Lessons for from past experience

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.

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