Overview:
Model-Centered
Instruction (MCI) is a set of principles to guide instructional designers in
selecting and arranging design constructs, so it is appropriately called a design theory. It favors designs that
originate with and maintain the priority of models as the central design
structure.
Background: A Layered View
of Design—MCI
is closely tied to a layered view of designs. This view assumes that a designer
organizes constructs within several somewhat independent layers characteristic
of instructional designs: the model/content layer, the strategy layer, the
control layer, the message layer, the representation layer, the media-logic
layer, and the management layer. The designer selects and organizes structures
within each layer in the process of forming a design. The designer also aligns
the structures within layers with those of other layers to create a vertical
modularity in the design that improves its manufacturability, maintainability,
and the reusability of designed elements. A design layer is typified by:
characteristic design goals, building-block constructs, design processes,
design expression and construction tools, and principles to guide the
arrangement of structures. Over time, a layer becomes associated with
specialized skill sets, publications, and a design culture. Instructional
theories provide principles to guide design within one or more of these layers,
but no theory provides guidelines for all of them, suggesting to designers the
wisdom of subscribing to multiple local theories of design rather than a single
monolithic theory.
MCI Theory: Model-Centered
Instruction, as any design theory, can be described in terms of the
prescriptive principles it expresses for each of these layers.
Content: The content of instruction
should be perceived in terms of models of three types: (1) models of
environments, (2) models of cause-effect systems (natural or manufactured), and
(3) models of human performance. Together these constitute the elements
necessary for performance and therefore for learning. Content should be
expressed relative to the full model structure rather than simply as facts, topics,
or lists of tasks.
Strategy: The strategy of
instruction should be perceived in terms of problems. A problem is defined as
any self-posed or instructor/designer-posed task or set of tasks formed into
structures called “work models” (Gibbons, et al., 1995). These are essentially
scoped performances within the environment, acting on systems, exhibiting
expert performance. Problems may be presented as worked examples or as examples
to be worked by the learner. During problem solution instructional augmentations
of several kinds may be offered or requested. Dynamic adjustment of work model
scope is an important strategic variable.
Control: Control (initiative)
assignment should represent a balance between learner and instructor/designer
initiatives calculated to maximize learner momentum, engagement, efficient
guidance, and learner self-direction and self-evaluation. Instructional
controls (manipulative) should allow the learner maximum ability to interact
with the model and the instructional strategy’s management.
Message: Contributions to the
message arise from multiple sources which may be architecturally modularized:
(1) from the workings of the model, (2) from the instructional strategy, (3)
from the controls management, (4) from external informational resources, and
(5) from tools supplied to support problem solving. The merging of these into a
coherent, organized, and synchronized message requires some kind of message or
display management function.
Representation: MCI makes no limiting
assumptions about the representation of the message. Especially with respect to
model representation, it anticipates a broad spectrum of possibilities—from
externalized simulation models to verbal “snapshots” and other symbolics that call up and make use of models learners
already possess in memory.
Medial-Logic: MCI makes no assumptions
regarding the use of media. Its goal is to achieve expressions that are
transportable across media. The selection of the model and the problem as central
design constructs assist in this goal.
Management: MCI makes no assumption
about the data recorded and used to drive instructional strategy except to the
extent that it must parallel the model’s expression of the content and align
also with the chosen units of instructional strategy.
Scope/Application:
When the designer enters design from the model/content layer, the priority of concerns follows this order:
(1) What is the appropriate cause-effect model (or system) the learner should interact with?
(2) What is the appropriate level of denaturing (reduction in fidelity and granularity) of models for a given learner?
(3) What sequence or set of problems should the learner solve as a lens into or a mask on this model?
(4) What resources and tools should be available as solving takes place?
(5) What additional instructional augmentations should be supplied to support the solving of the problem?
Designers
can (and do) enter design at any layer, placing highest priority on one of
them. Design decisions made within the priority layer, however, then constrain
decisions within the remaining layers and often either create or destroy other
layers and sub-layers of the design. This principle leads to important insights
into the order of instructional design activities and thus layers provide a
basis for generating and ordering design processes dynamically.
An
analysis approach called the Model-Centered Analysis Process (MCAP) identifies
the elements of all three model types and relates them directly to problems.
This automatically unites the specification of the learning environments,
instructional functionalities, surface dramatics, and logical structures (if a
computer is to be involved, which is not assumed).
Example:
A
model-centered design is centered around the model(s)
selected by the designer. This is often a difficult and subtle choice. It is
easy, for example, for a designer to mistakenly provide an interactive panel
simulation for chemical analysis equipment when what is needed is observation
and interaction with an expert model of interpreting the outcome of chemical
tests. The panel model can become the center of the designer’s attention
because it is concrete and programmable, shifting attention away from the more
important performance model that the learner would benefit from more.
Principles:
The principles of model-centered instruction are:
1. Experience: Learners should be given
maximum opportunity to interact for learning purposes with one or more systems or
models of systems of three types: environment, system, and/or expert
performance. The terms model and simulation are not synonymous; models can be expressed in a variety of
computer-based and non-computer-based forms.
2. Problem solving: Interaction with systems
or models should be focused by the solution of one or more carefully selected
problems, expressed in terms of the model, with solutions being performed by
the learner, by a peer, or by an expert.
3. Denaturing: Models are necessarily
denatured from the real by the medium in which they are expressed. Designers
must select a level of denaturing matching the target learner’s existing
knowledge and goals.
4. Sequence: Problems should be arranged
in a carefully constructed sequence for modeled solution or for active learner
solution.
5. Goal orientation: Problems selected
should be appropriate for the attainment of specific instructional goals.
6. Resourcing: The
learner should be given problem solving information resources, materials, and
tools within a solution environment (which may exist only in the learner’s
mind) commensurate with instructional goals and existing levels of knowledge.
7. Instructional augmentation: The learner
should be given support during solving in the form of dynamic, specialized,
designed instructional augmentations.
References:
Duffin, J.W. & Gibbons, A.S. (2001) Decompressing and Aligning the Structures of CBI Design. [download as Word document]
Gibbons,
A. S. (in press). Model-Centered Instruction. Journal of Structural Learning and Intelligent Systems. [download
as PDF document]
Gibbons,
A. S., Bunderson, C. V., Olsen, J. B., &
Robertson, J. (1995) Work models: Still beyond instructional objectives. Machine-Mediated
Learning, 5(3&4), 221-236.
Gibbons,
A. S., & Fairweather, P. G. (1998) Computer-Based Instruction: Design and
Development. Englewood Cliffs, NJ: Educational Technology Publications.