Month: April 2009 Page 1 of 2

This posts contains the last content of what (I hope) will become the “Past Experience” section of Chapter 2 of my thesis. Previous content for this section is already on the blog, including: History of technology-mediated learning, Paradigms of e-learning, e-learning usage – quality, and e-learning usage – quantity.

The aim of this post is to draw some lessons from the past. It won’t be exhaustive, I’m sure there are many other lessons to draw, I’d be interested in hearing them if you know of any. However, for the purposes of the thesis, I’m hoping the following will be “good enough”. As with the other posts, this is a first draft. It hasn’t been through a good proof read, but hopefully is sufficiently readable. In fact, while putting this post together I chopped, changed and added bits, which I haven’t spent a great deal of time checking.

Lessons for e-learning

The purpose of the “Past Experience” section of the Ps Framework is two-fold: examine the history of e-learning, and then draw any lessons or conclusions that may improve the implementation of e-learning within universities. The previous sections have examined the history of e-learning by examining the origins of e-learning in the history of technology-mediated learning through the 1900s (Section 2.1.2 – History of technology-mediated learning), examining the evolving paradigms observable in the history of e-learning (Section 2.1.3 – Paradigms of e-learning), and reporting on the usage of industrial e-learning in terms of quanity and quality (Section 2.1.4 – Industrial e-learning usage – quantity and quality). The purpose of this section is to identify lessons can be drawn from this history of e-learning and describe their potential implications.

This section (2.1) on “Past Experience” commenced with the following quote from Santayana (2009) (emphasis added)

Progress, far from consisting in change, depends on retentiveness. When change is absolute there remains no being to improve and no direction is set for possible improvement: and when experience is not retained, as among savages, infancy is perpetual. Those who cannot remember the past are condemned to repeat it.

This section identifies three lessons labeled using the emphasized sections of the Santayana quote. Each of the following three sections suggest that there is evidence of the three lessons. “Consisting in change” suggests that technology-mediated learning is always changing and that any approach to e-learning must embrace and respond to change effectively. “Retentiveness – or lack thereof” suggests that the history of technology-mediated learning is one where lessons are forgotten, a flaw that needs to be remedied. Lastly, “Infancy is perpetual” suggests that the history of technology-mediated learning, in terms of significant effective the practice of learning and teaching, is one of perpetual infancy. One where important insights into how to transform learning and teaching are continually forgotten.

Consisting in change

A significant feature of the history of technology-mediated learning has been that new paradigms or approaches to learning are driven, to a large extent, by the development of new technology. Each of the major applications of technology to learning covered in the History of technology-mediated learning section (2.1.2) were made possible through the advent of a new type of technology. Table 2.3 provides a summary of the technological spark that enabled each movement observed within technology-mediated learning. Table 2.4 summarises a similar connection between specific technological sparks and each of the paradigms of e-learning identified in section 2.1.3.

Table 2.3 – Technology-mediated learning movements and their technological sparks

TML movement	Technological spark
Audio-visual	Stereo-graphs, photos, motion pictures, radio, television
Teaching machines	Mechanical machines and the industrial revolution
Computer-based learning	Early computers
Computer-mediated communication	Mainframes, communication software, networks
Computer-managed learning	Personal computers, local-area networks

As shown in Section 2.1.3 it is possible to identify at least five different paradigms or models of e-learning since 1990 (see Table 2.1). All of these paradigms arise, to a large extent, because of the development of a new technology that promises to provide solutions to problems with previous practice. Table 2.3 provides a brief summary of the technology/paradigm connection. The current pre-dominant paradigm, industrial e-learning, appears to have been, as shown above (Section 2.1.4), somewhat less than successful in transforming learning and teaching at universities and the promise of the next paradigm is already being promoted. Learning 2.0 – the post-industrial e-learning paradigm – is challenging existing models by enabling and requiring pedagogical, organizational and technological innovation (Ala-Mutka, Bacigalupo et al. 2009). Suggesting that the cycle is starting over once again.

Table 2.3 – E-learning paradigms and their technological sparks

e-learning Paradigm	Technological Spark
Text-based CMC	Internet technologies provide solutions to problems with proprietary, main-frame based CMC systems.
Lone ranger	Increasing availability of simple Internet-based systems to academics (not involved in existing CMC research) in their everyday practice generates interest amongst lone ranging staff in applying it to learning and teaching.
Cottage industry	Lone rangers develop collections of technology, leveraging scripting languages and open-source tools – especially those associated with Web development, to make it easier for others to use the Internet in their teaching
Industrial	Commercial vendors (and more recently open source communities) develop integrated systems, often adapted from those of lone rangers, of technology that are “enterprise ready”
Post-industrial	The rise of social software, blogs, wikis etc. generate interest in how these can be harnessed to address problems with industrial e-learning

As described in Section 2.1.3 a paradigm embodies a particular worldview and requires a certain organizational structure and set of skills to operate effectively. Consequently, it is possible to observe institutions, heavily invested in a previous paradigm, that have been slow in adopting the next paradigm, for example, some traditional distance education institutions and the move to industrial e-learning. While a slower, more considered adoption process can be both positive and negative in its impacts, the ability to adopt new paradigms is important. It is suggested that the ability to cope with change is an important component of any approach to e-learning within universities.

A lack of retentiveness

Santayana (2009) in the quote that led off the “Past Experience” section (section 2.1) of this chapter suggests that progress depends on retentiveness, the ability to retain experience. The following provides a few examples of how e-learning has demonstrated an ability to forget or simply be ignorant of Past Experience. The examples are drawn from a number phases from the history of technology-mediated learning and are far from exhaustive.

As shown above in the work of Malikowski et al (Malikowski, Thompson et al. 2007) the quiz functionality of an LMS is the most used function for evaluating students with upwards of 20% of staff using the feature. Heines (2004) complains that he is yet to see a test item banking program that enforces even the most basic, long-established rules of test construction and bemoans LMS vendors who are not familiar with terms such as “item analysis”, “difficulty index”, “discrimation index”, and “standard deviation”. Just some of the fundamental knowledge arising out of the teaching machines, programmed instruction and computer assisted instruction movements. Another example is that Skinner (1958) establishes as one of the important features of any teaching machine the requirement that students compose their response rather than select it from a set of alternative in order to test their ability to recall, rather than recognize the answer.

One of the more dominant themes in research around e-learning has been the practice of comparison studies. Studies in which some new technology-mediated learning approach has its effectiveness compared against some other method, typically traditional face-to-face teaching. The continuation of such studies ignore the fact that much of the research tradition of the audio-visual instruction movement was based on comparison studies (Saettler 2000) and that a key finding from these studies was little or no significant difference between media and an identified need to change the focus of research (Reiser 2001). The continuation of comparison studies ignores the significant bodies of research to suggest that students lean equally well regardless of the technology or media used (Russell 1999) and that it is the instructional methods, not the technology, that influence student learning (Clark 1994).

The technology-mediated learning hype cycle – perpetual infancy

This section describes an observable technology-mediated learning hype cycle in the history given above and argues that this on-going cycle contributes directly to the on-going perpetual infancy of technology-mediated learning. The simple cycle used here consists of three steps:

1. growing revolution;
   A new technology is created and identified as a potential solution to a number of perceived problems with learning and teaching. Interest is generated in the application of the technology and a growing technologist’s alliance – in the form of researchers, educators, professional associated, vendors and instructional technology support people – is formed. The technology promises to revolutionise education.
2. minimal impact; and
   It is recognised that the impact of the technology has been somewhat more problematic and limited. Stories of failure and criticism of the technology arise. Use of the technology achieves a stable level of use – perhaps non.
3. resolution of dissonance.
   The failure of the expected revolution is explained away through rationalisation and allocation of blame to poor leadership, poor implementation, intransigence of followers, limited resources or the wrong technology – which often leads to the choice of a new technology and a repetition of the cycle.

The above is a very simple description of a cycle that is talked about in more detail about a number of authors in technology in education (van Dam 1999; Reiser 2001) and in other related fields such as management fads in higher education (Birnbaum 2000), and information technology (Fenn and Raskino 2008). The cycle described above draws mostly on the work of Reiser (2001) and Birnbaum (2000). Table ?? provides an illustration of this cycle for a small sub-set of the technologies discussed in the above history. It uses quotes from associated literature to represent the different phases in the cycle for each technology.

Note: In the thesis, the following tables are joined into one large table. That doesn’t work on the web, or at least within this blog template. So the table has been split

Evidence for TML hype-cycle for Audio-visual

Stage	Audio-visual
Growing revolution	“Books will soon be obsolete in the schools…It is possible to teach every branch of human knowledge with the motion picture. Our school system will be completely changed in the next ten years.” Thomas Edison from Saettler (1968) “Radio may come as a vibrant and challenging textbook of the air.” Darrow quoted in (Reiser 2001)
Minimal impact	Cuban (1986) suggests that in the 20 years following the peak of expectations in the 1930s, radio had very little impact on instructional practices. “The role played in formal education by instructional television has been on the whole a small one.., nothing which approached the true potential of instructional television has been realized in practice . . . . With minor exceptions, the total disappearance of instruc tional television would leave the educational system fundamentally unchanged.” Carnegie Commision on Educational Television quoted in (Reiser 2001)
Resolution of dissonance	Resier (2001) reports on literature identifying teacher resistance, the expense of installing and maintaining television sets, and the inability of television alone to create the various conditions necessary for student learning as the reasons behind the limited adoption of instructional television

Evidence for TML hype-cycle for teaching machines

Stage	Teaching machines
Growing revolution	“There are more people in the world than ever before, and a far greater part of them want an education. The demand cannot be met simply by building more schools and training for more teachers. Education must become more efficient. …. In any other field a demand for increased production would have led at once to the invention of labor-saving capital equipment.” (Skinner 1958)
Minimal impact
Resolution of dissonance	“The intellectual inertia and conservatism of educators who regard such ideas as freakish or absurd, or rant about the mechanization of education” Pressey quoted in (Petrina 2004) “Pressey’s machines succumbed in part to cultural inertia; the world of education was not ready for them. But they also had limitations which probably contributed to their failure.” (Skinner 1958) “By the 1960s, interest in “teaching machines” evolved into “programmed instruction” with the realization that program was more important than the machine” (Saettler 2000)

Evidence for TML hype-cycle for computer-based learning

Stage	Computer-based learning
Growing revolution	“the processing and the uses of information are undergoing an unprecedented technological revolution……One can predict that in a few more years millions of school-children will have access to what Philip of Macedon’s son Alexander enjoyed as a royal prerogative: the personal services of a tutor as well-informed and responsive as Aristotle.” (Suppes 1966)
Minimal impact
Resolution of dissonance	“However, in spite of the work that had been done, by the end of the 1970s, CAI had had very little impact on education (Pagliaro, 1983)” (Reiser 2001) “Learning management systems have emerged from the ashes of early mainframe-based CMI systems, thanks to the LAN, WAN, Intranet and Internet, driven by large databases in servers.” (Szabo and Flesher 2002)

Evidence for TML hype-cycle for personal computers

Stage	Personal computers
Growing revolution	“the computer is going to be a catalyst of very deep and radical change in the educational system” (Papert 1984)
Minimal impact	By 1995 substantial numbers of teachers report little or no use of computers for instructional purposes and, where used, computer use was primarily used for drill and practice and the teaching of skills such as word-processing (Reiser 2001).
Resolution of dissonance

Evidence for TML hype-cycle for e-learning

Stage	e-learning
Growing revolution	Green and Hayward (1997) suggest it will have a profound effect on the structure of higher education. Peter Drucker suggested that within 30 years, big university campuses will be relics (Lenzer and Johnson 1997). While Duderstadt et al (2002) suggest the technology and emerging technology threaten the survival of the current form of the university. Peters (2002) suggests that e-learning will force a radical restructuring of our educational institutions.
Minimal impact	“In this sense, then, I do not share the view of Harasim et al. (1995) or Peters (2002) when they argue that e-learning is a “paradigm shift.” Rather, it is old wine in new bottles, at least at present.” (Bates 2004) “Indeed, the formal use of computer technologies in many areas of higher education could best be described as sporadic, uneven, and often ‘low level’” (Selwyn 2007) “One step ahead for the technology, two steps back for the pedagogy” (Mioduser, Nachmias et al. 1999).
Resolution of dissonance	“In particular, there is criticism that institutions and governments are not doing enough to prepare managers, teachers, instructors, and students for the organizational, institutional, and cultural changes needed to make e-learning successful” (Bates 2004)

A question that arises from the recognition of the technology-mediated learning hype cycle is whether or not it will happen with e-learning. Whelan (2005) asks won’t the same disappointment occur with web-based technologies? For Reiser (2001) and Whelan (2005) the answer is to suggest that e-learning will be different due to the improved affordances provided Internet-based technologies to support learning. In particular, Whelan (2005) suggests that the two-way interactive nature of the technology allows for learners to explore multiple perspectives, in multiple formats and puts them in charge of constructing their learning experience. There appear to be significant problems with this more positive view including, but not limited to: ignorance of the need to respond to on-going change in e-learning (see the Consisting in change section above) and forgetting the “grammar of school”.

In talking about the school system – the extension to universities seems appropriate -Tyack and Cuban (1995) propose the idea of a grammar of school as an explanation for difficulties faced when attempting to implement reforms. Papert (1995) describes school as a system in which the major components – curriculum content, epistemological framework, organisational structure and knowledge technology – have developed mutually supportive and matched forms. This grammar of school means that any change is seen as nonsensical as an ungrammatical utterance (Cavallo 2004). In addition to “feeling wrong”, Papert (1995) suggests it is possible for people that accept the grammar of school to interpret, and consequently transform drastically, the ungrammatical utterance into the nearest utterance that fits with the grammar of school.

The two traditional methods of teaching that remain at the forefront of teaching with technology are the lecture and the blackboard (Miller 2000). As shown in the usage of e-learning section (section 2.1.4) the majority of current usage of industrial e-learning focuses on content distribution, it continues to “use new technologies in traditional ways, repeating past inadequacies and constraints with the new media” (Miller 2000). Papert’s (1995) explanation for this is that the new “knowledge technology” is being pulled back by the other components in the system to maintain the grammar of the school.

Carvallo (2004) suggests that the problem is not with knowledge about learning, but with the limitations of models for growth and change at the systemic level. In terms of technology-mediated learning within universities it is not difficult to see the problems. First, universities are inherently resistant to change (Jones and O’Shea 2004). But rather engage in processes that focus on how to encourage change within the grammar of school, most approaches to e-learning concentrate on the features, technical details and pricing of different systems. (Britain and Liber 2000), and focus on technical, financial and administrative aspects (Coates, James et al. 2005). The models used view education and its reform as a sequence of depersonalized, decontextualised steps carried out by willing, receptive and non-transforming agents (Cavallo 2004). The focus has not been on the human dimensions, scaling-up and embedding of innovation and the associated management of change (Tham and Werner 2005). There has been little attention paid to thinking about systemic change or developing alternative models for the development of learning environments and making changes in the grammar of school (Cavallo 2004).

References

Ala-Mutka, K., M. Bacigalupo, et al. (2009). Learning 2.0: The impact of Web 2.0 innovation on education and training in Europe. Seville, Spain, Institute for Prospective Technological Studies.

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.

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

Britain, S. and O. Liber. (2000). "A Framework for Pedgogical Evaluation of Virtual Learning Environments."   Retrieved 21 Nov, 2006, 2006, from http://www.leeds.ac.uk/educol/documents/00001237.htm.

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

Clark, R. (1994). "Media will never influence learning." Educational Technology Research and Development 42(2): 21-29.

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.

Cuban, L. (1986). Teachers and machines: The classroom use of technology since 1920. New York, Teachers College Press.

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.

Fenn, J. and M. Raskino (2008). Mastering the Hype Cycle. Boston, Massachusetts, Harvard Business School Press.

Green, M. and F. Hayward (1997). Forces for Change. Transforming Higher Education: Views from Leaders Around the World. M. Green. Phoenix, Arizona, The Oryx Press: 3-26.

Heines, J. (2004). Technology for Teaching: Past Masters Versus Present Practices. Online Learning: Personal Reflections on the Tranformation of Education. G. Kearsley, Educational Technology Publications: 144-162.

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

Lenzer, R. and S. Johnson (1997). Seeing Things as They Really Are. Forbes.

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.

Miller, I. (2000). "Distance learning – a personal history." The Internet and Higher Education 3(1-2): 7-21.

Mioduser, D., R. Nachmias, et al. (1999). Web-based learning environments (WBLE): Current state and emerging trends. World Conferenceon Educational Multimedia, Hypermedia and Telecommunications.

Papert, S. (1984). "New theories for new learnings." School Psychology Review 13(4): 422-428.

Papert, S. (1995). "Why School Reform is Impossible." The Journal of the Learning Sciences 6(4): 417-427.

Peters, O. (2002). Distance Education in Transition – New Trends and Challenges. Oldenburg, Germany, Biblioteksund informationssystem der UniversitŠt Oldenburg.

Petrina, S. (2004). "Sidney Pressey and the Automation of Education, 1924-1934." Technology and Culture 45(2): 305-330.

Reiser, R. (2001). "A History of Instructional Design and Technology: Part 1: A History of Instructional Media." Educational Technology Research and Development 49(1): 1042-1629.

Russell, T. (1999). The no significant difference phenomenon. Montgomery, AL, International Distance Education Certification Center.

Saettler, P. (2000). The evolution of American educational technology, Information Age Publishing.

Santayana, G. (2009). The Life of Reason, BiblioLife.

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

Skinner, B. F. (1958). "Teaching Machines." Science 128: 969-977.

Suppes, P. (1966). The Uses of Computers in Education. Scientific American: 207-220.

Szabo, M. and K. Flesher (2002). CMI Theory and Practice: Historical Roots of Learning Managment Systems. World Conference on E-Learning in Corporate, Government, Healthcare and Higher Education, Montreal, Canada, AACE.

Tham, C. M. and J. Werner (2005). "Designing and evaluating e-learning in higher education: a review and recommendations." Journal of Leadership & Organizational Studies 11(2): 15-25.

Tyack, D. and L. Cuban (1995). Tinkering towards utopia: A century of public school reform. Cambridge, MA, Harvard University Press.

van Dam, A. (1999). "Education: the unfinished revolution." ACM Computing Surveys 31(4es).

Whelan, R. (2005). "Instructional technology & theory: A look at past, present and future trends." Connect: Information Technology at NYU(Spring/Summer 2005): 13-17.

The following post is a continuation of posts from the “Past Experience” section of chapter 2 of my thesis. This part of chapter 2 is looking at the usage of e-learning within higher education. A previous post provided the introduction to the section and also covered usage from a quality perspective – i.e. how good is the learning and teaching.

The aim of this post is to briefly examine what is known about the quantity of usage of e-learning within institutions. It does this by focusing on three different perspectives:

• Institutional – how many universities have adopted an LMS (just about all).
• Course of faculty – how many courses/staff are using an LMS (was low, but now increasing)
• Service or feature – how many of the features of an LMS are being used in those courses (predominantly content distribution).

As with the previous posts this is a summary. Consequently I have probably missed aspects and nuances. If you have any suggestions please fire away.

Also, as with other posts, I have not done a good proof-reading job on the content before I post them on the blog. At the moment, my emphasis is getting the content done as quickly as possible. Proof-reading will need to wait until later, when I have the energy and state of mind. If you pick up any, let me know.

Quantity – how much is done

The previous section provided an overview of the quality of usage of industrial e-learning. This section seeks to examine the quantity of usage of industrial e-learning and will do so at three levels: organisational, courses and academics and features. The organisational section briefly examines what level of adoption industrial e-learning, in the form of LMSes, has amongst individual universities. The primary unit of teaching within a university and the primary organising construct within the LMS is that of a course. Typically the design and nature of each course is the responsibility of a particular academic. The course and academics examines adoption of LMSes at this level. Finally, each LMS provides a broad array of features and services that can be used to support learning. The features section examines how broadly these features are adopted within courses.

Organisations

The almost universal approach to the adoption of e-learning at universities has been the implementation of Learning Management Systems (LMS) such as Blackboard, WebCT, Moodle or Sakai (Jones and Muldoon 2007). Despite the associated complexities and risks almost every university seems compelled to have one (Coates, James et al. 2005). CMS have become perhaps the most widely used educational technologies within universities, behind only the Internet and common office software (West, Waddoups et al. 2006). Harrington, Gordon et al (2004) suggest that higher education has seen no other innovation result in such rapid and widespread use as the CMS. By 2005 almost every higher education institutions is or has plans to make use of a CMS (Salmon 2005). West, Waddoups et al (2006) express surprise at how quickly CMS have been adopted by universities, institutions which are know for reluctance towards change. Oblinger and Kidwell (2000) comment that the movement by universities to online learning was to some extent based on an almost herd-like mentality. Even though the perceived drivers for CMS are contestable, the perceived need for a CMS seems to be entrenched in the higher education sector (Wise and Quealy 2006).

Course Management Systems (CMS) are an essential feature of instructional technology at universities (Warger 2003). The 2003 Campus Computing project reports that more than 80% of United States universities and colleges utilize a CMS (Morgan 2003). Elgort (2005) cites work that indicates that 86% of 102 UK universities are using a CMS; all 18 surveyed New Zealand based institutions used a CMS; and all 33 Australian universities participating in a survey also used a CMS. Smissen and Sims (2002) found that 34 of the 37 Australian universities were using one of two CMS – Blackboard or WebCT. The almost universal adoption within the Australian higher education sector, a sector that has traditionally aimed for diversity and innovation, of just two commercial LMSs, which are now owned by the same company, is somewhat surprising (Coates, James et al. 2005). The mindset in recent times has focused on the adoption of the one-size-fits-all LMS (Feldstein 2006).

Courses

Even with the universal implementation of the LMSs, the level of adoption of those systems within many institutions has been limited (Jones and Muldoon 2007). In 2002, Lynch, reported in Shea et al (2005), estimates that while eighty percent of US-based four year colleges provide faculty access to LMSes, only twenty percent of staff use them in their courses. Vodanovich and Piotrowski (2005) report that of the 74% of faculty surveyed as being positive towards using the Internet for education, 70% view it as effective but only 47% actually used it for education. Other best practice implementations, recommended by LMS vendors, report no more than 55% staff adoption rates (Sausner 2005). Most universities are struggling to engage a significant percentage of students and staff in e-learning (Salmon 2005).

Even with a concerted effort to encourage adoption of the LMS, less than two-fifths of faculty in some disciplines use the LMS, and even then usage is limited to a small number of tools (Yohon, Zimmerman et al. 2004). Experience from one Australian university shows that as late as the second half of 2006, after over six years of institutional use of an LMS, only just over half of all courses offered had course websites (Jones and Muldoon 2007). Badge et al (2005) report about sixty percent adoption amongst staff but use is almost entirely for content distribution with some limited use of online assessment.

Features

Coates et al (2005) suggest that it is the uptake and use of features, rather than their provision, that really determines their educational value. While there is not sufficient research into LMS usage for a formal meta-analysis, patterns have begun to appear (Malikowski 2008). A pattern that fits with the content-centric focus of the quality of industrial e-learning observed in the previous section. The usage pattern observed by West et al (2006) is that instructors rarely adopt all of the features of an LMS. Malikowski (2008) found the nearly half of all faculty members use one feature or less with those using multiple features significantly more likely to have experience with interactive technologies. Rather than adopt all features of an LMS, instructors face many smaller adoption decisions as they perform a cost/benefit analysis of each individual feature (West, Waddoups et al. 2006).

Malikowski et al (2007) propose a model for synthesising research into LMS feature usage that consists of five categories of feature, a suggested order in which features are adopted and an indication of how often features are used. The model is shown in Figure 1.

Figure 1 – Flowchat of LMS feature usage research categories (adapted from Malikowski, Thompson et al. 2007)

The five categories in the Malikowski et al (2007) model are:

1. transmitting course content;
   Including the provision of files, grade information, and announcements to the entire class.
2. Creating class interactions;
   Interaction between course members either synchronously or asynchronously including LMS email, discussion forums, interactive chat etc.
3. Evaluating students;
   Tools, such as quizzes and assessment drop boxes, that aid in the evaluation of student learning.
4. Evaluating courses and instructors;
   Features, primarily surveys, that enable the evaluation of the course or instructor.
5. Computer-based instruction;
   Based on very simple features, when compared to much earlier research mentioned in a previous section. Features in current LMS relate to the adaptive release of content or other services based on student activity.

The Malikowski et al (2007) model also identifies these categories based on level of observed use with transmitting content most used; evaluating students and creating class interactions moderately used; and evaluating courses and instructions and computer-based instruction rarely used. This is illustrated through a series of tables that draw on usage figures from research literature. Table 1 is an adaptation and summary of this work. The last two categories are not shown in Table 1 due to extremely limited reported data on usage.

Note: In the thesis this is one table. However, that doesn’t work on the narrow confines of the blog. So I have to break it up into 3 different tables – which is what Malikowski et al (2007) did. They actually discussed in much more detail each category.

Location	N	Transmitting content
>38 American institutions (Woods, Baker et al. 2004)	862	86%	Not reported	59%
University of Wisconsin-Milwaukee (Morgan, 2003)	342	80%	81%	57%
University of Wisconsin-Whitewater (Morgan, 2003)	276	67%	87%	47%
University of Wisconsin-Stout (Morgan, 2003)	166	71%	67%	58%
University of Nebraska at Lincoln (Ansorge and Bendus 2003)	192	69%	not reported	not reported
Private US University (Dutton, Cheong et al. 2004)	191	1st and 2nd of 17	5th of 17	9th of 17

Table 1 – Summary of LMS usage for transmitting content (adapted from Malikowski, Thompson et al. 2007)
^a Results were provided for multiple semesters, only the most recent semester (spring 2002) shown here.
^b Results presented as a ranked list of 17, most used features first.

Location	N	Creating class interaction
		Asyncrhonous	Synchronous
38 American institutions (Woods, Baker et al. 2004)	862	25%	3%
University of Wisconsin-Milwaukee (Morgan, 2003)	342	28%	“Low levels”
University of Wisconsin-Whitewater (Morgan, 2003)	276	28%	“Low levels”
University of Wisconsin-Stout (Morgan, 2003)	166	24%	“Low levels”
University of Nebraska at Lincoln (Ansorge and Bendus 2003)	192	17%	1%
Private US University (Dutton, Cheong et al. 2004)	191	5th of 17	Last of 17

Table 2 – Summary of LMS usage for creating class interaction (adapted from Malikowski, Thompson et al. 2007)

^a Results were provided for multiple semesters, only the most recent semester (spring 2002) shown here.
^b Results presented as a ranked list of 17, most used features first.

Location	N	Evaluating students
		Quiz	Drop box
38 American institutions (Woods, Baker et al. 2004)	862	75% never in exams 59% never for quizzes	56% never use
University of Wisconsin-Milwaukee a (Morgan, 2003)	342	25% used assessments	Not reported
University of Wisconsin-Whitewater a (Morgan, 2003)	276	21% used assessments	Not reported
University of Wisconsin-Stout a (Morgan, 2003)	166	27% used assessments	Not reported
University of Nebraska at Lincoln (Ansorge and Bendus 2003)	192	Not reported	Not reported
Private US University b (Dutton, Cheong et al. 2004)	191	15th of 17	Not reported

Table 3 – Summary of LMS usage for evaluating students (adapted from Malikowski, Thompson et al. 2007)
^a Results were provided for multiple semesters, only the most recent semester (spring 2002) shown here.
^b Results presented as a ranked list of 17, most used features first.

References

Ansorge, C. and O. Bendus (2003). The pedagogical impact of course management systems on faculty, students, and institution. Web-based learning: What do we know? Where do we go? R. Benning, C. Horn and L. PytlikZillig. Greenwich, CT, Information Age Publishing: 169-190.

Badge, J. L., A. J. Cann, et al. (2005). "e-Learning versus e-Teaching: Seeing the Pedagogic Wood for the Technological Trees." Bioscience Education E-Journal 5.

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.

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.

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

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

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).

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.

Malikowski, S. (2008). "Factors related to breadth of use in course management systems." Internet and Higher Education 11(2): 81-86.

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.

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

Oblinger, D. and J. Kidwell (2000). "Distance learning: Are we being realistic?" EDUCAUSE Review 35(3): 30-39.

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.

Sausner, R. (2005). Course management: Ready for prime time? University Business.

Shea, P., A. Pickett, et al. (2005). "Increasing access to Higher Education: A study of the diffusion of online teaching among 913 college faculty." International Review of Research in Open and Distance Learning 6(2).

Smissen, I. and R. Sims (2002). Requirements for online teaching and learning at Deakin University: A case study. Eighth Australian World Wide Web Conference, Noosa, Australia.

Vodanovich, S. J. and C. Piotrowski (2005). "Faculty attiudes towards web-based instruction may not be enough: Limited use and obstacles to implementation." Journal of Educational Technology Systems 33(3): 309-318.

Warger, T. (2003, July 2003). "Calling All Course Management Systems." University Business  Retrieved 30 December, 2006, from http://universitybusiness.ccsct.com/page.cfm?p=315.

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 http://www.infodiv.unimelb.edu.au/telars/talmet/melbmonash/media/LMSGovernanceFinalReport.pdf.

Woods, R., J. Baker, et al. (2004). “Hybrid structures: Faculty use and perception of web-based courseware as a supplement to face-to-face instructions.” Internet and Higher Education 7(4): 281-297.

Yohon, T., D. Zimmerman, et al. (2004). "An exploratory study of adoption of Course Management Systems and accompanying instructional changes by faculty in the Liberal Arts and Sciences." Electronic Journal of e-Learning 2(2): 313-320.

The following post is a continuation of posts from the “Past Experience” section of chapter 2 of my thesis. It follows on from previous posts including: Ps Framework, History of technology-mediated learning, and the paradigms of e-learning.

I’m currently working on the “e-learning usage” section. The aim here is to look at the quality and quantity of usage of e-learning over the last 10 years or so – i.e. in the industrial e-learning paradigm. The quality and quantity overviews are part of the same section, so the following includes the current introduction to the overall section and then gets into the discussion of quality. Hopefully the quantity section will be up ASAP.

As always, I’m more than happy to here suggestions for improvement, disagreement or any comments in general.

Eventually, at some stage in the thesis, I will be arguing that the reason behind the less than stellar quality of most industrial e-learning is due to a combination of over-emphasis on the technology and its promise; and an on-going ignorance of what it takes to improve the majority of learning and teaching at a university. In particular, I think I’ll argue that this is the same ignorance that results in the majority of face-to-face teaching suffering from the same limited quality and that the current practices around industrial e-learning, rather than helping, are actually making things worse. At least for the majority of academics, though not the “long rangers” or Edupunks.

Industrial e-learning usage – quantity and quality

This section seeks to summarise what is known about the usage of e-learning within higher education. As shown in the previous section, the use of e-learning within higher education can be traced back to around the early 1990s. Rather than examine the entire history of e-learning, this section will focus on the usage of the predominant and current e-learning paradigm – industrial e-learning. This section examines usage of industrial e-learning from two perspectives. The first perspective is that of the quality of the learning experience for all participants. The second perspective is in terms of the quantity of usage, in terms of number of staff and students, organizations and the tools they use. In summary, while there has been widespread adoption of industrial e-learning by institutions, the quality of the e-learning is questionable and the level of use, while growing, is still not deep nor broad.

Quality

Research into teaching within higher education has developed a rich body of knowledge that links the quality of student learning outcomes with the conceptions of learning and a link between the conceptions of teaching held by academics and their approaches to teaching (Kember and Kwan 2000; Norton, Richardson et al. 2005; Eley 2006; Gonzalez 2009). Kember (1997) identified two main orientations to teaching and five underlying conceptions positioned as well-defined points on a continuum. The two main orientations are:

1. teacher-centered/content-oriented; and
   The focus is on the content to be taught and most associated with a focus on what the teacher does. Students are considered to be passive recipients of information.
2. student-centered/learning-oriented.
   The focus is on the learning process and most associated with a focus on what the student does. Students are seen as actively involved in the construction of their own learning.

Figure insert cross ref is a representation of Kember’s (1997) multiple-level categorisation model. An important point is that a transition from content-oriented to learning-oriented is a significant transition, while moving along the spectrum between the two conceptions under-pinning each orientation is relatively easy (Kember 1997).

Figure 1 – A multiple-level categorisation model of conceptions of teaching (Kember 1997)

In terms of which of these orientations is of the higher quality, it seems that the research literature is in increasing agreement. Herrington et al (2005) offer a 1974 quote from Olson and Bruner: “The acquisition of knowledge as the primary goal of education can be seriously questioned”. A model of learning that focuses on the deep engagement of students with complex and realistic tasks is preferable to a model that focuses on information provision (Herrington, Reeves et al. 2005). Theories of learning that currently hold greatest sway are those based on constructivist principles that suggest learning occurs through the active construction of knowledge supported by various perspectives within meaningful contexts with social interactions playing a critical role (Oliver 2000). These theories fit directly within Kember’s (1997) student-centred/learning-oriented orientation to teaching.

Given this recognition and fit it would be expected that the primary use of e-learning would be to support a student-centered/learning oriented orientation to teaching. In fact, it appears that the teacher-centred/content oriented dominants the practice of industrial e-learning. Much of the current research shows that academics use LMSes primarily to transmit course documents to students (Morgan 2003; Dutton, Cheong et al. 2004; Malikowski, Thompson et al. 2006). In the rush for universities to place courses on the Internet it is evident that the acquisition of knowledge remains the paramount goal for many educators (Herrington, Reeves et al. 2005). With few exceptions, almost all universities that have adoped a VLE have taken an approach where the VLE substitutes for existing media and have retained existing pedagogy (Salmon 2005) based on information distribution. For example, Dutton et al (2004) founding their study that most uses were anchored in traditional approaches to teaching with the technology primarily used as a substitute for the copier or projector.

Reeves, cited in Nichols (2003) describes the use of technology in education as far from innovative. From the evidence it is clear that there is an increasing use of industrial e-learning, however, there is not widespread change in pedagogy (Browne, Jekins et al. 2006). Industrial e-learning, for the most part, has involved fairly unsophisticated use of the available tools (Benson and Palaskas 2006). What limited use of technology there has been has sought to extend classroom pedagogy, either through the modest addition of resources or to extend the physical reach of the seat time-based teaching paradigm (Duderstadt, Atkins et al. 2002). Evidence suggests that universities are primarily using the LMS for administrative purposes with only a limited impact on pedagogy (OECD 2005). The vase majority of e-learning uses the same design and delivery model as on-campus courses (Twigg 2001).

Consequently, it is possible to suggest that the impact of industrial e-learning on the quality of learning has been somewhat limited. Harrington et al (2004) There is no evidence to suggest that adoption of an LMS leads to any increase in student learning to increases in the quality of teaching. Instead, the primary advantage behind use of an LMS was considered to be convenience to students (Harrington, Gordon et al. 2004). The value of e-learning is in maximising access to training opportunities and falls short of the potential for engaging learners in new ways (Pittard 2004). On most campuses the LMS is used to supplement traditional classroom courses (Warger 2003). In stark contrast to the imaginative and informal uses that students and faculty make of technologies, formal use of technology in most of higher education can be best described as sporadic, uneven and often low-level (Selwyn 2007).

However, given that web-based teaching is still less than ten years old, the application of the web to teaching and learning is still evolving (Bates 2004). Research into how people learn online is still in its infancy and more research is needed in order to understand the design of online learning that is engaging and effective (Herrington, Reeves et al. 2005). Research into the pedagogical issues that arise from the implementation of an LMS is still in its infancy (Coates, James et al. 2005). There is a need for further research to refine strategy dimensions defining approaches to teaching using the web (Gonzalez 2009). There are suggestions that no one approach, theory or solution is adequate for the design of e-learning (McLoughlin and Luca 2001).

References

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.

Benson, R. and T. Palaskas (2006). "Introducing a new learning management system: An institutional case study." Australasian Journal of Educational Technology 22(4): 548-567.

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.

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

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

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

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

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).

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

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.

Malikowski, S., M. Thompson, et al. (2006). "External factors associated with adopting a CMS in resident college courses." Internet and Higher Education 9(3): 163-174.

McLoughlin, C. and J. Luca (2001). Quality in online delivery: What does it mean for assessment in e-learning environments? 18th Annual Conference of the Australian Society for Computers in Learning in Tertiary Education, Melbourne.

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

Nichols, M. (2003). "A theory for eLearning." Journal of Educational Technology and Society 6(2): 1-10.

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

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.

Oliver, R. (2000). When teaching meets learning: Design principles and strategies for Web-based learning environments that support knowledge construction. ASCILITE’2000, Coffs Harbour.

Pittard, V. (2004). "Evidence for E-learning Policy." Technology, Pedagogy and Education 13(2): 181-194.

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

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

Twigg, C. A. (2001). "Innovations in Online Learning: Moving Beyond No Significant Difference."   Retrieved 30 December, 2006, from http://www.center.rpi.edu/Monographs/Innovations.html.

Warger, T. (2003, July 2003). "Calling All Course Management Systems." University Business  Retrieved 30 December, 2006, from http://universitybusiness.ccsct.com/page.cfm?p=315.

I’m currently working on chapter 2 of my thesis – the literature review. Mine is using the Ps Framework as the organising structure and also as part of the contribution of the thesis. I’m currently working on the “Past Experience” component of the Ps Framework. Recently, I posted the History of technology mediated learning section. It provides a brief overview of technology-mediated learning prior to e-learning – defined as using the Internet.

In writing that section it became readily apparent from the waves of different technology-mediated learning that nothing is ever forever, and yet many of the folk writing within a particular wave seem to think it will. At the same time, I’ve been reading and observing folk talking about the current wave of e-learning focusing on learning management systems (LMS) in the same way. The assumption that there is no other way to approach e-learning.

This strikes me as troubling and very short-sighted. So I’ve been tempted to include the following in the literature review to highlight that the current LMS approach to e-learning is just one of a collection of paradigms. That we will move onto something different and that a responsible and informed organisation would be aware of and planning for this paradigm change.

Not sure whether this will end up in the thesis. Still has some significant room for improvement. For example, connecting the increasing pressures towards the corporatisation of universities with the rise of the industrial paradigm. Or perhaps whether the Edupunk movement fits with post-industrial paradigm or whether it is a continuation of the lone ranger paradigm. Not to mention I haven’t given the text a thorough proof read.

Paradigms of e-learning

E-learning, as defined here, rose to widespread use during the 1990s with, as shown in the previous section, connections with a range of different and prior movements within the history of technology-mediated learning. This section identifies and seeks to understand a number of different paradigms, movements or discourses within the rise of e-learning. The aim is to illustrate that the models and perspectives underpinning and informing e-learning, like those of the various movements within technology-mediated learning, are changing and that with this change comes different perspectives about what is appropriate, what works and importantly how to best support e-learning. The six paradigms described here are meant as illustrative examples to achieve the purpose of recognising that different paradigms of e-learning have and will continue to exist and that understanding this offers insight for the design and support of e-learning. It is likely that it may be possible to identify additional paradigms and additional dimensions of these paradigms, but that is beyond the scope of this thesis.

A “paradigm” can be defined as the set of assumptions adopted by a professional community in order to allow its members to share perceptions and engage in commonly shared practices (Hirschheim and Klein 1989). The paradigm selects the ideas to accepted and rejected and grants privilege to certain logical operations to the deteriment of others (Morin 1999). Similarly, a discourse organises and constrains what can be said and done. Different discourses, like paradigms, may contain a distinctive set of rules and procedures which govern what counts as meaningful or senseless, true or false, normal or abnormal (Davis and Sumara 2006). Paradigms and discourses provide a particular framing for the problem and how it is understood. The common set of assumptions held by a community of a problem provide a vision of what the technology should and how progress should be measured (Allen 2000).

Not being aware of the existence and significant difference between paradigms can hinder evolution. Apart from embodying a particular way of understanding the world, the influence of a paradigm to e-learning also creates an inertia within organistions that can slow down moving from one paradigm to another. Allen (2000) makes the point that communities, such as organizations, make a social commitment through decisions to employ resources that are difficult to reverse and can explain how particular innovation paths are enabled, and others are constrained. For example, Bates (2008) suggests that universities with a history of operating within the paradigm of large scale autonomous distance education have been slower in adopting e-learning.

The following table provides a summary of the six paradigms that are described in more detail below. The time period for each paradigm provides a broad indication of when the particular paradigm was most dominant within e-learning. It is possible to find evidence of some paradigms, or aspects of some paradigms, throughout the history of e-learning. It is suggested that the “post-industrial” paradigm has not yet achieved, and may not achieve, a level of dominance.

Period	Title	Description
Late 1980s-late 1990s	Text-based CMC	Arising out of the CMC movement. Focus on using Internet for collaboration/communication by using the Internet to address issues with proprietary systems. Proprietary CMC systems ported to the Internet.
Early 1990s to mid-1990s	Lone ranger	Individual academics, generally not from a CMC paradigm, start using Internet tools as part of their teaching. Including pre and post Web.
Cottage industry	mid-1990s-1999/2000	Lone ranger attempts leveraged by the construction of small-scale systems to support the use of e-learning. Often many systems per institution.
Industrial	Late 1990s to TBA	Inefficiency, duplication etc lead to adoption of single enterprise system.
TBA-??	Post-industrial	Problems with monolithic, institutional focus of industrial lead to development of alternatives

Text-based CMC

The computer-mediated communications (CMC) movement outlined in the previous section (Section Error! Reference source not found.) support communication through the use of large time-shared computers to which all participants would log on to via terminals of phone lines. By the early 1990s there were over 900,000 hosts on the Internet and the number was growing by over 1000 per day and accelerating (Press 1992). The rise of the Internet, its availability to universities and a growing range of text-based communication tools such as Usenet news and Internet email enabled the CMC based learning practices to move to the Internet and address some perceived problems such as cost and support (Atkinson and Castro 1991; Gregor and Cuskelly 1994). The Internet offered alternatives to the three main services of CMC identified by Kaye (1989): electronic mail through Internet-based email; Computer conferencing through Usenet news and mailing lists; and information banks through a combination of these and services such as FTP. Arising out of the origins of the CMC movement, the emphasis in this phase was on the use of the Internet to enable communication and collaboration.

By no means was the use of the Internet for CMC within universities widespread. By 1994, using the Internet for any purpose was limited to a fraction of academics at US universities with significant differences in usage between academic disciplines (Goodman, Press et al. 1994). As late as 1993 the Internet still did not play a central role in consideration of the future of CMC. In an article (Holden and Wedman 1993) examining the future issues associated with CMC the Internet is mentioned a handful of times and is positioned as one of three widespread networks enabling CMC. Moving from existing CMC system to the use of the Internet as a medium for CMC was, to some extent, a paradigm shift for those institutions already heavily invested in non-Internet CMC.

Lone ranger

Many, if not most, innovations around learning and teaching are created by “lone rangers” (Jones, Stewart et al. 1999). The “lone ranger” approach is by far the most common model of e-learning course development (Bates 2004). The lone rangers are individual academics who are energetic and early adopters of innovation motivated by a desire to improve the accessibility and quality of their teaching (Taylor 1998). At its best the lone ranger approach lays a foundation for new teaching methods based on technology, however, it often happens in spite of institutional interest, tends to produce pockets of isolated activity and often fails to have any impact or recognition at the institutional level (Taylor 1998).

The invention of the World-Wide Web and its capabilities to present multimedia made online education increasingly accessible and expanded the range of disciplines that could be offered online (Harasim 2000). The rise of the web made it clear that e-learning that used the Web as the primary interface were becoming the most successful (Stiles 2007). The relative ease of web-publishing encouraged lone-ranging academics from a range of disciplines to experiment with the new interface in a variety of ways. This contributed to the development of the second of the models for online courses identified by Harasim (2000), and perhaps the current primary model, based around information publishing.

Cottage industry

A limitation of the lone ranger approach is that quality teaching with technology requires expertise in a range of tasks, not just learning design and it is difficult for teachers to gain this breadth of knowledge without workload or quality impacts (Bates 2004). There is a gap between the lone rangers and the majority of academic staff that is unlikely to be bridged without assistance (Jones, Stewart et al. 1999). The mid to late 1990s saw widespread recognition that the majority of academic staff simply did not have the skills or time to individually design their use of Internet technologies (Goldberg, Salari et al. 1996; Jones and Buchanan 1996).

To address this gap, the mid to late 1990s saw the development of a diverse collection of intranet-based systems, home-built virtual learning environments, off-the-shelf products and customized groupware solutions by different schools, faculties or research initiatives (Dron 2006). At their best these systems were tailored to the needs of the learners and teachers in their original context. At their worst they were often unreliable, poorly maintained and each academic grouping having their own system contributed to issues around duplication, scalability and consistency. The de-centralised origins of many of these systems meant that few integrated with central management systems which led to duplication of user databases and often led to inconsistencies and disparities (Dron 2006).

Industrial

As use of e-learning increases institutional management start to identify concerns around quality, duplication, lack of standards, and costs; and consequently start the process of setting priorities, establishing technical standards, providing support and controlling budget and workload (Bates 2007). A number of institutions questioned whether they needed to be in the business of building e-learning systems. The need for management to address these issues, the arrival of commercial Learning Management Systems (LMS – further explanation of the LMS in the Product section insert cross ref) vendors and the rise of enterprise software contributed to the adoption of the LMS as an enterprise system. The LMS shifted from being based on the bottom-up work of the loan rangers into the very embodiment of a top-down institutional strategy, to a dominant element of higher education’s information technology capability (Katz 2003).

By 2006, Browne et al (2006) to see two key trends in e-learning in the UK higher education system: the first is the on-going preference of institutions to use commercial systems; and an emerging trend towards open source systems. Eventually the market for LMS matured with a range of mergers and takeovers resulting in the overwhelming domination of the market by two products: a commercial product in Blackboard and an open source product in Moodle (Stiles 2007). It will be argued within the Product section (insert crossref) that an open source LMS does not represent a paradigm shift, but instead simply allows a university to continue the existing industrial paradigm by using ERP-based methodologies to maintain the LMS.

Browne et al (2006) suggest that the on-going preference by institutions for commercial systems could be “interepreted as inertia due to expensive ‘lock in'”. Landon et al (2006) suggest that user dependency on these systems signals an end to the “exuberant exploration of competing systems” and suggests a future focused on meeting user demand and making systems ever more efficient to use. Wilson et al (2006) suggest that the focus in recent years on the improvement of the technology of the LMS has lead to the marginalization of software and techniques that do not fit within the LMS patter. The industrial VLE model represents a hegemony in which the institution controls the environment (Stiles and Yorke 2006).

Post-industrial

The limitations of industrial e-learning, the subsequent negative experiences of students and academic staff and the development of alternate technologies has contributed to the evolution of e-learning practice into e-learning 2.0 (Downes 2005). An evolution that can be seen as a change in paradigm or discourse around e-learning as it questions the assumptions of the industrial paradigm of e-learning (Jones 2008). Apart from the limitations of the industrial model, Stiles and Yorke (2006) identify three developments that are helping create the post-industrial challenge to industrial e-learning. These are the growth of:

1. service-oriented architectures and cloud computing;
   These technologies enable a post-industrial approach to e-learning systems where parts can be included as and when needed and control can also be granted when and where needed (Dron 2006).
2. systems, such as ePortfolios, where the question of information ownership is less than clear; and
   The ePortfolio is a personal place that belongs to the student to create and showcase their work (Downes 2005). Increasingly ePortfolios, like other applications, exist outside of the institution’s LMS.
3. Web 2.0 and social software.
   The evolution of the Web into Web 2.0, has resulted in a Web that is no more open, personalised, participative and social (Ravenscroft 2009). Social software and informal instant communication technologies can help spread control move evenly through the learning system (Dron 2006).

These developments challenge the institutional approach in terms of ownership of processes, systems and information and create uncertainties around institutional strategy and policy (Stiles and Yorke 2006). These changes represent a major challenge to the hegemony of the LMS (Stiles 2007). It is clear that social software is part of an evolving paradigm that has contributed to a new and important family of technology-mediated learning practices that require conceptualised and investigated (Ravenscroft 2009). There is a need to consider how learning can be reformulated to address the tension between a highly structured and authority driven view of learning and the more collaborative and volatile nature of the social web (Ravenscroft 2009). In order to be ready for the changes ahead, there is a need for institutions to be reconsidering their strategies and policies now (Stiles 2007). However, it is still early days and it is arguably time to focus on projects that stimulate reflection and asking of questions, rather than jumping prematurely to specific solutions (Ravenscroft 2009).

References

Allen, J. (2000). "Information systems as technological innovation." Information Technology & People 13(3): 210-221.

Atkinson, R. and A. Castro (1991). The ADEnet project: Improving computer communications for distance education students. Quality in Distance Education: ASPESA Forum 91, Bathurst, NSW: Australia, Australian and South Pacific External Studies Association.

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.

Bates, T. (2007). Strategic Planning for E-Learning in a Polytechnic. Making the Transition to e-Learning. M. Bullen and D. Janes. Hershey, PA, Idea Group Inc: 47-65.

Bates, T. (2008). Transforming distance education through new technologies. The International Handbook of Distance Education. T. Evans, M. Haughey and D. Murphy. Bingley, UK, Emerald Press: 217-235.

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.

Davis, B. and D. Sumara (2006). Complexity and education: Inquiries into learning, teaching, and research. Mahwah, New Jersey, Lawrence Erlbaum Associates.

Downes, S. (2005) "E-learning 2.0." eLearn Volume,  DOI:

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.

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.

Goodman, S., L. Press, et al. (1994). "The global diffusion of the Internet: patterns and problems." Communications of the ACM 37(8): 27-31.

Gregor, S. and E. Cuskelly (1994). "Computer-mediated communication in distance education." Journal of Computer Assisted Learning 10(3): 161-181.

Harasim, L. (2000). "Shift happens: online education as a new paradigm in learning." The Internet and Higher Education 3(1-2): 41-61.

Hirschheim, R. and H. Klein (1989). "Four paradigms of Information Systems Development." Communications of the ACM 32(10): 1199-1216.

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

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

Jones, D. and R. Buchanan (1996). The design of an integrated online learning environment. Proceedings of ASCILITE’96, Adelaide.

Jones, D., S. Stewart, et al. (1999). Patterns: Using Proven Experience to Develop Online Learning. Proceedings of ASCILITE’99, Brisbane, QUT.

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

Kaye, A. (1989). Computer-mediated communication and distance education. Mindweave: Communication, computers and distance education. R. Mason and A. Kaye. Oxford, UK, Pergamon Press: 3-21.

Landon, B., T. Henderson, et al. (2006). MIT Peer Comparison of Course/Learning Management Systems, Course Materials Life Cycle, and Related Costs, Massachusetts Institute of Technology 90.

Morin, E. (1999). Seven complex lessons in education for the future. Paris, France, UNESCO Publication.

Press, L. (1992). "The Net: progress and opportunity." Communications of the ACM 35(12): 21-25.

Ravenscroft, A. (2009). "Social software, Web 2.0 and learning: status and implications of an evolving paradigm." Journal of Computer Assisted Learning 25(1): 1-5.

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

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Anyone who knows me, knows that I have a particular disdain for the perspective that e-learning within a university can be treated as an IT project and in particular as the implementation of an Enterprise Resource Planning (ERP system). i.e. the LMS is an ERP and should be implemented as one. Some previous rants can be found on this blog, including:

• one ring to rule them all;
• comparison to the past;
• ignoring the messiness of e-learning;
• Edupunk versus “vanilla implementation”; and
• Ignoring the protean nature of ICTs.

Another flaw in current practice

Time for another argument against the view that LMS implemetation and consequently the support and development of e-learning within a university should not be treated as an IT project and certainly not as an IT project implementing an ERP. Actually, the following is probably not exactly an argument against that. It’s probably better characterised as an illustration of just how disconnected institutional strategies around LMSes and elearning are from research about the implementation of ERP systems.

The LMS implementation will improve L&T and solve all ills

The implementation of new enterprise level software is really hard and expensive. In order to justify this organisations have to make all sorts of claims. Some examples I’ve seen include:

• Adoption of {insert LMS name} will aid the institution in becoming a recognised leader in flexible learning.
• This is an opportunity to improve e-learning experiences for both staff and students by planning for course redesigns upfront.
• The transition can be utilised to establish quality check and reviews process and focus on producing quality online course delivery.

ERP implementation results in performance degradation

I’ve always thought that these sorts of views are just plain silly and ignore the reality of what people have to go through when a new system, especially when around something as variable and important as teaching, is introduced. Apart from personal experience, I’m also aware that this is one of the common findings from the ERP literature. In my thesis wanderings, I’ve come across a quote to illustrate this from Katz (2003)

In late 2002, the EDUCAUSE Center for Applied Research (ECAR) conducted research on enterprise systems in higher education, focusing on the big three administrative systems: student, financial and human resources. Among the many findings of the study is the observation that implementers of these systems initially experience a loss of functionality and a degradation of performance as employees grapple to come to terms with the new technologies and processes that these systems force.

Further down

In the context of course management systems, recent ECAR research suggests a similar socialization curve. The implementation of new software is often accompanied by a short-term loss in productivity as new tools, methods and processes are assimilated. Teaching and learning are inherently and historically social activities and, as such, are even more subject to dislocations associated with new techniques and technologies.

Katz goes onto report that ECAR findings that once the users and the organisation become familar with the new ERP system that productivity gains are reported. However, does this mean that the adoption of a new LMS will provide gains in terms of productivity or improvement in the learning and teaching experience?

You have to remember that the ECAR report focused on “greenfield” implementation of ERP systems. i.e. the institution had non-ERP systems and then implemented their first ERP system and consequently found productivity gains. By 2009, most universities have already implemented e-learning ERPs (i.e. an LMS). They are replacing one LMS with another.

I don’t believe the replacement of one LMS with another LMS will provide any significant increase in productivity. It is widely accepted that all LMS are essentially the same (and yes, I don’t think Moodle is all that different from Blackboard).

If the new ERP is essentially the same as the old ERP, then there will be no gains. Unless of course there were problems with how the old ERP was used or supported. And I don’t think implementing a new ERP is going to solve those problems.