I’m not bitter…. I’m better!

Image via Wikipedia

I’ve had a lot of visitors to my blog over the past week. Is it because of the WordPress DoSS attacks?  Is it because OU H809 course students are searching for info?  Is it my ex-employers checking to see if I’ve written anything about them?

Well don’t worry – I’m not bitter. Why should I be?  I had 11 great years at the college; lead on some amazing projects, built up a really sound and cutting-edge system.   Made a lot of fantastic friends and colleagues, especially when working on national projects such as BECTA TEN and MoLeNET.

I’m leaving on mutually agreed terms – by a Compromise Agreement.  So I wasn’t sacked and I didn’t resign.  It is time to move on, head held high.

What next then?  Well, it’s rather an uncertain future – employment options look bleak but I’m optimistic about things.  I’ve already looked at starting an MSc at the University of Liverpool – The one I started with The OU is possibly not one that I can transfer across easily… as it is an entirely different subject area.

I guess I’ll be looking for a job after April 1st to get me going and see where it leads.

Any offers will be gratefully appreciated.  Hint hint.

National 'snowday'

National ‘snowday’

Originally uploaded by colhawksworth

Never mind the British obsession with the weather; us urbane international hipsters (or should it be erstwhile self-deluded village dwellers) take-up the challenge and crack-on with a bit of mobile working (didn’t it used to be called tele-working?)

What’s the problem? we’ve got Internet access, devices, information and learning systems to connect to, oh and that Web thing too. Do we actually have to ‘go’ to a place of work? Aren’t we already there – we can work ‘anywhere’. (BTW: the coffee is damn fine at home)

.fin.

Logic tells me that this blog post *should* be in chronological order… however, I suspect that as with most of my other blog entries, it will wind-up being a stream of consciousness.  This will be a ‘blog-in-progress’ – as I remember all the noteworthy things that happened in 2009.

This year (2009) seemed to be hectic all the way! – considering the recession there were  plenty on conferences to attend and project funding to bid in for.

• I didn’t have a good start to the year and for personal reasons ended up not being able to finish my OU Post Grad studies.  Though, as the year went on, I missed studying and  decided to start a CTTLs course – so I can teach Post-16 courses.

• Our college successfully managed to get a MoLeNET Phase3 project – and so far it’s been a wonderful and inspiring experience.

• I made lots of new contacts across the globe via tools such as Twitter, FriendFeed, etc. It’s rather incredible to think that I have far more dialog with folks I’ve never met, in other parts of the world – than I do with some colleagues at my own college.  I guess that’s something a psychologist would probably have a field-day with… though a sociologist might simply shrug and tell me that it’s because we have a commonality and Community of Practice, rather than simply working at the same location with nothing else in common.

• I got an iPhone – a major improvement (as far as I’m concerned) to the iPaq Windows phone I previously had and the Palm Treo before that, etc, etc.  I love Apple and I love the iPhone – yes, I am aware that there are major flaws [3g had no video, etc] BUT Apple tech is simple and functional.  Whenever I have to use a MS Windows PC now, I find it such a pain and far too cumbersome.  I want my tech to be as intuitive as it is functional.

Early adopters or early majority?

Rogers' modified by Moore

Rogers’ Diffusion of Innovation model modified by Geoffrey Moore.

Whilst Rogers mentions early adopters as the opinion leaders. Moore argued that this is actually vital in order to target the early majority – he argued that there is a chasm between early adopters and early majority – explaining why some innovations become merely a minority interest.

Rogers does not agree with Moore:
‘…some scholars claimed that a discontinuity exists between innovators and early adopters versus the early majority, late majority and laggards (Moore, 1991). Past research shows no support for this claim of a ‘chasm’ between certain adopter categories. (Rogers, 2003, p.282)

Further reading:

MIT – Moving from Technology-Centered to Human-Centered Products

The Life Cycle of a Technology

Proven Models

‘Crossing the Chasm’ – Wikipedia entry

Les Robinson – ‘Enabling Change’

Activity 2.7: Reading the introduction and conclusion

Questions and hypotheses
The survey was looking for ‘rare’ information within institutions; about strategies and activities, to understand more precisely the reasons, development, inhibitors, etc. of e-learning development.
The key questions were:

• ‘Why do different kinds of tertiary institutions engage in e-learning, and what forms of engagement are favoured?’
• ‘What do institutions perceive to be the pedagogic impact of e-learning in its different forms?’
• ‘How do institutions understand the costs of e-learning, and how does this affect financial management?’
• ‘How might e-learning impact on staffing and staff development?’
• ‘Do particular types of student (e.g. by gender, mode of study, domicile, discipline, etc.) favour e-learning?’

Differences between how the two studies (OECD and OBHE) tackled these questions.
The OECD Centre for Educational Research and Innovation (OECD/CERI) surveyed 19 tertiary education institutions from 13 countries in order to understand e-learning practices and associated issues. The qualitative survey was complemented by quantitative evidence, (2004 survey of online learning by the Observatory on Borderless Higher Education (OBHE)).

CERI partnered with the Observatory on Borderless Higher Education (OBHE), to carry out a larger survey of online learning in Commonwealth universities covering similar topics in 2004. The quantitative data was to be complement the OECD/CERI survey.

The OECD/CERI survey was qualitative and designed to provide in-depth coverage of the issues, it was critical to have a small number of respondents. A problem was that it is difficult to generalise this small-scale qualitative findings. A large-scale survey (Observatory survey) by the OBHE was used for comparative purposes (Garrett and Jokivirta, 2004; Garrett and Verbik, 2004).

The Observatory survey is a rare example of a quantitative international survey of e-learning in higher education. Data provided quantitative coverage of many of the same issues as the OECD/CERI survey in some Commonwealth countries. Whereas the small OECD/CERI survey covered 19 institutions, the Observatory survey covered 122. This enabled some data to be put into a broader context, which checked whether OECD/CERI findings were in line with more general data. The two studies complemented each other.

The limitations noted by the report in each of these studies.
The study should not be taken as a representation of e-learning take-up in tertiary education institutions in the OECD area. This is typical of any qualitative survey. The study mainly used the Observatory survey, which realistically represents the UK, Canada, and Australia (more advanced overall in e-learning), with wider results from other studies in the US. In the surveys, there might be a (self-) selection bias. In the OECD/CERI survey there may have been more enthusiasm about e-learning as well as some overestimations in its merits and barriers. Conversely it could be that the Observatory survey attracted a disproportionate number committed to online delivery, and it may have over­ estimate Commonwealth universities. The focus of the OECD/CERI survey could have overlooked the cross-institutional subjects communities within higher education – the growth in electronic resource collections within national groups in the UK.

Activity 2.5: Reflecting on the research methods

1. In the discussion of task A11 (pp. 279–81) the account of the students’ utterances is plausible, but why is transcript data to be preferred to the video data for such a visual task?

It is not clear ‘why’ they have chosen transcript data over video data (which can also show ‘gestured’ action) – perhaps it is because they are adamant they are solely using !KwicTex, which can only assess transcript data.

2. A criticism sometimes made of quantitative research is that it uses preconceived categories rather than letting findings ‘emerge’ from the data. The ‘Commentary’ on task A11 (pp. 280–1) is qualitative rather than quantitative, but it could be argued that it also uses preconceived categories.

For example, Elaine’s words before the intervention, ‘No, because it will come along like that’, and the fact that the next utterance is by John on the next question are interpreted as, ‘She gives a reason to support her view and this is not challenged.’

Her words after the intervention, ‘Now we’re talking about this bit so it can’t be number 2 it’s that one. It’s that one it’s that one’ are interpreted as, ‘In proposing number 4 Elaine is building on these two earlier failed solutions’ (p. 281).

Wegerif and Mercer have prior expectations about ‘exploratory talk’, defined as ‘talk in which reasons are given for assertions and reasoned challenges made and accepted within a co-operative framework orientated towards agreement’ (p. 277).

So notions such as ‘reason’, ‘support’, ‘challenge’ and ‘failed solution’ have specific, preconceived meanings. Do you think it would be possible to avoid the use of preconceived categories when analysing this data?

In this scenario, I do not think it would be possible to avoid the use of preconceived categories. I would imagine the only way the software could interpret the conversation would be to have some prior inputted categories to enable cataloging.

Just to expand this a little – Conversation is generally spontaneous and certainly at this age (primary 9/10), children can often use incorrect words and badly formulated sentences and have a tendency to link them together with words such as ‘because or cos’. Quite often children actually begin an answer with ‘because’, instead of reasoning their argument out loud.

3. Again in relation to task A11, what evidence might support the following claim on p. 281?

‘In the context of John’s vocal objections to previous assertions made by his two partners his silence at this point implies a tacit agreement with their decision.’

The transcript data could not reveal ‘implied agreement’ with their decision. in order to look at the evidence fully, video footage would need to be introduced, and perhaps a voice-over commentary added to the transcript data to explain this ‘silence’. This isolated instance should not be looked at as valid evidence; John may be a child who is lacking in confidence and will always become silent when challenged by his peers, whether he has given a right or wrong answer.

4. On p. 281, the authors claim:

‘It was generally found to be the case that the problems which had not been solved in the pre-intervention task and were then solved in the post-intervention task, leading to the marked increase in group scores, were solved as a result of group interaction strategies associated with exploratory talk and coached in the intervention programme.’

When you read this claim, did you ask yourself if the researchers had looked at whether this was also true of the control group? If time allows, feel free to look at the papers in which fuller accounts of the study appear.

The use of the word ‘generally’ in a research paper, would suggest one of two things: either the researchers have no evidence to back up their findings and have added a term ‘general’ in order to cover all eventualities – or they do have further evidence, which has not been included in the research paper for some reason, however, I expect that this would be noted in the text.

5. In the post-intervention talk around problem A11, John says, ‘No, it’s out, that goes out look’.

This utterance doesn’t use the words ‘cos’, ‘because’, ‘if’, ‘so’ or a question word, but it is plausible that John is giving a reason. How might one deal with such a problem?

Going back to my answer to question 2. Conversation is generally spontaneous and certainly at this age (primary 9/10), children can often use incorrect words and badly formulated sentences and have a tendency to link them together with words such as ‘because or cos’.

One possible way to deal with this problem is to use some key ‘action’ or ‘doing’ words as categories. Another way could be to actually scan every sentence

6. Are you convinced that the study effectively demonstrates the authors’ case that:

‘the incorporation of computer-based methods into the study of talk offers a way of combining the strengths of quantitative and qualitative methods of discourse analysis while overcoming some of their main weaknesses’?

I think that whilst the theory does offer some way of combining the strengths of quantitative and qualitative methods of discourse analysis while overcoming some of their main weaknesses, the practicality of the technology may have proved to be a stumbling block. I can think back to the earliest version of voice-to-text transcribers we used in the early 1990′s – the theory was fantastic, however the voice recognition software was quite shoddy. With the advances in technology, modern version of the same product are pretty near 100% accurate and can ‘learn’ many different users’ voice patterns.

7. What does the computer add to the analysis?

I think that using a computer speeds up the key word identification process and allows for rapid cross-referencing of inputted texts.

8. What is the status of computer-based text analysis 10 years on? Spend 20 minutes trying to answer this question by searching the web.

Having spent 20 mins (or possibly more) searching on different engines, I came up with the following two sites of note.

http://tactweb.humanities.mcmaster.ca/tactweb/doc/catahist.htm
http://kh.hd.uib.no/tactweb/homeorg.htm

9. How does this paper compare with Reading 1?

Both of the papers deal with different aspects of collaborative learning at both ‘ends’ of the education system. Reading 1 is about e-learning and Reading 2 is pure research (computer-based). On a personal level, Reading 2 was by far a more difficult paper do read and deconstruct!

Activity 2.4: Reading the paper (finished)

So, here, finally is my attempt to locate points from within Wegerif and Mercer (1997) and apply them to the 4 basic questions from week 1.

Research Questions
p271…Computer-based analysis has mainly been applied to the study of large corpora of written texts. In this paper we propose that such methods are also of great value for research on children’s talk and the joint construction of knowledge in the classroom.
p271…We believe that the incorporation of computer-based methods into the study of talk offers a way of combining the strengths of quantitative and qualitative methods of discourse analysis while overcoming some of their main weaknesses.

Setting
p272 …A study of talk amongst pupils in primary school classrooms.

Concepts
p275 …We argue that computer-based transcript analysis offers a way beyond this apparent divide through enabling a more context sensitive approach to combining qualitative analysis with systematic comparison and evaluation.
p276 …The very concept of recording assumes that there is more concrete original event or events which is recorded through being abstracted in some way.
p276 …Time required for analysis and the space required for presentation mean there is a de facto relationship between degree of abstraction useful in the data and the sample size of a study or the degree of generalisation.

Wider Literature
p277…Intervention programme – eight lessons coaching exploratory talk, defined as talk – reasons given for assertions and reasons challenges made and accepted within a co-operative framework. Mercer, (1995). Each lesson integrated three stages. …The intervention programme included some computer software to present information and problems in a way that encouraged children to formulate hypotheses, share information, question assumptions and reach joint decisions.
p278 …Evaluation of intervention programme combined analysis of classroom talk and interaction throughout the programme with the use of a pre- and post-intervention comparison of children’s problem solving. Pre- to post-intervention comparison – two kinds of data: scores from a group reasoning test and an analysis of the recorded talk of certain focal groups of children. …Research design possible to statistically link changes in test score measures to changes in linguistic features, also possible to relate extracts of transcripts to groups talking together to their work on specific problems.

Methods
p272 …We used a software package specifically developed for analysis of transcriptions. In the research described, this software was used to link a detailed analysis of the quality of children’s talk with the quantitative data of the same children’s scores on a test of reasoning.
p276 …The difficulty in effectively combining the strengths of quantitative and qualitative methods of discourse analysis in the study of collaborative learning is the problem of integrating different levels of abstraction in the data.
p276 …The value of computer-based text analysis, we suggest, lies in its ability to perform a mediating role between these two positions. Concordancing software such as !Kwictex enables a rapid movement between different levels of abstraction. Units of analysis such as words, utterances or conversational turns can be abstracted from the transcript to form a separate list.
p277 …The use of such a tool therefore facilitates the inter-relation of different levels of abstraction in the data. An essential and attractive feature of the method we are proposing here is that it maintains a connection between relatively concrete data, such as recordings of events, and relatively abstract data such as word counts or test scores. …Quantitative analysis of test scores does not replace a qualitative analysis of collaborative interactions, both co-exist and contribute to an overall understanding. …This new methodology enables discourse analysts to present their findings in ways that should be more explicit and convincing to a critical audience.
p278 …With this research design, it was possible to statistically link changes in test score measures to changes in linguistic features – also possible to relate extracts of transcripts of groups talking together to their work on specific problems.
p283 …’key word in context’ analysis took one linguistic feature which a detailed qualitative analysis had indicated was associated with one group’s solution of one reasoning problem, tested the generality of this association across the transcripts of this group in the pre-intervention and post-intervention tests.

Findings
p281 …We have used this analysis to support a claim that the children’s use of exploratory talk was a significant factor in their finding the solution of the problem. …may not be convincing to some educational researchers, who might argue – we simply chose such an example of talk because it supported, rather than tested, our claim. …value of using a computer-based analysis to augment a qualitative study can be appreciated. !KwicTex, enabled us to extract all uses of specific linguistic features across the full transcripts, in immediate context of utterances in which they occur.
p283 …Showed a marked shift in the number of times this key word was used from the pre- to the post-intervention condition. Showed an evolution in the way this key term was used in context. Use of !KwicTex in this way makes it possible to explore rapidly the contextualised use of such linguistic features. …Our ‘key word in context’ analysis showed how the general occurrence of specific words can be tracked over a set of related transcripts. In research it is difficult to present many such analysis in a way that does not take up much space and overburden the reader. …Generalised – it is necessary to move up a level of abstraction, make a count of ‘key usages’. Key usages, not simply a key word but a key word being used to serve a particular function.
p284-5 …Computer-basead text analysis using !KwicTex demonstrates a difference between the post-intervention task talk of the three focal groups and their pre-intervention task talk. …Basis – interrelating different levels and types of data to produce an overall interpretation which integrated qualitative and quantitative dimensions. …argued that the use of computer-based methods of text analysis facilitated this integration through allowing the researcher to move rapidly between different levels of abstraction in dealing with transcript data. …most concrete data represented by the full transcript of each reported event, use of specialist computer software facilitated generation of data – selected ‘key words’ and a count of ‘key usages’. The final level of abstraction and generality ,was afforded by the results of the group reasoning tests. Interrelating these four levels of analysis enabled us to draw two conclusions. That the exploratory features of talk responsible for solving the problem illustrated through a qualitative analysis were features generally found more in the post-intervention talk of the children doing a reasoning test task than in the pre-intervention talk of the children doing the same task. That this change in the style of talk of the children towards exploratory talk was matched by an increase in group reasoning.

Implications
p285 …Using computer-based transcript analysis to help combine qualitative and quantitative methods – can produce an overall interpretation – more convincing than either if used alone. Computer-based transcript analysis, special role in bringing data representing different levels of abstraction together into dynamic relationship – different kinds of analysis reflect upon and inform each other. The computer-based method we propose can facilitate this approach because it enables the abstraction of different levels of linguistic data without ever leaving behind the original linguistic contexts of the actual words spoken.

Activity 2.4: Reading the paper (III)

The reason why I made my previous posting, was to outline the incredible quantity of citations made in this paper, within the opening two pages. Looking at the Key Questions as part of the Activity, I think the wording used to outline the pro-Coding and Counting argument is very definite. “…These and other studies using similar coding methods have produced interesting and valuable results.” It is clear that the Wegerif and Mercer are in agreement with this style of research and are more than likely going to employ it within their own.

However, in contrast, the language used within the section ‘Critiques of coding approaches to the analysis of talk’, is rather less defined. “…It might be thought that using two or more independent coders…”

Activity 2.4: Reading the paper (II)

Notes on: Wegerif and Mercer (1997)

The paradigm Debate in Classroom Discourse Analysis

Coding and Counting
Methods for analysing classroom talk, developed from well-established methodological tradition called ‘systematic observation’ (Croll, 1986) – talk data is reduced to coded categories, then statistically compared – categories vary according to focus of the research.

Teasley (1995) – study the talk of children working in pairs on a problem-solving task, each utterance attributed to one of fourteen mutually exclusive categories, including ‘prediction’ and ‘hypothesis’. Transcripts coded by two coders and agreement measured to ensure reliability. Count of categories in different groups correlated with outcome measures to draw conclusions about the kinds of utterances which promote effective collaborative learning. Other studies of collaborative learning have used some version of this coding.

King (1989), used measures such as length of utterance to investigate variables affecting the success of collaborations.

Kruger (1993) counted utterances considered indicative of ‘transactive reasoning’ , and correlated their incidence with measures of the success of children’s problem solving.

Barbieri & Light (1992) measured the incidence of plans and explanations expressed in talk.

Azmitia & Montgomery (1993) looked for talk features indicative of scientific reasoning.

Concept of ‘Socio-cognitive conflict’, (Doise & Mugny, 1984, and Perret-Clermont, 1980), and Joiner (1993) counted the number and type of disagreements in interactions and related these to problem-solving outcome measures.

These and other studies produced interesting and valuable results. Strength as opposed to more qualitative methods lies in their capacity to handle large corpora of data; offer explicit criteria for comprehensively categorising the whole of a data set; offer basis for making systematic comparisons between the communicative behaviour of groups; offer a basis for relating this behaviour to measures of the outcomes of collaborative activity. Critics point to weaknesses.

Critiques of this method
Summary of critiques
Edwards & Mercer (1987:11) – coded analysis is often presented as a fait accompli and the the prior interpretative movement that generated the codes form the data is often obscured or forgotten.
Draper & Anderson (1991) identify four specific kinds of problem:
1. Utterances are often ambiguous in meaning, making coding difficult or arbitrary.
2. Utterances may have – indeed often have – multiple simultaneous functions, which is not recognised by most coding schemes which naturally involve the assignment of utterances to mutually-exclusive categories.
3. The phenomena of interest to the investigator may be spread over several utterances, and so any scheme based on single utterances as the unit of analysis may not capture such phenomena.
4. Meanings change, and re-negotiated during the course of the ongoing conversation.

References
Azmitia, M. and Montgomery, R. (1993) Friendship, transactive dialogues, and the development of scientific reasoning. Social Development 2 (3), 202-21.
Barbieri, M. and Light, P. (1992) Interaction, gender and performance on a computer-based task. Learning and Instruction 2 (3): 199-213.
Croll, P. (1986) Systematic Classroom Observation. Lewes, Sussex: The Falmer Press.
Doise, W. and Mugney, G. (1984) The Social Development of Intellect. Oxford: Pergamon Press
Draper, S. and Anderson, A. (1991) The significance of dialogue in learning and observing learning. Computers and Education 17 (1), 93-107.
Edwards, D. and Mercer, N. (1987) Common Knowledge: The Development of Understanding in the Classroom. London: Methuen/Routledge.
Joiner, R. (1993). A dialogue model of the resolution of inter-indevidual conflicts: Inplications for computer-based collaborative learning. Unpublished PhD thesis, The Open University.
King, A. (1989) Verbal interaction and problem solving within computer aided learning groups. Journal of Educational Computing Research 5, 1-15.
Kruger, A. (1993) Peer collaboration: conflict, cooperation or both? Social Development 2 (3).
Perret-Clermont, A.N. (1980) Social Interaction and Cognitive Development in Children. London: Academic Press.
Teasley, S. (1995) The role of talk in children’s peer collaborations. Developmental Psychology 31 (2), 207-20.

Activity 2.4: Reading the paper (I)

Notes on Abstract and Introduction

Using Computer-based Text Analysis to Integrate Qualitative and Quantitative Methods in Research on Collaborative Learning.

Rupert Wegerif and Neil Mercer
Centre for Language and Communications, School of Education, Open University, Milton Keynes, MK7 6AA, UK.

This paper argues that there are great potential benefits in incorporating computer-based text analyses into methods for researching talk and educational activity in classrooms. The first part of the paper discusses the strengths and weaknesses of some existing approaches to he study of talk and collaborative activity. The second part of the paper suggests ways that computer-based analysis of transcribed talk can integrate qualitative and quantitative methods, and in so doing overcome some of their respective weaknesses. This integrated approach is illustrated by a recent study of primary school children’s talk and joint activity while working at the computer.

Introduction
Computer-based text analysis is one of the fastest growing areas of linguistics (Stubbs, 1996). Some have even described its impact on research in linguistics as a ‘revolution’ (Baker et al., 1993).

“We propose that such methods are also of great value for research on children’s talk and the joint construction of knowledge in the classroom.”

A brief discussion of the strengths and weaknesses of commonly-used methods for analysing classroom talk. The use of coding schemes analyse talk into functional categories – enable researchers to deal with large amounts of data, and normally use explicit, publicly-verifiable criteria to make the categorisations. However, they are of limited value for exploring the construction of knowledge – not sensitive to the ways that the content and context of talk develops over time.

Qualitative – rely essentially on the interpretative analysis of transcribed speech. Can be sensitive to content and context, but have been criticised for the apparent reliance of their exponents on the detailed interpretation of short excerpts selected from larger, unseen speech corpora.

“We believe that the incorporation of computer-based methods into the study of talk offers a way of combining the strengths of quantitative and qualitative methods of discourse analysis while overcoming some of their main weaknesses.”

Illustrate claims for computer-based discourse analysis by applying it to the study of talk amongst pupils in primary school classrooms. Used a software package specifically developed for analysis of transcripts . Link detailed analysis of quality of children’s talk with quantitative data of the same children’s scores on a test of reasoning.

References
Stubbs, M. (1996) Text and Corpus Analysis. Oxford, Blackwell.
Baker, M., Francis, G. and Tognin-Bonelli, E. (eds) (1993) Text and Technology. Amsterdam and Philadelphia: Benjamin’s.