Alternative to clickers – freeing up the physical location limitation

In a previous post I outlined some broad ideas of how to understand “lectures”. At the crux of it was an initial stab at a “taxonomy/framework” for understanding characteristics of lectures. In this initial stab there were three main dimensions: participants, physical space, and time. Each had some additional sub-points.

As one example, a sub-point of physical space was physical co-location. i.e. for most lectures there is a requirement that you be within the same physical space. There are various ways around this limitation. For example, my institution has a significant physical, networking and support infrastructure around video-conferencing that allows folk to be in a number of physical locations – though still generally on the institution’s campus.

The point of this “framework” was to allow some initial comparisons of the various approaches. For example, clickers have been pushed by publishers as a way of increasing interaction (one of the sub-points under Participants is “limited interaction or participation”). However, most clickers retain the limitation of the same physical space. The technology used in most clickers means that the participants have to be in the same room. Which causes problems with using them over video-conferencing.

Alternative technologies for clickers

With the rise of mobile phones, especially those with web capabilities, it would appear straight forward to move clickers away from using infrared or radio frequency technologies to using the Web, SMS or increasing Twitter. I thought a simple tool that provides support for tracking the Twitter back channel and using it for polls etc during a presentation might be useful. It would certainly get around the same physical location limitation. Having vicariously lived through the EdMedia conference via twitter comments while on a road trip to Longreach, reinforces this perspective.

I was pretty sure that someone else has already through of this idea, so was going to spend some time searching at some stage. I’ll still do that but I did come across a commercial alternative while reading a post from Tomorrow’s Professor.

The name of this service – Poll Everywhere – makes the point about location independence (though I wonder if it being a US-based company has implications for folk using the SMS version). There’s a video on the home page, the integration with Powerpoint looks neat, but it’s not available for the Mac – though there is a work around using a Deskbar widget. It appears that there is a RESTful API and a wiki.

There is a free account, limited to 30 or so responses. An instructor plan that allows 400 responses costs $399 a semester – I’m assuming $USD.

So it is being done. Anyone know of any open source versions? A search for latter date.

Students and e-learning – a start to the People section

The following is the first step in the People component of the Ps Framework from chapter 2 of my thesis. The first bit (“People”) is the introduction to the thesis section and the following (“Students”) is the first major section of that component. Hopefully, over the next week and in fairly quick progression the remaining sections of the People component will get posted.

As always, this stuff is version 1 draft quality and there is always going to be more that can and probably should be included, however, I’m currently going for “good enough” rather than “too good”.

The suggested four sections that conclude the “People” section are a work in progress and may change. I’m wondering whether the “chasm” section should be included within the “People involved in e-learning section”. Time will tell.

People

Any excellence demonstrated by a University is not a product of technology, it is a product of the faculty, students and staff who play differing roles in the pursuit of scholarship and learning (Dodds 2007). It comes from the people. Teaching and learning are two of the most highly personalised processes (Morgan 2003). It is clear that consideration of the human dimension is critical to education (Watson 2006). Personal characteristics have been found to influence e-learning implementation (Siritongthaworn, Krairit et al. 2006) and most universities are still struggling to engage a significant percentage of students and staff in e-learning (Salmon 2005).

While the success of an information systems innovation can be determined in a number of ways, there has been a range of work that marks a shift from organisational measures, such as delivery on-time and on budget, to more user focused measures including system usage (Behrens, Jamieson et al. 2005). It is the uptake and use of features, rather than the provision of those features, that really determines education value (Coates, James et al. 2005). The perceptions of the people who may potential use an information and communication technology play a significant role in their adoption and use of that technology (Jones, Cranston et al. 2005). The beliefs held by those involved in the educational process, regardless of how ill-informed, can have a tremendous impact on the performance of both students and teachers and how effectively technology may be utilised (Stewart 2008).

In considering adoption, it is important to recognise agency, the ability of the individuals or groups within universities to consciously or unconsciously respond to and change practices (Trowler and Knight 1999). Especially since taking full advantage of e-learning will require university administrators, lectures and students to think differently about teaching and learning (Volery 2001). Individuals and groups within the same institution will often have very different, even conflicting, views of best practice in learning and teaching that will influence priorities, including the implementation of e-learning (Luck, Jones et al. 2004). The members of an organisation will vary greatly in their individual characteristics, including their willingness to adopt an innovation like e-learning (Jones, Jamieson et al. 2003).

This section examines, arguably, the most important component of the Ps Framework, People. It draws on the literature to discuss people associated issues that impact upon the implementation and practice of e-learning within universities. It does this through the following major sections:

  • People involved in e-learning;
    An examination of what is known about the characteristics and purpose of the different types of people involved with e-learning within universities.
  • The e-learning chasm;
    Describes an important finding regarding different categories of people involved with e-learning that offers an explanation of less than effective implementations.
  • People and cognition; and
    A brief examination of what is known more generally about people, cognitation and how that may effect the implementation and practice of e-learning.
  • Lessons from People for E-learning.
    Offers one distillation of what has been previously described into particular lessons that may help inform the implementation of e-learning.

People involved in e-learning

The first step taken here to examine issues around people that impact upon learning and teaching is to review what is known about the various roles associated with e-learning. The roles examined here include: students, teaching staff, leaders and managers, technical staff and instructional designers. Each of these roles are examined in turn in the following sections.

Students

An essential component of facilitating learning is understanding learners, and particularly their learning styles, attitudes and approaches (Alexander 2001; Oblinger 2003). Not surprisingly, university students play the key role in their own learning, however it is striking how recently the notion has been contested, or even ignored (Goodyear and Ellis 2008). Students are not educated solely through the efforts of teaching staff, but also through the contributions of fellow students (Jongbloed, Enders et al. 2008). A student’s experience of university is embedded in a complex environment made up of diverse, interdependent elements with students’ characteristics as one set of elements (White 2006). A familiarity with the evolving characteristics of adult learners and a sensitivity to their diverse needs improve facilitation of their academic journey (Semmar 2006). This section draws on the literature to develop a semblance of familiarity.

Non-traditional working adults over the age of 26 now comprise over 50% of the post-secondary student population within the United States and are the fastest growing market segment and the largest audience for e-learning (Ausburn 2004). Table 2.1 compares characteristics of university students in the United States between 1970 and 1999. The 70% of students in 1999 labelled non-traditional are students who have delayed enrolment, attend part-time, work full-time, have dependents, are single parents or did not graduate from high school (Oblinger 2003). Speaking in the UK context Jones and O’Shea (2004) report on rapidly changing educational patterns with many more part-time students, mature students and students from more diverse backgrounds, often with lower levels of qualification.

Table 2.1 – Data on student characteristics in the United States (1970 and 1999) (adapted from Oblinger 2003)
Characteristic 1970 1999
Enrolment 7.4m 12.7m
2-year enrolment 31% 44%
Part-time 28% 39%
Women 42% 56%
Older than 25 28% 39%
Non-traditional N/A 73%
Have dependents N/A 27%
Employed N/A 80%

Consequently, students are no longer insulated from external pressures and they have to deal with real world concerns including student loans, poor accommodation and part-time-working and yet many students still aspire to the assumed richness of campus-based education (Haywood 2002). However, there is a significant trend towards students spending less time on-campus and in class and more time in paid employment (Russell 2008). Lock-step approaches to learning, that consist of regular study schedules and weekly modules, are increasingly in conflict with the need for flexibility of these students (Herrington, Reeves et al. 2005). Differences between individuals increase with age, consequently adult education must make provision for differences in style, time, place and pace of learning (Knowles, Holton et al. 2005). Adults value options, variety, self-directedness and effective two-way communication with their classmates and instructor (Ausburn 2004). A large group of students, with significantly different characteristics, find asynchronous e-learning highly suited to their lifestyles and requirements (Hitt and Hartman 2002).

Surveys of student experience and attitudes towards technology, do show an evolution from students having less technology experience than expected through to more recent surveys showing significant personal and social experience with technologies (Hardy, Haywood et al. 2008). A number of researchers have found evidence of young people using technology frequently and creatively in ways that has transformed their experience of childhood and adolesence in comparison to former generations (Somekh 2004). Students (and staff) accustomed to the convenience of modern technology use in banking, mobile communications and web-based retailing do have changing expectations of the use of technology to support their university experience (Duderstadt, Atkins et al. 2002). There does, however, exist an extreme difference between the experience of technology at home and the experience at school that can only be accounted for by the institutional functioning of education systems as a whole (Somekh 2004). Students are increasingly seeing the use of technology in education as inadequate (Oblinger 2003). The growing expectations of technology use within education present an exciting, though potentially disruptive and complex problem (Hardy, Haywood et al. 2008).

There is a line of literature that suggests that an affinity for e-learning is particularly strong amongst students who have grown up with computers and the Internet. Students who have been labelled as the net generation (Tapscott 1998), digital natives (Prensky 2001) or millenials (Oblinger 2003). Students who grew up with computers and often with a broadband connection to the Internet and who, at least in the US, use the Internet (87%), use it daily (51%), play games online (81%), get news online (76%), and use the Internet to communicate with one another (Salaway, Katz et al. 2006). It has been suggested that growing up using this technology has fundamentally changed the way these students think and process information and consequently they are no longer the people educational systems were designed to teach (Prensky 2001).

However, it has been suggested that arguments for the changes in the brains of digital natures is a helpful illusion based on unfounded estimates and a faulty chain of logic (Sheely 2008). New students, with a self-reported high level of competence and confidence with information technology, are relatively conservative in their approach to study prefer to work with traditional face-to-face locations and methods with online sources used as on demand supplements (Hardy, Haywood et al. 2008). Claims about the media habits of digital natives do not appear to carry over to what students expect, or do, in universities (Goodyear and Ellis 2008). These students are confident about their use of ICT and digital media, but they do not want them to erode or substitute for face-to-face teaching and social interaction (Joint Information Systems Committee (JISC) 2007). Two large scale surveys of undergraduate students (Kvavik, Caruso et al. 2004; Salaway, Katz et al. 2006) in the United States – 28,724 respondents for the 2006 survey – reached similar findings including that students prefer a “moderate” amount of technology in their courses and that while many fit the net generation characterisation, many do not.

There is a rich body of knowledge arising from research into higher education that has established a relationship between students’ conceptions of learning, their approaches to study and eventual learning outcomes (Gonzalez 2009). Student resistance can be a behavioural impediment to the implementation of e-learning (Siritongthaworn, Krairit et al. 2006). The expectations and values are a constraint on innovation (Dutton, Cheong et al. 2004). Hirschheim (1992) found that a majority of students taking the Internet version of class, which was virtually identical to a face-to-face version of the class, believed that they were receiving a lower level of education. Perceptions of a lower level of education appear to arise because of the changed learning experience where the e-learning students missed out on traditional face-to-face experience such as lectures and face-to-face discussions (Hirschheim 1992). Participation in traditional classroom formats are still considered an important experience by all students, which suggests that the cultural context of higher education and the resulting student expectations place an additional constraint on e-learning innovations (Dutton, Cheong et al. 2004).

It is dangerous to make assumptions about students’ adoption or rejection of educational technology as their choice and practices are shaped in quite subtle ways (Goodyear and Ellis 2008). Selwyn (2007) sees students as making active choices informed by the signals they pick up from teachers, the curriculum, assessment and workplace demands. Consequently, the diversity in backgrounds and expectations of students forms one of the greatest challenges facing higher education today (Oblinger 2003). Individual differences including gender, system experience, prior knowledge, spatial ability, culture, occupational experience and cognitive styles have a significant effect on the behaviour of learners (Sabry and Baldwin 2003). The combination of diversity from a range of factors that make up the e-learning system means that there is no one student experience of e-learning (Alexander 2001). The growing percentage of adult learners and their preference for variety and flexibility (Herrington, Reeves et al. 2005; Knowles, Holton et al. 2005) only increases this diversity.

However, there are some common factors that are significant determinants of student satisfaction with e-learning including prompt and informative feedback on work, clarity of faculty expectations, high levels of participation by other students time available to devote to the course adequate technical support and training (Alexander 2001). White (2006) suggests that students most value lecturers that are passionate about teaching and readily recognise its absence and how organisational priorities impact on how lecturers approach their teaching responsibilities. Sheely’s (2008) description of the digital native argument as a helpful illusion arises because in the end the digital native argument ends with a description of how students learn and an exhortation for educators and educational institutions to prepare to deal with students who learn this way. However, rather than preferring some new and unusual way of learning these students learn by constructing knowledge through authentic experiences in social situations, in other words, how humans have always learnt (Sheely 2008).

References

Alexander, S. (2001). "E-learning developments and experiences." Education and Training 43(4/5): 240-248.

Ausburn, L. (2004). "Course design elements most valued by adult learners in blended online education environments: an American perspective." Educational Media International 41(4): 327-337.

Behrens, S., K. Jamieson, et al. (2005). Predicting system success using the Technology Acceptance Model: A case study. Australasian Conference on Information Systems’2005, Sydney.

Coates, H., R. James, et al. (2005). "A Critical Examination of the Effects of Learning Management Systems on University Teaching and Learning." Tertiary Education and Management 11(1): 19-36.

Dodds, T. (2007). "Information Technology: A Contributor to Innovation in Higher Education." New Directions for Higher Education 2007(137): 85-95.

Duderstadt, J., D. Atkins, et al. (2002). Higher education in the digital age: Technology issues and strategies for American colleges and universities. Westport, Conn, Praeger Publishers.

Dutton, W., P. Cheong, et al. (2004). "The social shaping of a virtual learning environment: The case of a University-wide course management system." Electronic Journal of e-Learning 2(1): 69-80.

Gonzalez, C. (2009). "Conceptions of, and approaches to, teaching online: a study of lecturers teaching postgraduate distance courses." Higher Education 57(3): 299-314.

Goodyear, P. and R. A. Ellis (2008). "University students’ approaches to learning: rethinking the place of technology." Distance Education 29(2): 141-152.

Hardy, J., D. Haywood, et al. (2008). Expectations and reality: Exploring the use of learning technologies across the disciplines. 6th Networked Learning Conference. Halkidiki, Greece, Lancaster University.

Haywood, T. (2002). Defining moments: Tension between richness and reach. Digital Academe: The New Media and Institutions of Higher Education and Learning. W. Dutton and B. Loader. London, Routledge: 39-49.

Herrington, J., T. Reeves, et al. (2005). "Online Learning as Information Delivery: Digital Myopia." Journal of Interactive Learning Research 16(4): 353-367.

Hirschheim, R. (1992). "The Internet-Based Education Bandwagon: Look before you leap." Communications of the ACM 48(7): 97-101.

Hitt, J. and J. Hartman (2002). Distributed learning: New challenges and opportunities for institutional leadership. Washington, American Council on Education: 28.

Joint Information Systems Committee (JISC) (2007). Student expectations study. London, Author.

Jones, D., M. Cranston, et al. (2005). What makes ICT implementation successful: A case study of online assignment submission. ODLAA’2005, Adelaide.

Jones, D., K. Jamieson, et al. (2003). A model for evaluating potential Web-based education innovations. 36th Annual Hawaii International Conference on System Sciences, Hawaii, IEEE.

Jones, N. and J. O’Shea (2004). "Challenging hierarchies: The impact of e-learning." Higher Education 48: 379-395.

Jongbloed, B., J. Enders, et al. (2008). "HIgher education and its communities: Interconnections, interdependencies and a research agenda." Higher Education 56(3): 303-324.

Knowles, M. S., E. F. Holton, et al. (2005). The Adult Learner. Oxford, Butterworth-Heinemann.

Kvavik, R., J. Caruso, et al. (2004). ECAR study of students and information technology, 2004: Convenience, connection and control. Boulder, CO, EDUCAUSE Center for Applied Research.

Luck, J., D. Jones, et al. (2004). "Challenging Enterprises and Subcultures: Interrogating ‘Best Practice’ in Central Queensland University’s Course Management Systems." Best practice in university learning and teaching: Learning from our Challenges.  Theme issue of Studies in Learning, Evaluation, Innovation and Development 1(2): 19-31.

Morgan, G. (2003). Faculty use of course management systems, Educause Centre for Applied Research: 97.

Oblinger, D. (2003). "Boomers, gen-Xers and millennials: Understanding the new students." EDUCAUSE Review: 37 – 47.

Prensky, M. (2001). "Digital natives, digital immigrants." On the Horizon 9(5): 1-6.

Russell, C. (2008). E-learning adoption in a campus university as a complex adaptive system: mapping lecturer strategies, University of Leicester. PhD: 250.

Sabry, K. and L. Baldwin (2003). "Web-based learning interaction and learning styles." British Journal of Educational Technology 34(4): 443-454.

Salaway, G., R. Katz, et al. (2006). The ECAR Study of Undergraduate Students and Information Technology, 2006. Boulder, USA, EDUCAUSE Center for Applied Research.

Salmon, G. (2005). "Flying not flapping: a strategic framework for e-learning and pedagogical innovation in higher education institutions." ALT-J, Research in Learning Technology 13(3): 201-218.

Selwyn, N. (2007). "The use of computer technology in university teaching and learning: a critical perspective." Journal of Computer Assisted Learning 23(2): 83-94.

Semmar, Y. (2006). "Distance learners and academic achievement: The roles of self-efficacy, self-regulation and motivation." Journal of Adult and Continuing Education 12(2): 244-256.

Sheely, S. (2008). Latour meets the digital natives: What do we really know. Hello! Where are you in the landscape of educational technology? Proceedings of ASCILITE Melbourne 2008, Melbourne.

Siritongthaworn, S., D. Krairit, et al. (2006). "The study of e-learning technology implementation: A preliminary investigation of universities in Thailand." Education and Information Technologies 11(2): 137-160.

Somekh, B. (2004). "Taking the Sociological Imagination to School: An analysis of the (lack of) impact of information and communication technologies on education systems." Technology, Pedagogy and Education 13(2): 163-180.

Stewart, D. P. (2008). "Technology as a management tool in the Community College classroom: Challenges and Benefits." Journal of Online Learning and Teaching 4(4).

Tapscott, D. (1998). Growing up digital: The rise of the Net Generation. New York, McGraw-Hill.

Trowler, P. and P. Knight (1999). "Organizational socialization and induction in universities: Reconceptualizing theory and practice." Higher Education 37(2): 177-195.

Volery, T. (2001). "Online education: An exploratory study into success factors." Journal of Educational Computing Research 24(1): 77-92.

Watson, D. (2006). "Understanding the relationship between ICT and education means exploring innovation and change." Education and Information Technologies 11(3-4): 199-216.

White, N. (2006). "Tertiary education in the Noughties: the student perspective." Higher Education Research & Development 25(3): 231-246.

Confirmation bias, the Tolstoy Syndrome and pattern entrainment

I’m currently working on the People component of the Ps Framework as part of my thesis. One of the sections of the People component will be “People and cognition”. It will seek to illustrate that people are not rational decision-makers, that we have all sorts of significant flaws in how we make decisions and that these flaws significant impact upon the implementation and practice of e-learning (the topic of the thesis).

In writing another blog post I visited the Wikipedia article on conformation bias and found out about “Tolstoy syndrome” from which the following two quotes come.

I know that most men, including those at ease with problems of the greatest complexity, can seldom accept the simplest and most obvious truth if it be such as would oblige them to admit the falsity of conclusions which they have proudly taught to others, and which they have woven, thread by thread, into the fabrics of their life.

and

The most difficult subjects can be explained to the most slow-witted man if he has not formed any idea of them already; but the simplest thing cannot be made clear to the most intelligent man if he is firmly persuaded that he knows already, without a shadow of doubt, what is laid before him.

Other connections

If you want some idea of how important this particular cognitive bias is, then let me show you some other ideas I’ve seen which build on this. At least for me, the sheer prevalence of ideas which encapsulate this idea give some indication of its impact.

Other connections include:

Implications for e-learning

When it comes to learning and teaching at universities the folk most likely to be accused of suffering from “Tolstoy’s syndrome” are the teaching staff. If only those recalcitrant academics would accept new and more modern (i.e. effective) ideas around learning and teaching everything would be okay. This often results in prescriptive approaches to improving learning and teaching (e.g. all courses shall use PBL) which I think are destined to fail and be effected by technology gravity.

In the People section I’m working on I’m looking at the following groups of people: students, teaching staff, instructional designers, managers and leaders, and technical staff. As these are all (generally) people they all suffer from Tolstoy’s syndrome and consequently it’s a major issue in e-learning implementation and use.

Management’s entrainment prevents or limits the institution’s ability to accept different approaches like PLEs. Technology folk’s entrainment around the scarcity and expensive nature of technology and the limited technical knowledge of end-users limits how they perceive information technology can be harnessed effectively to improve learning and teaching. Lastly, students have expectations of university education meaning face-to-face lectures and tutorials which heavily online, group based forms of education break.

Tolstoy’s syndrome needs to be considered.

References

McDonald, J. and A. Gibbons (nd). “Technology I, II, and III: criteria for understanding and improving the practice of instructional technology ” Educational Technology Research and Development.

PhD Update #15 – Some progress and an absence

Some progress made this week, but it comes as a prelude to a week in which not much, if anything will be done. This week, after a couple of days at work, the family and I are off to visit Longreach, my sister and her family. We won’t get back until after my normal “updates” day. So, I’ll miss the updates for next week.

What I’ve done

Last week I said that I would

  • Complete the process component. – DONE
  • Make significant progress on the People component. – not even started.

I’ve just posted the last section of the Process component (Lessons from process) and completed the other section earlier in the week (Learning and teaching processes).

What I’ll do next week

As suggested above, it’s likely to be bugger all. However, any work I do attempt will be focused on:

  • Making some progress on the People component of the Ps Framework.

Lessons from process for university e-learning

I told myself that I would get this section completed today before I went home and I have achieved that goal. Perhaps, however, I have engaged in a bit of “task corruption”. A couple of days ago I was listening to a podcast about how the United States education system might be improved. One of the panelists suggested that one of the strategies employed by various school sectors to improve graduation was to make it easier to graduate. Just perhaps, in order to get this posted, I’ve relaxed my standards more than normal. Time will tell if it is too far.

In case you haven’t been following along, this is the last section of a part of chapter two of my thesis. Chapter 2 is meant to be a “review” of the literature around e-learning in an attempt to demonstrate my understanding of the field and to identify what I think might be some holes in current knowledge. Holes that my brilliant thesis will fill through one potential approach. I’m using the idea of the Ps Framework to structure my Chapter 2. This is the last component of the Process part of the Ps Framework.

As I’m finishing sections of the thesis I’m posting them to this blog. Mostly to provide a sense of achievement and/or completion (associated with that is to produce visible outputs for my supervisor who resides a few thousand kilometers away). But also in the hope/belief that making this stuff available might provide some level of help to others or perhaps to me.

Consequently, these are version 0 drafts. The need improvement. Feel free to comment.

Lessons from process

The previous sections examining process have established the existence the teleological and ateleological approaches to processes, both within the realm of universities and e-learning and more broadly in organizations and other endeavours. The above and else where in this chapter it has been established that there is a growing move towards the adoption of teleological approaches within universities and e-learning. This section seeks to describe the following lessons associated with process for the practice of e-learning within universities:

  • The assumptions of teleological processes appear not to hold.
  • Process must be aware of and match the context.
  • Revolutionary change through teleological processes may not be necessary.
  • There appears to be a need for both teleological and ateleological.

Taken as a whole these lessons would appear to suggest that an over-reliance on solely teleological processes for the implementation of e-learning within universities is destined to result in less that positive outcomes.

The assumptions of teleological processes appear not to hold

Introna (1996) identifies three assumptions that must be met in order for teleological design processes to be possible: stable and predictable behaviour, designers able to manipulate system behaviour and the ability to accurately determine goals. As demonstrated in the section titled Weaknesses of teleological design there is a plethora of evidence to suggest that these three assumptions do not hold in many modern organizations. It would appear, due to the nature of universities and their main participants, to be less likely to exist within universities and their practice of e-learning. It would appear possible that many of the perceived limitations and problems with e-learning within universities (cross reference to Lessons from Past Experience) may arise from the adoption of processes approaches based on non-existent assumptions. It has been suggested that despite its prevalence and its status as the dominant discourse, the teleological approach seems not to have provided the returns required by organisations seeking to maximise value from information and communication technologies (McConachie, Danaher et al. 2005).

Process must be aware of and match the context

The Institution section (insert cross reference) of this chapter sought to show the differences that exist within universities as an institution working on the assumption, illustrated through perspectives from a number of authors (Butler and Fitzgerald 2001; Parchoma 2006; Nichols 2007), that such an understanding was important for successful implementation of e-learning. The examination of process in the preceding sections reinforces the importance of context in two important ways: the inappropriateness of teleological processes to the university context and the importance of responding to the specific, local context.

There is limited support from research to support teleological models as effective for facilitating change within universities (Kezar 2001). Contributing factors to the poor results of teleological models include: the inability to clearly state missions and goals, lack of centralised decision-making, short-term orientation of teleological models and the inertia of long-standing structures (Birnbaum 2000). These findings are suggestive that, as per the previous section, the assumptions necessary for teleological approaches to operate, do not hold within universities.

Whether or not e-learning becomes an effective intervention depends on how it is used and the context in which it is used (Cradler 2003). The importance of context, especially to the institutional implementation, was emphasised in the above by a number of authors (Oliver and Dempster 2003; Stiles 2004; Sharpe, Benfield et al. 2006). While teleological processes can and should pay significant attention to the context while setting the purpose, the broader characteristics of such processes end up limiting the capability of the process to be aware of and respond to contextual changes and requirements. The effectiveness of e-learning is hampered by artifical boundaries created by teleological processes, boundaries that fail to engage with the complexity, flexibility and fluidity of university learning and teaching (Jones, Luck et al. 2005).

Revolutionary change and its relationship with teleological and ateleological design

The perceived need for revolutionary change within universities and their practice of learning and teaching seem to be, in part, driving the emphasis on teleological processes. The perception often is that such revolutionary change is only possible through large-scale change typical of teleological processes than the incremental change more typical of atelological processes. Phillips (2005) provides one example of this perspective:

For universities to adapt to the changing circumstances they find themselves in, radical, rather than incremental change is needed, and this requires all stakeholders to re-evaluate their paradigm of university education.

Such radical change is most often associated with teleological design processes driven by a visionary leader. However, the assumption of the teleological approach is that everyone agrees on this single vision and works in one direction with no disagreement (Bamford and Forrester 2003). As shown above, the inappropriateness of such an assumption can lead to a break down in teleological design. It will fail. Schien (1985) criticises teleological change models through their inability to incorporate radical change and its emphasis on isolated change. Traditional, teleological approaches to systems design and deployment have not produced desired results in situations requiring systematic change (Cavallo 2000). While Weick (2000) suggests that the talk of revolution, discontinuity and upheval included in the rhetoric of planned, transformational change presents a distorted view of how successful change works.

Returning to the importance of context, the diffusion of major change is difficult to achieve within loosely coupled systems (Morgan 2006). Universities are traditionally loosely coupled and in such organizations it is more common to find improvisational and on-going change which can lesson the need for major change (Weick 1976). Cavallo (2004) suggests that it is possible to achieve large-scale growth on the basis of a large number of little contributions. The simultaneous creation of small-scale continuous adjustments across organizational units can cumulate and create substantial change as long as these isolated innovations can travel and be seen as relevant to a wider range of purposes (Weick and Quinn 1999).

There appears to be a need for both teleological and ateleological

Generating knowledge that will underpin effective practice in change management may entail discarding established dichotomous concepts such as planned and emergent processes (Pettigrew 2000). Discussion of ateleological or emergent change is not an argument against teleological or planned change, it is instead a dispute with the increasingly unreflective manner of most organizational change initiatives (Bamford and Forrester 2003). A significant contributor to this is that teleological models are so ingrained that people often forget that these ideas have not always existed (Kezar 2001). In part, perhaps, because planned or teleological change has dominated the theory and practice of change management for the past fifty years (Bamford and Forrester 2003).

Returning to the notion of matching context. An important contributing factor to achieving successful change is to adopt the most appropriate type of change for the type of change being undertaken and the circumstances within which the change is being undertaken (Burnes 2004). This may mean that a synthesis of the most productive elements of both teleological and ateleological approaches is crucial to addressing the plethora of issues competing for the attention of university decision-makers (Jones, Luck et al. 2005). Jones and O’Shea (2004), in the context of e-learning within a university, agree with Mintzberg that a dynamic and flexible interplay between deliberate and emergent strategy assists with the management of change.

References

Bamford, D. and P. Forrester (2003). "Managing planned and emergent change within an operations management environment." International Journal of Operations and Production Management 23(5): 546-564.

Birnbaum, R. (2000). Management Fads in Higher Education: Where They Come From, What They Do, Why They Fail. San Francisco, Jossey-Bass.

Butler, T. and B. Fitzgerald (2001). "The relationship between user participation and management of change surrounding the development of information systems: A European perspective." Journal of End User Computing 13(1): 12-25.

Cavallo, D. (2000). "Emergent design and learning environments: Building on indigenous knowledge." IBM Systems Journal 39(3&4): 768-781.

Cavallo, D. (2004). "Models of growth – Towards fundamental change in learning environments." BT Technology Journal 22(4): 96-112.

Cradler, J. (2003). "Research on E-learning." Learning & Leading with Technology 30(5): 54-57.

Introna, L. (1996). "Notes on ateleological information systems development." Information Technology & People 9(4): 20-39.

Jones, D., J. Luck, et al. (2005). The teleological brake on ICTs in open and distance learning. Conference of the Open and Distance Learning Association of Australia’2005, Adelaide.

Kezar, A. (2001). "Understanding and Facilitating Organizational Change in the 21st Century: Recent Research and Conceptulizations." ASHE-ERIC Higher Education Report 28(4).

McConachie, J., P. Danaher, et al. (2005). "Central Queensland University’s Course Management Systems: Accelerator or brake in engaging change?" International Review of Research in Open and Distance Learning 6(1).

Morgan, G. (2006). Images of Organization, SAGE Publications.

Nichols, M. (2007). "Institutional perspectives: The challenges of e-learning diffusion " British Journal of Educational Technology 39(4): 598-609.

Oliver, M. and J. Dempster (2003). Embedding e-learning practices. Towards strategic staff development in higher education. R. Blackwell and P. Blackmore. Milton Keynes: UK, Open University Press: 142-153.

Parchoma, G. (2006). "A Proposed e-Learning Policy Field for the Academy." International Journal of Teaching and Learning in Higher Education 18(3): 230-240.

Phillips, R. (2005). "Challenging the primacy of lectures: The dissonance between theory and practice in university teaching." Journal of University Teaching and Learning Practice 2(1): 1-12.

Schein, E. H. (1985). Organisational Culture and Leadership: A Dynamic View. San Francisco, CA, Jossey-Bass.

Sharpe, R., G. Benfield, et al. (2006). "Implementing a university e-learning strategy: levers for change within academic schools." ALT-J, Research in Learning Technology 14(2): 135-151.

Stiles, M. (2004). "Is an e-learning strategy enough?" Educational Developments 5(1): 13-14.

Weick, K. (1976). "Educational Organizations as Loosely Coupled Systems." Administrative Science Quarterly 21(1).

Weick, K. (2000). Emergent change as a universal in organisations. Breaking the code of change. M. Beer and N. Nohria. Boston, MA, Harvard Business School Press: 223-242.

Weick, K. and R. Quinn (1999). "Organizational change and development." Annual Review of Psychology 50: 361-386.

The reason *insert label* talk about gurus is because they can’t spell the word charlatan

A little while ago, I was sparked by Dilbert and my own prejudice against external consultants to contribute two posts (1 and 2) critical of the assumptions underlying the idea of and the contribution of such folk. In some thesis reading today, I came across this great quote the continues my basic assumption of the basic silliness of a reliance on external consultants.

the reason American businessmen talk about gurus is because they can’t spell the word charlatan — (Micklethwait & Wooldridge 1996:11)

I came across the quote and the reference to the book it came from while reading Weick and Quinn (1999) which appears, so far, to be a very interesting paper around organisational change. More on this soon, I think.

According to the Amazon reviews, the “Witch Doctors” book (Micklethwait and Wooldridge, 1996) looks kind of interesting as well. Not the least of the reasons is that I expect you could see some correlations between the management gurus and e-learning/university gurus.

Dave Snowden jokes that consultants and their ideas infect business first. Then, just as or after they fail, they flee to infect governments. Birnbaum (2000) suggests that they then move from government into universities (e.g. TQM). A sentiment which my experience supports.

References

Birnbaum, R. (2000). Management Fads in Higher Education: Where They Come From, What They Do, Why They Fail. San Francisco, Jossey-Bass.

Micklethwait, J. Wooldridge, A. (1996). The Witch Doctors: Making sense of management gurus, Three Rivers Press

Weick, K. and R. Quinn (1999). “Organizational change and development.” Annual Review of Psychology 50: 361-386.

Learning and teaching processes

The following is the penultimate section from the Processes section of chapter 2 of my thesis. It aims to talk about the types of processes used for learning and teaching (including e-learning). It seeks to use the lens of teleological and ateleological processes. This version has been finished quickly and could be made better. I’m trying hard to get “good enough” done and out so I can make progress. I guess supervisor feedback will tell the tale.

A related aside

Before I leap into the section of the thesis, I’d like to raise some thoughts that occurred while working on this section. An example of Jon Udell’s idea of getting a half-baked idea out there. This one is considerably less than half-baked, I’m formulating it as I write this.

Almost without exception the processes used by “official” instructional designers appear to be teleological processes. I do not have a lot of time for teleological processes and hence this makes me question. Are they teleological? What would an ateleological approach to designing a course look like? Does that question make sense?

If you talk about how most university courses get created then, at least at my institution, most don’t have instructional designers involved. Consequently most pay only the vaguest lip service to methodologies.

In fact, most courses aren’t designed. They are tweaked from the previous offering. In most cases, tweaked equals copied. Is this teleological design? It’s getting late, time for bed.

I think the nonsensical querying in the above gives some indication that I need to come back to this at a latter date. Any pointers more than gladly welcome.

Learning and teaching processes

The previous sections examined the various strategic and management policies used by universities to inform the context within which learning and teaching takes place. This section examines the processes used to design and deliver learning and teaching within Universities. While the design of e-learning has been argued to be different in various ways (Irlbeck, Kays et al. 2006) the assumption here is that those differences are part of the same spectrum of processes and thus both e-learning and “non e-learning” are covered within the same section.

For Biggs (2001) the design of effective teaching involves three steps:

  1. Specification of desired outcomes to make clear what it is the students have to learn and to what level of skill or understanding.
  2. Teaching/learning activities are arranged so that students perform tasks that make it likely that they will attain the desired outcomes.
  3. Assessment is developed to determine if the outcomes are attained at varying levels of acceptability.

A lack of alignment between any of these makes it possible for students to escape with inadequate learning (Biggs 2001). Phillips (2005) describes outcomes-centred subject design that has a similar initial focus on learning outcomes but a slightly different set of subsequent steps: assessment tasks, learning activities and then content.

The design of learning and teaching within a university may follow a variety of different models. Many university teaching staff, in the absence of formal teaching qualifications, fall back onto the use of the didactic methods with which they were taught (Phillips 2005). More formal and systematic approaches to the design of learning and teaching, often called instructional design, can be traced back to World War II and the large number of psychologist and educators conducting research and developing training materials for the military services (Reiser 2001). Work in the early to mid-1960s led to a variety of concepts being linked together to form processes or models for systematically designing instructional materials (Reiser 2001). The majority, if not all, of these models are highly teleological and often reduce to the components of Analysis, Design, Development, Implementation and Evaluation, which are seen to operate within a linear sequence (Sims 2006) These models for the design of instruction tend to be modifications and elaborations of a basic problem-solving model tailored to the needs of instructional design (Smith and Ragan 2005). The connection with teleological processes is illustrated by Introna’s (1996) identification of problem-solving as the design process for teleological design (see Table 2.2).

Alternate models that have impacted on instructional design practices include rapid prototyping which focuses on the early development of a prototype before putting it through a series of rapid tryout and revision cycles until final release (Reiser 2001). While moving along the teleological/ateleological spectrum prototyping still retains teleological assumptions. For example, even though the design of the product is refined through experimentation, it still aims to achieve a specified goal and design occurs through the actions of explicit designers.

The teleological emphasis in instructional design models means that examples of situated towards the ateleological end of the spectrum are very rare. Irlbeck et al (2006) describe one, the Three-Phase Design (3PD) instructional design model, that is based on emergence theory and subsequently is designed to operate from the bottom-up through agent interaction following local rules and through use of feedback loops.

The 3PD model was developed to develop new perspectives on instructional design to respond to the requirement of online distance education for practices that are complex, flexible, dynamic and organic (Irlbeck, Kays et al. 2006). The teleological emphasis in much of discussion of e-learning course design still remains. For example, the development of effective, large-scale e-learning courses require well-understood development methodologies that require a clear articulation of what is to be achieved, independent of the technology and a development process to develop the course in the most efficient manner (Manton, Fernandez et al. 2004).

Even with the predominance of the teleological approach to the design of learning and teaching there are acknowledged weaknesses. The linear implementation models are partly to blame for current instructional design approaches being only moderately successful in taking advantage of the online medium (Irlbeck, Kays et al. 2006). The main criticisms of the ADDIE process are a lack of flexibility between stages and a tendency to result in identikit solutions (Manton, Fernandez et al. 2004).

As with the above processes there remain significant questions about the ability to develop a teleological blueprint for the design of learning and teaching. E-learning pedagogies are probabilistic in that there is no such thing as the perfect approach due to the diverse contexts, the diversity of the students and the varying teaching and learning demands of particular courses (Nichols and Anderson 2005). As with other branches of the social sciences there is no unifying mature theory in education and consequently a diversity of ideas, approaches and theories coexist in various states of cohesion and tension (Dillon and Ahlberg 2006). As they progress through their careers teachers will tend to hold different theories of teaching (Biggs 1999). The development of instructional systems depends on the character of the problem, there is no one best way, no optimal solution. Instead, everything depends on the situation and the skills available (Davis 1996).

Any approach to one technology and pedagogy for all is pretty much doomed to flap and then crash (Salmon 2005). Apart from the above reasons there are also those associated with the technology. Technological artifacts often generate new, unforeseen behaviours that may deviate from initial intentions, it is likely that secondary changes in patterns and behaviours will occur that will not be predictable (Westera 2004). E-learning practice cannot remain static because e-learning pedagogies are evolving through the continual emergence of new modes of practice and enhanced technological tools (Nichols and Anderson 2005).

Finally, there are views that make different assumptions. Dalsgaard (2006) argues that learning cannot be managed, only facilitated which suggests that the learning activities of students cannot be structured or pre-determined. Subsequently, the approach to e-learning is not to provide a single integrated system, instead it is to enable personal choice of a variety of loosely joined personal tools for indepdent construction and enagement in social networks (Dalsgaard 2006). Goodyear and Ellis (2008) that e-learning, rather than being a technological intervention, actually depends on a web of skilful activity, human relationships and subtle adjustements to a changing material environment and requires more organic or ecological methods of analysis and design.

References

Biggs, J. (1999). Teaching for quality learning at university. Buckingham, Open University Press.

Biggs, J. (2001). "The Reflective Institution: Assuring and Enhancing the Quality of Teaching and Learning." Higher Education 41(3): 221-238.

Dalsgaard, C. (2006) "Social software: E-learning beyond learning management systems." European Journal of Distance Education Volume,  DOI:

Davis, I. K. (1996). Educational technology: Archetypes, paradigms and models. Classic writings on instructional technology. D. P. Ely and T. Plomp. Santa Barbara, CA, Libraries Unlimited: 15-30.

Dillon, P. and M. Ahlberg (2006). "Integrativism as a theoretical and organisational framework for e-learning and practitioner research." Technology, Pedagogy and Education 15(1): 7-30.

Goodyear, P. and R. A. Ellis (2008). "University students’ approaches to learning: rethinking the place of technology." Distance Education 29(2): 141-152.

Introna, L. (1996). "Notes on ateleological information systems development." Information Technology & People 9(4): 20-39.

Irlbeck, S., E. Kays, et al. (2006). "The Phoenix Rising: Emergent models of instructional design." Distance Education 27(2): 171-185.

Manton, M., B. Fernandez, et al. (2004). "Forced to Conform? Using Common Processes and Standards to Create Effective eLearning." Journal of Interactive Media in Education 14: 15.

Nichols, M. and B. Anderson (2005). "Strategic e-learning implementation." Educational Technology & Society 8(4): 1-8.

Phillips, R. (2005). "Challenging the primacy of lectures: The dissonance between theory and practice in university teaching." Journal of University Teaching and Learning Practice 2(1): 1-12.

Reiser, R. (2001). "A history of instructional design and technology: Part II: A history of instructional design." Educational Technology Research and Development 49(2): 57-67.

Salmon, G. (2005). "Flying not flapping: a strategic framework for e-learning and pedagogical innovation in higher education institutions." ALT-J, Research in Learning Technology 13(3): 201-218.

Sims, R. (2006). "Beyond instructional design: Making learning design a reality." Journal of Learning Design 1(2): 1-7.

Smith, P. and T. Ragan (2005). Instructional Design. New York, John Wiley.

Westera, W. (2004). "On strategies of educational innovation: between substitution and transformation." Higher Education 47(4): 501-517.

Why minimium standards (probably) won't work and will probably become maximum standards

I’ve reached a phase in my thesis work that allows me, long after I should have, to return to Cavallo (2004). I had previously put in a place holder to remind me to go back to this paper. The full impact of the paper will likely become evident over the next few days, but this post focuses specifically on an issue I see arising locally.

The local issue

My current institution is adopting a new LMS. Which LMS isn’t important for this discussion. One of the approaches they will be using to ensure quality is that of “minimum standards”. i.e. organisational units will specify a list of minimum components and services that every course website will be required to have. More recently I hear that these minimal standards will be enforced as part of a quality assurance process that will involve an academic moderate, head of school and curriculum designers in checking that each course meets those minimum standards.

This approach has been coming for some time. Based on my current knowledge of how this is to be implemented I have significant disagreements with some of its assumptions, grave concerns about its short-term effectiveness and even graver concerns about the long-term impacts on the quality of learning and teaching at the institution.

Most of these concerns and disagreements are based around the fundamental model of change embedded within this approach. A model of change which Cavallo (2004) provides alternate possibilities.

Standards, change and problem representation

I feel that some of the differences in opinion on this issue may arise from differences in problem representation. I believe it possible that the folk pushing the minimum standards are representing the problem as “How do we ensure that students experience at least a basic experience with online course sites?”. Where as I think I’m representing the problem as “How do you improve, and continue to improve, the quality of the student experience?”.

The first problem representation lends itself more to the answer of standards. Where as my problem representation lends itself more, in my mind at least, to the solution being “learning” on the part of the academics in charge of those courses. In this, I agree with Cavallo’s (2004) point

As we see it, real change is inherently a kind of learning. For people to change the way they think about and practice education, rather than merely being told what to do differently, we believe that practitioners must have experiences that enable appropriation of new modes of teaching and learning that enable them to reconsider and restructure their thinking and practice. The limitations inherent in existing systems based upon information transfer models are as impoverished in effecting systemic development as they are in child development.

The view encapsulated in this quote suggests a number of weaknesses or perspectives of the minimum standards approach, including:

  • You can’t tell (through development of minimum standards) academics that they have to do something differently.
  • The only experience they will experience with the application and policing of minimum standards is someone telling and checking what they are meant to do.
  • Minimum standards are an impoverished method for effecting systemic development – i.e. it won’t work

Best practices and grafting

This quote comes from a bit earlier in Cavallo (2004)

The push towards scientific, research-based approaches aimed at improving education as mandated in the No Child Left Behind act [2] will suffer due to the implicit model of growth as a matter of grafting a series of discrete treatments into a complex system and assuming they will be applied faithfully and uniformly and will fit into the existing local cultures.

For me, this also illustrates a weakness of the minimal standards approach. To a certain extent the minimal standards approach is an example of applying “best practices”. The minimal set of standards is meant to encapsulate what is considered a minimal set of “best practices”.

Consequently, I see it very much related to what Cavallo (2004) is talking about above. i.e.

“scientific, research-based approaches” = best practices = minimal standards

Additionally, how the minimal standards are to be applied to each individual course is very similar to the second half of the Cavallo (2004) quote.

I’m suggesting that the “system” – the collection of actors and requirements – surrounding each course within a university is a complex system. At least that’s my experience and it seems some other knowledgeable folk agree with me

Archetypal examples of ill-structured problems are instructional design problems. (Jonassen, 1997)

Jonassen (1997) goes on to characterize ill-structured problems with the following and other points:

  • Appear ill-defined because one or more of the problem elements are unknown or not known with any degree of confidence.
  • Possess multiple solutions, solution paths, or no solutions at all, that is, no consensual agreement on the appropriate solution.
  • Possess multiple criteria for evaluating solutions.
  • Require learners to express personal opinions or beliefs about the problem, and are therefore uniquely human interpersonal activities.

I’ve complained before about the silliness of best practices. As Cavallo (2004) suggests such “grafting” on is questionable at the best of time.

Especially if you believe the assumption that these practices will be applied “faithfully and uniformly”. Remember, we’re talking about a university and academics. Academics are trained not to accept propositions uncritically and subsequently cannot be expected to adopt strategies without question or adaptation (Gibbs, Habeshaw et al. 2000).

I’ve complained previously about Quality compliance and task corruption and the risks run by prescription. The last post includes this quote from Knight and Trowler (2000)

Likewise, attempts to improve teaching by coercion run the risk of producing compliance cultures, in which there is `change without change’ , while simultaneously compounding negative feelings about academic work

Anyone else feel like I’m starting to repeat myself?

Minimum standards becoming maximum standards

I suggest that the negative feelings about academic work created by minimum standards and how they are implemented will result in the minimum standards actually becoming the maximum standards.

i.e. many of the academics will treat the minimum standards as the only things they need to do. It will limit the desire for innovation.

Another type of solution

So, if you’re aren’t going to do minimal standards, then what do you do? Well, funnily enough the solution I’ve used before is minimal standards.There is, however, a significant difference between the approach I advocate and that that I perceive as being embodied in the “bad” minimal standards.

I’m running out of time. So I won’t complete this here with an explanation of the solution we’ve used in the past. However, I will include a few connected quotes from Cavallo (2004)

The importance of contextualisation

We bring in powerful ideas about learning and through our practice illustrate how to put them to work. The possibility for spread and growth is not through the exact replication of the actions since the context will be different and the culture is dynamic. Rather, the goal is for the appropriation of the principles and the development of models of thinking so that the agents can adapt and apply with the ability to continually develop through reflection on the feedback and changing environmental conditions.

The absence of an established external purpose and the importance of knowing and responding to the problems of the actors involved.

When we run learning projects, we build upon and take practical action towards existing local concerns. We do not arrive with a fully pre-packaged project design. The design of learning projects evolves and changes in dialogue with personal, collective and local interests, conceptions, and needs. It does not assume that all host environments are the same and that one can merely impose a new model. This design dialogue is what generates involvement, commitment, and staying power — people are learning what they need to know to take action about issues that are important to them. Learners are motivated to master the knowledge they need to solve problems that mean something to them.

References

Cavallo, D. (2004). “Models of growth – Towards fundamental change in learning environments.” BT Technology Journal 22(4): 96-112.

Gibbs, G., T. Habeshaw, et al. (2000). “Institutional learning and teaching strategies in English higher education.” Higher Education 40(3): 351-372.

Jonassen, D. (1997). “Instructional design models for well-structured and ill-structured problem-solving learning outcomes.” Educational Technology Research and Development 45(1): 65-94.

Knight, P. and P. Trowler (2000). “Department-level Cultures and the Improvement of Learning and Teaching.” Studies in Higher Education 25(1): 69-83.

Examination focus and what it might tell us about learning and teaching

Phillips (2005) includes the following quote

In most university subjects, the dominant mode of teaching consists of lectures, tutorials and laboratory practical sessions (Laurillard 2002: 81), with assessment strongly focussed on examinations.

This has some connections with some work a colleague and I are doing around what history can tell us about e-learning and some ideas I have about experimenting with the assumptions and/or mythic nature underlying lectures.

History

One of the points we’re liable to draw from history is that introducing new technology (or a new LMS) into a university is not, by itself, going to change the quality of learning and teaching. Mainly because the conception of learning and teaching held by the academics isn’t going to change.

The bit about “assessment strongly focussed on examinations” provides one potential indicator about what those conceptions might be. i.e. this perspective seems to suggest that

focus on examination = indicator of somewhat limited conception of L&T

That’s a very bald and poor way of putting it, there are all sorts of exemptions and limitations, but that’s the crux of it.

So what’s the case at our institution? And is there any link between how heavily weight assessment is towards an and how e-learning is used?

In terms of exams, I can determine that for the 2nd major term in 2008 the assessment database I have access to indicates:

  • 527 courses were offered;
    This does, I believe, include a number of post-graduate/research courses. This means the percentage of courses with exams might be off – more work needed here.
  • 164 of those had an exam;
  • the average weighting of that exam of total assessment was just over 54%.

Seems to be a particular focus there. I wonder if that focus converts into something observable in the use of e-learning?

Assumptions

The major connection with the work on lectures, but also the history stuff, is shown with the following quote from Phillips (2005)

Sometimes, ICT has been used to replace face-to-face activities, but, often, this has been an unreflective replication of existing activities (Collis and van der Wende 2002; Harris, Yanosky et al. 2003).

I hope the “lecture” work can start to highlight some of those assumptions (which go beyond simply assumptions about learning and teaching to include organisational assumptions).

References

Phillips, R. (2005). “Challenging the primacy of lectures: The dissonance between theory and practice in university teaching.” Journal of University Teaching and Learning Practice 2(1): 1-12.

The planning fallacy, innovation and ateleological design

I occasionally get comments (usually agreement) on the quote that I include in my email signature. The quote

If a major project is truly innovative, you cannot possibly know its exact cost and its exact schedule at the beginning. And if in fact you do know the exact cost and the exact schedule, chances are that the technology is obsolete.

is from Joseph Gavin discussing the design of the Lunar module.

I like it because it expresses the huge disconnect that I see between the assumptions that underlie teleological design processes and innovation, especially in Universities. This causes me great problems because the majority of information technology and management folk in Universities don’t recognise the disconnect between innovation and telological processes. What’s worse is that they aren’t even aware that there are alternatives and that those alternatives are a better fit in some circumstances.

Support from psychology

This post from 37Signals (which has a variety of interesting perspectives in the comments) points to this post about the planning philosophy.

I should repeat that I don’t think teleological processes are inherently bad, just that they are a bad fit for projects that involve innovation, novelty or learning.