asked to pass a student
if they answer Q1 as 'a' and Q2 as 'a'. This also fits a passing student's
profile as determined from past records.
This technology continuously can update the test database
every time a new student takes the test allowing Knowledge Builder
to recreate the decision tree based on this new information. The test
itself improves, or learns, with additional profile data.
I developed such an adaptive testing system at Miami-
Dade Community College in 1988. Using records from non-adaptive tests
given throughout the semester, it generates an adaptive mid-term exam
and an adaptive final exam. For this, I received another award from
the League of Innovation.
Recent Advancements
The ability to manage complex, dynamic configurations
is an important advance in some knowledge-based systems. Configuration
systems, similar to those frequently employed in manufacturing and
assembly, configure products with numerous optional features. For
example, every Mack truck is unique - built to order with an assortment
of options making it truly unique. To configue a Mack truck for manufacturing,
the manufacturer must ensure that they combine the correct engine
with the correct transmission; the windshield wipers match the windshield,
and so on for numerous other options. The detail level goes down to
the threads of the wheel lugs. It takes thousands of constraints for
everything to fit perfectly. The choice of one-part type forces the
employment of other compatible parts while avoiding those that cause
conflicts. The manufacturer must also determine the exact order to
assemble the parts. With the Configurator features of a product such
as Knowledge Builder, this type of logic is easy to express.
This type of logic is also perfect for configuring training
systems. If you establish the training goals, review the training
record of the student, and give the student an adaptive assessment
test for placement details, then it is possible for the
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knowledge based system
to configure an individualized curriculum, different and perfectly
suited to each individual student. This curriculum then drives all
of the training and testing to achieve the specified training goals.
The Narwhale Student Advisement System
In 1996, I developed an Internet enabled student
advisement system for Miami-Dade Community College - project Narwhale.
The system enabled a student to enter a student number to access their
student records from anywhere in Florida. On receiving the student
record, which included standardized test scores and a list of completed
courses with status and grades, the student selected a major. The
Configurator (a predecessor of Knowledge Builder) contained the advisement
knowledge necessary to advise a student on the courses, the campus,
and the times for classes over the next two semesters. With just these
two entries, the Configurator generated a proposed schedule more accurately
and more consistently than the professional advisors whose advisement
knowledge was employed to build the system. Advisors, not faculty,
on the Miami-Dade staff created the graphical decision trees that
contains the system's knowledge components. The advisors distributed
the work between themselves to create the advisement knowledge for
over two hundred and fifty majors. The advisors on the Miami-Dade
staff were not programmers and they received only four hours of training.
If it is So Great, Why Isn't Everybody Using
It?
Knowledge based adaptive training is primarily under-
utilized because it requires slightly more thought than the less effective
approaches, and today's instructional technologists are generally
unaware of its benefits.
Today's instructional technologists create computer
driven multiple-choice tests with record keeping by using specialized
course authoring tools, HTML, and Learning Management Systems. They
also create hierarchically arranged tutorials and tests with multi-media
effects, etc. using these same tools. This is easy to do since it
requires only a surface knowledge of the domain being taught. Most
Internet enabled training systems are now built with the emphasis
on multi-media and simple statistical scoring rather than on optimal
individualized training.
That extra thought and effort to improve the
structuring of the test and tutorial materials, and their interdependencies,
is often skipped or not available to the instructional technologist.
The training most instructional technologists receive does not teach
them how knowledge based systems would enhance their ability to do
more efficient training and testing as well as including the multi-media
effects that enhance any learning experience.
To create an adaptive system one must add a deeper
layer of knowledge that defines how testing and tutorial materials
are related. To utilize this knowledge, the domain experts must acquire
the basic skills of working with a knowledge based system development
tool or they must work with someone who has those skills. Knowledge
based system technology has advanced so far, that anyone with basic
computer literacy skills can design, capture, and create the branching
logic needed in an adaptive system. Knowledge |
