learning - Academic Conferences Limited

Arno Louw
programmes, and within the parameters of reasonably practicable implementation. (UJ Senate
Teaching and Learning Committee 2009).
A proof to the growth in student numbers and the accompanying lecturer growth is shown in reports
from CenTAL for 2008 to 2010. The reports reveal an increase in students and lecturers using the
LMS. One can arguably agree that these numbers will inevitably become unlimited as usage of the
Internet, ICTs, Web 2.0 learning tools, and an LMS as learning tool increases over the next few years.
Consequently, the same approach for using these numbers was change when the 2010 report was
published. Therefore, the approach to what is seen as effective use of the Internet and World Wide
Web (WWW) as learning tools, has become biased toward the actual teaching needs from lecturers
and the learning needs of students. This discrepancy highlights an immediate need for lecturers to be
related to the rate of adoption to incorporating the Internet and the WWW into blended learning.
3. Methodology and data gathering
Over this decade, ICTs, Web 2.0 facilities and a new generation of students have also become part of
daily teaching practices and many faculties had to adapt to new and innovative ways of teaching and
learning. Since the University of Johannesburg has adopted a blended learning approach, the LMS
has been upgraded in 2002, 2006 and in 2010 the pilot launch of the new Blackboard system had
been launched in 2011. I have been involved in these processes and implementations and have
worked closely with over one hundred lecturers in nine faculties over four campuses. The process
followed for presenting the research in this paper, comes from notes, conversations, being involved in
implementation procedures and training many lecturers and students since 1998. The subsequent
involvement in many research project in this time, has delivered trends which I share within the scope
and limitation of this paper. It is intriguing to observe how lecturers adapt and come up with new
initiatives in spite of growing student numbers and limited physical classroom space on campuses.
Therefore, the discussions, conclusions, and given facts in this report can be substantiated with
literature, data from and data generated by the LMS currently in use. Apart from using data from
many cases, I will mostly focus on projects involving large classes in modules for Commercial Law, B
Tech bridging courses, Language Skills for Science, and Business Management. The findings,
conclusions and arguments are presented together with the discussions as to promote the integrated
nature of this research. However, a separate section for findings and conclusions will also be given as
an accumulation of what is assumed to be effective blended learning.
4. Impact and considerations
The diffusion theory has its origins in work done in 1943 in rural sociology. According to Jones,
Jamieson and Clark (2005), the Diffusion, per se, is defined as “a process that occurs over time and
can be seen as having five distinct stages – knowledge, persuasion, decision, implementation, and
conformation.” The diffusion theory framework involves four main elements: innovation,
communication channels, time and the social system. Furthermore, these elements “influence the
adoption or rejection of an innovation in a complicated, interdependent way” (op. cit.). The same
authors refer to Jacobsen (2000) who used the five stages in adopting technology for teaching and
learning and various other authors (Bronack & Riedl 1998; Schefstrom et al. 1998; Surry & Farquahar
1997) who used several diffusion theories used and relates specifically to an instructional technology
model. The common elements of most diffusion theories are quoted in table 1 as proffered by Jones,
Jamieson and Clark (2005) and given in relation to the UJ context.
Table 1: Common elements of diffusion theory
Influential Elements Description
Diffusion is a process that occurs over time and can be
Diffusion process
seen as having five distinct stages:
Adopter categories
Perceived attributes
Rate of adoption
Lecturers vary greatly in their willingness to adopt a
particular innovation. Individual characteristics can be
used to divide the population into categories:
How innovation is perceived, influences the adoption
decision. Five perceived attributes of an innovation
have been shown to have strong influence:
The relative speed with which lecturers in a social
system adopt innovation. Innovations are diffused over
time in a pattern that resembles an s-shaped curve.
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