amp MILLAR R (1989) The lsquohowrsquo and lsquowhatrsquo of learning physics European Journal of Psychologyof Education 4 pp 513ndash528

PROSSER M amp TRIGWELL K (1999) Understanding Learning and Teaching (Buckingham Society for Researchinto Higher Education and Open University Press)

PROSSER M TRIGWELL K amp WATERHOUSE F (2000) Studentsrsquo experiences of studying physics conceptsthe effects of disintegrated perceptions and approaches European Journal of Psychology of Education 15pp 61ndash74

RAMSDEN P (1991) A performance indicatory of teaching quality in higher education the Course ExperienceQuestionnaire Studies in Higher Education 16 pp 129ndash150

RAMSDEN P (1992) Learning to Teach in Higher Education (London Routledge)RAMSDEN P amp ENTWISTLE NJ (1981) Effects of academic departments on studentsrsquo approaches to studying

British Journal of Educational Psychology 51 pp 368ndash383RAMSDEN P BESWICK DG amp BOWDEN JA (1986) Effects of learning skills interventions on rst year

university studentsrsquo learning Human Learning 5 pp 151ndash164RAMSDEN P PROSSER M TRIGWELL K amp MARTIN E (1997) Perceptions of academic leadership and the

effectiveness of university teaching paper presented at the Annual Conference of the Australian Associationfor Research in Education Brisbane Australia

RICHARDSON JTE (1990) Reliability and replicability of the approaches to study questionnaire Studies inHigher Education 15 pp 155ndash168

RICHARDSON JTE (1994) A British evaluation of the course experience questionnaire Studies in HigherEducation 19 pp 59ndash68

ROBBINS TL amp DENISI AS (1994) Interpersonal affect and cognitive processing in performance appraisaltowards closing the gap Journal of Applied Psychology 79 pp 341ndash350

SADLER DR (1989) Formative assessment and the design of instructional systems Instructional Science 18pp 119ndash144

STEPHENSON J amp WEIL S (Eds) (1992) Quality in Learning a capability approach to higher education (LondonKogan Page)

SVENSSON L (1997) Skill in learning and organising knowledge in F MARTON D HOUNSELL amp NENTWISTLE (Eds) The Experience of Learning (Edinburgh Scottish Academic Press)

THOMAS PR amp BAIN JD (1984) Contextual dependence of learning approaches the effects of assessmentsHuman Learning 3 pp 227ndash240

THORLEY L amp GREGORY R (1994) Using Group Based Learning in Higher Education (London Kogan Page)TRIGWELL K amp PROSSER M (1991a) Improving the quality of student learning the in uence of learning

context and student approaches to learning on learning outcomes Higher Education 22 pp 251ndash266TRIGWELL K amp PROSSER M (1991b) Relating approaches to study and quality of learning outcomes at the

course level British Journal of Educational Psychology 61 pp 265ndash275

50 A Lizzio et al

TRIGWELL K amp PROSSER M (1997) Towards an understanding of individual acts of teaching and learningHigher Education Research and Development 16 pp 241ndash252

TRIGWELL KR amp SLEET RJ (1990) Improving the relationship between assessment results and studentunderstanding Assessment and Evaluation in Higher Education 13 pp 290ndash297

VAN ROSSUM EJ amp SCHENK SM (1984) The relationship between learning conception study strategy andlearning outcome British Journal of Educational Psychology 54 pp 73ndash83

WHELAN G (1988) Improving medical studentsrsquo clinical problem solving in P RAMSDEN (Ed) ImprovingLearning new perspectives (London Kogan Page)

WILSON KL LIZZIO A amp RAMSDEN P (1997) The development validation and application of the CourseExperience Questionnaire Studies in Higher Education 22 pp 33ndash53

Appendix 1 Conceptual Model of an Effective Academic Environment Underpinning the CourseExperience Questionnaire

Good Teaching an academic environment which is involving for students because staff

middot Empathic responsivenessshow an interest in studentsrsquo opinions and attempt to understand the dif culties students may behaving

middot Motivating expectationsexpress positive expectations and seek to motivate students to do their lsquobest workrsquo

middot Understandable explanationsprovide clear and useful explanations of ideas

middot Stimulating learning designswork to make subjects interesting

middot Helpful feedbackprovide feedback on progress

Clear Goals an academic environment where students know what is expected of them because staff

middot Aims and objectivesexplain lsquoright from the startrsquo the learning objectives of coursessubjects

middot Directionprovide progressive markers of lsquowhere we are goingrsquo

middot Standards of workexplain the standard of work expected

Appropriate Assessment an academic environment which enhances learning by using assessment which

middot Typeemphasises understanding not just the memorisation of facts

middot Timingis progressive and spaced over time

middot Developmental feedbackinvolves feedback beyond just grades or marks

Appropriate Workload an academic environment which effectively manages studentsrsquo workloads and stress by

middot Volume of worksetting a feasible amount of material to be covered

middot Breadth of curriculumfocusing the range of topic areas covered

middot Time availabilityallowing adequate time frames for work to be completed

Independence in Learning an academic environment which within the limits of practicality offers students adegree of choice in


middot Curriculum contentdeveloping areas of academic interest

middot Learning processeshow they learn material

middot Assessment modesthe forms or modes of assessment

Generic Skills an academic environment which provides opportunities for students to develop genericmetacompetencies through

middot Analysisusing analytical skills

middot Problem-solvingdeveloping problem-solving capabilities

middot Communicationdeveloping written communication skills

middot Applicationusing problem-solving skills to tackle unfamiliar problems

middot Collaborationworking as a team member

middot Self-regulationplaning their own work

Appendix 2 A Protocol to Investigate Studentsrsquo Perceptions of Subject Workload

Comparative Perceptions

What is your sense of the volumeamount of work in this subject compared to other subjects you have doneWhat is your sense of the dif culty of the material in this subject compared to other subjects you have done

Felt Compromises

What is your sense of any compromises or lsquoshort cutsrsquo to your learning you may have taken in this subjectWhich of these do you explain in terms of the course workloadWhich of these do you explain in other terms

Trade Offs

What is your sense of the balance between the breadth (the range of areas covered) and depth (how thoroughlythey are covered) of material in this subject

PrioritiesWhat for you have been the key ideasareas in this subject How well do you feel you understand each of

theseIf we were to give more time or emphasis to one area what should that be WhyIf we were to give less time or emphasis to one area what should that be Why

Contributing Factors

What is your sense of lsquoother factorsrsquo which might contribute to making this subjectrsquos workload feel or appeargreater than it is (eg poor subject structure lack of organisation dif culty accessing resources noveltyor unfamiliarity of learning processes)

Student Characteristics

What are your lsquotime commitmentsrsquo outside of universityWhat activities compete with university studyWhat skills attitudes or resources might assist you in better managing the workload in this subject

52 A Lizzio et al

Appendix 3 A Protocol to Investigate Studentsrsquo Perceptions of Assessment


How does the assessment in this subject compare with assessment you have experienced in other subjectsIn what ways is it similar or different Novel or repetitive

Relationship to Learning

In what ways if any did the assessment in this subject help your learning hinder your learning or was notrelevant to your learning

Priorities

If all you knew about this subject was the forms of assessment used then what conclusions would you reachabout it

What lsquomessagesrsquo do you take from the lsquoassessment packagersquo about what we valueIn discussions with staffabout assessment pieces what impressions did you get about what was lsquodesirablersquo or would be lsquorewardedrsquo

What is your reaction to this

Congruence

How consistent are the espoused goals of this subject and the actual goals addressed by the assessmentHow congruent are we in your eyes in what we say is important and what we actually assess

Scope

What is your sense of how well the assessment matched the curriculum In your opinion what if any wereimportant aspects of the curriculum (eg knowledge behavioural competence values etc) that were notassessed or could be better assessed

In what ways did the assessment givenot give you an appropriate chance to show what you had learnt

Timing

What are your perceptions of the relative timing and sequencing of assessment pieces in the subjectFor example was the rst piece lsquotoo earlyrsquo for people to do it justice or lsquotoo latersquo to provide feedback onprogress Were pieces too close together to allow improvement between efforts

Equity

What do you think about the lsquofairnessrsquo of the assessment Dif culty Amount Marking proceduresJusti cation of gradesmarks

Feedback

What do you think about the feedback you have received on your assessment What comments if any wouldyou like to make about its quality extent effect on your motivation and value in helping improve yourperformance

Arousal

How anxiety or stress producing was the assessment in this course Are arousal levels affecting your learningor enjoyment How

Informal Systems

What is your perception of the type or extent of informal assessment or feedback systems in this subject (egpeer interactionfeedback informal ungraded exercises)

Are there suf cient means apart from formal graded assessment for students to get feedback on lsquohow theyare goingrsquo

Involvement

What is your opinion of the level of involvement or participation you have as students in the assessmentprocess Would you prefer moreless or different involvement or level of choice in the negotiation of theforms or content of assessment

28 A Lizzio et al

system of three sets of variables the learning environment and student characteristics(presage) studentsrsquo approach to learning (process) and learning outcomes (product) In basicterms the model proposes that rstly personal and situational factors in uence a student toadopt a particular approach to learning which in turn mediates or in uences the types ofoutcomes achieved and secondly that presage factors (eg perceptions of the learningenvironment) can also directly in uence learning outcomes

Presage factors are as the term suggests those which exist prior to the time of learningand comprise two broad types the enduring personal characteristics brought to the learningsituation by the student (eg prior knowledge academic ability personality) and situationalcharacteristics which de ne the learning environment (eg teaching methods workloadcourse structure) As Biggs (1985) and others (Ramsden 1991) have argued the key elementin the practical application of this model is the proposition that it is studentsrsquo perceptions oftheir learning environment in light of their motivations and expectations which determinehow situational factors in uence approaches to learning and learning outcomes

Process factors describe how students approach their learning While there is somediversity in the terms used there is a fair degree of empirical evidence that students adopt twobasic orientations or approaches A lsquodeeprsquo approach to learning is described as striving forimproved understanding by applying and comparing ideas Conversely lsquosurfacersquo learninginvolves reproductive strategies with little attempt to integrate information (Marton amp Saljo1976 Thomas amp Bain 1984)

Product factors describe the learning outcomes (cognitive affective or behavioural)which students derive from the learning process Traditionally depth or accuracy oflearning has been summarily described through assessment scores (grade point averages)Affective or lsquofelt outcomesrsquo in a climate emphasising student evaluations of education havebecome increasingly discussed These are generally of two typesmdashglobal evaluations ofaccomplishment or expressed satisfaction with a course and speci c perceptions of particularskill development (eg key or transferable skills)

It is important to note that while the present study focuses on a primarily linear orunidirectional understanding of component relationships (ie student characteristics andperceptions in uence student approaches which in uence student outcomes) the relation-ships between these elements are best conceived as an interactive system (Biggs 1993 1999)A more complex analysis would consider rstly the relationships between all systemcomponents and secondly that such in uence can be reciprocal or mediated in nature(Meyer 1998) Trigwell amp Prosser (1997) further argue that the interpretation of interactionsin the model should include the concept of lsquotemporalityrsquo and propose that the modelrsquoselements are not a causal chain of independently constituted components over time but aresimultaneously present in studentsrsquo awareness

The present study is not concerned with systematically operationalising and testing allpossible elements of the 3P model The focus is somewhat more constrained We seek toaddress three questions Firstly do studentsrsquo perceptions of their learning environmentsystematically in uence learning approaches and outcomes Secondly if so which aspects ofthe learning environment have moreless in uence in this regard Finally which in uentialaspects of the learning environment appear to be most amenable to change towards the goalsof enhancing learning outcomes

Ramsden amp Entwistle (1981) were the rst to empirically establish a relationshipbetween approaches to learning and perceived characteristics of the academic environmentThis association was explored through a concurrent factor analysis of the scales of theApproaches to Studying Inventory (ASI Entwistle et al 1979) and the Course PerceptionsQuestionnaire (CPQ Entwistle amp Ramsden 1983) based on the responses of a large






30 A Lizzio et al










32 A Lizzio et al








Method

Sample


Measures



34 A Lizzio et al


















36 A Lizzio et al











38 A Lizzio et al









B Beta t B Beta t








40 A Lizzio et al








TA

BL

EII

S

um

mar

yre

sult

sof

mu

ltip

lere

gres

sion

anal

yses

wit

hC

EQ

scal

esas

pre

dic

tors

ofac

adem

icou

tcom

es

gen

eric

skill

sco

urs

esa

tisf

acti

on

and

acad

emic

achi

evem

ent

Aca

dem

icou

tcom

esA

cad

emic

envi

ron

men

tG

ener

icsk

ills

Cou

rse

sati

sfac

tion

Aca

dem

icac

hie

vem

ent

CE

Qsc

ale

(pre

dic

tors

)B

Bet

at

BB

eta

tB

Bet

at

Goo

dT

each

ing

019

02

78

98dagger

00

50

29

115

1Dagger

003

025

82

6DaggerC

lear

Goa

ls0

060

05

21

10

05

01

99

53Dagger

004

026

101

4DaggerA

sses

smen

t0

070

08

32

1Dagger0

01

00

52

21

001

008

315

daggerW

orkl

oad

006

00

62

66dagger

00

20

06

308

daggermdash

mdashn

sIn

dep

end

ence

022

02

18

12Dagger

00

40

15

704

Daggermdash

mdashn

s

p

005

dagger

p

001

Dagger

p

00

01

42 A Lizzio et al












Summary











44 A Lizzio et al











46 A Lizzio et al









Acknowledgements



REFERENCES














48 A Lizzio et al

























































50 A Lizzio et al








































Felt Compromises


Trade Offs








52 A Lizzio et al






Priorities




Congruence


Scope



Timing


Equity


Feedback


Arousal


Informal Systems



Involvement







30 A Lizzio et al










32 A Lizzio et al








Method

Sample


Measures



34 A Lizzio et al


















36 A Lizzio et al











38 A Lizzio et al









B Beta t B Beta t








40 A Lizzio et al








TA

BL

EII

S

um

mar

yre

sult

sof

mu

ltip

lere

gres

sion

anal

yses

wit

hC

EQ

scal

esas

pre

dic

tors

ofac

adem

icou

tcom

es

gen

eric

skill

sco

urs

esa

tisf

acti

on

and

acad

emic

achi

evem

ent

Aca

dem

icou

tcom

esA

cad

emic

envi

ron

men

tG

ener

icsk

ills

Cou

rse

sati

sfac

tion

Aca

dem

icac

hie

vem

ent

CE

Qsc

ale

(pre

dic

tors

)B

Bet

at

BB

eta

tB

Bet

at

Goo

dT

each

ing

019

02

78

98dagger

00

50

29

115

1Dagger

003

025

82

6DaggerC

lear

Goa

ls0

060

05

21

10

05

01

99

53Dagger

004

026

101

4DaggerA

sses

smen

t0

070

08

32

1Dagger0

01

00

52

21

001

008

315

daggerW

orkl

oad

006

00

62

66dagger

00

20

06

308

daggermdash

mdashn

sIn

dep

end

ence

022

02

18

12Dagger

00

40

15

704

Daggermdash

mdashn

s

p

005

dagger

p

001

Dagger

p

00

01

42 A Lizzio et al












Summary











44 A Lizzio et al











46 A Lizzio et al









Acknowledgements



REFERENCES














48 A Lizzio et al

























































50 A Lizzio et al








































Felt Compromises


Trade Offs








52 A Lizzio et al






Priorities




Congruence


Scope



Timing


Equity


Feedback


Arousal


Informal Systems



Involvement








32 A Lizzio et al








Method

Sample


Measures



34 A Lizzio et al


















36 A Lizzio et al











38 A Lizzio et al









B Beta t B Beta t








40 A Lizzio et al








TA

BL

EII

S

um

mar

yre

sult

sof

mu

ltip

lere

gres

sion

anal

yses

wit

hC

EQ

scal

esas

pre

dic

tors

ofac

adem

icou

tcom

es

gen

eric

skill

sco

urs

esa

tisf

acti

on

and

acad

emic

achi

evem

ent

Aca

dem

icou

tcom

esA

cad

emic

envi

ron

men

tG

ener

icsk

ills

Cou

rse

sati

sfac

tion

Aca

dem

icac

hie

vem

ent

CE

Qsc

ale

(pre

dic

tors

)B

Bet

at

BB

eta

tB

Bet

at

Goo

dT

each

ing

019

02

78

98dagger

00

50

29

115

1Dagger

003

025

82

6DaggerC

lear

Goa

ls0

060

05

21

10

05

01

99

53Dagger

004

026

101

4DaggerA

sses

smen

t0

070

08

32

1Dagger0

01

00

52

21

001

008

315

daggerW

orkl

oad

006

00

62

66dagger

00

20

06

308

daggermdash

mdashn

sIn

dep

end

ence

022

02

18

12Dagger

00

40

15

704

Daggermdash

mdashn

s

p

005

dagger

p

001

Dagger

p

00

01

42 A Lizzio et al












Summary











44 A Lizzio et al











46 A Lizzio et al









Acknowledgements



REFERENCES














48 A Lizzio et al

























































50 A Lizzio et al








































Felt Compromises


Trade Offs








52 A Lizzio et al






Priorities




Congruence


Scope



Timing


Equity


Feedback


Arousal


Informal Systems



Involvement





Method

Sample


Measures



34 A Lizzio et al


















36 A Lizzio et al











38 A Lizzio et al









B Beta t B Beta t








40 A Lizzio et al








TA

BL

EII

S

um

mar

yre

sult

sof

mu

ltip

lere

gres

sion

anal

yses

wit

hC

EQ

scal

esas

pre

dic

tors

ofac

adem

icou

tcom

es

gen

eric

skill

sco

urs

esa

tisf

acti

on

and

acad

emic

achi

evem

ent

Aca

dem

icou

tcom

esA

cad

emic

envi

ron

men

tG

ener

icsk

ills

Cou

rse

sati

sfac

tion

Aca

dem

icac

hie

vem

ent

CE

Qsc

ale

(pre

dic

tors

)B

Bet

at

BB

eta

tB

Bet

at

Goo

dT

each

ing

019

02

78

98dagger

00

50

29

115

1Dagger

003

025

82

6DaggerC

lear

Goa

ls0

060

05

21

10

05

01

99

53Dagger

004

026

101

4DaggerA

sses

smen

t0

070

08

32

1Dagger0

01

00

52

21

001

008

315

daggerW

orkl

oad

006

00

62

66dagger

00

20

06

308

daggermdash

mdashn

sIn

dep

end

ence

022

02

18

12Dagger

00

40

15

704

Daggermdash

mdashn

s

p

005

dagger

p

001

Dagger

p

00

01

42 A Lizzio et al












Summary











44 A Lizzio et al











46 A Lizzio et al









Acknowledgements



REFERENCES














48 A Lizzio et al

























































50 A Lizzio et al








































Felt Compromises


Trade Offs








52 A Lizzio et al






Priorities




Congruence


Scope



Timing


Equity


Feedback


Arousal


Informal Systems



Involvement


34 A Lizzio et al


















36 A Lizzio et al











38 A Lizzio et al









B Beta t B Beta t








40 A Lizzio et al








TA

BL

EII

S

um

mar

yre

sult

sof

mu

ltip

lere

gres

sion

anal

yses

wit

hC

EQ

scal

esas

pre

dic

tors

ofac

adem

icou

tcom

es

gen

eric

skill

sco

urs

esa

tisf

acti

on

and

acad

emic

achi

evem

ent

Aca

dem

icou

tcom

esA

cad

emic

envi

ron

men

tG

ener

icsk

ills

Cou

rse

sati

sfac

tion

Aca

dem

icac

hie

vem

ent

CE

Qsc

ale

(pre

dic

tors

)B

Bet

at

BB

eta

tB

Bet

at

Goo

dT

each

ing

019

02

78

98dagger

00

50

29

115

1Dagger

003

025

82

6DaggerC

lear

Goa

ls0

060

05

21

10

05

01

99

53Dagger

004

026

101

4DaggerA

sses

smen

t0

070

08

32

1Dagger0

01

00

52

21

001

008

315

daggerW

orkl

oad

006

00

62

66dagger

00

20

06

308

daggermdash

mdashn

sIn

dep

end

ence

022

02

18

12Dagger

00

40

15

704

Daggermdash

mdashn

s

p

005

dagger

p

001

Dagger

p

00

01

42 A Lizzio et al












Summary











44 A Lizzio et al











46 A Lizzio et al









Acknowledgements



REFERENCES














48 A Lizzio et al

























































50 A Lizzio et al








































Felt Compromises


Trade Offs








52 A Lizzio et al






Priorities




Congruence


Scope



Timing


Equity


Feedback


Arousal


Informal Systems



Involvement











36 A Lizzio et al











38 A Lizzio et al









B Beta t B Beta t








40 A Lizzio et al








TA

BL

EII

S

um

mar

yre

sult

sof

mu

ltip

lere

gres

sion

anal

yses

wit

hC

EQ

scal

esas

pre

dic

tors

ofac

adem

icou

tcom

es

gen

eric

skill

sco

urs

esa

tisf

acti

on

and

acad

emic

achi

evem

ent

Aca

dem

icou

tcom

esA

cad

emic

envi

ron

men

tG

ener

icsk

ills

Cou

rse

sati

sfac

tion

Aca

dem

icac

hie

vem

ent

CE

Qsc

ale

(pre

dic

tors

)B

Bet

at

BB

eta

tB

Bet

at

Goo

dT

each

ing

019

02

78

98dagger

00

50

29

115

1Dagger

003

025

82

6DaggerC

lear

Goa

ls0

060

05

21

10

05

01

99

53Dagger

004

026

101

4DaggerA

sses

smen

t0

070

08

32

1Dagger0

01

00

52

21

001

008

315

daggerW

orkl

oad

006

00

62

66dagger

00

20

06

308

daggermdash

mdashn

sIn

dep

end

ence

022

02

18

12Dagger

00

40

15

704

Daggermdash

mdashn

s

p

005

dagger

p

001

Dagger

p

00

01

42 A Lizzio et al












Summary











44 A Lizzio et al











46 A Lizzio et al









Acknowledgements



REFERENCES














48 A Lizzio et al

























































50 A Lizzio et al








































Felt Compromises


Trade Offs








52 A Lizzio et al






Priorities




Congruence


Scope



Timing


Equity


Feedback


Arousal


Informal Systems



Involvement








38 A Lizzio et al









B Beta t B Beta t








40 A Lizzio et al








TA

BL

EII

S

um

mar

yre

sult

sof

mu

ltip

lere

gres

sion

anal

yses

wit

hC

EQ

scal

esas

pre

dic

tors

ofac

adem

icou

tcom

es

gen

eric

skill

sco

urs

esa

tisf

acti

on

and

acad

emic

achi

evem

ent

Aca

dem

icou

tcom

esA

cad

emic

envi

ron

men

tG

ener

icsk

ills

Cou

rse

sati

sfac

tion

Aca

dem

icac

hie

vem

ent

CE

Qsc

ale

(pre

dic

tors

)B

Bet

at

BB

eta

tB

Bet

at

Goo

dT

each

ing

019

02

78

98dagger

00

50

29

115

1Dagger

003

025

82

6DaggerC

lear

Goa

ls0

060

05

21

10

05

01

99

53Dagger

004

026

101

4DaggerA

sses

smen

t0

070

08

32

1Dagger0

01

00

52

21

001

008

315

daggerW

orkl

oad

006

00

62

66dagger

00

20

06

308

daggermdash

mdashn

sIn

dep

end

ence

022

02

18

12Dagger

00

40

15

704

Daggermdash

mdashn

s

p

005

dagger

p

001

Dagger

p

00

01

42 A Lizzio et al












Summary











44 A Lizzio et al











46 A Lizzio et al









Acknowledgements



REFERENCES














48 A Lizzio et al

























































50 A Lizzio et al








































Felt Compromises


Trade Offs








52 A Lizzio et al






Priorities




Congruence


Scope



Timing


Equity


Feedback


Arousal


Informal Systems



Involvement





B Beta t B Beta t








40 A Lizzio et al








TA

BL

EII

S

um

mar

yre

sult

sof

mu

ltip

lere

gres

sion

anal

yses

wit

hC

EQ

scal

esas

pre

dic

tors

ofac

adem

icou

tcom

es

gen

eric

skill

sco

urs

esa

tisf

acti

on

and

acad

emic

achi

evem

ent

Aca

dem

icou

tcom

esA

cad

emic

envi

ron

men

tG

ener

icsk

ills

Cou

rse

sati

sfac

tion

Aca

dem

icac

hie

vem

ent

CE

Qsc

ale

(pre

dic

tors

)B

Bet

at

BB

eta

tB

Bet

at

Goo

dT

each

ing

019

02

78

98dagger

00

50

29

115

1Dagger

003

025

82

6DaggerC

lear

Goa

ls0

060

05

21

10

05

01

99

53Dagger

004

026

101

4DaggerA

sses

smen

t0

070

08

32

1Dagger0

01

00

52

21

001

008

315

daggerW

orkl

oad

006

00

62

66dagger

00

20

06

308

daggermdash

mdashn

sIn

dep

end

ence

022

02

18

12Dagger

00

40

15

704

Daggermdash

mdashn

s

p

005

dagger

p

001

Dagger

p

00

01

42 A Lizzio et al












Summary











44 A Lizzio et al











46 A Lizzio et al









Acknowledgements



REFERENCES














48 A Lizzio et al

























































50 A Lizzio et al








































Felt Compromises


Trade Offs








52 A Lizzio et al






Priorities




Congruence


Scope



Timing


Equity


Feedback


Arousal


Informal Systems



Involvement


40 A Lizzio et al








TA

BL

EII

S

um

mar

yre

sult

sof

mu

ltip

lere

gres

sion

anal

yses

wit

hC

EQ

scal

esas

pre

dic

tors

ofac

adem

icou

tcom

es

gen

eric

skill

sco

urs

esa

tisf

acti

on

and

acad

emic

achi

evem

ent

Aca

dem

icou

tcom

esA

cad

emic

envi

ron

men

tG

ener

icsk

ills

Cou

rse

sati

sfac

tion

Aca

dem

icac

hie

vem

ent

CE

Qsc

ale

(pre

dic

tors

)B

Bet

at

BB

eta

tB

Bet

at

Goo

dT

each

ing

019

02

78

98dagger

00

50

29

115

1Dagger

003

025

82

6DaggerC

lear

Goa

ls0

060

05

21

10

05

01

99

53Dagger

004

026

101

4DaggerA

sses

smen

t0

070

08

32

1Dagger0

01

00

52

21

001

008

315

daggerW

orkl

oad

006

00

62

66dagger

00

20

06

308

daggermdash

mdashn

sIn

dep

end

ence

022

02

18

12Dagger

00

40

15

704

Daggermdash

mdashn

s

p

005

dagger

p

001

Dagger

p

00

01

42 A Lizzio et al












Summary











44 A Lizzio et al











46 A Lizzio et al









Acknowledgements



REFERENCES














48 A Lizzio et al

























































50 A Lizzio et al








































Felt Compromises


Trade Offs








52 A Lizzio et al






Priorities




Congruence


Scope



Timing


Equity


Feedback


Arousal


Informal Systems



Involvement



TA

BL

EII

S

um

mar

yre

sult

sof

mu

ltip

lere

gres

sion

anal

yses

wit

hC

EQ

scal

esas

pre

dic

tors

ofac

adem

icou

tcom

es

gen

eric

skill

sco

urs

esa

tisf

acti

on

and

acad

emic

achi

evem

ent

Aca

dem

icou

tcom

esA

cad

emic

envi

ron

men

tG

ener

icsk

ills

Cou

rse

sati

sfac

tion

Aca

dem

icac

hie

vem

ent

CE

Qsc

ale

(pre

dic

tors

)B

Bet

at

BB

eta

tB

Bet

at

Goo

dT

each

ing

019

02

78

98dagger

00

50

29

115

1Dagger

003

025

82

6DaggerC

lear

Goa

ls0

060

05

21

10

05

01

99

53Dagger

004

026

101

4DaggerA

sses

smen

t0

070

08

32

1Dagger0

01

00

52

21

001

008

315

daggerW

orkl

oad

006

00

62

66dagger

00

20

06

308

daggermdash

mdashn

sIn

dep

end

ence

022

02

18

12Dagger

00

40

15

704

Daggermdash

mdashn

s

p

005

dagger

p

001

Dagger

p

00

01

42 A Lizzio et al












Summary











44 A Lizzio et al











46 A Lizzio et al









Acknowledgements



REFERENCES














48 A Lizzio et al

























































50 A Lizzio et al








































Felt Compromises


Trade Offs








52 A Lizzio et al






Priorities




Congruence


Scope



Timing


Equity


Feedback


Arousal


Informal Systems



Involvement


42 A Lizzio et al












Summary











44 A Lizzio et al











46 A Lizzio et al









Acknowledgements



REFERENCES














48 A Lizzio et al

























































50 A Lizzio et al








































Felt Compromises


Trade Offs








52 A Lizzio et al






Priorities




Congruence


Scope



Timing


Equity


Feedback


Arousal


Informal Systems



Involvement





Summary











44 A Lizzio et al











46 A Lizzio et al









Acknowledgements



REFERENCES














48 A Lizzio et al

























































50 A Lizzio et al








































Felt Compromises


Trade Offs








52 A Lizzio et al






Priorities




Congruence


Scope



Timing


Equity


Feedback


Arousal


Informal Systems



Involvement


44 A Lizzio et al











46 A Lizzio et al









Acknowledgements



REFERENCES














48 A Lizzio et al

























































50 A Lizzio et al








































Felt Compromises


Trade Offs








52 A Lizzio et al






Priorities




Congruence


Scope



Timing


Equity


Feedback


Arousal


Informal Systems



Involvement







46 A Lizzio et al









Acknowledgements



REFERENCES














48 A Lizzio et al

























































50 A Lizzio et al








































Felt Compromises


Trade Offs








52 A Lizzio et al






Priorities




Congruence


Scope



Timing


Equity


Feedback


Arousal


Informal Systems



Involvement





Acknowledgements



REFERENCES














48 A Lizzio et al

























































50 A Lizzio et al








































Felt Compromises


Trade Offs








52 A Lizzio et al






Priorities




Congruence


Scope



Timing


Equity


Feedback


Arousal


Informal Systems



Involvement


48 A Lizzio et al

























































50 A Lizzio et al








































Felt Compromises


Trade Offs








52 A Lizzio et al






Priorities




Congruence


Scope



Timing


Equity


Feedback


Arousal


Informal Systems



Involvement






























50 A Lizzio et al








































Felt Compromises


Trade Offs








52 A Lizzio et al






Priorities




Congruence


Scope



Timing


Equity


Feedback


Arousal


Informal Systems



Involvement
















Felt Compromises


Trade Offs








52 A Lizzio et al






Priorities




Congruence


Scope



Timing


Equity


Feedback


Arousal


Informal Systems



Involvement


52 A Lizzio et al






Priorities




Congruence


Scope



Timing


Equity


Feedback


Arousal


Informal Systems



Involvement


University Students' Perceptions of the Learning ...reforma.fen.uchile.cl/Papers/Learning...

Documents

Transcript of University Students' Perceptions of the Learning ...reforma.fen.uchile.cl/Papers/Learning...