Information Technology Reference
In-Depth Information
Weber, 1997). In a monistic view of science, the boundaries of the discipline are usually clear,
there is a paradigm (system of working) and infrastructure (journals, committees, etc.) that
provide coherence (Banville & Landry, 1989). While the research and publication areas of the
emerging discipline of Information Systems owed much to the social sciences, its practice
relied heavily on scientific, mathematic, and engineering disciplines. Software was closely
associated with the underlying hardware — detailed knowledge of the underlying architec-
ture and operation was essential if efficient programs were to be written. As business began
to use computers to process and filter huge quantities of data, the demand for systems and
their developers increased exponentially. The impact on computer technology in engineering
and manufacturing had reduced the number of skilled people required. Many of these people
migrated into Information Systems, bringing with them a mindset that was grounded in logical
thinking, rigor, and mathematical notation. While the skills required by Information Systems
(IS) graduates have been a frequently studied topic (Latham, 2000; Snoke & Underwood,
1999; Standing & Standing, 1999) (of which the debate usually centers on the relative
importance of technical skills, interpersonal and communication skills, and the depth of
business knowledge and skills), little has been written about the skills required by information
systems lecturers.
As near as it may be established, the average age of an Australian Information Systems
academic is 53 years. An informal analysis of the backgrounds and qualifications of
Australian Information Systems academics shows that the overwhelming majority has
qualifications in other disciplines and has migrated to Information Systems in later life, many
from computer science. Information Systems curricula show a heavy dependency on the
thinking of the 1980s and early 1990s. The traditional systems life cycle view, derived from
engineering disciplines, is no longer appropriate for many Information Systems projects. In
particular, Web commerce development projects require much shorter timelines and ability
to respond to rapidly changing requirements than older projects. Newer systems also require
different skills and thinking styles, and it is hard to see where these are being engendered
in university curricula.
Lecturers are unlikely to change their mindset and are insufficiently resourced to
customize the curriculum at a micro-level. The only practicable approach is to give students
the resources they need to customize the curriculum to suit their own cognitive styles.
DEVISING AN INITIAL METACOGNITIVE
PROGRAM FOR STUDENTS
To be effective, any metacognitive program would have to consist of three parts:
1.
A self-diagnostic tool
2.
Education in thinking styles, memory, and adapting strategies
3.
Continuing access to suitable resources as metacognitive awareness increases
It should be noticed that diagnosis is the first phase. Consultations with psychologists
suggested that students might incorrectly self-label if education preceded diagnosis, and this
would lead to inappropriate metacognitive strategies. Obviously, any diagnostic tools would
have to be as simple as possible while retaining usefulness. Taking these components in turn,
discussion follows.
