Assembling the heterogeneous elements for (digital) learning

Category: PsFramework Page 2 of 7

Learning theories and e-learning

It’s almost a month since the last post from the first draft of my thesis. So, after much time away here’s the next installment. It’s probably rougher than previous versions – which says something – I’m still getting back into the swing of things.

The following is meant to be a description of learning theory within the context of e-learning at universities. It’s not a complete or in-depth examination of learning theories. Instead, it tries to illustrate that what we know about learning theory (in the broadest possible definition) is hugely complicated, diverse and ever changing. The intent is to argue that this is in direct contrast to characteristics of the common approach taken by universities to support e-learning. That is, an approach that focuses on stability and inflexibility.

Learning theories

The previous section (Section 2.8.1) argued that the quality of student learning within a university context is heavily influenced by the thinking, planning and strategies adopted by the pedagogues responsible for individual courses. This section seeks to summarise the research, literature and theories that have arisen to guide the thinking of pedagogues around learning. The following section cannot do justice to complexity, diversity, breadth and depth of research into learning. Explanatory accounts of learning range across culture, biology and cognition providing a multitude of theoretical perspectives drawing on different methodological traditions and bringing different educational phenomena into focus (Bell 2004). The scientific literature on cognition, learning, development, culture and the brain are voluminous (Bransford, Brown et al. 2000). Education, like other branches of the social sciences, has no single, unifying mature theory, instead theories, ideas and approaches coexist in various states of cohesion and tension (Dillon and Ahlberg 2006). There are many schools of thought on learning, and no one school is used exclusively to design e-learning (Ally 2004).

Given the diversity of perspectives, methodologies and schools of research associated with a variety of perspectives of learning, it is beyond the scope of this thesis to give a complete accounting of the research around learning. Instead the aim of this section is to establish the diversity, complexity, uncertainty and contradictions inherent in this research as it applies to the practice of e-learning within universities. This starts with a description the four levels of learning “theory” before a brief discussion of technology and learning theory.

The four levels of learning “theory”

Given the diversity of disciplinary and theoretical perspectives related to learning even defining learning and learning theory can be difficult. Definitions of learning differ based on approach and intended purpose and often reveal more about the perspective from which the person offering the definition sees learning (Siemens 2006). Definitions of what a learning theory is will likely differ between psychologists, computer scientists, instructional designers and other disciplines. However, it is possible to extract from literature such as Ertmer and Newby (1993) four levels or perspectives on learning “theory” or research. These four levels are: epistemology, descriptive theories from science, learning theories, and instructional design theories. Each of these four levels is briefly examined below with particular emphasis on the diversity, complexity and ever-changing nature of views within each.

Epistemology is concerned with the nature of knowledge and how we come to know things (Driscoll 1994), what does it mean to know (Siemens 2006). Ertmer and Newby (1993) in examining the connection between epistemology and learning theory identify two fundamental perspectives of epistemology: empiricism – the view that experience is the primary source of knowledge – and rationalism – the view that knowledge derives from reason without the aid of the senses. Performing a similar task, Driscoll (2000) adds a third epistemological perspectives of nativism – the belief that knowledge is innate or present at birth. Pallas (2001) identifies the proliferation of epistemologies as one of the most confusing developments in educational research over the past quarter-century and goes on to list a “welter of names” – positivism, naturalism, postpositivism, empiricism, relativism, feminist standpoint epistemology, foundationalism, and postmodernism.

Descriptive theories from science arise from disciplines including, but not limited to, the various branches of psychology, neuroscience, and biology that seek to understand how various aspects of human learning function. Seidel, Perencevich and Allyson (2005) argue that psychology can provide descriptive laws that describe how cognitive development, learning, meta-cognition and other elements of learning actually occur. Driscoll (1994) illustrates the influence of disciplinary perspectives by illustrating how behavioural, cognitive and social psychologists develop different views of learning. The contribution of theories arising from science is not limited to learning theory. Goldman (1986) argues that an understanding of the architecture of the human mind-brain is essential for primary epistemology. He continues to argue that epistemology, the history of which has shown strong currents against being interdisciplinary, should be a multidisciplinary affair (Goldman 1986).

Learning theories seek to provide insight into the act of learning (Siemens 2006). A learning theory comprises a set of constructs linking observed changes in performance with what is though to bring about those changes (Driscoll 1994). Discussions of different learning theories (e.g. Ertmer and Newby 1993; Driscoll 1994) tend to focus on three distinct viewpoints: behaviourism, cognitivism and constructivism. These learning theories link closely to the three different perspectives of behavioural, cognitive and social psychologists mentioned in the previous paragraph. Historically it can be seen that, the cognitive perspective overthrew behaviourism in the 1950s and 1960s and also underwent a major shift in the 1980s and 1990s towards constructivism (Mayer 1996).

The on-going historical development of learning theories has not stopped. Mayer (1996) suggests that a fourth metaphor is likely with possibilities arising from either an assimilation and accommodation between the existing metaphors, or from an entirely new approach. One such entirely new approach may be provided by connectivism, a theory describing how learning happens in a digital age (Siemens 2005; Siemens 2006) based on the epistemological foundation of connective knowledge (Downes 2006). Table 2.3 provides a summary of the three existing broadly accepted learning theories and connectivism.

Table 2.3 – Learning theories (adapted from Siemens, 2006)
Property Behaviourism Cognitivism Constructivism Connectivism
How does learning occur? Black box—observable behaviour main focus Structured, computational Social, meaning created by each learner (personal) Distributed within a network, social, technologically enhanced, recognizing and interpreting patterns
Influencing factors Nature of reward, punishment, stimuli Existing schema, previous experiences Engagement, participation, social, cultural Diversity of network
What is the role of memory? Memory is the hardwiring of repeated experiences—where reward and punishment are most influential Encoding, storage, retrieval Prior knowledge remixed to current context Adaptive patterns, representative of current state, existing in networks
How does transfer occur? Stimulus, response Duplicating knowledge constructs of “knower” Socialization Connecting to (adding) nodes
Types of learning best explained Task-based learning Reasoning, clear objectives, problem solving Social, vague (“ill defined”) Complex learning, rapid changing core, diverse knowledge sources

Table 2.3 does not capture the full diversity of learning theories. Mayer (1996) describes the six versions of constructivism identified by Steffe and Gale (1995) as “social constructivism, radical constructivism, social constructionism, information-processing constructivism, cybernetic systems, and sociocultural approaches”. Further, when the three main theories are closely analysed it becomes apparent that there are many overlaps in the ideas and principles (Ally 2004). Classifications of learning theories and theorists are contradictory (Siemens 2006) and confusing due to the use of different labels for categories, the grouping of major models and theorists in different categories and the use of vague concepts. Identifying where within the basic learning paradigms a particular theorists fits can be confusing due to theorists and their ideas evolving over time and subsequent changes to their ideas (Sackney and Mergel 2007). Rather than three competing theories, these can be though of as a taxonomony of learning with behaviourism being used to teach the “what”, cognitivism the “how” and constructivism the “why” (Ertmer and Newby 1993).

Instructional design theories are prescriptive theories that offer explicit guidance on how to better help people learn and develop (Reigeluth 1999). The origins of formal instructional design procedures have been traced back to the development of military training materials during the Second World War (Reiser 2001). Interest grew strongly during the 1970s and 1980s with a large increase in the number of instructional design models (Reiser 2001). Perhaps forming the basis for Postman (1995) claiming that while educators were once famous for providing reasons for learning, they had now become famous for inventing a method. Leading to a situation where the initial impression of these theories is one of diversity, followed by being perplexed by so many theories being at odds with one another (Duchastel 1998).

As a source of further complication, Shulman (1986) introduces the idea of pedagogical content knowledge (PCK) that argues that treating a pedagogue’s content knowledge and the pedagogue’s knowledge of pedagogy as mutually exclusive domains resulted in teacher education that emphasised one over the other. PCK is the blending of knowledge about content and pedagogy into an understanding of how particular aspects of content knowledge are best organised, adapted and represented within instruction (Mishra and Koehler 2006). Treating this knowledge as separate is not sufficient for capturing the knowledge good teachers require (Shulman 1986). Numerous research studies such that no optimal pedagogy is effective regardless of the subject matter (Dede 2008).

The above has sought to illustrate that “learning theory” consists of theoretical perspectives from at least four different levels. Each of those levels is characterised by significant diversity and in some cases confusion. In addition, some of the levels impact upon other levels in unexpected ways. The next section briefly discusses what happens when technology is added to pedagogy.

Technology and learning theory

There exist many different conceptual frameworks for describing the relationships among learning theories, pedagogical strategies, instructional designs and information and communication technologies (Dede 2008). E-learning, in general, does not change the fundamental process of learning (Bates 2004). However, research into how people learn online is in its infancy and further research is needed to provide insight into how to develop engaging and effective online learning environments in higher education (Herrington, Reeves et al. 2005). Writing in 2004, Bates (2004) suggested that since the use of the web for learning and teaching is less than ten years old, its application of learning and teaching was still evolving.

What research had been done suggested that the three established learning theories (behaviourism, cognitivism and constructivism) all provide principles that can be used to design online instruction (Ally 2004). Any given pedagogical tool may incorporate perspectives from any of these three intellectual positions (Dede 2008). The actual applications of e-learning are highly dependent on the teacher’s epistemological preferences and their chosen pedagogy (Bates 2004).

In extending Shulman’s (1986) work on pedagogical content knowledge (PCK) into the concept of technological pedagogical content knowledge (TPACK), Mischa and Koehler (2006) argue that

there is no single technological solution that applies for every teacher, every course, or every view of teaching”. Quality teaching requires developing a nuanced understanding of the complex relationships between technology, content, and pedagogy, and using this understanding to develop appropriate, context-specific strategies and representations. Productive technology integration in teaching needs to consider all three issues not in isolation, but rather within the complex relationships in the system defined by the three key elements.

Dede (2008) makes a similar point that no application of technology to learning and teaching is universally good. Instead the best approach is to analyse the nature of the curriculum, students, and teachers in order to select the appropriate tools, applications, media and environments (Dede 2008).


Ally, M. (2004). Foundations of Educational Theory for Online Learning. Theory and Practice of Online Learning. T. Anderson and F. Elloumi. Athabasca, Canada, Athabasca University: 3-31.

Bates, T. (2004). The promise and myths of e-learning in post-secondary education. The Network Society: A Cross-cultural Perspective. M. Castells. Cheltenham, UK, Edward Elgar: 271-292.

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

Bransford, J., A. Brown, et al. (2000). How people learn: brain, mind, experience, and school. Washington, D.C., National Academy Press.

Dede, C. (2008). Theoretical perspectives influencing the use of information technology in teaching and learning. International Handbook of Information Technology in Primary and Secondary Education. J. Voogt and G. Knezek. New York, Springer: 43-59.

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.

Downes, S. (2006, 3rd October 2009). "Learning networks and connective knowledge." Instructional Technology Forum, from

Driscoll, M. (1994). Psychology of learning for instruction. Needham Heights, MA, Allyn & Bacon.

Driscoll, M. (2000). Psychology of learning for instruction. Needham Heights, MA, Allyn & Bacon.

Duchastel, P. (1998). "Prolegomena to a theory of instructional design."   Retrieved 4 October, 2009, from

Ertmer, P. and T. Newby (1993). "Behaviorism, Cognitivism, Constructivism: Comparing critical features from an instructional design perspective." Performance Improvement Quarterly 6(4): 50-72.

Goldman, A. (1986). Epistemology and cognition. Cambridge, MA, harvard University Press.

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

Mayer, R. (1996). "Learners as information processors: Legacies and limitations of educational psychology’s second metaphor." Educational Psychologist 31(3/4): 151-162.

Mishra, P. and M. Koehler (2006). "Technological pedagogical content knowledge: A framework for teacher knowledge." Teachers College Record 108(6): 1017-1054.

Pallas, A. (2001). "Preparing education doctoral students for epistemological diversity." Educational Researcher 30(5): 6-11.

Postman, N. (1995). The end of education. New York, Vintage Books.

Reigeluth, C. (1999). Instructional-design theories and models: A new paradigm of instructional theory. Mahwah, NJ, USA, Lawrence Erlbaum Associates.

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.

Sackney, L. and B. Mergel (2007). Contemporary learning theories, instructional design and leadership. Intelligent leadership: constructs for thinking education leaders. J. Burger, C. Webber and P. Klinck. New York, Springer: 67-98.

Seidel, R., K. Perencevich, et al. (2005). From principles of learning to strategies for instruction: empirically based ingredients to guide instructional development. New York, Springer.

Shulman, L. S. (1986). "Those who understand: Knowledge growth in teaching." Educational Researcher 15(2): 4-14.

Siemens, G. (2005). "Connectivism: A learning theory for the digital age." International Journal of Instructional Technology and Distance Learning 2(1).

Siemens, G. (2006, November 12, 2006). "Connectivism: Learning theory or pasttime for the self-amused."   Retrieved 9 September, 2009, from

Siemens, G. (2006). Knowing Knowledge,

Steffe, L. and J. Gale (1995). Constructivism in education. Mawah, NJ, Lawrence Erlbaum Associates.

Dede's "sleeping, eating and bonding" metaphor and the diversity of learning and its impacts for e-learning

Earlier this year I posted on Disruption and the “mythic” technologies of education and my views about consistency and diversity when applied to learning, especially e-learning within universities.

That post was sparked by a presentation by Gardner Campbell. Of the many things I found striking was the video of Chris Dede using “eating, sleeping and bonding” as a framework to understand the diversity inherent in learning.

As it happens, I’m currently working on the “pedagogy” component of my thesis. In particular, I’m working on the section I’m calling “Learning theories, research and advice for pedagogues”. A key point I’m looking to make is that diversity is inherent in learning. Hence the connection to Dede’s metaphor/framework.

The main driver for this post is that I’ve found a publication (Dede, 2008) in which Dede writes about the metaphor/framework and expands beyond the bit I heard in the video. I know it has its issues, but you have to love Google Books, without it I would not have found this book chapter. Nor could I link you to the page on which the metaphor/framework is discussed (it starts under the heading “Reconceptualizing media as empowering diversity in learning”).

The following are some other quotes from the book chapter that I found useful for my purposes

from an instrumental perspective, the history of tool making shows that the best strategy is to have simultaneously available a variety of specialized tools, rather than a single device that attempts to accomplish everything…

No educational ICT is universally good; and the best way to invest in instructional technologies is an instrumental approach that analyzes the natures of the curriculum, students, and teachers to select the appropriate tools, applications, media and environments..

To progress, the field of instructional design must recognize that learning is a human activity quite diverse in its manifestations from person to person, and even from day to day. The emphasis can then shift to developing pedagogical media that provide many alternative ways of teaching, which learners select as they engage in their educational experiences


Dede, C. (2008). Theoretical perspectives influencing the use of information technology in teaching and learning. International Handbook of Information Technology in Primary and Secondary Education. J. Voogt and G. Knezek. New York, Springer: 43-59.

PhD Update #24: off to the crocodile form

Another early update today – off to the Crocodile Farm as an excursion with the boys so today’s a write off. However, progress has been good and the end is nigh for chapter 2 – at least in first draft.

What I’ve done

The aim for this week was to complete the pedagogy component. The component will have three sections: The centrality of the pedagogue (done); Learning theories, research and advice for pedagogues (about half done); and Lessons from Pedagogy for e-learning (most of the ideas in place – these are generally quick to get done).

What I’ll do in the next week

Two main aims for next week:

  1. Complete the pedagogy section and get Chapter 2 all together.
  2. Get started on chapter 5.

Pedagogy – the centrality of the pedagogue and what they believe

The following is the first part of the Pedagogy component of the Ps Framework with forms part of Chapter 2 of my thesis. As with previous thesis posts this is a rough first draft of the content, feedback welcome. This is the first of three parts to this component. The next will say something about “learning theory” and the final will draw some lessons.


This thesis draws on the definition of e-learning as “the use of information and communications technology to enhance and/or support learning in tertiary education” (OECD 2005). Other sections of this thesis have covered the “information and communications technology” (Product insert cross ref) and “tertiary education” (Place insert cross ref) components of this definition. This section pays attention to the “learning” component. Since the purpose of this thesis is to formulate an information systems design theory for e-learning within universities this precludes from consideration some aspects of individual or informal learning. It suggests that the practice of e-learning will almost certainly involve some input from a teacher, hence the use of Pedagogy (not to mention it fits within the naming scheme of the “Ps Framework).

The importance of learning is summarised by the point made by Alavi and Leidner (2001)

Most would agree that the objective of using technology in learning should be to positively influence learning in one way or another; that is, the student should either learn something that he/she would not have learned without the technology or learn it in a more efficient manner.

However, the approach taken here does not start with a focus on the learner, instead, – in line with the use of Pedagogy – it assumes that within a university context the teacher remains a significant, perhaps the most significant, direct influence on student learning. Consequently, this section starts by justifying this perspective and describing its implications in The centrality of the pedagogue (Section 2.1.1). It then moves more generally to examine Learning theories, research and advice for pedagogues (Section 2.1.2) before drawing some Lessons from Pedagogy for e-learning (Section 2.1.3).

The centrality of the pedagogue and what they believe

Alavi and Leidner (2001), in discussing technology-mediated learning, suggest that it is important to conceptualize technology features and attributes in a manner directly relevant to instructional and learning processes. For quite sometime there has been a growing recognition that student-centered approaches to learning are the most effective. The learning theories of greatest current influence suggest that learning occurs through student’s active construction of knowledge supported by various perspectives within meaningful contexts with social interactions playing a critical role (Oliver 2000). It is a view that suggests the highest levels of student learning occur when the focus is on what the student does (Biggs 2001). The question then is why start with and focus on the teacher, the pedagogue, and what they believe? This section seeks to answer that question and connect the pedagogue with the other aspects of the Ps Framework.

While agreeing that the main aim of university learning and teaching, and e-learning in particular, should be a focus on improving student learning it is the nature of university courses that they are designed by pedagogues within a particular context. Trigwell (2001) – in developing a model to evaluate good teaching – argues that rather than separating learning, teaching, context and other aspects associated with university learning, all these aspects must be considered together and, in order for learning to be judged effective, they must be aligned. Figure 2.1 is a representation of Trigwell’s (2001) model of university teaching, it is intended as a set of concentric spheres. At the centre is the student and their learning, however, that learning is directly impacted upon by the strategies adopted by the teacher, which are in turn influenced by the other factors.

Trigwell's model of teaching

Figure 2.1 – Trigwell’s (2001) model of university teaching

Trigwell (2001) suggests that focusing more holistically on the combination of elements – especially on the teachers’ conceptions of teaching and a focus on students – makes the differences between teaching qualities more discernible and judgements easier. A focus on the strategies and technologies used by a teacher ignores the influence that their conceptions can have on how such strategies and technologies are used. Approaches to staff development that focus on the provision of prescribed skills and teaching recipes result, in many cases, in participants querying the feasibility of presented methods, defending methods they are already using, using new methods mechanically, or modifying methods intended to facilitate student learning into didactic transmission modes (Gibbs 1995; Trigwell 1995). A focus on strategies also ignores the likelihood that contextual factors also influence the appropriateness and implementation of strategies and techniques. Even a teacher with a student-centred conception of learning will adopt alternate strategies if the context is not appropriate.

Based on this argument, there is little value in examining the relative worth of various educational theories and pedagogical strategies without first having examined the context and the pedagogue’s thinking and planning. Various other sections of this chapter and other components of the Ps Framework (e.g. Place, Process, People and Product insert cross reference) have dealt with various aspects of the teaching and learning context. This section briefly repeats and expands on what is known about the thinking and planning of pedagogues within universities that was initially mentioned in the Past Experience section (insert cross reference). The following section (Section 2.1.2) examines what is known about learning and teaching strategies.

As outlined in the Past Experience section (insert cross reference) there is a significant body of literature that establishes the conceptions of learning and teaching held by academics and links those conceptions to the quality of student learning outcomes (Kember and Kwan 2000; Biggs 2001; Trigwell 2001; Norton, Richardson et al. 2005; Eley 2006; Gonzalez 2009). That literature generally places pedagogue conceptions into one of two main orientations: teacher-centered/content-oriented and student-centered/learning-oriented. Figure ?? shows a graphical representation of these orientations and five underlying conceptions identified by Kember (1997). As mentioned above, a student-centered/learning-oriented orientation is broadly agreed to contribute to better student learning outcomes.

There has been only a small amount of research on conceptions of and approaches to e-learning that allows understanding of this phenomenon (Gonzalez 2009). However, the level of reported work is increasing (Roberts 2001; Smyth, Mainka et al. 2007; Gonzalez 2009). Gonzalez (2009) in the most recent work and attempting to build on the work of Roberts (2003) identified three conceptions of e-learning: web for individual access and assessment, web for learning related communication and web for networked learning. Pedagogues with the first conception were found to have a content-centered orientation to learning and teaching while pedagogues with the other two conceptions of e-learning had or were moving towards a learning-centered conception of learning and teaching. Table 2.1 summarises the conceptions of e-learning identified by Gonzalez (2009) and describes the associated dimensions. Table 2.2 provides a description of approaches to e-learning that fit within the conceptions from Table 2.1 along a number of dimensions.

Table 2.1 – Dimensions delimiting conceptions of online teaching (adapted from Gonzalez 2009)
The web for individual access to learning materials and information; and for individual assessment The web for learning related communication (asynchronous and/or synchronous) The web as a medium for networked learning
Teacher Provides structured information/directs students to selected web sites Set up spaces for discussion/facilitates dialogue Set up spaces for communication, discussion and knowledge building/facilitates-guides the process
Students Individually study materials provided Participate in online discussions Share and build knowledge
Content Provided by lectuerer Provided by the lecturerer but students can modify – extend it through online discussions Built by students using the space set up by the lecturer
Knowledge Owned by lecturer Discovered by students within lecturer’s framework Built by students

The literature is also in general agreement that pedagogues generally teach the way they were taught (Dutton, Cheong et al. 2004). It has been suggested that in the absence of formal teaching qualifications, many university pedagogues teach in the didactic way that they were taught (Phillips 2005). Conceptions of teaching that are at the content end of the orientation spectrum. What’s more this predilection shapes the outcomes from the introduction of e-learning as educators see the technology as a means for carrying on doing what they have done before with more expensive technologies (Dutton, Cheong et al. 2004). In an effort to survive the difficulties of coping with the new introduced technology pedagogues can focus on content rather than the process of educating the student (Herrington, Reeves et al. 2005). Increasingly, organisational priorities can also negatively impact upon how pedagogues approach their teaching responsibilities with the consequence that students can sense the pedagogue’s distance from teaching (White 2006).

Table 2.2 – Dimensions delimiting approaches to online teaching (adapted from Gonzalez 2009)
Informative/individual learning focuses Communicative/Networked learning focused
Intensity of use Small range on media and tools used to support learnign tasks and activities (mainly sources of information with small opportunities for interaction and communication) Wide range of media and tools used to support learning tasks and activities (with emphasis on interaction and communication)
Resources Web pages with information. Lecture notes. Links to websites. Web pages with information. Lecture notes. Links to web sites. Discussion boards. Chat. Blogs. Spaces for sharing. Animations. Videos. Still images.
Role of the learner Select and present information Design spaces for sharing and communication. Support the process.
Role of the students Study individually information provided Participate in a process of knowledge building

Changing conceptions of learning and teaching

The relationship between conceptions of learning and teaching has implications for educational change (Tutty, Sheard et al. 2008). Change towards more sophisticated forms of teaching is only possible if the pedagogue’s conception of teaching are addressed first (Ho, Watkins et al. 2001). There is little evidence to show that pedagogue’s conceptions of teaching will develop with increasing teaching experience or from formal training (Richardson 2005). Pedagogue’s approaches to teaching change slowly, with some change coming after a sustained training process (Postareff, Lindblom-Ylanne et al. 1997). Given that it appears most university pedagogues hold content-centred conceptions of learning and teaching and that the majority of e-learning appears focused on distributing content, there appears to be a need to change the conceptions held by pedagogues.

Changing pedagogues’ conceptions of teaching, however, are a necessary but not sufficient condition for improved student learning. While pedagogue’s are likely to adopt teaching approaches that are consistent with their conceptions of teaching there may be differences between espoused theories and theories in use (Leveson 2004). While pedagogues may hold higher-level view of teaching other contextual factors may prevent use of those conceptions (Leveson 2004). Environmental, institutional, or other issues may impel pedagogues to teach in a way that is against their preferred approach (Samuelowicz and Bain 2001). While conceptions of teaching influence approaches to teaching, other factors such as institutional influence and the nature of students, curriculum and discipline may also influence teaching approaches (Kember and Kwan 2000). Prosser and Trigwell (1997) found that pedagogue’s with a student-focused approach were more likely to report that their departments valued teaching, that their class sizes were not too large, and that they had control over what was taught and how it was taught. Other contextual factors that frustrate pedagogues’ intended approaches to teaching may include senior staff with traditional teacher-focused conceptions raising issues about standards and curriculum coverage and students who induce teachers to adopt a more didactic approach (Richardson 2005). In addition, teachers who experience different contexts may adopt different approaches to teaching in those different contexts (Lindblom-Ylanne, Trigwell et al. 2006).

Efforts to improve teaching have often failed because the complexity of teaching has been underestimated and such attempts should consider the integrated system of relationships that constitute the teaching experience as a whole (Leveson 2004). One such important complicating influence are differences that have found differences between discipline areas (Lindblom-Ylanne, Trigwell et al. 2006), which suggest a need to understand teaching from both a general and discipline-specific perspective (Leveson 2004). Beliefs about teaching vary markedly across different disciplines and these variations are related to the pedagogue’s beliefs about the naure of the discipline they are teaching (Richardson 2005).

There is a lack of empirical evidence that development in conceptions of teaching will result in prompt improvement in teaching practice (Ho, Watkins et al. 2001). There is at least one alternate model (Guskey 1986; Guskey 2002) of teacher change that suggest it is the experience of successful implementation that changes the attitudes and beliefs of pedagogues. Pedagogues believe change will work because they have seen it work and this experience is what changes their conceptions of teaching and learning (Guskey 2002). Existing research informs us of the static relationship between existing conceptions and teaching practice, but has limited findings in terms of the dynamics of the way changes in teaching conceptions are transferred to changes in teaching practice and at what rate (Ho, Watkins et al. 2001).

The way e-learning is adopted in tertiary education is most likely explained by the pedagogues’ approaches to teaching, in general, which are often the result of their conceptions about teaching and learning (Elgort 2005). As above, institutional factors play a mediating role. In examining conceptions of e-learning held by academic staff Gonzalez (2009) that institutional factors and the nature of the students were the most relevant contextual factors influencing teaching. Rhetorical claims espousing e-learning seek to appeal to a pedagogues’ vision with an emphasis on innovation at the expense of reflection on pedagogues’ thinking and practices (Convery 2009). The unrealistic expectations of e-learning inhibit pragmatic attempts by pedagogues to integrate technology into classroom contexts and contribute to pedagogues being blamed for the failure of technology to fulfill its promise (Convery 2009).


Alavi, M. and D. E. Leidner (2001). "Research commentary: technology-mediated learning – a call for greater depth and breadth of research." Information Systems Research 12(1): 1-10.

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

Convery, A. (2009). "The pedagogy of the impressed: how teachers become victims of technology vision." Teachers and Teaching 15(1): 25-41.

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.

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.

Gibbs, G. (1995). Changing lecturer’s conceptions of teaching and learning through action research. Directions in Staff Development. A. Brew. Buckingham, SRHE and Open University Press.

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

Guskey, T. (1986). "Staff development and the process of teacher change." Educational Researcher 15(5): 5-12.

Guskey, T. (2002). "Professional development and teacher change." Teachers and Teaching: theory and practice 8(3/4): 381-391.

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

Ho, A., D. Watkins, et al. (2001). "The conceptual change approach to improving teaching and learning: An evaluation of a Hong Kong staff development programme." Higher Education 42(2): 143-169.

Kember, D. (1997). "A reconceptualisation of the research into university academics’ conceptions of teaching." Learning and Instruction 7(3): 255-275.

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.

Leveson, L. (2004). "Encouraging better learning through better teaching: a study of approaches to teaching in accounting." Accounting Education 13(4): 529-549.

Lindblom-Ylanne, S., K. Trigwell, et al. (2006). "How approaches to teaching are affected by discipline and teaching context." Studies in Higher Education 31(3): 285-298.

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

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

Lessons from product for e-learning

This post contains the last section of the “Product” component of chapter 2 of the my thesis, at least a rough first draft version of it. This is getting to the crux of my argument and problem with how most universities implement e-learning (adoption of an LMS) and it refers back to many of the other components of the Ps Framework. This will eventually become part of my EDUCAUSE’09 presentation and I’m thinking of re-working the following from thesis speak into something a little more leading.

Since this post relies on many of the other sections of the thesis (including one that isn’t written yet) and that the way I’ve been blogging these sections means that there is probably no easy way for you to connect all the dots. Going to the thesis page is probably the easiest, as all the posts should be linked from there.

Lessons from product for e-learning

Based on the above examination of the Product component of the Ps Framework for e-learning this section draws two lessons for e-learning: that the outcome of technology implementation is emergent and unpredictable and that the LMS model of e-learning is inappropriate.

The outcome of technology implementation is emergent and unpredictable

The impact and outcomes of the implementation of technology within a social system like a university cannot be predicted. The nature of the technology will interact with the people, processes and requirements of the organisation in complex and unpredictable ways. In ways that only become obvious after the fact and are not likely to repeat.

The LMS model appears to be inappropriate for e-learning

The selection and implementation of a learning management system embodies (at least) two standard assumptions: the product model and the procurement strategy. The product model of an LMS is an integrated, enterprise system sourced from a single vendor. The procurement strategy, using the three efficient procurement strategies proposed by Saarinen and Vepsalainen (1994), is that of package acquisition. Both of these models are inherently inflexible. This creates a problem in that the nature of e-learning within universities is such that it needs high levels of flexibility.

Best practice advice for the implementation of integrated, enterprise systems is to implement vanilla, to implement the system as provided (Robey, Ross et al. 2002; Pozzebon, Titah et al. 2006; Wagner, Scott et al. 2006). This advice recommends that it is cheaper to modify the organisation to fit the capabilities of the enterprise system (Gosain 2004; Strong and Volkoff 2004). The modification of the system to meet organisational needs, either initially or in response to lessons and changes in context, is deemed to be too expensive. The enterprise systems themselves are often designed in a way to make such modification or integration with other external systems difficult. The package acquisition procurement strategy is most appropriate for routine systems (Saarinen and Vepsalainen 1994). Routine systems are those where requirements very stable, they do not change, and there is high certainty that they can be identified correctly.

Based on the examination of the Ps Framework in this chapter it is suggested that a key requirement for e-learning within universities is flexibility. Table 2.4 presents a summary of the lessons from each of the components of the Ps Framework and suggests a connection between these and requirements for flexibility (the table uses a simple scale of high, low or none to indicate requirements for flexibility). For example, the lesson that a university represents a type of complex adaptive system (insert cross ref) in which on-going change is a traditional and increasing suggests a need for a high level of flexibility. Table 2.4 suggests that 4 of the 7 Ps components suggest a requirement for high flexibility and the remaining three suggest a requirement for low to high flexibility. This suggests that e-learning within universities requires significant levels of flexibility.

Table 2.4 – How flexible does an e-learning system need to be: Lessons from the Ps Framework
Ps Component Lessons
Requirement for flexibility
Place insert cross ref Change is traditional, inherent and necessary
Inconsistent requirements, tensions and paradox
It is complex
People People mean variety
Academic staff aren’t prepared or rewarded for teaching
Most students, academic staff and people are conservative
People mean agency
People are central
Process Assumptions of teleological processes appear not to hold
Process must be aware of and match the context
Revolutionary change and its relationship with teleological and ateleological design
There appears to be a need for both teleological and ateleological
Purpose Problems with a singular view of purpose
Problems with purpose proxies
low to high
Past experience Consisting in change
Retentiveness – or lack therof
The technology-mediated learning hype cycle – perpetual infancy
Pedagogy There is no one learning theory
Most academics don’t use any
low to high
Product The outcome of technology is emergent low to high

There is significant literature suggesting that there should be a fit between organisational requirements and its information technology. Weak fit promotes the existence of risk-related behaviours in organizations (Hogarth and Dawson 2008). The recognition that the assumptions within an LMS provide little or no flexibility and that the components of the Ps Framework for e-learning within universities suggest a requirement for significant flexibility suggests a weak fit between organisational requirements for e-learning and the predominant form of information technology used to fulfil those requirements.


Gosain, S. (2004). "Enterprise Information Systems as objects and carriers of institutional forces: the new iron cage." Journal of the Association for Information Systems 5(4): 151-182.

Hogarth, K. and D. Dawson (2008). "Implementing e-learning in organisations: What e-learning research can learn from instructional technology (IT) and organisational studies (OS) innovation studies." International Journal on E-Learning 7(1): 87-105.

Pozzebon, M., R. Titah, et al. (2006). "Combining social shaping of technology and communicative action theory for understanding rhetorical closuer in IT." Information Technology & People 19(3): 244-271.

Robey, D., W. Ross, et al. (2002). "Learning to implement enterprise systems: An exploratory study of the dialectics of change." Journal of Management Information Systems 19(1): 17-46.

Saarinen, T. and A. Vepsalainen (1994). "Procurement strategies for information systems." Journal of Management Information Systems 11(2): 187-208.

Strong, D. and O. Volkoff (2004). "A roadmap for enterprise system implementation." IEEE Computer 37(6): 22-29.

Wagner, E., S. Scott, et al. (2006). "The creation of ‘best practice’ software: Myth, reality and ethics." Information and Organization 16(3): 251-275.

Two calves

Product models – LMS, BoB and alternatives

The following completes the “alternate models” section of the Product component started in a previous post. It’s a bit rough and ready, but hopefully good enough.

Product models

The ERP market was one of the fastest growing and most profitable areas of the software industry during the last three years of the 1990s (Sprott 2000) and has tended to dominate the IT field (Light, Holland et al. 2001). It was at this same time – the late 1990s – that the availability of commercial LMS and their use within universities became increasingly prevalent. Perhaps then, it is not surprising that in terms of the underlying product model an LMS appears to be very close to that of a single-vendor Enterprise Resource Planning (ERP) system. In both cases, all the required functionality is provided in one, integrated package sourced from a single provider. In comparing the literature it is possible to see significant commonality between the advantages and disadvantages of an LMS and that of ERP system. The aim of this section is not to repeat the advantages and disadvantages of LMS – covered somewhat in the “LMS characteristics and limitations” section in Section 2.1.2 – or ERPs – covered in more detail in the relevant literature (Kallinikos 2004; Light 2005). It is instead to establish the existence of other potential product models and compare these with the ERP model. In addition, towards the end of this section the additional complicating and recent factor of user-owned technology is raised.

There are two approaches to the design of an LMS (Weller, Pegler et al. 2005):

  1. monolithic or integrated approach; and
    All common tools are provided by the one software package provided and supported by the one vendor. The predominant approach.
  2. best of breed approach.
    An alternative approach also termed a component or hybrid architecture. Aims to provide the same level of integration but the ability to select components that best suit the local context.

The same two approaches can be identified in the broader provision of enterprise information systems. It is possible to identify a reasonable spread of literature (Dewan, Seidmann et al. 1995; Geishecker 1999; Light, Holland et al. 2001; Hyvonen 2003; MacKinnon, Grant et al. 2008; Burke, Yu et al. 2009) examining various questions arising out of the difference between a monolithic ERP product model and the best of breed (BoB) model. This may not be all that surprising as such discussions have been billed as the “long-running debate” with the pendulum swinging from one view to the other and back again (Geishecker 1999). It is a debate that is encompassed be an even longer standing debate over the centralisation of decentralisation of computing, its focus on efficiency versus effectiveness and the supposed rational attempts at optimising the trade-off (King 1983). A debate that appears unresolvable due to the actual driving issues in the debate being the politics of organisation and resources and especially the apparently central issue of control (King 1983).

ERP adoption involves a centralised organisation of processes and a tendency to reduce autonomy and increase rigidity (Lowe and Locke 2008). Centralisation of control preserves top management prerogatives in most decisions, whereas decentralisation allows lower level managers discretion in choosing among options (King 1983). A BoB approach allows each department to select its own solution (Dewan, Seidmann et al. 1995). Light, Holland and Wills (2001) perform a comparative analysis of the ERP (monolithic or integrated) and best of breed (BoB) approaches to enterprise information systems and is summarised in Table 2.3.

Table 2.3 – Comparison of major differences between ERP and BoB (adapted from Light, Holland et al. 2001)
Best of breed Single vendor ERP
Organisation requirements and accommodations determine functionality The vendor of the ERP system determines functionality

A context sympathetic approach to BPR is taken A clean slate approach to BPR is taken
Good flexibility in process re-design due to a variety in component availability Limited flexibility in process re-design, as only one business process map is available as a starting point
Reliance on numerous vendors distributes risk as provision is made to accommodate change Reliance on one vendor may increase risk
The IT department may require multiple skills sets due to the presence of applications, and possibly platforms, from different sources A single skills set is required by the IT department as applications and platforms are common
Detrimental impact of IT on competitiveness can be dealt with, as individualism is possible through the use of unique combinations of packages and custom components Single vendor approaches are common and result in common business process maps throughout industries. Distinctive capabilities may be impacted on
The need for flexibility and competitiveness is acknowledged at the beginning of the implementation. Best in class applications aim to ensure quality Flexibility and competitiveness may be constrained due to the absence or tardiness of upgrades and the quality of these when they arrive
Integration of applications is time consuming and needs to be managed when changes are made to components Integration of applications is pre-coded into the system and is maintained via upgrades

Even in 1983, over twenty-five years ago, it was recognized that the terrain in which to decide between centralized and decentralized computing was continually changing (King 1983). This change is driven in no small part by the changing nature of technology from main-frames to personal computers to managed operating environments. Similarly, the smaller discussion between ERP and BoB has also been influenced by changes in technology. In the early to mid-1980s, the mainframe-dominant market automatically defaulted to an integrated ERP approach (Geishecker 1999). Most recently integration technologies like web services and service-oriented architectures (SOA) are seen to be enabling the adoption of BoB approaches (Chen, Chen et al. 2003). Such approaches are having an impact within the LMS field with attempts at implement a BoB LMS being enabled by the development of service-oriented architectures such as that be JISC (Weller, Pegler et al. 2005). Such an approach may allow a more post-industrial approach to the LMS allowing the taking of parts that are needed, when they are needed and granting control where it is needed (Dron 2006). Bailetti et al (2005) report on an early system that uses web services to implement a BoB approach.

In general, however, discussion about and comparison between ERP and BoB approaches to enterpise systems suffer the same limitation as the discussion of procurement strategies in the previous section. They are still based on the assumption that it is the responsibility of the institution, and its information technology department, to select, own and maintain all of the information systems required by users. Web 2.0, e-learning 2.0 (Downes 2005) and the rise of social software requires that organization of e-learning moves beyond centralized and integrated LMS and towards a variety of separate tools which are used and managed by the students in relation to their self-governed work. (Dalsgaard 2006). Stiles (2007) argues that in the future organizational needs will be best met by a BoB approach, however student initiated processes will be done using their choice of tools and services. An approach that provides students with a tool-box of loosely joined small pieces (Ryberg 2008).


Bailetti, T., M. Weiss, et al. (2005). An open platform for customized learning environments. International Conference on Management of Technology (IAMOT).

Burke, D., F. Yu, et al. (2009). "Best of Breed Strategies: Hospital characteristics associated with organizational HIT strategy." Journal of Healthcare Information Management 23(2): 46-51.

Chen, M., A. Chen, et al. (2003). "The implications and impacts of web services to electronic commerce research and practices." Journal of Electronic Commerce Reseaerch 4(4): 128-139.

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

Dewan, R., A. Seidmann, et al. (1995). Strategic choices in IS infrastructure: Corporate standards versus "Best of Breed" Systems. ICIS’1995.

Downes, S. (2005). "E-learning 2.0." eLearn 2005(10).

Dron, J. (2006). Any color you like, as long as it’s Blackboard. World Conference on E-Learning in Corporate, Government, Healthcare and Higher Education, Honolulu, Hawaii, USA, AACE.

Geishecker, L. (1999). "ERP vs. best-of-breed." Strategic Finance 80(9): 62-67.

Hyvonen, T. (2003). "Management accounting and information systems: ERP versus BoB." European Accounting Review 12(1): 155-173.

Kallinikos, J. (2004). "Deconstructing information packages: Organizational and behavioural implications of ERP systems." Information Technology & People 17(1): 8-30.

King, J. L. (1983). "Centalized versus decentralized computing: organizational considerations and management options." ACM Computing Surveys 15(4): 319-349.

Light, B. (2005). "Potential pitfalls in packaged software adoption." Communications of the ACM 48(5): 119-121.

Light, B., C. Holland, et al. (2001). "ERP and best of breed: a comparative analysis." Business Process Management Journal 7(3): 216-224.

Lowe, A. and J. Locke (2008). "Enterprise resource planning and the post bureaucratic organization." Information Technology & People 21(4): 375-400.

MacKinnon, W., G. Grant, et al. (2008). Enterprise information systems and strategic flexibility. 41st Annual Hawaii International Conference on System Sciences, Waikoloa, Hawaii.

Ryberg, T. (2008). Challenges and potentials for institutional and technological infrastructures in adopting social media. 6th International Confernece on Networked Learning, Halkidiki, Greece.

Sprott, D. (2000). "Componentizing the enterprise application packages." Communications of the ACM 43(4): 63-69.

Stiles, M. (2007). "Death of the VLE? A challenge to a new orthodoxy." Serials 20(1): 31-36.

Weller, M., C. Pegler, et al. (2005). "Students’ experience of component versus integrated virtual learning environments." Journal of Computer Assisted Learning 21(4): 253-259.

Procurement and software: alternate models for e-learning

And here’s the next bit of the Products component for chapter 2 of my thesis. The aim of this section is basically two argue that the LMS approach to e-learning embodies one view of how to procure software and one software model. I eventually aim to argue that both of these predominant models are essentially bad matches for the nature of e-learning within a university. The following is intended more to identify that there are alternatives than argue for the inappropriateness. That’s for later. But I doubt I’ve stopped it coming through.

This section focuses on procurement, I hope to have the product section up later today.

Procurement and software: alternate models for e-learning

As has been noted previously, within higher education the selection and purchase of an LMS has become the almost ubiquitous and unquestioned technical solution to the provision of e-learning. This singular approach can be said to embody a single approach to the procurement of software – “buy” – and a standard software model – the integrated, enterprise system. This section is based on the assumption that there are alternatives to both these models. There are different approaches to software procurement and different software product models that may be more appropriate for e-learning within universities, especially in light of recent changes within the broader information technology market place.

Procurement strategies for information systems

There is recognition that the choice of IS procurement strategy is critical for company operations and that different kinds of systems, require different kinds of resources and consequently different procurement strategies are applicable (Hallikainen and Chen 2005). Alignment between information technology and business is seen by scholars as an important principle for the success of IT deployment and implementation (Beukers, Versendaal et al. 2006). Saarinen and Vepsalainen (1994) propose the Procurement Principle as a prescriptive model for information systems investments. The principle is based on the assumption that optimal decisions about procurement are made when there is alignment between three choices: what type of system, what procurement strategy, and what type of organisational requirements (Wild and Sobernig 2007).

The Procurement Principle is based on transaction cost economics and draws on two inherent factors – specificity of system design and uncertainty of requirements – to develop three generic types of organisational requirements (Saarinen and Vepsalainen 1994):

  1. routine;
    Common to many or most organizations with stable requirements and low uncertainty.
  2. standard; and
    Common to a group of organizations, possibly within a given domain (Wild and Sobernig 2007), with some variety and uncertainty in requirements.
  3. speculative.
    Highly specific to one company and involve high uncertainty in terms of functionality, user interfaces and the competitiveness of the organisation.

In terms of the two inherent factors – specificity of design and requirements uncertainty – the above generic types represent systems on the diagonal. Saarinen and Vesalainen (1994) recognise other types of systems exist, suggest that they may be difficult to deal with and recommend solutions that modify requirements to fit with the three identified types or postponed.

Saarinen and Vesalainen (1994) identify generic types of developers that fit with these procurement strategies. The three types are:

  1. implementers;
    Employed by an external software development company these developers of high levels of product specific knowledge but only limited, common knowledge about the user organisation.
  2. analysts; and
    Commissioned by the client these staff are responsible for specifying user requirements and improving system solutions by drawing on their abilitiy to solve generic problems and specify complex integrated systems.
  3. innovators.
    Usually employed by the user organisation these developers have specialised knowledge about the user organisation, its users and information systems. They can communicate easily with the users and can specify and create new innovative solutions.

The appropriate matching of the type of requirements and the types of developer is now used to identify three efficient and generic procurement strategies. In large projects, the above three generic strategies will have to be combined and redefined in practice (Saarinen and Vepsalainen 1994). The three generic strategies are (Saarinen and Vepsalainen 1994):

  1. Routine systems can be best implemented by acquiring software packages from implementers.
  2. Standard applications require software contracting by analysts and possibly other outside resources for implementation.
  3. Speculative investments are best left for internal development by innovators.

These three generic strategies correspond to the three major approaches to information systems development: software product purchase, contractual customized development with outside vendors, and in-house development (Heiskanen, Newman et al. 2000). The selection and implementation of an LMS within a university represents software product purchase with some limited integration work. There is increasingly an absence of institutions adopting other approaches, either individually or in combination.

The over-emphasis on the software product purchase approach contributes to an increased in a techno-centric view. Due to the cost involved in modifying a complex software package most commercial systems require the institution to modify its practices to accommodate the system (Dodds 2007). So, rather than using IT to foster a culture of innovation by taking the point of view of the individual (Dodds 2007), or even the organisation, the focus is on the technology and its capabilities. As early as 1982 an alternate evolutionary approach, which appears much closer to in-house development, was recommended by Kerr and Hiltz (1982) for computer-mediated communication and found to be common with interactive systems which provide cognitive support. Kerr and Hiltz (1982) suggested that because the technology was so new, the possibilities for alternative functions and capabilities so numerous, and that users could not adequately understand what they might do with a new technology until they had an opportunity to experience it that an approach of feedback, evaluation and incremental implementation of new features was desirable.

The reasons identified by Kerr and Hiltz (1982) seem to fit two (requirements identity and requirements volatility) of the three categories of risks associated with requirements development identified by Tuunanen et al (2007) and shown in Table 2.2. If this observation remains appropriate for current practices around e-learning it would appear to question the alignment between the LMS procurement approach and the types of requirements that would make that approach the most efficient as identified by the Procurement Principle.

Table 2.2 – Requirements development risks (adapted from Tuunanen, Rossi et al. 2007)
Risks Definition
Requirements identity The availability of requirements; high identity risk indicates requirements are unknown or indistinguishable
Requirements volatility The stability of requirements; high volatility risk indicates requirements easily change as a result of environmental dynamics or individual learning
Requirements complexity The understandability of requirements; high complexity risk indicates requirements are difficult to understand, specify, and communicate

In addition, both the nature of the LMS and the procurement model assume that it is necessary that for the organisation to provide all of the components of the information system. In recent years the functionality and usability of technology available ot individuals has been outstripping that of technology provided centrally by institutions (Johnson and Liber 2008). Increasingly, university students and staff are using a collection of tools and systems they choose, rather than tools and systems selected, owned and maintained by the university (Jones 2008).


Beukers, M., J. Versendaal, et al. (2006). "The procurement alignment framework construction and application." Wirtschaftsinformatik 48(5): 323-330.

Hallikainen, P. and L. Chen (2005). "A holistic framework on information systems evaluation with a case analysis." The Electronic Journal Information Systems Evaluation 9(2): 57-64.

Johnson, M. and O. Liber (2008). "The Personal Learning Environment and the human condition: from theory to teaching practice." Interactive Learning Environments 16(1): 3-15.

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.

Saarinen, T. and A. Vepsalainen (1994). "Procurement strategies for information systems." Journal of Management Information Systems 11(2): 187-208.

Wild, F. and S. Sobernig (2007). Learning tools in higher education: Products, characteristics, procurement. Second Conference on Technology Enhanced Learning. Crete, Greece.

Learning Tools in Higher Education: Products, Characteristics, Procurement

Back to the PhD today, probably will do a couple of summaries of papers I’m reading. The focus is on the product models and procurement strategies used by Universities to solve the technical problem of e-learning. I start with a paper with the title “Learning Tools in Higher Education: Products, Characteristics, Procurement” (Wild and Sobernig, 2007)


Uses interviews of 100 European universities from 27 countries to identify the tools they use to facilitate learning, how intensively they are used and what procurement strategies are used.

Gives some rough figures of types of systems used. Gives a longitudinal feel to some previous studies.

Seems to indicate that European institutions seem to find it “very important to have an institutional platform run by the institutions them-
selves, however, with strong connections to the open-source world”.

I wonder if the results would be the same in the US or Australia where commercial LMS adoption has been more predominant – though changing somewhat.

The reporting of the findings are, to me at least, somewhat confusing.

The greatest value for me is pointing me to the literature (Saarinen et al, 1994; Heiskanen et al, 2000) that proposes an optimal relationship between types of requirements, types of system and types of procurement strategy. I’ll be using this in the PhD and potentially some papers.


Most unis using some sort of LMS. 250 commercial software providers,40 open source products – large and heterogenous products. Some evidence (Pituch and Lee, 2006) that functionality and interactivity drive usage.

What tools are being used today?

Products in the market

Participants report

  • 182 distinct tools occurred 290 times: LMS, content management, collaboration tools
  • Moodle most used – 44 instances, but only 15 of these not running in parallell with others.
  • WebCT – 14 installations.
  • 15 pure content management systems in 20 installations
  • 18 pure admin information systems – 19x.
  • 22 different authoring tols
  • 14 learning object repositories
  • 10 different assessment tools
  • 32 different collaboration tools with 51 installations
  • Most heavily used systems identified by highest active number of users – WebCT (twice), .LRN (once), CampusNet (once), Blackoard (once) and eLSe (once).

References a couple of other similar investigations of tools

Since one – five systems have vanished.

Portfolio characteristics

What activities did the tools support:

  • text-based communication – 87 (out of 100)
  • Assessments – 81
  • Quality assurance and evaluation – 53
  • Collaborative publishing – 52
  • Individual publishing – 44
  • social networking – 34
  • Authoring learning designs – 31
  • Audio/video conferencing – 31
  • Audio/video broadcasting – 25
  • User portfolio management – 23
  • simulations/online labs – 21

Text-oriented predominant. Multimedia lacking support

Following table compares reports of courses sites from two previous studies and this one – some issues in comparison.

Categories Paulsen (1999) Paulsen (2003) Wild and Sobernig (2007)
Up to 15 courses 68% 38% 22%
More than 15 25% 50% 56%

This study also found – 36% more than 100. 5% more than 1000.

Tool usage: 49/100 delivery and 54/100 course management.

Report on problems with calculating number of users because of varios difficulties.

Procurement strategies

Procurement decisions based on 3 types of requirements

  1. Speculative requirements – organisationaly unique or involve uncertainty.
  2. Standard requirements – common to organisations of a particular domain.
  3. Routine requirements – invariant across domain boundaries.

Literature suggest that in optimal cases, organisational choices are driven by these requirements. Suggests this choice represents a combination of

  • Software type – custom developed, packaged and off-the-shelf
  • Procurement strategy – in-house development (internal procurement), contracting and acquisition (both external procurement).

Same literature suggests an alignment between requirement types and organisational choices:

  • Predominantly speculative – internal development of custom software.
  • Standard requirements – customised, packaged software where customisation external contracted.
  • Routine requirements – off-the shelf software.

At this stage, the explanation of the findings from the survey are really hard to follow – at least for me. I would’ve though this should be easy. Keep that in mind when you read the following.

  • 40% follow procurement configurations considered optimal
  • 44% reported mixed configurations of requirements and procurement strategy
  • 5% report external procurement from external contractors
  • External procurement, when it does occur, predominantly with speculative requirements.
  • Internal development equally distributed across requirements – 21% speculative, 19% mixed, 18% standard
  • There are other percentages reported, but I can’t follow it and/or make sense of it with the ones I’ve summarised above


Heiskanen, A., M. Newman, et al. (2000). “The social dynamics of software development.” Accounting, Management & Information Technology 10(1): 1-32.

Paulsen, M. F.: Online Education. An International Analysis of Web-based Education and Strategic Recommendations for Decision Makers. NKI Forlaget, Bekkestua, Norway (2000)

Paulsen, M. F. (2003). “Experiences with Learning Management Systems in 113 European Institutions.” Educational Technology & Society 6(4): 134-148.

Pituch, K., and Lee, Y.: The influence of system characteristics on e-learning use. Computers & Education. 47(2) (2006) 222–244

Saarinen, T. and A. Vepsalainen (1994). “Procurement strategies for information systems.” Journal of Management Information Systems 11(2): 187-208.

Wild, F. and S. Sobernig (2007). Learning tools in higher education: Products, characteristics, procurement. Second Conference on Technology Enhanced Learning. Crete, Greece.

Learning requires willingness to suffer injury to one's self-esteem

Over recent weeks I have ignored Twitter, it was consuming too much time and I have to focus on writing the PhD. There is a cost involved to doing this, you miss out on some good insights.

Aside: The quality of the insights you gather from twitter are directly correlated with the quality of the people you follow. Listening to this podcast yesterday I heard the following description of the difference between Facebook and Twitter. Facebook is for the people you already know, Twitter is for those you don’t.

This morning I gave in and started up Nambu and have come across the following, very fitting quote

“Every act of conscious learning requires the willingness to suffer an injury to one’s self-esteem. That is why young children, before they are aware of their own self-importance learn so easily; and why older persons, especially if vain or important, cannot learn at all.” — Thomas Szasz, 1973

I plan to use this quote to argue that current approaches within universities – or at least those I’m familiar with – prevent learning.


I came across this quote via a tweet by Gardner Campbell pointing to the first lecture by Michael Wesch. The quote is the lead in to the lecture.

Thomas Szasz is a somewhat controversial figure, so perhaps not the perfect source for a quote. But the quote does capture what I see as a key aspect of learning – and one that I personally struggle with.

Learning means being wrong

Szasz suggests you have to be willing to suffer through injury to your self-esteem to learn. To get it wrong. This connects with many of the other insights, quotes and perspectives on learning that I’ve seen and discussed on the blog. I’m sure there are many more.

Additional support for this idea comes from confirmation bias, the Tolstoy syndrome and pattern entrainment and not to mention the Golden Hammer law and status quo adherence. All summed up nicely by a quote from Tolstoy

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.

In order to learn something new you have to be prepared to think anew, critically examine what you currently take for granted and hold it up to the light of new insights to see if it is found wanting. While learning something new, you will make mistakes. In fact, there are any number of quotes around innovation that posit the importance of failure

If you’re not failing every now and again, it’s a sign you’re not doing anything very innovative. — Woody Allen


The essential part of creativity is not being afraid to fail. — Edwin Land


Success is on the far side of failure. — Thomas Watson Sr

Fear of failure is embedded in academia

Jon Udell has argued that academia is heavily focused on not being seen to make mistakes. Researchers only release ideas that are fully baked, half-baked ideas are discouraged

As Gardner Campbell observes in this article

For an academic, “failure” is often synonymous with “looking stupid in front of someone.” For many faculty, and maybe for me back in the 1980s, computers mean the possibility of “pulling a Charlie Gordon,” as the narrator poignantly terms it in Daniel Keyes’s Flowers for Algernon.

Fear of failure is made worse by managerialism

For quite some time I have been arguing that teleological approaches to online learning – and I know expand that to broader styles of management – within higher education is ill-suited to the challenge (Jons, Luck, McConachie and Daner, 2005; Jones and Muldoon, 2007). Approaches to leadership and management that are driven by current over-emphasis on efficiency and accountability are based heavily on teleological assumptions and because of the mismatch end up damaging universities.

But worse, at least from the perspective of learning, such approaches to leadership – at least as often practiced – are hugely fearful of failure. They seek to avoid it as much as possible. The SNAFU principle is a humourous explanation of this tendency for authoritarian hierarchies to screw up.

Of course there is also much written in the management and organisational research about this tendency. This post covers a small sample of it and includes the following quote from Argyris and Schon (1978, p116)

In a Model 1 behavioral world, the discovery of uncorrectable errors is a source of personal and organisational vulnerability. The response to vulnerability is unilateral self-protection, which can take several forms. Uncorrectable errors, and the processes that lead to them, can be hidden, disguised, or denied (all of which we call ‘camouflage’); and individuals and groups can protect themselves further by sealing themselves off from blame, should camouflage fail.


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.

Jones, D. and N. Muldoon (2007). The teleological reason why ICTs limit choice for university learners and learning. ICT: Providing choices for learners and learning. Proceedings ASCILITE Singapore 2007, Singapore.

Other information systems in higher education

The following is the next short, and still somewhat questionable, section of the Product component. A previous post discussesd the limitations of an LMS, this section talks briefly about the other types of systems necessary for learning and teaching. The next section will talk about more abstract alternatives to those most commonly associated with the LMS.

Other systems

A university makes use of a large number of software applications partly because creating a single application to run a business as higher education is virtually impossible (Jones 2004). Universities have multiple constituencies – including parents, students, government, industry and alumni – and a need to maintain relationships with individuals that are now lifelong (Lightfoot and Ihrig 2002). Paulsen (2002) perceives e-learning to consist of a chain of four systems: content creation tools, learning management systems, student management systems, and accounting systems. The implication being that the LMS is only component of the information systems ecosystem of a university. Institutions now have applications for financial management, human resources, admissions, recruitment, payments, procurement, research databases, course management, online library reserves, classroom scheduling, patient records, grant and contracts management and email (Lightfoot and Ihrig 2002). Over recent times many institutions have moved to enterprise systems that integrate students, financial and human resource systems (Duderstadt, Atkins et al. 2002).

It has been suggested that contemporary learning environments should integrate academic and administrative support services directly into the students’ environment (Segrave and Holt 2003). All too often the systems are not interconnected and present the user with a fragmented view of the institution (Lightfoot and Ihrig 2002). There is a general lack of integration amongst these systems (Paulsen 2002). The development of robust, institutional, technical infrastructure has become a major area of activity (Conole 2002). Large scale enterprise systems, while useful to the administrative side of the university, can work at odds with the academic activities and force teaching and research to conform to business IT systems (Duderstadt, Atkins et al. 2002). Some attempts to increase the level of integration between academic and administrative systems has been done under the label of a managed learning environment (MLE).

While there remains some difficulty in defining the MLE as a concept there is agreement that the MLE involves a whole institution approach that links systems and facilities that are already provided across the institution (Holyfield 2003). A managed learning environment (MLE) will include administrative information about courses, resources, support and guidance, collaborative information, assessment and feedback – essentially linking up to back-end office systems and databases (Conole 2002). Beyond integration with administrative systems, to fully reap the benefits of an LMS, it has been suggested that institutions must integrate them with other systems including: identity directories, internal and external web sites, portals, library catalogs, multimedia and learning objects repositories, e-portfolios, email, calendar, instant messaging, wikis, blogs, web conferencencing, and other collaboration tools. (Molina and Ganjalizadeh 2006). It is hypothesized that institutions implementing integrated systems will improve their chances of becoming successful, large-scale e-learning providers (Paulsen 2002).

Discussion of the benefits of integration bring us back to some of the limitations of the LMS discussed above. Integration through the use of monolithic solutions like ERP systems increase complexity, offer limited flexibility and are not designed to collaborate with other autonomous applications (Irani 2002). Based on this view, the very nature of most LMS – as an example of a monolithic enterprise system – would appear somewhat less than well suited to integration within a MLE. The difficulty of integration and how alternative product models may provide different capabilities is part of the focus of the next section.


Conole, G. (2002). "The evolving landscape of learning technology." ALT-J 10(3): 4-18.

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.

Holyfield, S. (2003). Developing a shared understanding of the Managed Learning Environment – the role of diagramming and requirements gathering, JISC.

Irani, Z. (2002). "Critical evaluation and integration of information systems." Business Process Management Journal 8(4): 314-317.

Jones, D. (2004). "The conceptualisation of e-learning: Lessons and implications." Best practice in university learning and teaching: Learning from our Challenges.  Theme issue of Studies in Learning, Evaluation, Innovation and Development 1(1): 47-55.

Lightfoot, E. and W. Ihrig (2002). "Next-Generation Infrastructure." EDUCAUSE Review: 52-61.

Molina, P. and S. Ganjalizadeh. (2006). "Open Source Learning Management Systems."   Retrieved 28 December, 2006, from

Paulsen, M. F. (2002). "Online education systems in Scandinavian and Australian Universities: A Comparative Study." International Review of Research in Open and Distance Learning.

Segrave, S. and D. Holt (2003). "Contemporary learning environments: Designing e-Learning for education in the professions." Distance Education 24(1): 7-24.

LMS characteristics and limitations

This post follows on from previous posts and contributes the next bit of the Product component of my thesis.

Having given an overview of what an learning management system (LMS) is in the last post, this post looks at some of the characteristics and limitations of the LMS model. It’s not complete, but it’s a start.

LMS characteristics and limitations

The introduction of the LMS has started a new round in the struggle between the propensities of technology to define their own paths and academic’s appropriate desires to subordinate the technologies to the values and traditions of the academy (Katz 2003). As with any technology, LMS are not value neutral transmitters of facts but instead carry the values and priorities of their producers (Dutton and Loader 2002). While agreeing with the emergent perspective described by Markus and Robey (1988), the perspective that sees the uses and consequences of information technology emerge unpredictably from complex social interactions, that technology does not unambiguously determine outcomes. This section illustrated agreement with the view expressed by Kallinikos (2004) that systems can have profound effects on the structuring of work and the forms of human action they enable or constrain. This suggests that there exists some value in examining the characteristics and limitations of technical systems. Subsequently, this section draws on the literature around LMS to identify that characteristics and limitations of the LMS, and how those may enable or constrain learning and teaching.

Adoption of an enterprise LMS will require some standardisation of teaching and learning as all available functionality is provided by the system (Luck, Jones et al. 2004). An LMS, by its nature, is structured and has little capability for customisation (Morgan 2003). Current LMS are not customizable for instruction aimed at a specific audience with specific content. (Black, Beck et al. 2007). As two of the most highly personalised sets of processes within institutions of higher education, any attempt at standardising teaching and learning is likely to be radical, painful and problematic (Morgan 2003). The standardization inherent in an LMS exacerbates the pain of adoption by being standardized products designed to support a non-standard base of university academics with different disciplines, teaching philosophies and instructional styles (Black, Beck et al. 2007).

There is, however, value in the standardisation inherent within an LMS as it reduces institutional pain during the selection process (Black, Beck et al. 2007). The same standardisation built into an LMS helps organizations deal with support and training as there is a fixed set of functionality. The design of an LMS is more concerned with providing the organisation with the ability to produce and disseminate information by centralising and controlling services (Siemens 2006). The standardisation embedded in the design of an LMS can create a number of operational conditions that push teaching and learning in a particular direction (Luck, Jones et al. 2004), at the very least limiting possibilities to those supported by the LMS. Managerialism may be the easiest and most natural path for a centrally managed LMS to take (Dron 2006).

The LMS model with its nature as an integrated, enterprise system fits the long-term culture of institutional information technology and its primary concern with centralizing and controlling information technology services with a view to reducing costs (Beer and Jones 2008). An approach that increases tensions created by a long-term cultural divide within universities between the culture of administration – that values efficiency, principles of scientific management and standardized business processes – and the academic culture – more focused on tradition, erudition and innovation (Fernandez 2008). Management perceive information technology as a cost to be minimized while academics see it as a service to be customized for their idiosyncratic requirements (Jones 2004).

The design of an LMS embeds particular world views, for example, the Blackboard LMS – with its origins in the American higher education sector – embodies a a particularly American view with “course” as the standard organisational unit within the system (Dron 2006). Rather than a minor irritation, the inability to modify this assumption requires institutional practice to align with the system, rather than vice versa (Dron 2006). In addition, the course focus on most LMS make it difficult to support communities of students outside of the course structure or to involve non-course participants in online courses (Beer and Jones 2008).

In terms of support for pedagogy, there are views that LMS, in general, does not dictate either a discipline or a pedagogy (Katz 2003). However, there have been some designed with a pedagogical emphasis, generally constructivist, (Stiles 2007), though none have entered the mainstream. Many LMS embed traditional teaching paradigms into them through name, metaphor and user interface (Dutton, Cheong et al. 2004; Stiles 2007). Examples include the use of common terms such as blackboard and gradebook and the use of university buildings to structure the user interface (Dutton, Cheong et al. 2004; Dron 2006; Stiles 2007). While the use of familiar concepts make for a more intuitive interface (Stiles 2007), they can also lead to built-in constrains on the use of LMS (Dutton, Cheong et al. 2004). The very design of the LMS can encourage horseless carriage approaches to e-learning. Giving support for the observation that technology will most likely reinforce the old systems rather than the new paths (Lian 2000).

The values embedded in many common LMS reveal a residue that is clearly transmissive and adds to the banality, confusion and disapointment in the learning and teaching experiences (Sullivan and Czigler 2002; deFreitas and Oliver 2005; Salmon 2005). The tendency towards behaviourist approaches to learning – with an emphasis on parcelling up knowledge into bite-sized chunks – is one of the great weaknesses of the contemporary LMS (Weigel 2005). LMS are largely based on training-type models based on an overly simplistic understanding of the relationship between teachers, knowledge and student learning (Coates, James et al. 2005). A social constructivist approach to learning – with an emphasis on self-governed and problem-based activities – are not very well support by LMS (Dalsgaard 2006). The LMS assumption of a self-paced learner results in most LMS having limited interaction or collaboration tools such as simple chat rooms and discussion forums (Bonk 2002).

Most LMS support more or less the same pedagogy (Robson 1999). The nature of an integrated, enterprise system and its requirement for standardization means it is unlikely that a single LMS will support more than one instructional theory, if that. This would appear problematic given the significant diversity in instructional theories adopted within a single university – whether implicitly or explicitly acknowledged (West, Waddoups et al. 2006). LMS need to become more flexible and customizable in form and allow students and faculty to choose among pedagogies in their structure (Katz 2003) in enable adaptation of the tool to fit each unique situation (West, Waddoups et al. 2006).

The standard and pre-established boundary to learning within an LMS is a course (Weigel 2005). Access to the resources, activities and people associated with learning – and subsequently the learning itself – is restricted to those individuals associated with a particular offering of the course and further to the period when the course is offered (Beer and Jones 2008). Learners contribute to discussions that are closed and removed at the end of the course (Cameron and Anderson 2006). The model of many LMS implementations is equivalent to having students come on-campus blind-folded, taking them directly to their course-related activities, and not allowing them to see or speak to anyone not in their own course (Wise and Quealy 2006). Learning within an LMS is like “walled garden”, outside of the context of the learner’s everyday life, environment and informal learning (Mentis 2008). The focus on the course by LMS also places limits on management. For example, LMS provide only very limited functionality associated with reporting and usage monitoring at an institutional level across multiple courses (Morgan 2003).

The closed nature of many LMS go beyond restrictions on learning. Embracing a new LMS has high entry costs because there are few efficient migration tools (Molina and Ganjalizadeh 2006). Restrictions on migration of content, technical and financial factors can make it difficult for institutions to migrate between different systems (Coates, James et al. 2005). An on-going challenge to management is that observation that e-learning technologies are undergoing a continual process of change (Huynh, Umesh et al. 2003) and that any frozen definition of “best” technology is likely to be temporary (Haywood 2002). The high cost of changing systems can contribute to lock-in (Davis, Little et al. 2008).

LMS vendors are trying to position their systems as the center-point for e-learning (Siemens 2004). The assumption of an enterprise system is that it provides all of the necessary services in one integrated whole. There are, however, increasing perceptions that the LMS may be less significant within the an organisational online learning system (Davis, Little et al. 2008). The LMS may not, on its own, be sufficiently conducive to supporting the design, development and operations required within contemporary learning environments (Segrave and Holt 2003). This is a point expanded upon in the next section.


Beer, C. and D. Jones (2008). Learning networks: harnessing the power of online communities for discipline and lifelong learning. Lifelong Learning: reflecting on successes and framing futures. Keynote and refereed papers from the 5th International Lifelong Learning Conference, Rockhampton, Central Queensland 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.

Bonk, C. (2002). Collaborative tools for e-learning. Chief Learning Officer: 22-24, 26-27.

Cameron, D. and T. Anderson (2006). "Comparing Weblogs to Threaded Discussion Tools in Online Educational Contexts." International Journal of Instructional Technology and Distance Learning 2(11).

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.

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

Davis, A., P. Little, et al. (2008). Developing an infrastructure for online learning. Theory and Practice of Online Learning. T. Anderson. Athabasca, Canada, AU Press: 121-142.

deFreitas, S. and M. Oliver (2005). "Does e-learning policy drive change in higher education? A case study relating models of organisational change to e-learning implementation." Journal of Higher Education Policy and Management 27(1): 81-95.

Dron, J. (2006). Any color you like, as long as it’s Blackboard. World Conference on E-Learning in Corporate, Government, Healthcare and Higher Education, Honolulu, Hawaii, USA, AACE.

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.

Dutton, W. and B. Loader (2002). Introduction. Digital Academe: The New Media and Institutions of Higher Education and Learning. W. Dutton and B. Loader. London, Routledge: 1-32.

Fernandez, L. (2008). "An antidote for the Faculty-IT divide." EDCAUSE Quarterly 31(1): 7-9.

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.

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.

Jones, D. (2004). "The conceptualisation of e-learning: Lessons and implications." Best practice in university learning and teaching: Learning from our Challenges.  Theme issue of Studies in Learning, Evaluation, Innovation and Development 1(1): 47-55.

Kallinikos, J. (2004). "Deconstructing information packages: Organizational and behavioural implications of ERP systems." Information Technology & People 17(1): 8-30.

Katz, R. (2003). "Balancing Technology and Tradition: The Example of Course Management Systems." EDUCAUSE Review: 48-59.

Lian, A. (2000). "Knowledge transfer and technology in education: Toward a complete learning environment." Educational Technology & Society 3(3): 13-26.

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.

Markus, M. L. and D. Robey (1988). "Information technology and organizational change: causal structure in theory and research." Management Science 34(5): 583-598.

Mentis, M. (2008). "Navigating the e-Learning Terrain: Aligning Technology, Pedagogy and Context." Electronic Journal of e-Learning 6(3): 217-226.

Molina, P. and S. Ganjalizadeh. (2006). "Open Source Learning Management Systems."   Retrieved 28 December, 2006, from

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

Robson, R. (1999). WWW-based course-support systems: The first generation. WWW-Based Course-Support Systems Seminar, Seattle, Washington.

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.

Segrave, S. and D. Holt (2003). "Contemporary learning environments: Designing e-Learning for education in the professions." Distance Education 24(1): 7-24.

Siemens, G. (2004). "Learning Management Systems: The wrong place to start learning."   Retrieved January 12, 2007, from

Siemens, G. (2006). "Learning or Management System? A Review of Learning Management System Reviews." from

Stiles, M. (2007). "Death of the VLE? A challenge to a new orthodoxy." Serials 20(1): 31-36.

Sullivan, K. and P. Czigler (2002). "Maximising the educational affordances of a technology supported learning environment for introductory undergraduate phonetics." British Journal of Educational Technology 33(3): 333-343.

Weigel, V. (2005). "Course Management to Curricular Capabilities: A Capabilties Apporach for the Next-Generation Course Management System." EDUCAUSE Review 40(3): 54-67.

West, R., G. Waddoups, et al. (2006). "Understanding the experience of instructors as they adopt a course management system." Educational Technology Research and Development.

Wise, L. and J. Quealy. (2006, May, 2006). "LMS Governance Project Report." from

What is an LMS?

The following post is the next step in completing the Product component of chapter 2 of my thesis. An earlier post started off the Product component. This post is the first section in a broader section titled “University e-learning technology”. This post focuses on the learning management system (LMS).

The content of this first post is a rough first draft of a section of the thesis. Increasingly there are going to be insert crossref type measures reminding me to add cross references to other sections.

What is an LMS?

Learning management systems (LMS) are software systems that are specifically designed and marketed to educational institutions to support teaching and learning and that typically provide tools for communication, student assessment, presentation of study material and organisation of student activities (Luck, Jones et al. 2004). These systems are also referred to by a number of different terms including virtual learning environments (VLE), course management systems (CMS), learning support systems (LSS), and learning platforms (Mendoza, Perez et al. 2006). Currently widely use LMS include systems called: Blackboard, Angel, Moodle and Sakai. The speed with which the adoption of an LMS has spread through universities is surprising (West, Waddoups et al. 2006). A 2004 survey of universities found that 73% had adopted an institution-wide LMS, compared to 60% in 2002, with 90% expecting to make such a claim within five years (OECD 2005).

The core components of an LMS include tools for for synchronous and asynchronous communication, content storage and delivery, online quiz and survey tools, gradebooks, whiteboards, digital dropboxes, and email communications (Harrington, Gordon et al. 2004). There are more similarities than differences amongst LMS products, with most distinguishing themselves with micro-detailed features (Black, Beck et al. 2007). As mentioned in the Past Experience section (insert cross reference) and illustrated in Figure ?? (crossref) the commonality of LMS features have led Malikowski, Thompson and Theis (2007) to develop a model that abstracts LMS features into five categories: transmitting content, creating class interactions, evaluating students, evaluating course and instructions and computer-based instruction. Most LMS do not specify a discipline or pedagogy (Katz 2003).

The development of early LMS started primarily with internal development within universities. For example, WebCT one of the early dominant commercial LMS arose out of work at the University of British Columbia (Goldberg, Salari et al. 1996). However, due to the difficulty and costs of in-house development, during the late 1990s and early 2000s the majority of institutions moved to the adoption of commercial, proprietary LMS with Blackboard and WebCT dominating. A move illustrative of the shift of the LMS from being based on the bottoms-up energy of a small cadre of inventive faculty to being the embodiment of a top-down institutional strategy (Katz 2003). This shift marked the start of the industrial e-learning paradigm identified in the Past Experience section (insert cross ref).

The next cycle in LMS adoption was the rise of open source LMS. By 2005 several major universities were releasing their in-house LMS under open source rather than commercial licences (Coates, James et al. 2005). By 2006 there were two key trends in e-learning within the UK: an on-going preference for commercial systems and an emerging trend towards open source systems (Browne, Jekins et al. 2006). The significant number of available LMS may be an illustration of the novelty and relative immaturity of such systems, but it may also correspond to an over-empahsis on the technological infrastructure when the real challenge lies in the innovative and effective use of these systems in learning and teaching (OECD 2005).

Regardless of being commercial or open source, a university’s LMS forms the academic system equivalent of enterprise resource planning (ERP) systems in terms of pedagogical impact and institutional resource consumption (Morgan 2003). Another similarity with that of an ERPis the common idea behind the LMS identified by Dalsgaard (2006) that different tools are integrated into the single system which offers all the necessary tools to run and manage and e-learning course. There is an assumption that all learning activities and materials in a course will be organised and managed by and within the system (Dalsgaard 2006).

This similarity with ERP systems may raise similar concerns. An enterprise system, by its very nature, will impose its own logic on a company’s strategy, structure and culture and will push a company towards generic processes even when customised processes may be a source of competitive advantage (Davenport 1998). LMS are not pedagogical neutral technology, through their design they influence and guide teaching and work to shape and even define teachers’ imaginations, expectations and behaviours (Coates, James et al. 2005). The implementation of enterprise systems often reflects a conscious or unconscious move towards standardization (Morgan 2003).

Universities typically select an LMS through a process of comparison which evaluates each LMS on the basis of its functionality and how well this matches the needs of the institutions (Jones 2004). This is despite suggestions that decisions about university teaching and learning should not be restricted to checklist evaluations of technical and organizational factors (Coates, James et al. 2005). In addition, it has been observed that there are few, if any, distinguishable technical applications of features that allow for product differentation within the LMS market (Black, Beck et al. 2007). One of the sources of technology and pedagogical distinctions identified by Holt and Seagrave (2003) is the pre-occupation of different parties with different aspects of the selection of any one system. Particularly troubling is the observations that the users of these systems are often not the people who select them, the motivations for their acquisition are often unstated or ambiguous, and that the expectations of the investments in these systems are unclear (Katz 2003). In reviewing a number of reviews of LMS, Siemens (2006) identifies the most prominent limitation of the review models as the limited focus on broarder organisational views of learning.

The implementation of a LMS within a university is a small first step in what is likely to become a significant reshaping and renewal of teaching and learning – one of higher education’s most cherished and important activities (Katz 2003). This reshaping means that the selection of an institutional LMS is a high risk decision which involves a great deal of technological and institutional forecasting (Coates, James et al. 2005). It is possible that the difficulty of this forecasting is responsible for the observation that universities are not especially loyal with the majority having changed LMS, planning to change LMS or operating additional LMS (Paulsen 2003). The full-fledged implementation of an LMS is an expensive and support-intensive enterprise (Warger 2003) and brings significant change-management implications (Katz 2003). For this and other reasons it appears sensible to focus efforts away from LMS selection and towards issues related to adoption and implementation (Black, Beck et al. 2007).


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.

Browne, T., M. Jekins, et al. (2006). "A longitudinal perspective regarding the use of VLEs by higher education institutions in the United Kingdom." Interactive Learning Environments 14(2): 177-192.

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.

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

Davenport, T. (1998). "Putting the Enterprise into the Enterprise System." Harvard Business Review 76(4): 121-131.

Goldberg, M., S. Salari, et al. (1996). "World-Wide Web – Course Tool: An environment for building WWW-based courses." Computer Networks and ISDN Systems 28: 1219-1231.

Harrington, C., S. Gordon, et al. (2004). "Course Management System Utilization and Implications for Practice: A National Survey of Department Chairpersons." Online Journal of Distance Learning Administration 7(4).

Holt, D. and S. Segrave (2003). Creating and sustaining quality e-learning environments of enduring value for teachers and learners. 20th Annual Conference of the Australasian Society for Computers in Learning in Tertiary Education, Adelaide.

Jones, D. (2004). "The conceptualisation of e-learning: Lessons and implications." Best practice in university learning and teaching: Learning from our Challenges.  Theme issue of Studies in Learning, Evaluation, Innovation and Development 1(1): 47-55.

Katz, R. (2003). "Balancing Technology and Tradition: The Example of Course Management Systems." EDUCAUSE Review: 48-59.

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.

Malikowski, S., M. Thompson, et al. (2007). "A model for research into course management systems: bridging technology and learning theory." Journal of Educational Computing Research 36(2): 149-173.

Mendoza, L., M. Perez, et al. (2006). "Tailoring RUP for LMS Selection: A Case Study." CLEI Electronic Journal 9(1).

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

OECD (2005). E-Learning in Tertiary Education: Where do we stand? Paris, France, Centre for Educational Research and Innovation, Organisation for Economic Co-operation and Development.

Paulsen, M. F. (2003). "Experiences with Learning Management Systems in 113 European Institutions." Educational Technology & Society 6(4): 134-148.

Siemens, G. (2006). "Learning or Management System? A Review of Learning Management System Reviews." from

Warger, T. (2003, July 2003). "Calling All Course Management Systems." University Business  Retrieved 30 December, 2006, from

West, R., G. Waddoups, et al. (2006). "Understanding the experience of instructors as they adopt a course management system." Educational Technology Research and Development.

PhD Update #21 – End in sight for chapter 2

A bit of progress made on chapter 2 this week, sufficient to suggest that the end is nigh – at least for the first draft. That progress is in spite of only having limited time this week to work on the thesis because of work on Monday and having to baby sit the two boys on Wednesday.

What I’ve done this week

In the last update I said I’d complete the People component and get going on either pedagogy or product. I chose product.

In the last week I’ve

  • Completed the People, cognition, rationality and e-learning section of the People component.
  • Completed the Lessons for e-learning from People section which is the last section of the People component.
  • Completed the introduction and conceptions of technology section from the Product component.
  • Worked out the structure and re-organised most of the literature for the remaining sections of the Product component.
    The current planned structure is a little different than that outlined in the introduction. The current structure is
    • Conceptions of technology – DONE
      Broad discussion of how technology is perceived.
    • University e-learning technology – describe the technologies currently used
      • What is an LMS?
      • LMS limitations.
      • Other systems.
    • Other product models – the LMS is an integrated “ES”. There are other alternatives. Briefly mention these and the relative merits as opposed to the ES model.
    • Lessons for e-learning from Product.

What I’ll do next week

The aim is to complete the Product component and make significant progress on the Pedagogy component. If at all possible I’d like to complete both, but I’ve got a couple of other tasks to do at work next week which may prevent this from happening.

The product component of the Ps Framework

This post contains the start of the Product component of the Ps Framework that forms a section out of chapter 2 of my thesis.


Technology is a tool and like all tools it should fit your hand when you pick it up, you shouldn’t have to bio-re-engineer your hand to fit the tool. – Dave Snowden

E-learning, as used in this thesis, draws on the definition provided by the OECD (2005) where e-learning is defined as “the use of information and communications technology to enhance and/or support learning in tertiary education”. The purpose of the Product component of the Ps Framework and this section is to examine the nature of the information and communications technology – the product – used to implement e-learning within universities. Due to its pre-dominance within university e-learning, the emphasis will be on the class of integrated enterprise system known alternatively as the Course Management System (CMS), Learning Management System (LMS) or the Virtual Learning Environment (VLE).

This section first briefly examines some literature from information systems and more broadly about conceptions of technology and the information technology artifact (Section 2.1.1). It will then more onto to examine more closely the nature and characteristics of the technology currently used to support e-learning within universities (Section 2.1.2), before examining in detail some of the limitations of that technology (Section 2.1.3) and examining some alternatives (Section 2.1.4). Lastly, it seeks to draw some lessons for e-learning from this discussion of the Product component of the Ps Framework (Section 2.1.5).

Conceptions of technology

Bringing to the surface the common assumptions can be particularly useful in the design and implementation of a system – like e-learning within a university – in order to identify where stakeholder frames may be incongruent and internally inconsistent (Orlikowski and Gash 1994). In the past, and especially within the implementation of e-learning within universities, information technology has been taken for granted or assumed to be unproblematic. Such techno-rational conceptions illustrate a quite narrow perspective of what technology is, how it has effects and how and why it is implicated in social change (Orlikowski and Iacono 2001). Conceptions of technology within the practice of e-learning at universities appears particularly limited when it is observed that the almost universal university approach to e-learning has been the adoption of a particular type of system (Salmon 2005; Feldstein 2006; Jones and Muldoon 2007). This section seeks to briefly examine the variety of conceptions of technology found in the literature.

Gana and Fuentes (2006) identify two different ways of understanding technology and its management within society:

  1. technology as neutral; and
    The development of technology follows a linear process and oriented towards efficiency and economic yield through the application of technical rationality that can only be understood and applied by experts with adequate specialised understanding.
  2. technology as a social activity.
    Decisions about technology cannot be based exclusively on specialised technology knowledge but is instead a shared activity attempting to made sense of a complex array of forces arising from development being intrinsically woven together with society and social actors.

In examining conceptions of causal agency in the literature on information technology and organisational change, Markus and Robey (1988) identify three conceptions:

  1. the technological imperative;
    Technology is seen as an exogenous force which determines or at least strongly constrains the behaviour of individuals and organizations. Information technology is seen as shaping organizations, its processes and jobs. Empirical research has generated contradictor findings and it has been proposed that contingencies affect the relationship between information technology and structural change.
  2. the organisational imperative; and
    Assumes that organizations and the people within them have almost unlimited choice over technological options and almost unlimited control over the consequences. This perspective assumes that human actors rationally design information systems to satisfy organisational requirements. It assumes that system designers and management are able to manage the impacts of systems by paying attention to both technical and social concerns. Empirical support is limited and most studies fail to assess designers’ intentions and are consequently not complete tests of this imperative.
  3. the emergent perspective.
    Holds that the uses and consequences of information technology emerge unpredictably from complex social interactions. Central to this perspective are the role of computing infrastructure, the interplay of conflicting objectives and preferences, and the operation of non-rational objectives and choice processes. This perspective refuses to acknowledge a dominant cause of change, instead prediction requires detailed understanding of dynamic organisational processes, the intentions of actors and features of information technology.

Sproull and Kiesler (1991) draw on the history of prior technology to develop four points useful in thinking about the potential consequences of new communication technologies. These points are:

  1. Full possibilities of new technology are hard to foresee;
    Inventors and early adopters tend to emphasise the planned uses and under-estimate second-level effects.
  2. Unanticipated consequences arise from interactions;
    Efficiency effects have less to do with developing unanticipated consequences of technology than the changing of interpersonal interactions, social organisation, work procedures and ideas about what is important.
  3. Second-level effects often emerge slowly; and
    Such effects tend to arise only after people begin over time to understand, reflect and renegotiate changed patterns of behaviour and thinking.
  4. Second-level effects are not determined by technology.
    Rather than arising from autonomous technologies operating on a passive organisation or society, second-level effects are construct as technology interacts with, shapes, and is shaped by the social and policy environment.

In examining the information systems research literature – in the form of the 1888 articles published within the journal Information Systems Review from 1990 through 1999 – with the intent of discovering what IS researchers had done with the alternative conceptualisations of technology given in the 1980s – such as that given by Markus and Robey (1988) – Orlikowski and Iacono (2001) identified 14 specific conceptualisations of information technology. They clustered these into five broad meta-categories:

  1. tool view of technology;
    Representing the common and received understanding of technology as the engineered artifact, expected to do what was intended by its designers. A focus largely on technical issues independent of social or organisational arrangements within which it is developed and used.
  2. proxy view of technology;
    Attempts to capture critical aspects of information technology through the use of a surrogate and usually quantitative measure such as individual perceptions, diffusion rates or dollars spent.
  3. ensemble view of technology;
    Technology is seen as only one element of a package or web of components that are necessary in order to apply technology to some socio-economic activity. All variants of this view focus on the dynamic interactions between people and technology at various stages of its construction, implementation and use.
  4. computational view of technology;
    A view that focuses on the capabilities of technology represent, manipulate, store, retrieve and transmit information in support of processing, modelling or simulating aspects of the world. Typically focusing on the development of algorithms or models.
  5. nominal view of technology: technology as absent.
    Where technology is incidental or act as background information. The focus is on topics of interest to the IS field but with not specific connection with technology.

An initial change in technology can set the direction of a deviation-amplifying spiral, however, humans can affect technology design and policy and therefore influence second-level effects (Sproull and Kiesler 1991). Management, those responsible for creating the environment in which an organization operates, tends to concentrate on efficiency effects (Sproull and Kiesler 1991; Lacity and Hirschheim 1993). Such a focus can limit the level of disruption caused by information technology and its second-level effects contributing to the maintenance of the status quo. It is not uncommon for adoption of new technological opportunities which significantly deviate from the established socio-technical profile of a sector to be slow (Dolata 2009). Table 3.1 summarises perspectives about information technology, its purpose and the likelilood of sustaining or disruptive innovation (Christensen 1997).

Table 3.1 – Sustaining and disruptive perspectives of information technology
Source Sustaining Disruption
Strategic information systems (Clark 1994) For automation and office support As a source of strategic advantage
Communications technologies, networked organisation (Sproull and Kiesler 1991) Emphasizing efficiency effects Enabler of previously impossible practices
Web-based teaching and learning (Hannafin and Kim 2003) Harnessed to improve existing practices Enabling significant transformation by embracing different world views
Technology, innovation and firms (Christensen 1997) Sustaining – helping institutions improve existing products Disruptive – change the standard, a different set of benefits at lower costs

As shown in Table 3.1, in particular the Hannafin and Kim (2003) reference, various conceptions of information technology can be found within the literature associated with e-learning, some additional examples follow. It is most likely that technology will reinforce the old systems rather than the new paths (Lian 2000). Educators are likely to use the technology to do things the way they have always been done, but with new and more expensive equipment (Dutton and Loader 2002). Technology is not, of itself, liberating or empowering but serves the goals of those who guide its design and use (Lian 2000). The tools themselves are never value-neutral but are replete with values and potentialities which may cause unexpected responses (Westera 2004). The forms new media take are not technologically given, instead they are historically emergent and best understood by examining how social relations are inscribed in the technology and how the technology is shaped to provide specific functions (Adam 1998). The impact of new technologies depends crucially on the social context (Clegg, Hudson et al. 2003). While a e-learning system can be purported to support various aspects of learning, the reality is more complex, involving the context within which these systems are used and how they are adapted to specific student needs (Conole 2002).

The above brief examination of different conceptions of technology suggests that views of technology as neutral or deterministic are somewhat limited in the explanatory power. Instead, of quality e-learning arising simply out of the technology is unlikely. Instead the outcomes of e-learning are likely to arise in unpredictable and emergent ways out of the complex interplay between the technology, the organisation and the individuals. It also indicates that it is through this emergence that many unexpected effects arise and open up new possibilities.


Adam, A. (1998). Artificial knowing: gender and the thinking machine. London, Routledge.

Christensen, C. (1997). The innovator’s dilemma: when new technologies cause great firms to fail. Boston, Harvard Business Press.

Christensen, C. M. (1997). The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail. Boston, Harvard Business School Press.

Clark, R. (1994, 14 July 1994). "The Path of Development of Strategic Information Systems Theory." 2003, from

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.

Conole, G. (2002). "The evolving landscape of learning technology." ALT-J 10(3): 4-18.

Dolata, U. (2009). "Technological innovations and sectoral change. Transformative capacity, adaptability, patterns of change: An analytical framework." Research Policy 38(6): 1066-1076.

Dutton, W. and B. Loader (2002). Introduction. Digital Academe: The New Media and Institutions of Higher Education and Learning. W. Dutton and B. Loader. London, Routledge: 1-32.

Feldstein, M. (2006). Unbolting the chairs: Making learning management systems more flexible. eLearn Magazine. 2006.

Gana, M. T. S. G. and L. A. T. Fuentes (2006). "Technology as ‘a human practice with social meaning’ – a new scenery for engineering education." European Journal of Engineering Education 31(4): 437-447.

Hannafin, M. and M. Kim (2003). "In search of a future: A critical analysis of research on web-based teaching and learning." Instructional Science 31: 347-351.

Jones, D. and N. Muldoon (2007). The teleological reason why ICTs limit choice for university learners and learning. ICT: Providing choices for learners and learning. Proceedings ASCILITE Singapore 2007, Singapore.

Lacity, M. and R. Hirschheim (1993). Information systems outsourcing: myths, metaphors and realities. Chichester,, John Wiley & Sons.

Lian, A. (2000). "Knowledge Transfer and Technology in Education: Toward a complete learning environment." Educational Technology & Society 3(3).

Lian, A. (2000). "Knowledge transfer and technology in education: Toward a complete learning environment." Educational Technology & Society 3(3): 13-26.

Markus, M. L. and D. Robey (1988). "Information technology and organizational change: causal structure in theory and research." Management Science 34(5): 583-598.

OECD. (2005, 17 January 2006). "Policy Brief: E-learning in Tertiary Education."   Retrieved 5 December, 2006, from

Orlikowski, W. and D. Gash (1994). "Technological frames: Making sense of information technology in organizations." ACM Transactions on Information Systems 12(2): 174-207.

Orlikowski, W. and C. S. Iacono (2001). "Research commentary: desperately seeking the IT in IT research a call to theorizing the IT artifact." Information Systems Research 12(2): 121-134.

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.

Sproull, L. and S. Kiesler (1991). Connections: new ways of working in the networked organization. Cambridge, MIT Press.

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

Lessons for e-learning from people

This is the last section from the People component of chapter 2 of my thesis. It is an attempt to derive some lessons from the previous sections that are relevant to the practice of e-learning.

This leaves me with two components of the Ps Framework to go and Chapter 2 is complete – at least to first draft stage. The two remaining are Product and Pedagogy. I believe both should be fairly quick ones to write, hopefully I’m right.

Lessons for e-learning from People

The previous sections have examined various aspects associated with the People involved with e-learning. This has included descriptions of the characteristics of the people (Students, Academic Staff, Leaders and Managers and Support Staff) involved with e-learning (Section 2.1.1); the chasm (Section 2.1.2) that exists between the visionaries and the pragmatists; and some notions of rationality (Section 2.1.3). This section draws on those descriptions to identify some potential lessons for the practice of e-learning within higher education.

People mean variety

The perceptions and beliefs around technology and learning and teaching play a significant role in the adoption and use of e-learning (Jones, Cranston et al. 2005; Stewart 2008). Different groups of people – academic staff, students, management and information technology practitioners – within the same institution will bring different and often conflicting views (Luck, Jones et al. 2004) or technological frames (Orlikowski and Gash 1994) to organizational information technology projects such as e-learning. Even within groups (e.g. students or academic staff) there is incredible variation in needs, requirements and tastes (McCormack and Jones 1997). In fact, increasing diversity within the student body is one of the defining trends in modern higher education. Additionally, changes in the context of higher education (see Place crossref) are increasing diversity between academics. More generally, individuals within an organisation will vary greately in their willingness to adopt an innovation like e-learning (Jones, Jamieson et al. 2003).

An awareness and sensitivity to the increasingly diverse needs of students can improve their learning experience and outcomes (Semmar 2006). The variation in academics, students and disciplines combined with the absence of any unifying educational theory or practice suggests that there is no one correct method for implementing an online course (McCormack and Jones 1997). Approaches to the organisational implementation of e-learning that enable, support and encourage this diversity may be easily implemented, more readily adopted and potentially more innovative.

Academic staff aren’t prepared or rewarded for teaching

Academic staff are trained, selected and evaluated on the discipline expertise and their ability to perform quality research. The experience and training of academic staff not only focuses on discipline and research expertise it can, and often does, socialise aspiring academics towards a vision of academic work that emphasises these tasks (Austin 2002). While universities promote the importance of teaching the create ambiguous, even contradictory expectations by rewarding academic staff primarily for research (Zellweger 2005) and creating environments where spending more time teaching is a negative influence on academic pay (Fairweather 2005).

Most students, academic staff and people are conservative

Findings from neuro-science and psychology identify a strong tendency in people to gravitate toward the familiar and away from the unfamiliar (Bailey 2007). Social cognitive research suggests that an individual’s knowledge is cognitively structured through experience and interaction, which creates knowledge structures that focus attention on information consistent with existing structures (and past experience) while masking information inconsistent with those structures (Davidson 2002). Geoghegan’s (1994) use of Moore’s (2002) chasm suggests that the vast majority of potential adopters like gradual change and are risk averse (see Table 2.3).

Given this background, it is not all that surprising to find that new students, even those with a high level of competence and confidence with information technology, are conservative in their approach to study and learning approaches (Hardy, Haywood et al. 2008). Similarly, it is not at all surprising – especially when given the nature of rewards for academic staff – to find that most applications of e-learning within higher education can be characterised as horseless carriage applications. Where the attempt is made to develop new actions based on old adaptations to obsolete contexts (Anderson 2004). The work of Geoghegan (1994) suggests that the current limited adoption and limited quality of e-learning may arise from models of e-learning implementation that fail to effectively engage with this conservativism.

People mean agency

At most, new technologies and systems enable rather than dictate change (John and La Velle 2004). When a technical innovation threatens to disrupt established methods, teachers, administrators, students and technology staff will resist, assimilate, subvert or otherwise appropriate what is being proposed or imposed (Dutton, Cheong et al. 2004). Academic staff, as knowledge workers, have considerable autonomy about how they perform tasks and often can and do resist the imposition of changes to routine (Jones, Gregor et al. 2003). Attempts to impose changes tend to induce camouflage, conformance (Snowden 2002) or task corruption (White 2006). It appears unlikely that students and academic staff will objectively observe and evaluate the perceived advantages of e-learning and adopt imposed changes to practice. Instead, it appears more likely that the use of e-learning will emerge unpredictably through the interplay between the agency of the people involved, the implementation context and the material property of the supporting technologies. Failure to effectively engage with this unpredictable emergence may result in less than favourable outcomes.

People are central

The main point of this entire section is that the quality of e-learning within universities arises from the quality of the people within universities and their ability to harness and not be unduly constrained by the technology or the organisational processes and structures. It has been observed that most universities are still struggling to engage a significant percentage of students and staff in e-learning (Salmon 2005) and that the quality of what engagement there is, is limited. Given these observations, it is suggested that techno-rational approaches that fail to engage with people and the experiences are unlikely to create significant, sustainable improvements. It is possible, that current limitations around the organizational implementation of e-learning arise due to organizational approaches and practices that are not yet effectively engaging with the needs and characteristics of the people involved in e-learning.


Anderson, T. (2004). Toward a theory of online learning. Theory and Practice of Online Learning. T. Anderson and F. Elloumi. Athabasca, Canada, Athabasca University: 33-60.

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.

Bailey, C. (2007). "Cognitive accuracy and intelligent executive function in the brain and in business." Annals of the New York Academy of Sciences 1118: 122-141.

Davidson, E. (2002). "Technology frames and framing: A socio-cognitive investigation of requirements determination." MIS Quarterly 26(4): 329-358.

Dutton, W., P. Cheong, et al. (2004). "An ecology of constraints on e-learning in higher education: The case of a virtual learning environment." Prometheus 22(2): 131-149.

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.

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

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.

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.

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

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.

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.

McCormack, C. and D. Jones (1997). Building a Web-Based Education System. New York, John Wiley & Sons.

Moore, G. A. (2002). Crossing the Chasm. New York, Harper Collins.

Orlikowski, W. and D. Gash (1994). "Technological frames: Making sense of information technology in organizations." ACM Transactions on Information Systems 12(2): 174-207.

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.

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.

Snowden, D. (2002). "Complex Acts of Knowing." Journal of Knowledge Management 6(2): 100-111.

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).

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

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

People, cognition, rationality and e-learning

The following is the second to last section for the People component of chapter 2 of my thesis. The basic aim of this section is to establish that people are generally not rational and methods that assume that they are, are destined to fail. It’s my proposition that most of the organisational approaches to e-learning and, more generally, learning and teaching at universities suffer this flaw.

I’m not the first to make this observation and I almost certainly haven’t expressed it as well as it can be in the following. Again, I’m satisficing for the purposes of completing the thesis. There’s a thesis in this topic alone.

People, cognition and rationality

The practice of e-learning within universities has arisen at a time when changes in broader society are increasing the emphasis on accountability, efficiency and managerialisation (see Place cross ref for more discussion). From the late 1990s onwards the practice of e-learning within universities has been dominated by an industrial paradigm associated with the use of enterprise information systems (see Past Experience cross ref). Both factors are usually characterised as having a strong techno-rational basis. A techno-rational discourse seeks the use of quantitative data and measurement to ensure accountability (Kappler 2004). Enterprise systems are an extreme application of a techno-rational perspective (Dillard and Yuthas 2006). A techno-rational approach to management sees it as a scientifically rational and efficient application of neutral knowledge on a par with the natural sciences (Morgan 1992). It is a school of through aimed at marginalizing the role of intuitive thinking through the use of analytical tools and technical solutions (Vanharanta and Easton 2009). This section draws on a range of literature to briefly outline observations that suggest limitations of such techno-rational approaches that impact upon the implementation of e-learning within universities.

At the level of the individual, there is significant research to indicate that people do not make rational decisions. It has been shown that when making decisions people rely on strategies such as rules of thumb and heuristics to simplify decisions, several of which suffer from systematic biases that influence judgement (Tversky and Kahneman 1974). Cognitive biases are mental behaviours that negatively impact upon decision quality in a significant number of decisions for a significant number of people; they are inherent in human reasoning (Arnott 2006). Arnott (2006) develops a taxonomy of 37 cognitive biases identified by psychological research. Humans have primitive emotional parts to our brains that can strongly influence – both negatively and positively – the choice we make (Morse 2006). A common example from organisational life is provided by Keil and Robey (1999) who identify the “deaf effect” where by people remain deaf in the presence of reported troubles in the hope that they can avoid dealing with difficult problems and perhaps to also disassociate themselves from a failing endeavour. Even if people can make rational decisions there appear to be limits on that rationality. Given a complex environment, there are limits to the ability of human beings to adapt optimally, or even satisfactorily (Simon 1991).

Cecez-Kecmanovic, Janson and Brown (2002) describe Weber’s (1978) view that there is a limit on rationality because the mutual judgements of rational action between actors will differ to the degree to which their beliefs and values differ, and that such belief and value conflicts cannot be resolved in a rational way. Weber’s view is that substantive rationality within organizations is inherently limited because of the inevitability of value conflict (Cecez-Kecmanovic, Janson et al. 2002). Individually inherited cultural belief systems significantly bias normal human thought and perception, belief systems that are added to as we inherit organisational beliefs based on a long line of learned and rigidly held inaccuracies (Bailey 2007).

These ideas of individually and organisationally different cultural beliefs connect with the idea of technology frames. The understandings that members of a social group come to have of technological artefacts arising from knowledge of the particular technology and the local understanding of specific uses in a given setting (Orlikowski and Gash 1994). These structures provide templates for problem solving and evaluation, focus attention on information consistent with existing structures while hiding inconsistent information and fill gaps in information with information consistent with existing knowledge structures (Davidson 2002). Differences in technological frames between those involved with information systems projects can lead to actions that hamper technology implementation (Orlikowski and Gash 1994).

Techno-rational approaches to management treat people as objects to be manipulated in accordance with scientific laws (Morgan 1992). Such approaches embody a deterministic approach that views potential adopters as predisposed to adopt innovations that are quantifiably superior from some technical perspective (Surry and Farquhar 1997). The expanded technological determinist view suggests that it is technology that shapes the forms of society and organizations (Jones 1999). Increasingly, however, interest in explaining the organisational consequences of information systems had led to positions that privilege human agency over social structure and technological features (Boudreau and Robey 2005). Information systems development is then not a case of people with clearly-defined goals applying technologies with clearly-defined properties to achieve clearly defined organisational effects (Jones 1999). Boudreau and Robey (2005) show that the organisational consequences of an ERP system – known to be notoriously inflexible once configured and implemented and where adoption is typically motivated by a desire for greater control – could be shaped and enacted through use rather than simply embedded in technical features. The trajectory of emergence of use is not wholly determined either by human agency or the material property of the technology, but rather by the unpredictable interplay of the two.

Research into decision making around information systems projects has revealed that such decisions are rarely logical or rational (Bannister and Remenyi 1999). Decision making about the implementation of information systems is not a techno-rational process with many decision makers relying on intuitions or instincts and simple heuristics to simplify decision making (Jamieson and Hyland 2006). The practice of innovation and change development within universities can never be a mere rational process (Jones and O’Shea 2004). Awareness of the problems associated with the limitations in individual and organizational rationality can help minimize the negative effectives of irrational bias in individual and organizational decision making opens the possibility of adopting approaches and practices that can improve outcomes.


Arnott, D. (2006). "Cognitive biases and decision support systems development: a design science approach." Information Systems Journal 16: 55-78.

Bailey, C. (2007). "Cognitive accuracy and intelligent executive function in the brain and in business." Annals of the New York Academy of Sciences 1118: 122-141.

Bannister, F. and D. Remenyi (1999). "Value peception in IT investment decisions." Electronic Journal of Information Systems Evaluation 2(2).

Boudreau, M.-C. and D. Robey (2005). "Enacting integrated information technology: A human agency perspective." Organization Science 16(1): 3-18.

Cecez-Kecmanovic, D., M. Janson, et al. (2002). "The rationality framework for a critical study of information systems." Journal of Information Technology 17: 215-227.

Davidson, E. (2002). "Technology frames and framing: A socio-cognitive investigation of requirements determination." MIS Quarterly 26(4): 329-358.

Dillard, J. and K. Yuthas (2006). "Enterprise resource planning systems and communicative action." Critical Perspectives on Accounting 17(2-3): 202-223.

Jamieson, K. and P. Hyland (2006). Factors that influence Information Systems decisions and outcomes: A summary of key themes from four case studies. 17th Australasian Conference on Information Systems, Adelaide, Australia.

Jones, M. (1999). Information systems and the double mangle: Steering a course between the scylla of embedded structure and the charybdis of strong symmetry. Information Systems: Current Issues and Future Chalenges. T. Larsen, L. Levine and J. DeGross. Laxenburg, Austria, IFIP: 287-302.

Jones, N. and J. O’Shea (2004). "Challenging hierarchies: The impact of e-learning." Higher Education 48(3): 379-395.

Kappler, K. (2004). NCATE: Wolf in shepherd’s clothes. Critical perspectives on the curriculum of teacher education. T. Poetter, T. Goodney and J. Bird. Lanham, MD, University Press of America: 19-40.

Keil, M. and D. Robey (1999). "Turning around troubled software projects: An exploratory study of the de-escalation of commitment to failing courses of action." Journal of Management Information Systems 15(4): 63-87.

Morgan, G. (1992). Marketing discourse and practice: Towards a critical analysis. Cricital management studies. M. Alvesson and H. Willmott. London, SAGE: 136-158.

Morse, G. (2006). "Decisions and desire." Harvard Business Review 84(1): 42-51.

Orlikowski, W. and D. Gash (1994). "Technological frames: Making sense of information technology in organizations." ACM Transactions on Information Systems 12(2): 174-207.

Simon, H. (1991). "Bounded rationality and organizational learning." Organization Science 2(1): 125-134.

Surry, D. and J. Farquhar (1997). "Diffusion Theory and Instruction Technology." Journal of Instructional Science and Technology 2(1): 269-278.

Tversky, A. and D. Kahneman (1974). "Judgment under uncertainty: Heuristics and biases." Science 185(4157): 1124-1131.

Vanharanta, M. and G. Easton (2009). "Intuitive managerial thinking; the use of mental simulations in the industrial marketing context." Industrial Marketing Management In Press.

Weber, M. (1978). Economy and society. Berkeley, CA, University of California Press.

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