Integrating BAM into Moodle – Can it be done?

Posted in bam, icddu on July 9, 2009 by davidtjones

Blog Aggregation Management (BAM) is a little project of mine that’s been going since 2006. It’s an example of, what I think, is a more appropriate product model for e-learning systems – essentially small pieces loosely joined/best of breed/PLE. Currently BAM is based on the infrastructure provided by Webfuse, another project of mine (which embodies and enables the better product model).

Trouble is that come 2010 Webfuse is history as my current institution cans Webfuse in favour of Moodle. There are about 5 or 6 courses at that institution that currently use BAM and many more that could probably use it. So, if there is to be a future for BAM it will have to be ported to Moodle. This is the first step in checking to see if this can be done. It will be subsequently be followed by whether or not it should be done and whether it will be done.

The following isn’t a real blog post. It’s more a unstructured collection of ad hoc, formative reflections as I confuse myself diving through the Moodle world as a ill-informed newbie.

How do you find out developing something for Moodle?

Each system embodies a way of looking at the world, a set of terms and concepts. Essentially I need to get some sort of insight into its structure, language and world view. From there I can make some vaguely informed decision as to whether the BAM worldview has any hope of living nicely with the Moodle world-view. I need some resources.

Well, the Moodle site has this pointer towards development resources. Of course, there’s also the constructivist approach recommended by Dan Poltawski. There’s a lot to be said for that approach, but it’s a little heavyweight for my current requirements.

What other stuff is there on the Moodle main site?

  • FAQS! There’s a FAQ for development.
    But most of that seems to be low level code related stuff. I’m looking for a bigger picture.
  • There are some pointers to information about creating new modules or plugins. – apparently there are 22 different types of plugins.
  • the manuals
  • The coding guide

The following list of resources is something I’ll probably have to come back to at a later date when/if coding commences.

Absence of definitions or an overview

I’m about 4 or 5 hours into my examination of Moodle and whether BAM might go into it. The biggest problem I have is that I haven’t been able to find an overview. Something that defines terms such as blocks, activity modules etc and shows how they all link together.

All of the developer docs like “how to develop a block” just leap straight into answering the question. None seem to offer a description or pointer to a description of what a block is and how it compares to other components.

This set of powerpoint slides (by Sam Marshall) on creating Moodle modules seems to be the best so far.

Perhaps this Moodle Programming course might help fill the hole.

Nature of modules

Each module has it’s own directory. Will include a list of files, directories for specific purposes.

Each module can specify capabilities – who can do what?

Existing work

As others have pointed out, Moodle already has blogs and there is also a project currently looking at improving the Moodle blog component. Actually, that’s a 2008 project. The project blog just seems to peter out – no final “it’s done” post. However, according to this it completed successfully and contributed code which is available as patches and may also be merged into Moodle 2.0

Looking into that project brings me to a thread on the Moodle site (you may have to login to see the thread) about the blog component. It starts off with a post from Martin Dougiamass explaining some of the initial rationale.

That post reinforces the point that there is a strong model underpinning Moodle and how it should work that drives the design decisions. At least on an initial read, the aim of Blogs in Moodle was to provide a blog like facility that fit within the Moodle model. The idea of integrating BAM into Moodle comes from a different perspective and there might be some interesting clashes of perspective/assumptions.

An assumption behind BAM is that you actually want the students to be posting their comments on the open web, to enable some of the serendipity related fun to happen. Moodle appears to be based on an assumption of a much tighter integration.

What type of plugin?

If implemented, I’m assuming BAM will be some kind of plugin and one of those listed here. In the following, I’m documenting my investigations about which type BAM might be.

From the initial list, without looking forward, I’m guessing that BAM might be or be related to one of the following:

  • Activity module
  • Assignment type
    This looks potentially interesting/related. BAM is mostly used at the moment as an assignment. So it’s inclusion here probably fits current operation. However, currently BAM is separated from the assignment stuff which allows a bit more flexibility….mmm. There are examples of non standard assignment types. Which are some sort of plugin to the existing assignment component.
  • Gradebook or Portfolio plugins

What’s next?

Well, I should perhaps follow the published guidelines. Completing the first step – make sure it’s a good idea – is first. I’ll need to

  • Check to see if the blog improvements offer something close to BAM.
  • Dig around a bit more in Moodle to see if BAM can be implemented.
  • Ask a question on the forums.

Academics – the next part of the People section

Posted in Chapter 2, PsFramework, design theory, elearning, phd, thesis on July 7, 2009 by davidtjones

The following is the next part of the People section for chapter 2 of my thesis. The People section was started a week ago with this post. This one takes up the task of saying something about academic staff, subsequent and soon to be completed sections will look at management, academic staff developers and technology staff.

While I think some of the stuff in the following is important and overlooked, I just can’t help feeling that it is, not to put to fine a point on it, crap. But hopefully it is good enough for the thesis. Happy reading.

Academic staff

A common definition for the term ‘academic’ is not simple to arrive at as the characteristics that define an academic are increasingly problematic (Williams 2008). Defining what it is to be an academic is not a given, but is a matter of dynamic relationships between social and epistemological interests and structures (Barnett 2000). In addition, the nature academic work has changed with time. During the eighteenth and early nineteenth centuries in the USA academic work included tasks associated with the supervision of dormitory accommodations and ministerial work in the community (Schuster and Finkelstein 2006). In spite of these problems this section seeks to provide an overview of some of what is known about academics, teaching, learning and e-learning.

While change in the tasks academics perform continues and there is increasing diversity between academics, there is a minimal, shared understanding that the occupational role of an academic involves two distinctive responsibilities within the context of a university: research or scholarship, and teaching (Williams 2008). It is through the performance of these tasks that academic faculty members can be said to be the essential production force of universities (Xu and Meyer 2007). While fairly common, the balance between these two tasks suffers from the same temporal change and increasing diversity as definitions of academics. In comparing the distribution of effort by academic staff between the early 1970s and late 1980s, Finkelstein, Seal and Schuster (1998) reveal a drop in the amount of time spent teaching – from 60-66% to 54% – and an increase in time researching – 14% to 20%. This in contrast to the increasing pressure within this new century to refocus academic attention on student learning (Schuster and Finkelstein 2006).

Calls to recognise the profession of teaching as the central role of the academic can be traced back to the arguments of many mid-19th century reformers at Oxford who saw such recognition as crucial for both the survival of the university and of academe as a career (Engel 1975). There have been questions about what profession an academic fulfils. Is an academic a discipline expert/researcher or a teacher or educator. Piper (1992) suggests academics are discipline experts. This conclusion is based on academics generally lacking any teacher training and the lack of status arising from them demonstrating teaching qualifications, knowledge and experience. In addition, when academics leave universities it is generally to return to discipline-based roles and not that of educators (Piper 1992). Taylor (1999) suggests that when it comes to teaching academics are craft workers who learn to teach largely through imitation. It is as researchers, as discipline experts, that academics display professional attributes, not as teachers (Taylor 1999). Similarly, academics are typically not trained as teachers or course designers, but as disciplinary experts (Ziegenfuss and Lawler 2008). Academics come to teaching with immense amounts of content knowledge but little or no knowledge of teaching and learning (Weimer 2007).

Academics are discipline professionals, not teaching professionals. While there are growing trends towards short teacher training courses for academics, it is unreasonable on that basis alone to expect a comparable level of competence between research specialisations and teaching (Booth and Anderberg 2005). Graduate students – academics in training – traditionally do not receive instruction on how to teach (Folkers 2005). Where training in teaching is available, many advisors of these students actively discourage them from engaging in such training (Stice, Felder et al. 2000). The graduate student experience appears to socialize aspiring academics primarily to a vision of academic work that emphasises research and disciplinary expertise, in spite of rhetoric about the growing importance of student learning (Austin 2002). The majority of an academic’s knowledge of how to teach is gained while teaching through observation, imitation and trial and error (Passmore 2000).

Academic interest and focus on teaching is further impacted by exposure to ambiguous, even contradictory, role expectations. Academics are expected to engage equally in research and teaching and yet work towards promotion criteria that primarily value achievements in research (Zellweger 2005). There is no question that funded research and publication of results in scholarly journals is the dominant criteria in universities world-wide and this is, at least a contributing, if not causal factor in this limitations of university learning and teaching (Knapper 2003). While a review of promotion criteria and weightings from UK universities found widespread adoption of formal parity between teaching and research for mid-range academics, it found that promotion to senior ranks were based almost exclusively on research excellence and did not allow applications based on teaching activities (Parker 2008). Fairweather (2005) found that spending more time teaching in the classroom remains a negative influence on academic pay and that the trend is worsening most rapidly in institutions whose central missions focuses on teaching.

Through their position as discipline experts, academics possess high levels of scientific capital and consequently have been difficult to manage (Kolsaker 2008). A difficulty increased by conceptions of academic freedom that see it as freedom for the academic to speak their minds, teach in accordance with their own interests and to enjoy security of tenure (Nixon, Beattie et al. 1998). Individual academics are, by definition, very autonomous individuals and there has generally been no tradition for tightly controlling the actions of faculty members within universities (Waeraas and Solbakk 2009). Academics are knowledge workers (Jones, Gregor et al. 2003). For knowledge work, the means of production is the knowledge held by knowledge workers and it is totally portable and an enormous capital asset (Drucker 2001). Consequently, academics have considerable autonomy about how they perform tasks, a fact that enables and encourages diversity. It would be difficult to find two academics who take identical approaches to teaching the same content (Mishra 2005). Academics recognize no boss and see themselves as individual entrepreneurs with little desire for collective action and little interest in the larger university (Dearlove 2002).

At the same time academics wield a relatively large amount of power, including the ability to set, or at least influence, organizational processes (Folkers 2005). It is unlikely that any reform within a university will succeed without the support of academics (White and Myers 2001). Since technology use continues to remain an individual choice, how faculty members perceive and use technologies is important (Xu and Meyer 2007). Teaching academic staff are at the heart of the on-going negotiation between teaching, learning and new technology (Goodson and Mangan 1995). They are key to the successful integration of educational technology in the teaching and learning process (Zellweger 2005).

New technologies at most enable rather than dictate change (John and La Velle 2004). While technology may be the stimulus, the essential matters are complex and will be the purview of academics (Oblinger, Barone et al. 2001). The success of e-learning is primarily a result of faculty buy in (Lynch 2002) and the extent to which faculty are supported as they develop innovative approaches to using technology in teaching. Addressing the concerns of faculty is an important factor (Nichols 2007). Improved integration of technology can be facilitated by understanding current faculty trends and issues and by adapting specific strategies suited to the needs and contexts of faculty within their individual institutions (Howell, Saba et al. 2004).

Technology is restructuring the fabric of higher education and influencing the work done by academics (Valimaa and Hoffman 2008). Teaching staff are often suspicious of any changes to traditional pedagogies and are often expected to adopt innovations whilst under significant workload (Jones 2008). E-learning can directly challenge traditional pedagogies and consequently are likely to generate resistance (Folkers 2005). Several writers have described how the lack of compatibility with existing pedagogies may cause academics to resist using technology in learning and teaching (Holden and Wedman 1993). Left to their own paradigms academics will generally use their university’s course management system as a supplement to their preferred teaching style (Ullman and Rabinowitz 2004). Academics only use e-learning tools if they are aligned with their beliefs about teaching and learning (Elgort 2005).

As mentioned in the Past Experience section (insert cross reference) research into teaching within higher education has developed a rich body of knowledge that links the quality of student learning outcomes with the conceptions of learning and a link between the conceptions of teaching held by academics and their approaches to teaching (Kember and Kwan 2000; Norton, Richardson et al. 2005; Eley 2006; Gonzalez 2009). A relationship captured in Figure 2.1 adapted from Trigwell (2001). The conception of learning held by teachers has a major influence on the planning of courses, the development of teaching strategies and ultimately on the what and how students learn (Alexander 2001). In order to change the way teaching staff approach teaching, it is necessary, and very difficult, to change their conceptions of teaching and learning (Trigwell and Prosser 1996).

Trigwell's model of teaching

The predominant form of learning within universities remain the teacher-centred, classroom education (Piccoli, Ahmad et al. 2000). The majority of existing academics have not studied using a Learning Management System (LMS) nor have they seen how e-learning can be used in a range of teaching situations (Newland, Jenkins et al. 2006). Consequently, their experiences and values are predominantly those of the face-to-face paradigm (Newland, Jenkins et al. 2006). Universities are replete with resources in the form of intelligent individual who are rarely appropriately directed to pedagogical innovation nor are self-motivated to radically transform their teaching (Salmon 2005).

One result of this tendency has been for academics to be characterised as barriers to e-learning and labelled technology averse, luddites and digital immigrants (Xu and Meyer 2007). In opposition to this view are observations that find academics using computers in both their everyday lives and their research. Academics make extensive use of technology in research and scholarship, in many cases this use drives the evolution of the technology to meet their particular needs (Duderstadt, Atkins et al. 2002). Xu and Meyer (2007) report on a 1998 survey that shows 70% of academics had a computer at home. Jones and Johnson-Yale (2005) report on a survey of over 2000 academics that finds that academics have long-term exposure to the Internet and computer use.

There are a number of explanations arising from the literature that offers possible reasons for the mismatch between academics’ general use of technology and their limited or non-existent use of technology for teaching. The effort required to master new technologies, contend with glitches, or to bend their teaching to fit technologies provided on campus hinders rather than helps their teaching (Jones and Johnson-Yale 2005). In terms of technology, McGill and Hobbs (2008) found that even when academics perceived high levels of institutional support for e-learning they were less than satisfied as they perceived that the learning management system did not support their teaching activities.

Barriers to instructor acceptance of e-learning has been categorized into: personal, attitudinal, and organizational (Pajo and Wallace 2001). The time required to learn about new technology has been suggested as an important, and in some cases, the most significant factor inhibiting use (Pajo and Wallace 2001; Newton 2003). Career-minded academics are skeptical of investing time in e-learning believing the effort to have low returns both financially and intellectually (Ruth 2006). This is, at least in part, due to universities marginalizing the importance of e-learning within the promotion and tenure process (Schell 2004). Green (2002) identifies a continuing irony of campus efforts to promote e-learning is the fact that few institutions provide formal recognition and reward for faculty efforts. Often, personal satisfaction may be the greatest, even only, reward for the adoption of new technologies (Jones and Johnson-Yale 2005). It remains that case that in the majority of institutions, recognition and promotion arises from research activity and not innovative teaching developments (Newland, Jenkins et al. 2006).

Issues associated with academic staff adoption of e-learning can be correlated with the Rogers (1995) diffusion of innovations (Newland, Jenkins et al. 2006). Where the rate of adoption is driven by a complex combination of factors including the nature of the institution and associated social systems, the efforts by organizational change agents, the type of communication channels used to share information, how e-learning is perceived by academics and the type of innovation decision they are allowed (Jones, Jamieson et al. 2003). Perceptions are influenced by other factors including demographic and professional characteristics (Xu and Meyer 2007). Beyond this it is possible that two individuals could, and usually do, perceive the a given innovation differently (Jones, Jamieson et al. 2003).

Spotts (1999) found that faculty decide to use technology if they perceive technology to provide a relative advantage in terms of improving student learning, enhancing instruction or making their job less demanding. However, for some staff e-learning is perceived to be of lower quality, perhaps due to subjective attitudes toward an approach with which they are uncomfortable, unfamiliar and which is perceived to threaten their job (Huynh, Umesh et al. 2003). E-learning is also perceived to bring pain factors that minimise any relative advantage (Black, Beck et al. 2007) such as excessive preparation time, conflict over intellectual property rights, lack of recognition, and technical, operational and administrative difficulties.

Much of e-learning has been driven by early adopters who were technology champions, however, there is evidence to suggest that many academics are reluctant to adopt e-learning and yet may feel pressure from their institutions (McGill and Hobbs 2008). In the early 1990s, Geoghegan (1994) suggested that this second wave is slow in adopting technology not because of an aversion to technology, but due to an aversion to risk, inadequate support and the lack of a compelling reason to disrupt existing practice. The ways in which academics experience their work inhibit them adopting what the research consensus suggests are ways to be better teachers (Knight and Trowler 2000). There is a yearning for safety which underpins much of what an academic does in research – filling in the details of dominant research paradigms – and teaching – reliance on pedagogic methods that give both teacher and student an easy time (Barnett 2000). Amongst a list of factors limiting adoption of technology Stewart (2008) lists the following fears: technology taking away real learning, job loss, technophboia, and loss of autonomy through conformity.

Much of the e-learning literature contains an assumption that if the virtues of e-learning are demonstrated then academics will adopt it (Oslington 2005). Learning to teach in new ways requires more than applying new theoretical knowledge disseminated using formal modes, it requires a culture in which innovative teaching is expected and rewarded, where teams or departments replace isolated individuals as the unit of change, strategies which involve collaboration and reflection and support through encouragement, recognition and resources (Johnston 1996). Academics are not likely to simply adopt e-learning if its virtues are demonstrated, instead adoption is only likely if it is within their interest to do so (Oslington 2005). The limited quality and quantity of e-learning within higher education (insert cross reference to Past Experience) is often not due to a set of easily overcome deficiencies, barriers or misunderstandings, instead, it is a product of the wider game of higher education and the strategic interests of those who play it (Selwyn 2007).

It is then not surprising that faculty may be more willing to adopt e-learning if they are not forced to quickly abandon long-established practices (Howell, Saba et al. 2004). The invoking of an earlier pedagogic regime within a new environment is an attempt to give academics reassurances of stability and continuity (Cousin, Deepwell et al. 2004). The ability for academics to draw on their own pedagogic repertoires, practical wisdom and relative control to shape the ways innovation is implemented should limit reliance on over-deterministic accounts of global tendencies and focus attention to take account of local conditions and the range of possible responses to particular pressures (Clegg, Hudson et al. 2003). The interpersonal and cultural issues may well overshadow the, by comparison, simple issues of funding and technological infrastructure (Folkers 2005).

Self-identified change is a key component of successful implementation, while change that is perceived as imposed is not (Hersey and Blanchard 1988). A gentle and affirming change strategy, with an emphasis on interpersonal and social activity, can minimise the anxiety and uncertainty academics tend to associate with change and lead to effective diffusion (Nichols 2007). Rather than focus on the techniques and technologies associated with teaching, initiatives aimed at developing academic practice should focus on facilitating and supporting a more reflective approach to teaching (Ramsden 1998; Biggs 1999; Prosser and Trigwell 1999). When implementing e-learning within existing Universities staff engagement is the most complex and important success factor (Collis 1998).

References

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

Austin, A. E. (2002). "Preparing the next generation of faculty: Graduate school as socialization to the academic career." The Journal of Higher Education 73(1): 94-122.

Barnett, R. (2000). Realizing the University in an age of supercomplexity. London, SRHE and Open University Press.

Barnett, R. (2000). "Supercomplexity and the curriculum." Studies in Higher Education 25(3): 255-265.

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

Black, E., D. Beck, et al. (2007). "The other side of the LMS: Considering implementation and use in the adoption of an LMS in online and blended learning environments." Tech Trends 51(2): 35-39.

Booth, S. and E. Anderberg (2005). "Academic development for knowledge capabilities: Learning, reflecting and developing." Higher Education Research & Development 24(4): 373-386.

Clegg, S., A. Hudson, et al. (2003). "The Emperor’s new clothes: globalisation and e-learning in higher education." British Journal of Sociology of Education 24(1): 39-53.

Collis, B. (1998). Implementing change involving WWW-Based course support across the faculty. ACEC’98.

Cousin, G., F. Deepwell, et al. (2004). Theorising implementation: variation and commonality in European approaches to e-learning. Networked Learning Conference 2004.

Dearlove, J. (2002). "A continuing role for academics: The governance of UK Universities in the Post-Dearing era." Higher Education Quarterly 56(3): 257-275.

Drucker, P. (2001). Management Challenges for the 21st Century, Collins Business.

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.

Eley, M. (2006). "Teachers’ conceptions of teaching, and the making of specific decisions in planning to teach." Higher Education 51(???): 191-214.

Elgort, I. (2005). E-learning adoption: Bridging the chasm. Proceedings of ASCILITE’2005, Brisbane, Australia.

Engel, A. (1975). Emerging concepts of the academic profession at Oxford 1800-1854. The University in Society. Vol 1, Oxford and Cambridge from the 14th to the early 19th Century. L. Stone. Princeton, NJ, Princeton University Press: 305-351.

Fairweather, J. (2005). "Beyond the rhetoric: Trends in the relative value of teaching and research in faculty salaries." Journal of Higher Education 76(4): 401-422.

Finkelstein, M., R. Seal, et al. (1998). The new academic generation. Baltimore, MD, John Hopkins University Press.

Folkers, D. A. (2005). "Competing in the Marketspace: Incorporating Online Education into Higher Education – An Organisational Perspective." Information Resources Management Journal 18(1): 61-77.

Geoghegan, W. (1994). Whatever happened to instructional technology? 22nd Annual Conferences of the International Business Schools Computing Association, Baltimore, MD, IBM.

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

Goodson, I. and J. M. Mangan (1995). "Subject cultures and the introduction of classroom computers." British Educational Research Journal 21(5): 613-628.

Green, K. (2002). Campus Portals make Progress; Technology Budgets suffer significant cuts, 2002 National Survey of Information Technology in Higher Education (summary). Encino, CA, The Campus Computing Project: 4.

Hersey, P. and K. Blanchard (1988). Management of organizational behavior: Utilising human resources. New Jersey, Prentice-Hall

Holden, M. and J. Wedman (1993). "Future issues of computer-mediated communication: The results of a delphi study." Educational Technology Research and Development 41(4): 5-24.

Howell, S., F. Saba, et al. (2004). "Seven strategies for enabling faculty success in distance education." Internet and Higher Education 7: 33-49.

Huynh, M., U. N. Umesh, et al. (2003). "E-Learning as an emerging entrepreneurial enterprise in universities and firms." Communications of the AIS 12: 48-68.

John, P. D. and L. B. La Velle (2004). "Devices and Desires: subject subcultures, pedagogical identity and the challenge of information and communications technology." Technology, Pedagogy and Education 13(3): 307-326.

Johnston, S. (1996). "Questioning the concept of ‘dissemination’ in the process of university teaching innovation." Teaching in Higher Education 1(3): 295-304.

Jones, D. (2008). PLES: framing one future for lifelong learning, e-learning and universities. Lifelong Learning: reflecting on successes and framing futures. Keynote and refereed papers from the 5th International Lifelong Learning Conference, Rockhampton, CQU Press.

Jones, D., S. Gregor, et al. (2003). An information systems design theory for web-based education. IASTED International Symposium on Web-based Education, Rhodes, Greece, IASTED.

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, S. and C. Johnson-Yale (2005). "Professors online: The Internet’s impact on college faculty." First Monday 10(9).

Kember, D. and K.-P. Kwan (2000). "Lecturers’ approaches to teaching and their relationship to conceptions of good teaching." Instructional Science 28(5): 469-490.

Knapper, C. (2003). "Three decades of educational development." International Journal for Academic Development 8(1-2): 5-9.

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

Kolsaker, A. (2008). "Academic professionalism in the managerialist era: a study of English universities " Studies in Higher Education 33(5): 513-525.

Lynch, M. M. (2002). The online educator: a guide to creating the virtual classroom. London, RoutledgeFalmer.

McGill, T. and V. J. Hobbs (2008). "How students and instructors using a virtual learning environment perceive the fit between technology and task." Journal of Computer Assisted Learning 24(3): 191-202.

Mishra, P. (2005). "On becoming a Web site." First Monday 10(4).

Newland, B., M. Jenkins, et al. (2006). Academic experiences of using VLEs: Overarching lessons for preparing and supporting staff. Technology supported learning and teaching: A staff perspective. J. O’Donoghue. Hershey, PA, Idea Group Publishing: 34-50.

Newton, J. (2003). "Implementing an institution-wide learning and teaching strategy: lessons in managing change." Studies in Higher Education 28(4): 427-441.

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

Nixon, J., M. Beattie, et al. (1998). "What does it mean to be an Academic? A colloquium." Teaching in Higher Education 3(3): 277-298.

Norton, L., J. Richardson, et al. (2005). "Teachers’ beliefs and intentions concerning teaching in higher education." Higher Education 50(????): 537-571.

Oblinger, D., C. Barone, et al. (2001). Distributed education and its challenge: An overview. Washington DC, American Council on Education: 56.

Oslington, P. (2005). "Incentives in On-line Education." Journal of Higher Education Policy and Management 27(1): 97-104.

Pajo, K. and C. Wallace (2001). "Barriers to the uptake of web-based technology by university teachers." Journal of Distance Education 16(1): 70-84.

Parker, J. (2008). "Comparing research and teaching in university promotion criteria." Higher Education Quarterly 62(3): 237-251.

Passmore, D. L. (2000). "Impediments to adoption of web-based course delivery among university faculty." ALN Magazine 4(2).

Piccoli, G., R. Ahmad, et al. (2000). "Knowledge management in academia: A proposed framework." Information Technology and Management 1: 229-245.

Piper, D. W. (1992). "Are professors professional? The organisation of University examinations." Higher Education Quarterly 46(2): 145-156.

Prosser, M. and K. Trigwell (1999). Understanding learning and teaching: The experience in higher education. Buckingham, SRHE / Open University Press.

Ramsden, P. (1998). Learning to Lead in Higher Education. London, Routledge.

Rogers, E. (1995). Diffusion of Innovations. New York, The Free Press.

Ruth, S. (2006). "E-Learning: A Financial and Strategic Perspective." EDUCAUSE Quarterly 29(1): 22-30.

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.

Schell, G. (2004). "Universities marginalize online courses." Communications of the ACM 47(7): 53-56.

Schuster, J. and M. Finkelstein (2006). The American Faculty. Baltimore, MD, John Hopkins University Press.

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.

Spotts, T. H. (1999). "Discriminating factors in faculty use of instructional technology in higher education." Educational Technology & Society 2(4).

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).

Stice, J., R. Felder, et al. (2000). "The future of engineering education IV. Learning how to teach." Chemicel Engineering Education 34(2).

Taylor, P. (1999). Making Sense of Academic Life: Academics, universities and change. London, SRHE / Open University Press.

Trigwell, K. (2001). "Judging university teaching." The International Journal for Academic Development 6(1): 65-73.

Trigwell, K. and M. Prosser (1996). "Changing approaches to teaching: A relational perspective." Studies in Higher Education 21(3): 275-284.

Ullman, C. and M. Rabinowitz (2004). Course Management Systems and the Reinvention of Instruction. Technical Horizons in Education. October 2004.

Valimaa, J. and D. Hoffman (2008). "Knowledge society discourse and higher education." Higher Education 56(3): 265-285.

Waeraas, A. and M. Solbakk (2009). "Defining the essence of a university: lessons from higher education branding." Higher Education 57(4): 449-462.

Weimer, M. (2007). "Intriguing connections but not with the past." International Journal for Academic Development 12(1): 5-8.

White, J. and S. Myers (2001). "You can teach an old dog new tricks: The faculty’s role in technology implementation." Business Communication Quarterly 64(3): 95-101.

Williams, K. (2008). "Troubling the concept of the ‘academic profession’ in 21st Century higher education." Higher Education 56(5): 533-544.

Xu, Y. and K. Meyer (2007). "Factors explaining faculty technology use and productivity." Internet and Higher Education 10(2): 41-52.

Zellweger, F. (2005). Strategic Management of Educational Technology: The Importance of Leadership and Management. 27th Annual EAIR Forum. Riga, Latvia.

Ziegenfuss, D. and P. Lawler (2008). "Collaborative course design: changing the process, acknowledging the context, and implications for academic development." International Journal for Academic Development 13(3): 151-160.

PhD Update #16 – return from a break

Posted in phd, thesis on July 5, 2009 by davidtjones

As mentioned a fortnight ago I’ve had much of the last two weeks doing non-PhD stuff including a road trip to Longreach. So this update is somewhat light on.

What I’ve done

Last update I said I would by now have:

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

To some extent that’s been done. I have a structure and I have completed a draft of the first major section on Students. That draft includes an overview of what I think the structure for the People component will be.

I’ve spent today wondering the literature gathering perspectives on some of the other sections. I have sufficient information on this stuff, I need to write it up. That’s next week’s job.

What I’ll do next week

There’s a trend developing here with weekends. Next weekend I’ll be off celebrating a couple of anniversary related events in Adelaide. This means Friday through Tuesday will be lost to the PhD.

In this case, I aim to have another update on the Thursday before I leave. The main aim from now until then will be to:

  • Make as much progress as possible on the People component.

I’m hoping to have at least 2 or 3 days writing.

How do you “apprehend the future”

Posted in elearning, icddu on July 1, 2009 by davidtjones

The following is an attempt to reflect upon an EDUCAUSE Review article by Bryan Alexander entitled “Apprehending the Future: Emerging Technologies, from Science Fiction to Campus Reality”. I’m doing this because I believe the topic, at least at first glance, has connections with the new role I’m meant to fulfill at my current institution.

Summary

Provides an overview of five different methods that can be used to apprehend what the future might hold for higher education in terms of technology and its application. The methods are:

  1. The environmental scan.
  2. The delphi method.
  3. Prediction markets.
  4. Scenarios.
  5. Crowdsourcing.

The articles givesEach of the methods get the following treatement:

  • A brief description;
  • Pointers to relevant examples; and
  • A summary of the advantages and disadvantages.

The last main section recognises that all of these methods are at best, partial solutions and raises a number of challenges they face, including:

In reflecting on the problems with these methods the article suggests a number of reasons for consuming resources to undertake them. The reason I like most and which is classed as the best is that the intellectual exercise prepares the individuals and the institution.

Comments

Provides a good overview of the methods listed. What I found most interesting were the pointers to the relevant examples of each method that exist within the university/educational technology fields.

The following are some nit picks. Whether you think them relevant may be a factor of your perspective or opinion. None of them limit the value of the article.

Seems to miss some disadvantages

For example, I believe that the reliance on experts in the Delphi model is a limitation especially when dealing with potential paradigm shifts – probably connected to the unknown-unknowns mentioned in the latter parts of the article. Experts are experts because they have a large number of deeply complex mental patterns associated with a certain world view that have been built up over time. A paradigm shift encapsulates radical change in that world view which makes expert knowledge somewhat less than appropriate.

The example I’ve seen first hand is that of print-based distance education experts faced with the rise of the Internet. Or more broadly, the hypermedia community when faced the idea of the World-Wide Web.

This page on the Delphi Method seems to suggest that this is a weakness, but perhaps not for the reasons I give.

Prediction markets are not “wisdom of the crowds”

The article associates prediction markets with the wisdom of the crowds. I’m influenced here by my following of Dave Snowden who argues, quite effectively in my opinion, that prediction markets are not examples of the wisdom of crowds. Though the Wikipedia page on the wisdom of crowds thinks they are.

Scenarios

Showing my bias/Snowden influence – a Snowden post on scenario planning

Snowden and his group have developed the Future Backwards as an alternative to scenario planning. So it might belong here as another method.

Reflections

I need to take the time to visit and examine each of the examples given of the methods. By combining the results of those with my own thinking and experience should help something interesting arise. As the article points out, the intellectual exercise of reflecting on the findings will help expand perceptions and better prepare for thinking about the future.

The next two sections make related points. Essentially, I’m trying to develop an argument that apprehending the future is only half the argument.

The role of context

The focus of the methods discussed is on knowing what the future brings. It focuses outward, not inward on the local context. I think both is needed.

The point I’m trying to make is that it’s not just about the nature of the next wave of technology but it’s how that technology is combined with the problems faced within particular contexts that can generate interesting approaches. Sometimes those approaches can be totally unthought of by the original developers of the technology. The article offers a William Gibson quote

“the street finds its own uses for things”

that captures some of this. The street or each unique context may generate a new and interesting application that the experts don’t see as they are divorced from the complexities of the context. They’ve abstracted away all those lower problems and consequently miss some stuff.

A famous Allan Kay quote seems to have the essence of what I’m trying to get at

the best way to predict the future is to invent it

.

You don’t know what you’ve got until you build it

Related to the above point is the assumption that if you know which future technologies are coming then you can predict the impact it will have on your local context. This assumes that the local context is, in the sense of Snowden’s Cynefin framework, is simple or complicated. In such systems cause and effect exist. You, or an appropriately skilled expert, can predict what will happen when you introduce a new technology.

Personally, I believe that the context in which learning and teaching takes place within a university is complex. It is the type of system where cause and effect cannot be predicted. You never really know what is going to happen until you try it. Also, if you try the same thing at different times, or in slightly different contexts, you are likely/certain to get different outcomes.

The article does make the point that

One challenge to any futures method is the sheer complexity of the future.

I’m suggesting that the context is another source of complexity that needs to be considered.

A Snowden suggestion is safe fail probes. Matching such probes with the options and possibilities identified by the approaches described within this article could prove useful.

Perhaps a focus on response is better?

One last thought, perhaps it’s more important to build into the system the ability to respond quickly to near-term changes, rather than predict long-term changes.

Alternative to clickers – freeing up the physical location limitation

Posted in cddu, iLecture, icddu on June 30, 2009 by davidtjones

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

Posted in Chapter 2, PsFramework, design theory, elearning, phd, thesis on June 29, 2009 by davidtjones

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

Posted in Chapter 2, cddu, design theory, icddu, thesis on June 23, 2009 by davidtjones

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

Posted in phd, thesis on June 21, 2009 by davidtjones

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

Posted in Chapter 2, PsFramework, design theory, elearning, phd, thesis on June 21, 2009 by davidtjones

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

Posted in Chapter 2, PsFramework, design theory, elearning, phd, thesis on June 21, 2009 by davidtjones

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.