Assembling the heterogeneous elements for (digital) learning

Month: September 2007

It's all over: no need to select an LMS

There are inclinations amongst some at my university, as with many others, to move onto a different learning management system (LMS). Which creates n important questions – which new LMS?

We shouldn’t worry. At least that’s one possible conclusions to be drawn from Black et al (2007).

The argument is that there are so few important differences between the various LMS that “it makes sense to focus efforts away from LMS selection and toward the “other side of the LMS” or issues related to adoption and implementation of the systems”.

The paper then proceeds to use Rogers’ diffusion theory to offer advice about how to address the adoption and implementation side of the story. In particular, how various limitations of an LMS need to be addressed.

My problem is that the authors don’t seem to mention the alternative. If all LMSes are the same and if they are so problematic, then why should an institution chose to adopt an LMS?


Erik Black, Dennis Beck, Kara Dawson, Susan Jinks, Meredith DiPietro (2007). The ohter side of the LMS: Considering implementation and use in the adoption on an LMS in online and blended learning environments, Tech Trends, 51(2): 35-39

PhD Thesis Timetable

A meeting with the PhD supervisor must mean a new planned timetable for completion of the PhD. In order to maximise fear of embarasement if/when I miss the timelines I’m making it public. It also serves the purpose of helping people understand why I might be ignoring them over the next 8 months or so.

There are likely to be six chapters in the thesis and the following are based on the idea that the draft chapters to the supervisor don’t need to be polished prose. It’s sufficient if they have the structure and the argument is visible and understandable.

To supervisor

  • 1st Oct – Chapter 2
  • 12th Nov – Chapters 3 and 4
  • 3rd Dec – Chapter 5
  • 7th Jan – Chapter 6

From supervisor

  • 1st Feb, 2008 – All back to student

To supervisor

  • 3rd Mar – 1st draft

It does seem to imply that I will be away from work for most of February.

I'm a "uber cool high nerd"

You know someone is procrastinating when they take these sorts of tests. says I'm an Uber Cool High Nerd.  What are you?  Click here!

IS diffusion theory research – hints for e-learning implementation

I’ve previously written here about the value I believe which diffusion theory brings to helping understand, design and support the implementation of e-learning within a university context.

Diffusion of Innovations theory has been “>used significantly within the information systems research community. That research has consistently found that three perceived characteristics of an innovation are important antecedents to the adoption of an innovation

  1. Technical compatibility.
    How similar is the innovation to what the potential adopters are currently doing?
  2. Technical complexity.
    How complex is for potential adopters to understand and adopt the innovation?
  3. Relative advantage.
    How much do the potential adopters perceive that they need the innovation?

An innovation that has HIGH compatibility, LOW complexity and HIGH advantage is much more likely to be adopted.

These measures are subjective and are based on the perceptions of the individual participants.

Lessons for Web3dx

My previous post about diffusion theory used the theory to understand what we might need to do with the Web3Dx project.

Concentrating just on these three characteristics, I feel that for most staff their perception will of Web3D will be

  • Low compatibility
    Immersive 3D worlds are very different from what they’ve done before. They will need to have and use new software to get into this.
  • High complexity
    For non-gaming staff using these 3D worlds will be difficult. Understanding and incorporating them into their teaching will also be very difficult.
  • Uncertain relative advantage
    Staff will be uncertain just how to use the technology and what advantage it might have.

So it doesn’t look good. As we’re involved in the project we need to develop tactics we can use to turn around the above perceptions.

Lessons for e-learning development

The above, at least to me, is further evidence to support the proposition that ateleological development is a “better way” to develop university e-learning systems.

Ateleological development, would by its nature, concentrate on implementing innovations that are more likely to have high compatibility (the innovation would be a small change from current practice), low complexity (it would be implemented in as simple and transparent a way as possible) and high relative advantage (it would be chosen to solve a specific problem identified by folk within the system).

Based on diffusion theory, such an approach would lead to greater levels of adoption.

E-learning and information systems – a connection?

I currently work in e-learning for which I use the OECD definition – “the use of information and communication technologies to support and enhance learning and teaching”. My original discipline as an academic is the information systems discipline. To some extent, I believe this background provides some useful insights that help with the implementation of e-learning within a university context.

The following is an excerpt from my thesis which attempts to give my perceptions of the information systems discipline. It was written at least 3 or 4 years ago and is almost certainly going to have some limitations and will certainly provide enough for a range of folk to disagree.

Posting it here is in part to share with my work colleagues to provide them with some idea of why I think and act the way I do. My slant on IS does give some hints to my stance on certain research related questions. Also, it is partly to make me feel like I’m making some progress.

The connections I make

  • Both e-learning and IS are focused on learning how best to use IT within a social setting. You might even argue that e-learning is a particular type of information system, that e-learning is a sub-set of information systems.
  • That “otherware”, the people, processes etc, or e-learning are the most difficult problem to solved. There there is no consistency or rational, objective reason behind much of what otherware does.

The IS discipline

Any attempt to develop a description of the core of the information systems discipline is liable to displease someone. The information systems discipline is somewhat unique in the amount of on-going discussion and negotiation about what is the core of the discipline. This discussion is often referred to as an identity crisis in so far as IS has not carved out its own niche within the academy or industry (Benbasat & Zmud, 2003; Fitzgerald & Adam, 1996; Khazanchi & Munkvold, 2000). During 2003 and 2004 another round of discussion about the core of the information systems discipline flared up (Alter, 2003; Benbasat & Zmud, 2003; Iivari, 2003; Weber, 2003). Rather than be drawn into this important, difficult, and interesting debate this section seeks to provide one, reasonably widely accepted, description of the discipline.

At its core this work is about the development and maintenance of Information Technology (IT) within a specific type of social setting. IT is technology used to acquire and process information for human purposes and is typically instantiated as complex organisations of hardware, software, procedures, data and people (March & Smith, 1995). The study of the effective design, delivery, use and impact of IT in organisations and society is the main aim of research in Information Systems (Keen, 1987). Lee (2000) defines the Information Systems (IS) field as concerning itself with research and practice about the problems and solutions that emerge from the interactions at the interface between the technological and the behavioural.

du Plooy (2003) describes an information system as consisting of three subsystems: the hardware, software and “otherware” (Figure 3.1). It is the consideration of all three subsystems, and in particular the addition of “otherware”, which differentiates information systems from other related disciplines such as computer science and information technology. The inadequacy of computer science in addressing problems associated with the use of computers in organisational contexts has played a large part in the emergence of the IS discipline (Fitzgerald & Adam, 1996). “Otherware” is defined as including the system’s goals, the owner, users, operational procedures, and the tasks and responsibilities of the people involved.

Software and hardware are designed artefacts intended to be deterministic and reliable. Given certain inputs, that the output of a computing system is usually predictable (duPlooy, 2003). “Otherware” involves people, who may have agendas and goals that differ vastly from those of the organization (Markus, 1983). For this, and other reasons, “otherware” is non-deterministic (duPlooy, 2003). Inclusion of “otherware” brings with it added complexity, greater imprecision, the possibility of different interpretations of the same phenomena, and the need to take these issues into account when considering an appropriate research approach (Galliers & Land, 1987).

Information Systems
An information system (duPlooy, 2003, p. 111)

The goal within the field of information systems is to better understand how individuals, groups, organizations and society can use information systems more effectively and more efficiently (Weber, 1997). As such, information systems can be seen as an applied rather than a pure discipline (Adams & Courtney, 2004; Iivari, 2003; Moody & Buist, 1999; Nunamaker, Chen, & Purdin, 1991). Information systems researchers and practitioners attempt to understand the use of IT artefacts in order to be able to develop “better” ones (Iivari, 2003).


Adams, L., & Courtney, J. (2004). Achieving relevance in IS research via the DAGS framework. Paper presented at the 37th Hawaii International Conference on System Sciences, Hawaii.

Alter, S. (2003). SideStepping the IT Artifact, Scrapping the IS Silo and Laying Claim to “Systems in Organizations”. Communications of the AIS, 12(30), 494-526.

Benbasat, I., & Zmud, R. (2003). The Identity Crisis within the IS Discipline: Defining and Communicating the Discipline’s core properties. MIS Quarterly, 27(2), 183-194.

duPlooy, N. F. (2003). Information systems as social systems. In J. Cano (Ed.), Critical Reflections on Information Systems: A Systematic Approach. Hershey: IDEA Group Inc.

Fitzgerald, B., & Adam, F. (1996). The future of IS: Expansion or Extinction. Paper presented at the First Conference of the UK Academy of Information Systems, Cranfield Univeristy.

Galliers, R. D., & Land, F. F. (1987). Choosing appropriate information systems research methodologies. Communications of the ACM, 30(11), 900-902.

Iivari, J. (2003). The IS Core – VII: Towards Information Systems as a Science of Meta-Artifacts. Communications of the AIS, 12(37), 568-581.

Keen, P. G. W. (1987). MIS Research: Current Status, Trends and Needs. In R. A. Buckingham, R. A. Hirschheim, F. F. Land & C. J. Tully (Eds.), Information Systems Education: Recomendations and Implementation. Cambridge: Cambridge University Press.

Khazanchi, D., & Munkvold, B. E. (2000). Is Information Systems a science? An inquiry into the nature of the Information Systems discipline. The DATA BASE for Advances in Information Systems, 31(3), 24-42.

Lee, A. S. (2000). Irreducibly Sociological Dimensions in Research and Publishing. MIS Quarterly, 24(4), v-vii.

March, S. T., & Smith, G. F. (1995). Design and Natural Science Research on Information Technology. Decision Support Systems, 15, 251-266.

Markus, M. L. (1983). Power, politics and MIS implementation. Communications of the ACM, 26, 430-440.

Moody, D., & Buist, A. (1999). Improving Links Between Information Systems Research and Practice: Lessons from the Medical Professions. Paper presented at the Proceedings of the 10th Australasian Conference on Information Systems.

Nunamaker, J. F., Chen, M., & Purdin, T. (1991). Systems development in information systems research. Journal of Management Information Systems, 7(3), 89-106.

Weber, R. (1997). Ontological foundations of information systems. Melbourne, Australia: Coopers and Lybrand Australia.

Weber, R. (2003). Still desperately seeking the IT artifact. MIS Quarterly, 27(2), iii-xi.

Design-based research and theory

Yippee!! Some actual thinking and writing about thesis related material.


My PhD thesis is titled “An information systems design theory for e-learning” (PDF of recent paper) and positions itself as an example design research within the information systems discipline. It seeks to generate a design theory for aiding universities with how they should implement e-learning at an organisational level.

For the longest time it was a struggle to see how this work would become a PhD. I’m not worthy. Imagine my surprise when in the last ten years or so there’s been an explosion of interest in design research both in information systems and in education. Visions of academic respectability became more likely.

The problem

Of course, being the contrarian that I am, I didn’t like much of what I read. Actually, that’s an overstatement. I had a vague sense of unease which I, at first, attributed to my lack of knowledge of the literature, and just my general lack of knowledge. I’m not so sure. Let’s start with design-based research, the term widely used in the educational field.

I’ve often seen in the DBR literature quotes similar to the following from Bell (2004)

Scholars came to engage in design-based research to better understand how to orchestrate innovative learning experiences among children in their everyday educational contexts as well as to simultaneously develop new theoretical insights about the nature of learning.

What gets me about this quote, or at least my interpretation of this quote is the apparent lack of importance or legitimacy given to the “how” knowledge and the on-going primacy given to learning theory.

Scholars engage in DBR to understand “how” – but the aim is to develop learning theory.

Can’t the “how” knowledge be theory in its own right? What do the DBR/educationalists define as theory? What is “learning theory”?

Is this simply a bit of nit picking or am I simply showing my ignorance?

Some light?

One of the “what is DBR” pages mentions a paper by Edelson (2002) which seems to engage in this a little more.

But, the problem is that it appears to define what I understand as design theory as either a design framework or a design methodology.

Another of the “what is DBR” pages mentions a talk by Sandoval which identifies the following types of knowledge that DBR can produce

  • Design knowledge (Edelson, 2002)
  • Ontological innovation (DiSessa & Cobb, 2004)
  • Local instructional theories (Cobb)

Another “what is DBR” page has a link to a DBR lit review that draws on Barab & Squire (2004) to identify the major tenents of DBR

  • results in the production of theories on learning and teaching
  • employs an interventionist approach
  • takes place in a naturalistic context
  • is iterative

The same literature review includes the following paragraph

What is interesting about Edelson’s discussion of the types of theory generation possible with DBR is his rather liberal use of the term “theory”. He hence allows design frameworks and design methodologies to fall into this category. Kelly (2004) reacts against the liberal use of this term, quoting the National Academy of Sciences definition of theory, which states that a theory is “a well-substantiated explanation of some aspect of the natural world that can incorporate facts, laws, inferences, and tested hypothesis” (p. 123). Articulating that the use of theory requires hard-fought consensus among scientists, he argues for the use of “working words” which are less strong, such as “framework” or “hypothesis”.

So it would appear that Edelson may provide some support for my thoughts/beliefs.


Bell, P. (2004). On the theoretical breadth of design-based research in education. Educational Pyschologist. 39(4): 243-253

Carrick, Web3D and Sustainability

I’m lucky enough to be involved with a Carrick funded project looking at how to integrate 3D immersive technologies into university education. My fellow project team members have already been put through the uplifting experience that is my pessimism. I’m quite sure that experience was enough for them to reconsider the value of having be involved.

In doing some reading for my PhD thesis (yes I am procrastinating by writing this post) I came across a paper titled Sustaining model systems of educational activity: Designing for the long haul.

It includes the following

In a certain sense, the answer to the question of why successful innovations fail and have constantly to be reinvented was already well known: institutions welcome innovations so long as they are compatible with institutional goals and are supported by external funds. But the host institutions do not integrate the innovations into their core activities, so when the extra money goes, so does the innovation.

Which nicely summarises my fear about the Carrick project and consequently, in order not to be seen as a whinger, it is necessary to come up with some brilliant ideas to prevent this from happening.

So, being a typical academic lets borrow the collective wisdom of much smarter people.

Reading further down the same paper you find the authors discussion how they really don’t know any “universal” ideas to ensure sustainability. They do, however, suggest that they have identified some critical events that influence sustainability.

These include

  • Surfacing of incompatibilities.
    The partners in the project, as they go further into it, realise that there exists some incompatibilities between the project requirements and their capabilities. In terms of the Web3D project I think the major challenges here are around the use of 3D immersive environments. I’m not sure we know just how complex embedding them into a course is and how well the staff and students will take to it. You can guess, given that I’m a pessimist, what I think.
  • Dynamism negatively influencing sustainability.
    The very short memory span of institutions lead to the original reasons for the project disappearing or no longer being important.
  • Dynamism positively influencing sustainability.
    Ad hoc events can also help.

Isn’t that hugely insightful? Oh well, wasted some PhD time. Procrastination achieved.

Why "modeller-broker" orientation is inherently limited – bridging the gap

The modeller-broker orientation

Here at CQU there is a mindset amongst a surprisingly large number of folk that align closely with a quote from Land (2001)

I think we work much more effectively by working with departments we know are active, then try to get some examples out to other people. They see that it works and then we try to bring them on board.

Land (2001) calls this the “modeller-broker” approach and describes it as

‘Trojan horse’ approach of working alongside colleagues to demonstrate good practice or innovation. ‘Do as I do’ rather than ‘do as I say’

Why is it limited?

My argument is not that there is no value in this approach. There is some value. My argument is that this approach, by itself, is not sufficient to engage a significant number of folk. It quickly degenerates into “preaching to the converted”.


Land places this orientation into the diffusion of innovations literature. I’ve been a fan of diffusion theory as a tool to generate some insight, some guidance in this problem of engaging widespread adoption of elearning for quite sometime. I’ve written about it recently.

In that previous writing I’ve talked about observability as being one of the five perceived characteristics of an innovation that are likely to encourage adoption. It’s actually one of the least likely of the five to positively influence adoption decisions. There are also a large range of other factors which diffusion theory identifies.

Compatibility with previous practice and the level homophily (similarity in beliefs) between innovators and later adopters are two important ones.

The difference beween early adopters and the mainstream

Geoghegan (1998) identified the following differences between the early adopters and the mainstream.

Early adopters


Favour revolutionary change Favour evolutionary change
Visionary Pragmatic
Project oriented Process oriented
Risk takers Risk averse
Willing to experiment Want proven applications
Generally self-sufficient May need significant support
Horizontally connected Vertically connected

Geoghegan (1994) developed the above in the early 90s to answer the following observations

Despite massive technology expenditures over the last decade or so, the widespread availability of substantial computing power at increasingly reasonable prices, and a growing “comfort level” with this technology among college and university faculty, information technology is not being integrated into the teaching and learning process nearly as much as people have regularly predicted since it arrived on the educational scene three or four decades ago. There are many isolated pockets of successful technology implementations. But it is an unfortunate fact that these individual successes, as important and as encouraging as they might be, have been slow to propagate beyond their initiators; and they have by no means brought about the technologically inspired revolution in teaching and learning so long anticipated by instructional technology advocates.

It’s over 10 years later and I don’t think much has changed. There is almost certainly a great deal more adoption of technology. But it is little more than “horseless carriage” stuff. The performance of previous practice with a different type of tool/medium.

The chasm

Geoghegan (1994), drawing on the work of Moore (1991), proposes that there is a “chasm” between the enthusiasts and the pragmatists.

Geoghegan (1994) argues that these two groups bring entirely different criteria for deciding whether or not to adopt an innovation. They need different approaches to “market” the innovation.

Why the gap hasn’t been bridged?

Geoghegan identifies four reasons why the gap hasn’t been bridged

  1. Ignorance of the gap.
    This is demonstrated in the “modeller-broker” orientation. There is not recognition that the two groups are entirely different. That simply modelling the new innovation is not going to be sufficient for the pragmatists.
  2. The “technologists’ alliance.
    It is argued that the faculty enthusiasts, university instructional technology support organisations and the IT vendors form a community that, while successful in developing innovations, is a failure in engaging with the pragmatists. The language, concerns and interests of this alliance is completely different to that of the pragmatists. There was a “one size fits all” approach that was not working. This alliance tends to focus on disruptive innovations at the expense of the incremental advances favoured by the mainstream.
  3. Alienation of the mainstream.
    The focus on “disruptiveness” of enthusiasts can alienate and anger the mainstream. The enthusiasts are able to operate in a support vacuum which pragamatists can’t and consequently there are problems when enthusiasts move on. The type of disruptive change favoured by the enthusiast tend to produce disruptive side-effects that magnify the overall cost of adoption.
  4. No compelling reason to adopt.
    In order to interest the mainstream, as with anyone, it is necessary/desirable to have a compelling value that can be expressed in terms attractive to the desired audience. For the pragmatists, this means an application that offers value substantially in excess of the costs of adoption.

Bridging the gap

Geoghegan (1994) offers four factors in bridging the gap

  1. Recognition.
    Simply recognise the gap, recognise that the pragmatists are different to the enthusiasts. Recognise their needs and include them in the process.
  2. Vertical orientation.
    The support for pragmatists should place more emphasis on peer support and sharing rather than on the assistance of enthusiasts from a narrow range of technically oriented disciplines. The support staff with experience and credibility in a broad range of discipline areas that combine moderate technical knowledge with a solid understanding of the culture of the disciplines involved.
  3. Compelling value.
    Whatever innovation should clearly demonstrate, in terms important to the pragmatist, that it can perform an important, existing task better than current practice or can address a previously, unavailable and important problem. The side effects and risk of failure should be minimal and ease of use should be high.
  4. Institutional commitment.
    Demonstrated through appropriately visible and worded commitments, appropriately targetted and resources awards for using IT and an effective support division or resources.

Why bother?

So why did I spend all this time on the above? Well mostly because I believe that the above fits very nicely and supports my views about ateleological development of e-learning. Which just happens to be the topic of my PhD.

I think an ateleological process for implementation organisational e-learning within a university engages with the above problems, it offers a way to bridge the gap.


William Geoghegan (1994). Whatever happened to instructional technology?, Paper presented at the 22nd Annual Conference of the International Business Schools Computing Association, Baltimore, MD

Land, R. (2001). “Agency, context and change in academic development.�? The International Journal for Academic Development. 6(1): 4-20

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