academic research in education, technology and media: mapping the ...

academic research in education, technology and media: mapping the ‘field’ ‘First sight’ research report January 2013

‘Learning with New Media’ Research Group Faculty of Education, Monash University Melbourne, Australia http://newmediaresearch.educ.monash.edu.au/lnmrg @LNM_Monash

Suggested citation: Selwyn, N., Johnson, N., Bulfin, S. and Henderson, M. education, technology and media: mapping the field’ Faculty of Education

(2013) ‘Academic research in Melbourne, Monash University

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0)

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executive summary During the summer of 2012 an online survey was developed and hosted for ‘research active’ academic researchers working in the broad area of educational technology and educational media. Completed surveys were gathered from 462 researchers. This initial report is intended to give a ‘first sight’ of the emerging findings from these data. The survey focused on three broad research questions: • • •

Who is conducting research in the area of education, technology and media? What methods (or kinds of methods) of data collection and analysis are privileged in this research? What is the use of theory in shaping the kind of research work that gets done?

Data from the survey produced a number of notable findings •

Three broad groups of researchers working in the area of education, technology and media could be identified. These were researchers working in what can be termed the area of ‘education & teaching’, those working in ‘cognition & design’, and those in the ‘social sciences & humanities’. Notable differences could be found between these three groups in terms of research interests, methodological capabilities and use of theory.

•

One of the interests underlying many people’s research in education, technology and media was ‘learning’, and to a lesser degree ‘teaching’. This appears to exert a strong influence on researchers’ uses of theory (i.e. a broad preference for psychologyinfluenced explanations of learning or no use of theory at all), and method (i.e. methods that purport to describe learning taking place).

•

There is clearly a preference amongst many researchers for relatively basic forms of descriptive research. The survey also highlighted a disinterest in advanced quantitative data collection and analysis. On one hand, this finding perhaps reflects the persistence of the long-standing ‘paradigm wars’ within many areas of social research. However, this finding might also be due to people’s personal lack of quantitative expertise, coupled with the perceived poor quality of the quantitative work that tends to get published in the area of education, technology and media.

•

The survey also highlighted a lack of engagement with theory amongst many respondents. Some respondents’ notion of what constituted useful ‘theory’ appeared to often relate to specific ideas, concepts and frameworks that would not be usually considered to be theoretically-grounded or particularly theoretically-sophisticated. Further thought and discussion needs to take place regarding the apparent absence of bone fide ‘theory’ from this area of academic research.

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•

Much of the open-ended data conveyed a suspicion towards – and reluctance to engage with – aspects of research that did not provide good ‘explanations’. On one hand, these responses reflected an understandable demand for useful and useable research. However, these responses also reflected overly simplistic expectations of clear ‘answers’. As such, there is clearly a need for all ‘producers’ of research to be more explicit in their use of theory and methods. Conversely, there is an accompanying need for all ‘consumers’ of research in the area of education, technology and media to be willing and able to engage with more complex explanations and analyses.

The report concludes with some suggestions for a number of short-term ‘research capacity building’ opportunities for broadening the use of methods and theory in educational technology research. It also highlights some emerging issues in relations to longer-term improvements to what will undoubtedly continue to be an expanding area of academic education research over the next ten years and beyond.

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introduction Amidst the long-standing criticisms of the quality of education research in general (see Oancea 2005), the areas of educational technology and educational media are often portrayed to be particularly weak in terms of research robustness and rigour. Individual examples of outstanding research notwithstanding, the academic study of educational technology and educational media research often lacks empirical and theoretical consistency and rigour. Thus as Margaret Roblyer (2005, n.p.) was driven to conclude, there are a number of ‘special problems’ that can be said to recur throughout the research literature in education, technology and media: … these weaknesses include fragmented and uncoordinated approaches to studying technology resources and strategies, methods that lack rigor or are illmatched to the research questions at hand, and poorly written reports that render problematic subsequent attempts at replication and follow-up. We in the field of educational technology have a clear and imminent challenge. We must design and carry out research that will both address past concerns about methods and findings and clarify the directions we should take in the future. Despite Roblyer’s call-to-arms, some commentators see these shortcomings as reflecting a deep-rooted lack of coherence in this area of academic research. Indeed, while growing rapidly in size and scope, academic research into education and digital technology and media remains a distinctly ‘fuzzy’ area. Most researchers would probably concede that this is not a stable, easily defined ‘field’ in the sense of more established and clearly demarcated areas of academia. Of course, the fluidity and hybridity of research backgrounds and interests in this area can be seen as an asset. As Friesen (2009, p.12) described of what he termed ‘e-learning research’: It is interdisciplinary in that it seeks to combine and explore the interconnections between new and different approaches from different fields and specialisations; it is multidisciplinary in that it simultaneously tries to respect the multiplicity of differences that can separate one research approach from another. These observations may well be justified, yet it could be argued that this sense of productive plurality is well-hidden in much research on education and technology – at least as presented in the many journals, conferences, blogs and other public forms of research dissemination. In this sense, it might be more accurate to describe academic research in education, technology and media in more disorganized and dysfunctional terms. As Selwyn (2012, p.1) argued recently: ‘Education and technology’ is not an easily identifiable or especially coherent field of study. What is often referred to in broad-brush terms as ‘ed-tech’ or ‘ed-media’ refers in practice to an assortment of researchers and writers brought together through inadvertently shared interests in technology use in education. Rather than

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being an area of sustained academic study, education and technology tends to attract a transient ragbag of individuals hailing from the learning sciences, instructional design, social psychology, computer science, teacher education, media studies, sociology, literacy studies and beyond. All these different ‘groups’ have their own particular interests and motives for studying technology and education. Understandably, most people feel little collective impetus to make this ‘non-field’ anything more that the sum of its parts. This exaggeratedly harsh assessment (as well as Friesen’s excessively harmonious assessment before it) clearly requires more thought, consideration and – above all – empirical investigation. Indeed, while all the observations considered so far in this report may well be accurate, they are too broad-brush and impressionistic to be of genuine use. In order to move these debates forward, this report offers an empirically informed analysis of the academic researchers working in the broad area of educational technology and educational media. In particular it addresses the following areas of concern: •

• • • •

Who is conducting research in the area of education, technology and media? What subject traditions do these researchers see themselves as working in? Do they form a coherent ‘field’ or ‘group(s)’? What methods of data collection and analysis are privileged in research on education, technology and media? What kinds of methods are absent? What is the use of theory in shaping the kind of research work that gets done? How does research capacity appear to vary between different groups of researchers? What opportunities are there for strengthening the ‘research capacity’ of academics working in education, technology and media research?

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research methods These research questions are addressed through analysis of data collected by a recent survey of academic researchers whose work encompasses education, technology and media. An online survey was developed and hosted for researchers deemed to be ‘research active’ in the broad areas of educational technology/educational media. A working definition of ‘research active’ was used of having published one peer-reviewed journal article relating to educational technology and/or educational media in the past five years. The survey was designed to take no more than 15 minutes to complete, and to collect information about the nature and form of researchers’ experience and expertise in terms of research designs, methods of data collection and analysis, their personal and professional backgrounds, and use of theory in their research activities. Invitations were sent during the summer of 2012 to first-named authors of all empirical articles published between the summer of 2007 and the summer of 2012 in four prominent educational technology/ educational media journals (Computers & Education, British Journal of Educational Technology, Australian Journal of Educational Technology, and Learning Media & Technology). In addition, open invitations to participate were distributed through researcher discussion lists and forums in the UK, North America and Australasia. Completed surveys were gathered from 462 researchers. While an invitation to participate in a survey of this sort might be expected to result in a self-selecting sample of methodologically confident respondents, the sample could be said to be relatively representative of the wider population of academic researchers publishing in the English language. As can be seen in table 1, there was a roughly even distribution in terms of gender, age and career-stage. However, there was an under-representation of respondents from North America, East Asia and the ‘rest of the world’.

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Table 1. Background characteristics of the survey sample

n Gender Male Female

per cent

235 223

51.3 48.7

Country North America Australia/ New Zealand UK/Ireland Europe (exc. UK/Ireland) East Asia Rest of the world

76 106 133 90 39 18

16.5 22.9 28.8 19.5 8.4 3.9

Age of Researcher 20-29 years 30-39 years 40-49 years 50-59 years 60 years or more

17 116 151 126 47

3.8 25.4 33.0 27.6 10.3

Nature of current role Research intensive role (>50% time) Non-research intensive role (50% or less)

152 304

32.9 65.8

246

53.2

216

46.8

85 72 300

18.6 15.8 65.6

Stage of career Early career (0 to 5 years) Mid career (6 to 15 years) Late career (>15 years)

131 145 174

29.1 32.2 38.7

Received formal research training Doctoral Research training No doctoral research training

406 56

87.9 12.1

Promoted position (i.e. senior lecturer or equivalent) Non-promoted position Nature of current university Research focused university Teaching focused university University focused equally on teaching and research

(N.B. summed totals may not add up to 462 due to missing responses to particular items; summed percentages may not add up to 100 due to rounding up/down).

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results 1. Who is working in academic educational technology research? Respondents were asked to nominate the academic areas that their digital technology/digital media related research was aligned most closely to. The full range of academic areas can be been in table 2, highlighting a variety of different disciplines and traditions. However, simple component analysis (appendix table A.1) suggests that respondents’ alignments with these 27 different areas of study appeared to cluster around three dominant themes. These related to three different areas all relating to what could be termed ‘education & teaching’ (component three); eight areas relating to the broad theme of ‘cognition & design’ (component one); and finally eight areas relating to what could be described as ‘social sciences & humanities’ (component two) ‘Education & teaching’

• • •

Educational studies Teacher education/ pedagogical sciences Social psychology

‘Cognition & design’

• • • • • • • •

Cognitive psychology Human computer interaction Computer sciences Instructional design/ learning design Learning sciences Educational psychology Artificial intelligence Engineering

‘Social sciences & humanities’

• • • • • • • •

Media studies Cultural studies Communications studies Anthropology Philosophy Sociology English/ literacy/ new literacies Creative arts and design

For the purpose of subsequent analysis, respondents were assigned to whichever of these ‘groups’ that their nominated areas implied that they were aligned predominantly with. This led to the following distribution: • • •

predominantly ‘education & teaching’ – 61.4 per cent predominantly ‘cognition & design’ – 24.0 per cent predominantly ‘social sciences & humanities’ - 14.6 per cent

Of course, if we examine the range of areas that respondents felt aligned with, then there is considerable interplay between these broad groups of interest and expertise. While the

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majority of respondents were aligned primarily with ‘education & teaching’, most of these also encompassed some element of ‘cognition & design’ and/or the ‘social sciences & humanities’. Indeed, over one quarter of respondents were aligned with a combination of ‘education & teaching’ and ‘cognition & design’. A further one-fifth of the sample was aligned with subjects spanning all three groups (see figure 1). The research interests and topics of these three different groups of researchers varied. As can be seen in figures 2 to 4, ‘learning’ was a prominent topic of interest in ‘cognition & design’ and ‘education & teaching’, but far less so in the ‘social sciences & humanities’. The broad topic of ‘technology’ was evident in the responses of researchers in ‘education & teaching’, whereas the other groups reported more varied and nuanced technology related interests. Responses from researchers aligned primarily with ‘education & teaching’ also featured a range of issues related to teachers and teaching, but less often related to students and the spaces/places of educational provision (e.g. schools, classrooms and so on). As might be expected, responses from researchers in the ‘social sciences & humanities’ reflected a greater interest in terms such as digital, media, culture, social and literacy. As can be seen in table A.2, the background characteristics of these three different ‘groups’ varied along a number of notable lines. Respondents working in ‘cognition & design’ were more likely to be male, working in later stages of their careers and based in Europe and East Asia. Respondents working in the ‘social sciences & humanities’ were more likely to be female, working in non-promoted positions and in relatively early stages of their careers, and be based in North America and UK/Ireland. Conversely, respondents working in ‘education & teaching’ were older – with over 44 per cent aged 50 years or more. Table 2. Academic areas that respondents nominated their research in digital technology/media as most closely aligned to Educational studies Teacher education/ pedagogical sciences Instructional design/ learning design Learning sciences Media studies Educational psychology Human computer interaction Communications studies Computer sciences Cultural studies Cognitive psychology Sociology Other specialist areas of education English/ literacy/ new literacies Creative arts and design Science subjects Anthropology Artificial intelligence Social psychology Business studies Philosophy Mathematics Engineering Developmental psychology History Socio linguistics Geography

50.0 44.8 33.8 23.6 20.6 20.1 19.7 17.3 15.2 14.1 11.9 10.8 9.7 9.5 8.7 6.3 5.6 5.4 5.4 4.3 4.1 3.9 3.2 2.6 1.7 1.7 1.3

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25.1%

12.9%

14.8%

Cognition & design

21.3%

Education & teaching

11.7%

4.8%

9.3%

Social sciences & humanities

Figure 1. Percentages of respondents aligned with at least one subject in each of the three academic ‘groups’ (NB. circles/ areas are not to scale)

Figure 2. Reported topics of research interest for the ‘education & teaching’ group

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Figure 3. Reported topics of research interest for the ‘cognition & design’ group

Figure 4. Reported topics of research interest for the ‘social sciences & humanities’ group

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2. What methods of data collection and analysis are privileged in educational technology research? What kinds of methods are absent? Respondents were asked to rate their expertise with a range of methods of data collection and analysis. For each item respondents indicated their expertise along the following lines: • • • •

Have no working knowledge of this skill; Can ‘consume’ this skill – i.e. feel comfortable in understanding others’ research that uses this skill; Have used this skill in my own research – i.e. have personally used this skill in research; Have expertise for this skill – i.e. consider themselves as having expertise in the advanced use of this skill.

As can be seen in table 3, there was a clear predominance of expertise and experience in the use of descriptive research strategies and collaborative research (most notably action research and other participatory designs). Similarly, in terms of methods of data collection there was a clear predominance of interviewing, surveys and observations (table 4). As can be seen in table A.3, expertise in terms of different research strategies varied along a number of lines. Significantly, respondents claiming to have expertise and/or to have used experimental research strategies were more likely to be aligned with the cognition & design, be in later stages of their careers and located in North America, Europe and East Asia. Familiarity with historical research was more likely to be reported by respondents based in North America and UK/Ireland, those aged sixty years or over and working in the social sciences & humanities. Familiarity with futures studies was more likely to be reported by male rather than female respondents. Some differences were also evident in terms of familiarity with methods of data collection (table A.4). For example, respondents claiming to have expertise and/or to have used prototyping were more likely to be aligned with ‘cognition & design’, while the use of secondary data was more prominent in the ‘social sciences & humanities’. Younger researchers (i.e. aged between 20 to 29 years) were noticeably less likely to report expertise or use of synthesizing existing research, analysis of secondary data and documentary analysis. Experiments/quasi experiments were less likely to be reported by respondents from the ‘social sciences & humanities’, those working in teaching focused universities and in the UK/Ireland.

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Table 3. Familiarity with different types of research strategy

Descriptive research (e.g. single case studies, evaluations) Collaborative research (e.g. action research, participatory methods) Comparative research Experimental research Prototyping/ design based research Ethnographic research Longitudinal research Cross-sectional research Historical research Futures studies/ forecasting

No working knowledge 15.4

Can consume 7.6

Have used 27.1

Have expertise 50.0

20.6

19.0

28.4

32.0

21.2 28.1 38.3 30.1 30.3 40.9 43.3 48.3

25.5 29.4 26.0 33.5 35.7 33.8 35.9 35.9

27.9 21.4 20.3 21.4 19.7 14.1 12.8 10.6

25.3 21.0 15.4 14.9 14.3 11.3 8.0 5.2

Table 4. Familiarity with different methods of data collection

Interviews Surveys Observation Experimental design/ quasi-experimental design Visual/audio data collection Synthesizing existing research (e.g. systematic reviews, meta-analysis) Analysis of secondary data Documentary analysis Prototyping/ design based research

No working knowledge 15.2 15.4 18.8 28.4 29.9 27.7

Can consume 3.9 6.7 10.6 23.6 15.8 25.5

Have used 30.3 35.7 31.2 24.2 31.0 26.4

Have expertise 50.6 42.2 39.4 23.8 23.4 20.3

29.4

20.8

32.5

17.3

32.9 39.0

25.3 24.2

24.7 20.3

17.1 16.5

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In terms of familiarity with different methods of data analysis, the most commonly reported methods were relatively simple in nature – i.e. various forms of content analysis and graphs/charts, calculating means and frequencies (see table 5). For the purposes of comparative analysis, the various methods of data analysis were collated into the following categories of ‘basic’ and ‘advanced’ according to respondents’ familiarity with them: Basic qualitative:

Content analysis; discourse analysis; narrative analysis; textual analysis; grounded theory approach.

Advanced qualitative:

Conversational analysis; interpretative approach; quasi-statistical approach; interactionism; ethnomethodology; semiotics; typologies.

Basic quantitative:

Frequencies; means, standard deviations; graphs and charts; cross-tabulations; handling missing values; corrective weightings; probability; correlation (bivariate); comparing means (e.g. ANOVA, t-tests); comparing frequencies (e.g. Chi-squared, Mann-Whitney).

Advanced quantitative:

Regression (multivariate); principal components/factor analysis; classification/cluster analysis; multi-level modelling; log-linear modelling; time-series analyses; spatial analysis.

Respondents were judged as having ‘basic’ and ‘advanced’ skills if they indicated having used/having expertise in two or more of the methods. Along these lines, 66.0 per cent of the respondents could be judged as having basic quantitative skills, and 37.4 per cent advanced quantitative skills. Conversely, 62.8 per cent of the sample could be judged as having basic qualitative skills, and 45.9 per cent advanced qualitative skills. As can be seen in table A.5, these skills were patterned in a number of ways. For example, advanced quantitative expertise was more prominent in male respondents, those working in ‘cognition & design’, respondents working outside of the UK/Ireland and Australia/New Zealand, and researchers in later stages of their careers. While advanced qualitative skills were more prominent in the social sciences & humanities, they were otherwise less obviously patterned by the background of respondents.

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Table 5. Familiarity with different methods of data analysis

No working knowledge

Can consume

Have used

Have expertise

22.7 29.4 30.3 39.2 32.5 32.0 42.9 45.0 58.0 56.1 66.0 63.0

14.7 24.7 25.3 20.1 25.1 29.7 26.4 23.8 26.2 28.6 25.8 25.3

32.9 27.7 26.8 24.7 26.8 25.5 21.4 21.9 11.3 11.7 6.1 9.5

29.7 18.2 17.5 16.0 15.6 12.8 9.3 9.3 4.5 3.7 2.2 2.2

22.3 26.2 27.1 38.3 35.9 38.5

8.9 12.1 12.1 12.8 15.6 15.6

27.7 23.8 24.7 20.3 20.6 21.6

41.1 37.9 36.1 28.6 27.9 24.2

36.8 40.7 44.8 40.9 48.7 51.1 53.7 62.8 66.2 65.8 71.9

15.8 18.6 16.5 19.5 16.9 21.2 20.3 22.5 23.2 23.4 20.6

23.4 19.7 20.8 22.9 19.3 17.5 17.1 9.1 7.1 8.2 6.3

24.0 21.0 18.0 16.7 15.2 10.2 8.9 5.6 3.5 2.6 1.3

Qualitative data analysis Content analysis Discourse analysis Textual analysis Interpretative approach Grounded theory approach Narrative analysis Conversational analysis Quasi-statistical approach Semiotics Ethnomethodology Interactionism Typologies Quantitative data description and analysis Graphs and charts Means, standard deviations Frequencies Cross-tabulations Comparing means (e.g. ANOVA, t-tests) Comparing frequencies (e.g. Chisquared, Mann-Whitney) Correlation (bivariate) Regression (multivariate) Handling missing values Probability Principal components/factor analysis Classification/Cluster analysis Corrective weightings Multi-level modelling Log-linear modelling Time-series analyses Spatial analysis

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3. What is the use of theory in shaping the kind of research work that gets done? Over one third of the sample (34.8 per cent) reported that they made deliberate use of theory in their research. As can be seen in table A.6 this was more likely with respondents from the UK/Ireland and the ‘rest of the world’, those in non-promoted positions and early stages of their careers, and those in the ‘social sciences & humanities’. This latter finding notwithstanding, the majority of theories arising from an open-ended item in the survey related to psychological theories of learning (see table 6). That said, one of the most striking findings from this section of the survey was the considerable ambiguity as to what was understood as constituting ‘theory.’ Many of the answers appeared to relate to a field of research or a method of research rather than a theoretical approach per se. For example, over a third of responses to this item (n=90) gave responses that were research traditions or fields rather than recognised theories per se (e.g. ‘action research’, ‘CSCL’).

Table 6. Ten most commonly reported theories used by respondents. NB. Data are percentage of respondents providing a response (n=257)

n 56 42 38 30 17 17 16 15 15 11

Constructivism/ connectivism/ constructionism Cognitive/ psychological theory Vygotsky/ socio-cultural theory Activity theory Grounded theory Critical theory Community of inquiry/ practice TPCK Semiotics/ linguistics theory/ symbolic interactionism Actor Network Theory

Per cent 21.8 16.3 14.8 11.7 6.6 6.6 6.2 5.8 5.8 4.3

Against this background, findings from the subsequent sections of the survey relating to respondents’ use of theory throughout specific elements of the research process need to be read with caution (see table 7). Here, nearly two-thirds of respondents reported using theory at some stage of the research process (in contrast to the earlier finding of 34.8 per cent of the sample reporting that they made deliberate use of theory in their research). As responses to the open-ended items suggested, it would appear that these data reflect a broad interpretation of what constitutes ‘theory’. Here respondents were making reference to their use of theory in terms of ‘qualitative’, ‘quantitative’, ‘digital native’, ‘ethnography’ and so on.

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Table 7. What role (if any) have these theories played in your educational technology research? (n=257)

Informed the identification of research ideas/ research topics Informed the formulation of research questions Informed the formulation of research hypothesis Informed the nature of data collection/ choice of research methods Informed the nature of data analysis Provided a means of interpreting research data/findings

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Never

Occas.

42.2 42.4 49.6 42.6 43.5 43.9

6.5 3.9 6.9 6.3 5.2 3.2

Sometimes 24.5 23.6 22.3 22.9 24.9 20.6

Always 26.8 30.1 21.2 28.1 26.4 32.3

4. Respondents’ views on the strengths and weakness of research in the area of education, technology and media The final sections of the survey offered a series of open-ended items where respondents were invited to submit their opinions on the strengths and weakness of research in the area of education, technology and media. Although these items were intended to relate to the use of theory, in practice responses covered a broad range of issues. In particular, two issues recurred throughout these data – relating to: (i) the use of theory; and (ii) the use of quantitative research approaches. i) The uses and mis-uses of theory The use and mis-use of theory was one of the two prominent themes that emerged in the responses to the open-ended items towards the end of the survey. First was the issue of the nature and appropriateness of theory in the academic literature relating to education, technology and media research. A surprising number of responses relating to this theme were broadly critical of the use of ‘theory’ – which was often argued to lack relevance to practical issues: ‘Some of the research in this area is over-theoretical and has therefore contributed to creating gaps between academic researchers and practitioners’ [UK/Ireland, education & teaching] ‘There are far too many publications that are based upon ideas or philosophies rather than empirical evidence. … often interfere with identification of practical solutions’ [North America, education & teaching] ‘Theoretical approaches remain just that. Theories unless they are supported by evidence. Digital technology/digital media unfortunately is the dumping ground of much poorly conceived theory with limited empirical underpinnings and dubious connection to practice’ [ANZAC, no group] This issue was highlighted particularly with regards to perceptions of theory obscuring people’s core interest in better understanding teaching and learning: ‘It seems that there's a lot of work in this area doesn't add to our understanding of how this generation of technology users go about the business of learning and doing’ [ANZAC, education & teaching] ‘I've also had occasion to read papers recently that appear inspired by cultural theories, and have found them an intriguing cross-over from the humanities, but I wonder how far they genuinely illuminate our understanding of teaching and learning with digital technologies, and how far they are simply an interesting intellectual exercise’ [UK/Ireland, education & teaching] These responses also highlighted dissatisfaction with the self-referential use of theory:

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‘If you look at the literature on games and learning there is an abundance of theoretical papers that are fairly redundant and often citing one another, however without providing any empirical evidence in support of the theoretical notions portrayed’ [Europe, cognition & design] ‘As someone who resides in Education and who wants to be recognised in the field in UK, I am bound by the conventions of educational research which is dominated by sociology. I am unhappy about this because many sociological papers revel in the complexity of the theory to the extent that they present little or no robust data to support the theory. Using sociological theories means authors can use long Latin words which make the paper look impressive - but it is all meaningless’ [UK/Ireland, education & teaching] As another respondent reasoned, this predominance of theory precluded the valuing of common-sense understandings and ideas: ‘‘Common sense' knowledge cannot be used to argue; but someone else's statement in a book can! How would this be different?’ [UK/Ireland, education & teaching] On the other hand, a smaller but equally vociferous set of responses offered counterarguments to these views – reasoning that research in the area of education and technology was constrained and limited by a lack of theoretical rigour and preference for practical explanations. As one respondent put it bluntly: “much of the literature strikes me as atheoretical” [North America, social sciences & humanities]. As was observed elsewhere, much of the work in the area of education and technology “reads like a magazine article” [ANZAC, education & teaching]. As two other respondents contended: ‘I find the current widespread ‘unscientific’ approach in much of the literature to be very unhelpful. Far too much of the literature appears to be in pursuit of ‘best practice’ without questioning what constitutes ‘good practice’ in the first place. We need to adopt a more critical approach in our thinking, build up a comprehensive evidence base before we can begin to construct hypotheses ready for testing. Just as Creationists stand in the way of real scientific research, e-learning enthusiasts blindly carrying out pseudo-research in the hope of catching a glimpse of the second coming of technology enhanced learning do precious little to further the progress of our knowledge’ [UK/Ireland, education & teaching] ‘I feel strongly that the literature of technology enhanced learning has been hindered because it has been too dominated by enthusiasts desperately searching for any ‘evidence’ which supports their pre-conceived notions that e-learning tools will bring about a revolution in teaching and learning. However, the reality is that this anticipated transformation today appears to be no nearer than it ever was. I believe that researchers have done themselves and our discipline no favours by adopting such an evangelical approach – ‘real’ researchers (both from the positivist and interpretivist camps) will look down on such unprincipled approaches (indeed I am reluctant to use the word ‘approach’ to describe the current state of affairs).

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Surely, it is time that researchers in this field took a step back, reflect upon the methodological principles which inform their work, and begin to consider genuine theoretical principles’ [UK/Ireland, education & teaching]

Amidst these responses was the perceived tendency of some academics working in the area of education, technology and media to conflate ‘popular concepts’ with a rigorous theoretical approach, leading to an abundance of ‘pseudo’ theories: ‘Generally the area is under-theorised with a default tendency to over-emphasise constructivism and popular concepts with no empirical base, like 'digital nativism' etc.’ [UK/Ireland, social sciences & humanities] ‘Connectivism - holds some interesting ideas and perspectives, but when stated/postulated as a theory it is problematic, incoherent, messy, technology driven’ [Europe, education & teaching] ‘Digital natives is interesting as a metaphor, but should be pruned before put into usage’ [Europe, education & teaching]

It was also reasoned that the field of education and technology ‘borrowed’ theoretical concepts and perspectives in an uncritical and sometimes inappropriate manner: ‘Its striking that approaches come and go very quickly without due consideration to the particular context of e-learning. For example I have seen activity theory come in and out, ANT becoming popular and I guess later to be dropped, CoP as an idea. We borrow from them without at times really understanding them’ [UK/Ireland, education & teaching] Seen from this perspective, then, a number of respondents argued that encouraging the more rigorous and appropriate use of any theory would improve the field: ‘The literature can tend to be uncritical at times, especially in its ahistorical/determinist or positivist claims/context, or where education is not contextualised inside a socio-political structure. I wouldn't wish to criticise anyone's use of theory - using some is a starting point for critique’ [UK/Ireland, education & teaching] ‘Any time that theory is invoked - as opposed, say, to the needs and learning styles of digital natives - that is probably a good thing!’ [North America, social sciences & humanities] ‘There is room for a range of diverse theories and even if they are inadequate they provide useful background information for building on, extending or addressing gaps’ [ANZAC, education & teaching]

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ii) The merits and limitations of quantitative approaches The second recurring theme in these open-ended responses related to the use of quantitative data in education and technology research. Here, a considerable degree of suspicion and even hostility to the use of quantitative research in the field was evident. A common argument was that quantitative approaches lacked a depth and richness of explanation – offering, as one respondent put it, “insufficient attention to why an effect has occurred” [ANZAC, cognition & design]. As was also argued: ‘I think quantitative approaches - that overly emphasise number crunching but do not reveal the context and the individual do not present a 'real' picture and certainly not a 'human picture'’ [ANZAC, education & teaching] This insufficiency of explanation was felt by some respondents to reflect the fast changing nature of the topic: ‘Statistical analysis. Things change far too rapidly in today's technology for the results to be particularly meaningful and they don't explain WHY things happened only that they did for that brief window’ [North America, cognition & design] ‘Experimental and statistical - too much trying to nail a shifting target which is not well problematised’ [UK/Ireland, cognition & design] Primarily, then, these criticisms related to the lack of correspondence between quantitative research and the complex contexts of technology use in education: ‘It makes no sense to include statistical approaches as most work is laden with context, especially in higher education’ [ANZAC, education & teaching] ‘Practically all positivist studies that believe they can ‘measure’ phenomena which are too complex to be reduced to numbers’ [Europe, education & teaching] ‘Purely psychological approaches often neglect social framework conditions and/or external restrictions (lack of finance, historical framework conditions, decision structures...)’ [Europe, education & teaching]

These views were countered, however, by a smaller number of respondents who reasoned that academic research in the area of education, technology and media was in clear need of more use of rigorous quantitative approaches. This, it was argued, would expand the analytic capability of research as well as giving it wider academic credibility: ‘Research which relies upon qualitative evidence to support interpretations. The 'science' academic community are never convinced. To use ICT across all

22

disciplines to support learning and teaching, there needs to be unequivocal, statistical evidence from advocates’ [East Asia, cognition & design] From this perspective, the research literature in education, technology and media was felt to suffer from the inappropriate use of quantitative research approaches. One respondent, for example, criticised the overuse of “rating scales [that] seem not really valid” [Europe, education & teaching]. Other criticisms centred on the tendency of small-scale studies to make inappropriate generalisations about wider populations: ‘Attempts to make absolute, rather than contextual, generalizations about what works and what doesn't. Often such research is experimental and of limited scope which compounds the problem’ [East Asia, education & teaching] ‘case studies involving a statistically insignificant number of cases - there is a tendency to generalize about the larger population and this is inappropriate’ [North America, education & teaching]

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points for discussion This modest piece of research has considered a few simple questions that are rarely considered amidst the cut-and-thrust of academic research in education, technology and media – namely ‘who are we?’, ‘what is it that we do?’ and ‘how might we do things better?’. If nothing else, the findings that have emerged from our survey data highlight the need for everyone working in this area to continue asking and exploring the issues that these questions bring up. Clearly there is scope for improving the rigour of academic work in this area, alongside an ongoing need to be more self-reflexive and self-critical. In many ways, these data provide empirical confirmation of what many people working in this area of academic research would have long suspected to be true. Certainly the tone of responses to the open-ended sections of the survey suggested a well-developed awareness amongst some respondents of the shortcomings of academic research in this area. As such, these findings may well simply confirm issues and problems that many people working in this area of academic research already suspected or observed for themselves. However, the fact that readers’ preconceptions may have been confirmed by this report does not mean that its findings cannot be used as a starting point for further discussion and thought. In this spirit, we conclude by considering some of the pertinent points that arise from these data: •

Firstly, while there is certainly a wide range of disciplines and subject areas represented within the body of researchers working in the area of education, technology and media, this is by no means a completely disparate and fragmented ‘field’. The three broad interest groups identified from our data suggest a set of common interests that may often transcend (and it could be argued subsume) the specific disciplinary origins and specialities of individual researchers. In other words, whether they were originally a mathematician, clinician or anthropologist by training, people’s interests in education, technology and media are perhaps more aligned than might be otherwise expected. In particular, the emergence of the three distinct ‘groups’ within this study’s data suggests that there are perhaps fewer differences within this area of research than some commenters might assume.

•

That said, there are certainly differences that need to be acknowledged instead of referring to a homogenous ‘field’ of research. Clearly, this is an area of research where the majority of people are aligned primarily to ‘educational’ issues – i.e. interested in issues relating to learning as well as the concerns of teachers and teaching. This majority group may, however, have a relatively unsophisticated view of ‘technology’ and a relatively limited repertoire of research approaches, methods and advanced data analysis skills. Conversely, while researchers aligned with the cognition & design may nominally share an interest in ‘learning’, their’s appears to be primarily technology focused rather than teacher focused, using descriptive, experimental and prototyping research, with strengths in quantitative methods. Finally, the smaller group of researchers aligned with the social sciences & humanities are more likely to see themselves as interested in ‘digital media’ rather than ‘technology’ per se, using descriptive, ethnographic and historical methods, with strengths in qualitative research.

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Interestingly, researchers in this group are most likely to see themselves as making use of ‘theory’, but not in ways that relate directly to their research. While many researchers and writers may see themselves as fitting across two or even all of these groups, the data suggest that these distinctions are clearly demarcated. •

The survey data suggested that one of the interests underlying many people’s research in education, technology and media is ‘learning’, and to a lesser degree ‘teaching’. This appears to exerts a strong influence on individuals’ preferences for theory (i.e. a broad preference for psychology influenced explanations of learning or no use of theory at all), and method (i.e. methods that purport to describe learning taking place). These preferences are understandable, but raise the question of what issues are being overlooked or obscured as a result. For example, is there a need for more research that deliberately seeks to look ‘beyond’ issues of learning and teaching? Certainly, the social and cultural interests of the minority ‘social sciences & humanities’ group highlights a number of different topics that may be pertinent to all studies of education, technology and media.

•

There is clearly a preference amongst many researchers for relatively basic forms of descriptive research. This is certainly not a problem in itself. Indeed, this present report is based solely on basic descriptive forms of data collection and analysis. However, the lack of advanced data collection and analysis techniques that appears to characterize the capabilities and ambitions of many researchers might be seen as cause for concern in terms of the overall ‘research capacity’ of this area of academic study.

•

The survey did highlight a surprising lack of interest in advanced quantitative data collection and analysis. Certainly the responses to the open-ended survey questions suggested a scepticism towards quantitative techniques. In one sense, this finding may reflect the persistence of the long-standing ‘paradigm wars’ within many areas of social research. However, this finding might also be due to the perceived poor quality of the quantitative work that tends to get published in the area of education, technology and media – which some respondents suggested was compromised by small sample sizes, indirect proxy measures, weak predictive and statistical power. This may also be due to people’s lack of ability in understanding fully studies based upon advanced quantitative analysis. Either way, there would certainly appear to be a need for increased exposure to and conduct of well designed, rigorous and interesting quantitative work in the area of education, technology and media.

•

The survey also highlighted a paucity of theoretical engagement (and perhaps theoretical ambition) amongst many respondents. It would appear that many respondents’ notion of what constituted useful ‘theory’ often related to specific ideas, concepts and frameworks that would not be considered to be theoretically grounded or particularly theoretically sophisticated. Further thought and discussion needs to take place regarding the apparent absence of bone fide ‘theory’ from the field.

•

Much of the open-ended data reflect a suspicion towards – and reluctance to engage with – aspects of research that are not seen as providing good ‘explanations’. This was especially noticeable in terms of people’s reactions to the use of theory and quantitative methods. On one hand, these responses reflect an understandable demand for useful and useable research. However, these responses may also reflect overly simplistic expectations of clear ‘answers’. As such, there is clearly a need for all

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‘producers’ of research to be more explicit in their use of theory and methods. However, there is perhaps an accompanying need for all ‘consumers’ of research in the area of education, technology and media to be willing and able to engage with more complex explanations and analyses. Of course, the self-report nature of these data should be seen as a limitation to the conclusions that can be drawn. It would certainly be desirable for these research questions to also be examined through a meta-analysis of the published research literature. This limitation notwithstanding, these findings certainly point to a number of short term opportunities for broadening the use of methods and theory in academic research in the area of education, technology and media. Regardless of personal preference, a strong argument could be made that all researchers should have familiarity with a range of research methodologies and associated approaches to data collection and analyses, and make more effort to consume the research of others. Indeed, ideally it would seem reasonable to expect academics working in this area to have the capacity to conduct (or at least consume competently) research of all kinds (see Rees et al. 2007). The need arises, therefore, to explore opportunities for ‘research capacity building’ within the mass of researchers working in this area. A number of well established means of building ‘research capacity’ exist throughout the social sciences that might be considered. For example, the technical methodological competencies of researchers could be improved through formal and informal modes of professional learning. This could be achieved through formal programmes of professional development and ‘research coaching’ which can be delivered either face-to-face or online (the latter being an especially appropriate mode of delivery given the nature of this area of research). The activities could be the responsibly of the learned societies that purport to represent researchers working in education technology and media research, or perhaps as a focus for the many conferences and journals in this area. While it may be more practical and successful for such activities to concentrate on doctoral students and early career researchers, academics of all ages and stages would clearly benefit from such assistance. Conversely, a strong case can also be made for more stringent methodological demands from journal editors, research funders and peer reviewers. At the very least, within the spaces where academic researchers interact and where academic research is communicated, there could be an encouragement of more respectful communication with proponents of different methodological positions and traditions. If nothing else, it could be argued that researchers need to make a conscious effort to interact in wider circles in order to stimulate an informal ‘enculturation’ into a greater range of different research traditions than is the case at present. In short, an argument could be made for everyone involved in academic research in education, technology and media to be more adventurous and ambitious in their engagement with unfamiliar traditions and disciplines. These immediate issues of self improvement notwithstanding, it is also worth considering the long term changes that might be desirable if this area of academic work is to mature and develop. For example, further thought could be given to the opportunities that might exist for increased multidisciplinary and interdisciplinary collaboration. Of course, it may well be that the area of education, technology and media is perhaps too broad for an effective multi-disciplinary or inter-disciplinary transformation to ever take place. Instead, it might be more productive to consider emerging trends in other areas of academia towards

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networked approaches to making academic sub-disciplines more than the sum of their (disparate) parts. As Christian Fuchs concludes, academic work in this area could explore the development of ‘trans-disciplinarity’ rather than inter- or multi-disciplinarity – i.e.: Trans-disciplinarity means a higher level system of research with a shared language, a unity in diversity of disciplines, approaches, methods, categories, theories and so on. It emerges from the communication of scientists who have different backgrounds but share an interest in a common topic of research from different angles (Fuchs 2008, p.4). Above all, then, any future improvement is likely to depend upon improved awareness, communication and respectful exchange between the many different researchers working in this area of academic research. This is such a rich and potentially significant area of education research that it is the obligation of everyone working in it, to strive to make it more than the sum of its parts. As this report suggests, there is some way to go before this becomes the case.

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references Friesen, N. (2009) ‘Re-thinking e-learning research’ New York, Peter Lang Fuchs, C. (2008) ‘Internet and society: social theory in the information age’ London, Routledge Oancea, A. (2005) ‘Criticisms of educational research: key topics and levels of analysis’ British Educational Research Journal, 31, 2, pp. 157-183 Rees, G., Baron, S., Boyask, R. and Taylor, C. (2007) ‘Research-capacity building, professional learning and the social practices of educational research’ British Educational Research Journal, 33, 5, pp.761-779 Roblyer, M. (2005) ‘Educational technology research that makes a difference’ Contemporary Issues in Technology and Teacher Education, 5, 2 [www.citejournal.org/articles/v5i2seminal1.pdf] Selwyn, N. (2012) ‘Ten suggestions for improving academic research in education and technology’ Learning, Media and Technology, 37, 3, pp.213-219

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appendix Table A.1. Component loadings based on a principal components analysis for 27 subject areas.

Cognitive psychology Human computer interaction Computer sciences Instructional design/ learning design Learning sciences Educational psychology Artificial intelligence Engineering Media studies Cultural studies Communications studies Anthropology Philosophy Sociology English/ literacy/ new literacies Creative arts and design Educational studies Teacher education/ pedagogical sciences Social psychology Science subjects Mathematics Developmental psychology Socio linguistics Other specialist areas of education Geography History Business studies

Component 2 -.013 .115 .081 -.036 .089 -.044 .090 -.023 .718 .700 .633 .556 .445 .410 .406 .349 .142 .031 .062 -.016 -.088 -.007 .151 -.020 .246 .260 .137

1 .630 .589 .586 .581 .578 .566 .411 .330 -.101 -.254 .039 .064 .069 -.101 -.001 .210 .170 .326 .216 .296 .149 .237 .020 -.019 -.044 -.165 .052

3 .049 -.206 -.360 -.030 .110 .071 -.356 -.242 -.093 -.124 -.065 .061 .273 .283 .041 -.289 .563 .527 .331 .235 .181 .178 .172 .068 .018 -.032 -.265

NB. Extraction Method: Principal Component Analysis (extracted components were rotated). Highlighted values denote retained variables for each component. Three items had cross loadings above 0.3 (Teacher education/ pedagogical sciences, computer sciences and artificial intelligence) - however these items had strong primary loadings of 0.56, 0.58 and 0.41 respectively.

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Table A.2. Background characteristics of respondents in each of the three ‘groups’

Education & teaching

Cognition & design

Social sciences & humanities

Gender Male Female

50.6 49.4

62.0 38.0

32.8 67.2


14.5 26.2 28.9 19.9 7.0 3.5

19.0 22.0 16.0 24.0 15.0 4.0

26.2 11.5 42.6 11.5 3.3 4.9


2.0 20.1 33.5 31.9 12.6

5.1 30.3 36.4 22.2 6.1

8.3 35.0 33.3 16.7 6.7

Nature of current role Research intensive role (>50% time) Non-research intensive role (50% or less)

32.9 67.1

31.0 69.0

36.1 63.9

Promoted position Non-promoted position

55.1 44.9

55.0 45.0

42.6 57.4

Nature of current university Research focused university Teaching focused university University focused equally on teaching and research

19.8 16.6 63.6

19.2 16.2 64.6

13.3 11.7 75.0


26.3 35.5 38.2

21.2 31.3 47.5

43.3 28.3 28.3

Received formal research training Doctoral Research training No doctoral research training

91.6 8.4

90.0 10.0

90.1 0.9

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Table A.3. Have used/have expertise with different research designs BY background characteristics of the survey sample

Descr

Coll

Comp

Cross

Long

Ethno

Exper

Proto

Hist

Futur

Gender Male Female

77.9 76.7

57.4 63.7

53.6 53.4

27.2 23.3

34.9 33.2

34.0 39.5

45.5 39.5

36.6 35.4

20.0 22.0

20.4 11.2


78.9 84.9 71.4 82.2 71.8 50.0

65.8 63.2 61.7 58.9 53.8 33.3

55.3 66.0 45.9 54.5 51.3 22.2

30.3 23.6 18.8 28.9 35.9 22.2

39.5 37.7 31.6 32.2 33.3 16.7

46.1 34.0 41.4 27.8 28.2 33.3

56.6 39.6 27.1 52.2 51.3 44.4

47.4 31.1 33.1 37.8 35.9 22.2

31.6 18.9 30.1 7.8 5.1 16.7

14.5 9.4 21.8 14.4 20.5 11.1


70.6 75.0 78.1 78.6 80.9

41.2 57.8 62.3 61.9 66.0

64.7 50.0 50.3 56.3 59.6

11.8 28.4 25.2 20.6 34.0

17.6 24.1 37.1 39.7 36.2

23.5 36.2 37.1 37.3 36.2

47.1 37.1 46.4 42.1 42.6

29.4 33.6 37.7 40.5 25.5

17.6 20.7 14.6 22.2 36.2

11.8 14.7 16.6 11.9 27.7

Nature of current role Non-research intensive role Research intensive role

78.0 78.3

59.5 64.5

54.6 52.6

23.0 30.9

31.2 40.8

34.9 40.8

39.8 49.3

30.9 46.7

20.7 21.7

14.5 19.1

Non-promoted position Promoted position

71.3 82.1

54.2 65.9

48.1 57.7

24.1 26.4

27.3 39.8

34.3 38.2

34.3 49.6

31.5 39.4

19.4 22.0

14.4 17.1


77.6 70.8 78.3

67.1 50.0 61.0

57.6 44.4 54.0

30.6 11.1 27.0

36.5 29.2 34.3

38.8 31.9 37.0

45.9 27.8 45.0

45.9 27.8 35.0

22.4 30.6 17.3

22.4 19.4 12.7


69.5 78.6 83.3

42.0 64.8 71.8

42.0 57.9 59.2

22.1 21.4 32.2

24.4 34.5 42.0

32.1 37.2 39.1

29.8 42.8 54.0

26.7 35.9 44.3

22.1 18.6 23.0

13.7 13.8 19.0

Academic group Education & teaching Cognition & design Social sciences & humanities

84.0 82.0 80.3

66.8 59.0 67.2

58.6 54.0 59.0

25.8 35.0 19.7

37.1 41.0 26.2

39.8 26.0 55.7

42.2 66.0 26.2

35.5 48.0 34.4

19.1 17.0 44.3

16.4 17.0 16.4

NB. Columns relate to the following categories: • • • • • • • • • •

Descriptive research (e.g. single case studies, evaluations) Collaborative research (e.g. action research, participatory methods) Comparative research Cross-sectional research Longitudinal research Ethnographic research Experimental research Prototyping/ design based research Historical research Futures studies/ forecasting

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Table A.4. Have used/have expertise with different methods of data collection BY background characteristics of the survey sample

Inter

Surv

Obser

Exper

Visual

Synth

Secon

Docum

Proto

Gender Male Female

80.0 82.5

81.3 74.4

69.8 71.7

52.3 43.5

51.1 58.3

46.8 47.1

51.9 47.5

40.9 42.2

38.7 35.0


85.5 87.7 78.2 82.2 71.8 55.6

82.9 85.8 71.4 78.9 76.9 55.6

80.3 74.5 67.7 66.7 64.1 61.1

63.2 49.1 27.8 61.1 56.4 44.4

59.2 56.6 60.9 51.1 35.9 27.8

48.7 51.9 50.4 41.1 38.5 27.8

47.4 56.6 53.4 40.0 41.0 61.1

39.5 48.1 40.6 41.1 35.9 38.9

42.1 33.0 33.8 43.3 35.9 27.8


76.5 78.4 80.1 84.1 85.1

82.4 73.3 78.8 81.7 74.5

64.7 64.7 70.9 76.2 72.3

47.1 43.1 52.3 47.6 48.9

41.2 55.2 54.3 54.0 59.6

29.4 49.1 41.1 49.2 57.4

29.4 50.9 46.4 50.0 63.8

11.8 39.7 39.1 41.3 66.0

35.3 37.9 36.4 41.3 25.5


80.3 84.9

76.0 84.2

68.4 77.6

44.1 57.9

51.0 63.2

45.7 50.7

51.0 48.7

41.4 44.1

32.6 46.7


76.9 84.6

73.1 82.1

66.7 74.0

40.7 54.5

53.7 54.9

42.1 50.8

49.1 50.4

38.0 45.1

33.8 39.4


83.5 76.4 81.0

78.8 75.0 78.0

71.8 68.1 71.0

52.9 29.2 51.7

58.8 48.6 54.3

42.4 54.2 46.3

40.0 50.0 52.3

41.2 37.5 43.3

49.4 25.0 36.3


71.0 86.9 85.6

69.5 80.0 84.5

61.1 73.1 77.6

34.4 51.7 58.0

48.1 56.6 58.6

41.2 49.7 51.1

48.1 49.7 52.3

32.1 42.8 48.9

28.2 35.9 46.0


87.5 87.0 83.6

85.5 89.0 63.9

77.3 79.0 65.6

49.6 70.0 27.9

59.8 52.0 65.6

51.2 47.0 54.1

52.6 47.0 70.5

48.0 36.0 44.3

36.7 54.0 27.9

NB. Columns relate to the following categories: • • • • • • • • •

Interviews Surveys Observation Experimental design/ quasi-experimental design Visual/audio data collection Synthesizing existing research (e.g. systematic reviews, meta-analysis) Analysis of secondary data Documentary analysis Prototyping/ design based research

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Table A.5. Have used/have expertise with different research approaches BY background characteristics of the survey sample

Basic quantitative

Advanced quantitative

Basic qualitative

Advanced qualitative

Gender Male Female

69.4 62.3

41.3 34.1

56.6 69.5

43.0 49.3


69.7 70.8 53.4 76.7 69.2 55.6

50.0 28.3 20.3 55.6 53.8 38.9

67.1 64.2 63.9 56.7 61.5 61.1

52.6 46.2 49.6 36.7 46.2 33.3


58.8 63.8 66.9 67.5 68.1

35.3 39.7 40.4 31.7 38.3

47.1 62.9 57.0 69.8 68.1

29.4 47.4 45.0 44.4 53.2


62.8 75.0

35.2 43.4

60.5 69.7

42.8 53.9


61.6 69.9

34.3 40.2

59.7 65.4

46.8 45.1


71.8 55.6 67.0

40.0 23.6 40.0

63.5 52.8 65.0

51.8 36.1 46.3


49.6 71.7 75.9

25.2 35.9 50.0

51.9 67.6 68.4

38.2 50.3 49.4


73.0 87.0 31.1

40.2 59.0 11.5

68.0 58.0 82.0

49.6 40.0 67.2

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Table A.6. Responses to item ‘I make deliberate use of theory in my research’ BY background characteristics of the survey sample

Per cent Gender Male Female

36.2 33.3


28.9 31.1 42.9 33.3 25.6 50.0


35.3 36.2 35.1 34.9 31.9

Nature of current role Research intensive role Non-research intensive role

30.3 35.9

Promoted position Non-promoted position

30.1 40.3


32.9 34.7 35.0


44.3 27.6 32.8


27.0 32.0 41.0

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