How academics use technology in teaching and learning: Understanding the relationship between beliefs and practice

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How academics use technology in teaching and learning: Understanding the relationship between beliefs and practice
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  How academics use technology in teaching andlearning: understanding the relationshipbetween beliefs and practice J. D. Bain à & C. McNaught w à School of Curriculum, Teaching and Learning, Faculty of Education, Griffith University, Nathan Queensland 4111, Australia w Centre for Learning Enhancement And Research, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong Abstract This paper reports on a detailed investigation into the beliefs and practices of teachers in22 computer-assisted learning projects in Australia in the mid-1990s. Detailed interview datawere obtained, supported by the project software and other curriculum materials. The in-terview transcripts and documentary material were collated and condensed into rich de-scriptions; these were then coded on a number of belief and practice dimensions. Theresulting profiles were clustered into five belief–practice categories: thoughtful instructors,pre-emptive professionals, conversational constructivists, learning facilitators and situatedknowledge negotiators. These complex, yet interpretable, patterns of relationships betweenbeliefs and practices are useful in understanding teachers’ reluctance to change theirteaching, one instance of which is the relatively limited uptake of technology in highereducation. Keywords belief dimensions, case descriptions, cluster analysis, computer-assisted learning, highereducation, practice dimensions. Why bother with beliefs? The aim of this research was to investigate the re-lationships between the design and outcomes of computer-assisted learning (CAL) in higher educationand the educational beliefs and practices of the aca-demics who develop and use such technology. Theresearch focused on Australian academics who were‘early adopters’ (Rogers 2003) of educational tech-nology and had been successful in obtaining compe-titive development funding for their projects. Ourresearch was prompted by several observations andquestions:  Like other teaching initiatives, CAL implementa-tions vary considerably in character: Why?  Many academics resist the use of CAL that has been‘created elsewhere’? Why?  Academic staff development units often report dif-ficulty in augmenting academics’ teaching andlearning methods. Why?  Many academics have adopted Internet-basedtechnologies to support their teaching, but often inlimited forms. Why?Although the answers to these questions are un-doubtedly complex, our working assumption has beenthat a common element among all of them is the edu-cational and epistemological beliefs underpinning aca-demics’ teaching, learning and assessment practices.So, to take our first bullet point above, we conjecturethat variation in the styles of CAL is attributable – in Correspondence: Prof. Carmel McNaught, Centre for LearningEnhancement And Research, The Chinese University of Hong Kong,Shatin, New Territories, Hong Kong. E-mail: Accepted: 10 January 2006 & 2006 The Authors. Journal compilation & 2006 Blackwell Publishing Ltd Journal of Computer Assisted Learning 22 , pp99–113 99 Original article  part – to variation in the educational assumptions of those designing it. Similarly, academics are not likely toadopt a teaching resource made elsewhere unless it‘fits’ with their assumptions about appropriate and vi-able methods for their content domain.There is growing evidence for the variety (Kember1997; Samuelowicz & Bain 2001) and the influence(Ramsden 1992; Hativa & Goodyear 2002; Samuelo-wicz & Bain 2002) of educational beliefs in thepractices of university teachers. One contrast toemerge with some consistency in the literature is be-tween academics who think of learning as reproducingestablished knowledge (and of teaching as organisingand presenting that knowledge appropriately), andothers who think of learning as the outcome of anunderstanding process, which, although assisted by theteacher, has to be constructed by the student (e.g.Trigwell et al . 1994; Roblyer 2002). Available evi-dence suggests that these two different orientations tothe education enterprise result in different learningexperiences and learning outcomes for students(Ramsden 1992; Biggs 1999).There are obvious parallels between these two‘prototypical’ orientations and some conventionalforms of CAL (e.g. knowledge recitation and proce-dural drill versus simulations and microworlds), butthere have been few systematic empirical analyses of these parallels (Laurillard 1993, 2002). Moreover, as apreliminary study in this series indicates (Bain et al .1998b), orientations to teaching with CAL are notlikely to be as stereotypic as the reproducing versusconstructing contrast outlined above. Bain et al .(1998a) analysed the archival records of 36 fundedCAL projects and reported seven distinct orientationsdefined in terms of their positions on five qualitativedimensions of CAL practice. Although the prototypicalorientations were clearly evident in the sample, therealso were other, more complexly defined, orientationsand these were more prevalent. Moreover, there was noclear relationship between orientations and ‘types’ of CAL (such as tutorial systems and databases).Similar conclusions (about the complexity and di-versity of academics’ educational orientations) holdfor general teaching and learning beliefs (Fox 1983;Samuelowicz & Bain 1992, 2001; Kember 1997), foreducational beliefs within a discipline (Quinlan 1999,2002), for beliefs about assessment (Samuelowicz &Bain 2002), and for beliefs about the supervision of research in higher degrees (Murphy et al . in press).Accordingly, we anticipated substantial variation andcomplexity in academics’ orientations to teaching withCAL when using a sample and methods designed toreveal the diversity. Key research questions Given the above, the aim of this study was to describeand interpret orientations to CAL practice, where weuse ‘orientation’ to refer to a unique pattern of edu-cational beliefs and practices involved in the designand use of a CAL package. Our main research ques-tions were as follows:1. What belief and practice dimensions are most usefulin the description of orientations to CAL practice?2. What is the range of variation in orientations toCAL practice? That is, what distinctive patterns of CAL beliefs and practices emerge in a broadsample of relevant cases?3. Do the patterns of beliefs and practices makesense? That is, is each orientation to CAL practiceunderstandable and internally coherent? Design and procedure Sample The cases were 22 CAL projects that had been fundedby the Committee for the Advancement of UniversityTeaching (CAUT), an Australian competitive grantingagency, during the period 1993–1995. The 22 projectswere sampled on the basis of a preliminary analysis of CAL practices (and implied beliefs) contained in theirCAUT grant applications and final reports. The ana-lytic framework reported by Bain et al . (1998a) wasused as the sampling plan. A wide range of disciplinesand CAL types was selected. Eighteen of the casesinvolved a single teacher and four cases involved twoteachers who had collaborated closely. The pairs of teachers were interviewed together. Interviews and other sources of evidence Participants were interviewed extensively about thedesign and use of their CAL and about their educa-tional and knowledge beliefs. There were two inter-views, each of about 2h duration. The interviews were 100 J.D. Bain & C. McNaught & 2006 The Authors. Journal compilation & 2006 Blackwell Publishing Ltd  semi-structured, being based on prepared schedules of questions and prompts. Although the schedules set outthe nature and order of the matters to be canvassed, theactual order and emphases were determined as eachinterview progressed. There were two teams of re-searchers, two per team, and each team handled thedata collection for 11 cases.The first interview focused on the CAL, beginningwith a demonstration of its use and then exploring theeducational needs that prompted its design and thestudent capabilities it was intended to foster (such asconceptual knowledge, procedures and skills, methodsof inquiry and/or ways of thinking in the discipline).Detailed discussion about the ways in which studentswork with the CAL also occurred in this interview.Broadly, this interview concentrated upon partici-pants’ educational practices, but very often these werediscussed and elaborated in connection with their be-liefs and values. This interview was video-recorded.The second interview began with discussion aboutthe ways in which students learn the discipline, and inparticular the content and skills associated with theCAL. The nature of the evidence academics had aboutthe learning of their students (e.g. how well studentsperform in their assessments) was an integral part of the discussion. The broader question of what is goodand poor teaching was explored, as was the issue of how the academics’ teaching experience (in generaland in relation to the CAL project) influenced theirbeliefs about teaching. This interview usually endedwith a discussion about the meaning of scholarshipand the possible relationship between teaching andresearch. Thus, the second interview was focused oneducational and epistemological beliefs, but anchoredto the educational contexts involved, particularly theCAL. This interview was audio-recorded.The discussions contained in the recordings of thesetwo semi-structured interviews were fully transcribed.The transcripts were analysed in conjunction with re-levant archival material (project reports, journal arti-cles, book chapters), curriculum resources (handouts,texts, exercises, assessments) and student commentsand evaluations. Data preparation The transcripts and documentary material were col-lated and condensed into rich descriptions comprisingdetails of the CAL; the ways in which the CAL wasused by students and staff; the educational context inwhich the CAL was used (content area, otherteaching and learning methods in use, learning ex-ercises and assessments, etc.); and the academics’educational beliefs and values. These descriptionswere drafted by the interviewing team, and thencarefully checked by the members of the otherteam. In most cases this involved viewing the firstinterview tape and then reconciling the rich des-cription against the interview transcripts and othersources of evidence. Data coding and initial dimension selection Previous belief–practice studies that we have con-ducted (Samuelowicz & Bain 1992, 2001, 2002; Bain et al . 1998a; Murphy et al . in press) commenced witha lengthy qualitative classification of the cases, fol-lowing which the categories thus formed were com-pared and contrasted to reveal the various bases(dimensions) of qualitative variation. This procedurewas not adopted for the present study. Instead the richdescriptions were first coded on belief and practicedimensions, and then categorised according to the si-milarities among their ‘profiles’ on those dimensions.Initially, 18 belief, 16 practice and two scholarshipdimensions were used, with some of the practice di-mensions being coded in two ways – on the propertiesof the CAL and on the CAL as used in its coursecontext (called practice-in-context). Most of the di-mensions were drawn from published sources (Reeves1992; Samuelowicz & Bain 1992; Reeves & Reeves1997; Bain et al . 1998a) but some were developedduring preliminary examination of the interviewtranscripts and project documentation. Most dimen-sions were five-point bipolar rating scales (cf. Reeves1992), but others involved qualitative differences thatcould be ordered from teaching-centred to learning-centred. Coding was based on the full weight of evi-dence available rather than on localised interviewcomments or archival details.There were several iterations of refinement of thedimension set to eliminate obvious redundancies andremove dimensions that were not reliably coded (asdetermined by inter-team differences that could not beresolved). During each iteration, all rich descriptionswere re-coded, and the two research teams cross- Technology in T&L: Beliefs & Practice 101 & 2006 The Authors. Journal compilation & 2006 Blackwell Publishing Ltd  checked their codings. At the completion of this verylengthy process (hundreds of hours), the number of dimensions, and the case codes for each, were wellstabilised. Table 1 lists the 40 dimensions that wereretained for subsequent analyses: 16 belief, 13 practiceand 11 practice-in-context dimensions (the last twosets having nine practices in common). Category formation The aim of the next stage of the analysis was to reducethe complexity of the data matrix (codes for 22 caseson 40 dimensions) to more manageable and inter-pretable proportions by grouping similar cases into asmaller number of categories (orientations) and redu- Table 1 . Belief and practice dimensions used for initial coding and after several iterations of refinement.Dimension 1 Scale 2 Epistemological beliefs Nature of discipline knowledge Abstract $ Situated Origin of to-be-learned knowledge Discipline/academic $ Student/negotiatedPedagogical beliefs Pedagogical philosophy Instructivist $ ConstructivistTeacher’s Role Didactive $ FacilitativeLearning theory Behavioural $ CognitiveValue of errors To be avoided or minimised $ Opportunities for learningIntended type of understanding Knowing more $ Knowing differentlyRole of discussion in learning Incidental $ CentralUnderstanding process  Reproductive/fragmentary  Reproductive/relational  Transformational Accommodation of students’ conceptions  Absent  Pre-emptive  ConversationalCurriculum beliefs Learning goal orientation Sharply focused $ UnfocusedTeaching focus Student development $ Academic disciplineCurriculum integration Sum of the parts $ Coherent integrationCurriculum progression  Linear aggregation  Jigsaw  Spiral elaboration Role of student collaboration  Minimal  Social  Cognitive  Knowledge & understandingCurriculum focus  Disciplinary/interdisciplinary ways of knowing  Professional/artistic performingCAL practices Task orientation Academic/abstract) $ Authentic/experiential Task structure  High (constrained) $ Low (open) Interactivity Navigational/mathemagenic $ Manipulative/constructive Learning control  Teacher-managed $ Student-managed Collaborative learning Unsupported $ Integrated Source of motivation Extrinsic $ Intrinsic Structural flexibility  Fixed time, place $ Open access Learning framework   Structured  Guided  Facilitated Learning process  Reproduction $ Construction Feedback to students   Minimal  Fixed  ResponsivePlace of CAL in the curriculum Adjunct $ CentralCAL contribution to assessment Adjunct $ CentralLearning focus  Knowledge  Reasoning  PerformanceGeneral practicesFocus of assessment Know more $ Know differentlyFlexibility of assessment Fixed $ Negotiated 1 Dimensions in bold are those finally adopted after clustering and simplification. Italicised dimensions were coded twice; once rating theCAL practice per se, and again rating the CAL practice as it functioned in the educational context. 2 Five-point bipolar scales areindicated by their polardescriptors separatedby $ ; qualitative scales are described by bulleted descriptorsthat are roughly ordered along a teacher-centred to learner-centred dimension. 102 J.D. Bain & C. McNaught & 2006 The Authors. Journal compilation & 2006 Blackwell Publishing Ltd  cing the dimensions to those most useful in distin-guishing the categories. To this end, we used the entirebelief–practice profile as the basis for category for-mation. That is, we sought groupings of cases suchthat the members assigned to a group had similarconfigurations of beliefs and practices.Although it may have been possible to group the 22belief–practice profiles with a qualitative ‘pattern-matching’ procedure, we elected instead to usenumerical pattern-matching methods to guide us incategory formation. Accordingly, we used numbers torepresent the codes on the five-point bipolar (andqualitatively ordered) dimensions, and subjected theresulting matrix to hierarchical clustering analysis(Ward 1963; Jain et al . 1999).Preliminary analyses of the 22  40 data matrixrevealed five belief–practice clusters that were verystable irrespective of the hierarchical clusteringmethod or distance measure used. To reduce methodcomplexity in subsequent analyses, we adoptedWard’s clustering method with two distance measures:Euclidean distance and Squared Euclidean distance.We next established that the same five clusters (cate-gories) appeared (a) when the five-point rating scaleswere simplified to three-point scales (to simplify in-terpretation and presentation); (b) when the 11 prac-tice-in-context dimensions were omitted (although onecase changed category, albeit to a ‘nearby’ category);and (c) when additional dimensions were omitted toeliminate non-discriminating dimensions. The fivecategories reported below are based on the 13 di-mensions shown in bold in Table 1. They include twodimensions from each of three broad belief groupings(epistemological, pedagogical and curriculum) plusseven practice dimensions. As a final check on thestability of this solution, we analysed the five-pointdata for these 13 dimensions and obtained the samefive-cluster solution as with the three-point scales.Having determined the likely categories in themanner just described we then checked each categoryprofile for internal coherence, and for its ability torepresent each of the cases assigned to a category. Thiswas a qualitative and time-consuming procedure. Al-though there were a few instances for which the ca-tegory profile seemed an insufficient description, weretained the dimensions and categories determinedpreviously because they were a defensible compro-mise between the complexity of the initial coding andthe simplifications needed to reveal and interpret thedominant patterns in the data. The resulting categoriesare such that there are small differences in profilewithin each category, but larger differences betweencategories. Findings Orientations to CAL The categories resulting from the method describedimmediately above are briefly described in Table 2,and illustrative cases are provided in Tables 4–8. It isimportant to note that, although we refer to the cate-gory patterns in this section, our later emphasis will beupon the individual cases whose patterns may differslightly from their category descriptions.The main point to note about the categories de-scribed in Table 2 is that they represent interpretableyet complex patterns of relationships between beliefsand practices. The two ‘extremes’ – the thoughtfulinstructors and situated knowledge negotiators – arerecognisable variants of the stereotypes noted earlier,yet they do not fully fit those stereotypes. For ex-ample, we refer to the instructors as thoughtful be-cause, despite their emphasis on instructing students inestablished discipline knowledge, they have takencareful account of past students’ difficulties and mis-understandings, albeit in a pre-emptive way (i.e. byrelying on analogies, animations and other devices toassist understanding and reduce misunderstanding).The situated knowledge negotiators , on the otherhand, view knowledge as emergent from context, ne-gotiable with the student, and generally not amenableto instruction. In between those extremes are cate-gories in which established discipline knowledge isemphasised (as with the thoughtful instructors), butemphasis is placed on students constructing their un-derstanding with some assistance from the teacher.Table 3 illustrates the ‘fit’ between the classic ste-reotypes of teacher-centred and learner-centred andour five categories. Illustrative case profiles Examples of case profiles for the five categories aresummarised in Tables 4–8. For each case, there is abrief description of the technology and the curriculum Technology in T&L: Beliefs & Practice 103 & 2006 The Authors. Journal compilation & 2006 Blackwell Publishing Ltd
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