Learning Styles and the Engineering of Learning Experiences

John Traxler  University of Wolverhampton.   UK.


There are currently many accounts of the development and use of new learning technologies but there are few references to any over-arching theoretical basis for these activities.   Consequently, whilst theories of learning style are important, there is no framework that automatically addresses them in the development or deployment of technology supported learning.

There are also other factors, such as tool-set, cost and conceptions of teaching style, that have a place. Integrating and reconciling such factors in a transparent and structured way is critical for the success of technology supported learning.

This paper draws on the engineering metaphor used in industrial software development to explore a paradigm for developing educational experiences that incorporate new technologies of learning. 

In creating artefacts we resort to a variety of paradigms to conceptualise the process of creation. Sometimes the model of experts (with apprentices) is used, sometimes the idea of automata is used.  In the first case, the nature of the creative process, for example the process of making a violin, can scarcely be articulated and only passes from one generation to the next by osmosis. In the second case, the nature of the creative process, for example the manufacture of volume cars, can not only be articulated but can also be delegated to machines.

The Engineering Paradigm

In the area of traditional engineering - civil, mechanical, etc - a vaguely defined set of practices, standards and tools have been fairly successful and engineering has superseded craft and cottage industries as a model for how industrialised societies meet many of their physical needs.

This vague notion of engineering has been turned into a much more specific agenda and adopted as the dominant paradigm for the development of computer software - hence, software engineering. In the course of the software community's debate about its own identity and its attempts to develop and clarify a sustainable and explicit production paradigm, an idealisation of engineering has emerged.  Later, systems thinking, with notions of purpose, synergy and components was incorporated into the engineering paradigm and the focus of computer professionals moved from the development of software to the engineering of information systems.

Courseware Engineering

The idea of engineering as idealised above can provide a useful framework for developing self-contained episodes of learning.

The idea had been explored before in the guise of courseware engineering.  Systems thinking would have suggested that this concept was doomed from the outset. It drew the system boundary too narrowly (around a piece of software rather than the total learning experience) and committed to "how" to teach before thinking about "what" to teach. The result was expensive multimedia software that was not integrated into courses (because of a lack of synergy with any of the other media being used to teach) or was quickly discarded (because adaptation, maintenance and re-use were difficult).

Learning Engineering

The proposed adoption of the engineering paradigm into learning is more fundamental: the appropriate system to analyse and design is the total learning experience. The learning experience with the most clearly defined boundary is the course and its purpose or goal as a system is defined in its Aims and Objects. If we are to proceed from analysis to design (We can consider other possible lifecycle models.), this means reasoning about the “what” before the “how”. If we can model accurately “what” the Aims and Objectives mean, we can check consistency and completeness. We can then proceed to synthesise an implementation or delivery of the module where each type of learning activity (in effect, each sub-system) is supported by the technology (presentation, workshop, web-site, book, hand-out etc.) most educationally appropriate and cost-effective.

This brings us back to learning styles.

If this seems to produce too much freedom of choice in how to develop courses, there are always constraints on the design of the module.  These include designing it for all types of learner (surface, serialists, holist, whatever) and for specific market and institutional characteristics (stability of content, student cohort size, distance learning mode, and so on).

Implementing the learning experience can be described as an optimisation exercise where the variables roam the space defined by the constraints.

The presentation will address how learning styles contribute to defining this space.

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