A method is proposed whereby the underlying domain knowledge is represented such that illustrative examples may be generated on demand. This method has the advantage that the generated example can follow changes in the domain in addition to allowing automatic customization of the example to the individual

Johannes, James D.

Selig, William John

English

NASA Technical Reports Server

N88:16400
The Use and Generation of Illustrative Examples
in Computer-Based Instructional Systems
William John Sellg
NASA/MSFC
and
James D. Johannes
The University of Alabama In Huntsville
ABSTRACT
Examples are both pervasive and necessary in the teaching of
new material. One of the more common types Is the Illustrative
example, used to clarify and Instantlate general statements. The
use of Illustrative examples In computer-based Instructional
systems to date, when they have been used at all, has been
general ly limited to some form of 'canned' text. This method has
the problem that as the system evolves, the canned supportive
material does not necessari ly follow along. This paper proposes a
method whereby the underlying domain knowledge Is represented
such that Illustrative examples may be generated on demand. This
method has the advantage that the generated example can follow
changes In the domain In addition to allowing automatic
customization of the example to the Individual.
I. Introduction
As the human race continues Its venture into space, the
supportive and operational systems necessary to accomplish this
goal become increasingly more complex. It is becoming ever harder
for an Individual to grasp the overall function of the systems
Involved and thus It Is becoming ever harder to effectively use
these systems. Owing to both the growing complexity and size of
our space effort, the need for efficient training systems I$
becoming critical. Computer based Instructional systems, while
still In various levels of development, hold out hope for Just
such efficiency of training due to their ability to tailor
themselves to individuals' needs and their ability to time-share
among many users [6,8]. In these Instructional systems, examples
will play an important role In Increasing the efficiency of
learning.
Examples have long been used In regular Instructional
materials to facilitate learning In a number of ways. Initial
examples introduce material and pique user Interest, thus
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motivating the learning experience. Application examples anchor
the learned material In a wider framework, thus Increasing
retention and understanding. Evidential examples and control
examples are used to support or test various Instructional
statements. Final ly, I I lustrative examples and counterexamples
are used to both clarify and define the limits of applicability
of general statements [2]. This paper Is specifically concerned
with the use and generation of these I I lustrative examples,
hereafter referred to as Just 'examples'
Current approaches to the use of examples In computer-based
Instructional systems Involve some form of presuppl led text to
determine what to say. These cover the range from 'canned' text
to templates to feature scripts [1]. Alternatively, the system
may contain an Internal expert to generate an example of a
'better way', as in some coaching systems [9]. The problem with
all of these systems, however, Is that they depend upon someone
deciding ahead of time what an appropriate example Is for some
given concept. In some domains, such as mathematics, this Is
adequate. In other domains, such as spacecraft or computer system
operations, the abi I ity to tailor the example to the user and his
working environment Is potentially more useful. This paper
proposes Just such a method.
This method of example generation uses a knowledge
representation scheme based upon Sowa's conceptual graph theory.
In this scheme there exists what Sowa cal Is a semantic net,
similar to a cross-I inked hierarchy, In which the basic
interrelationships among the primitive concepts in the domain Is
captured. Domain knowledge about higher level concepts and actual
entitles Is represented by conceptual graphs, simi lar to Schank's
conceptual dependency theory representation, but with a
potential ly unlimited set of relations [5,7]. To generate an
example from this domain representation, the concept to be
exempl ified Is also represented as a conceptual graph with
various attributes and parameters. This graph is then joined over
the domain representation to fill In the missing pieces, either
through ful I or partial matching on existing knowledge or
generation based upon domain first principles.
The most appl Icable related research is that of Edwina
Rlssland on constrained example generation (CEG), in which an
example is generated from an examples knowledge base using
prespeclfied constraints. CEG lists three methods of
'generation'; retrieval, modification and construction. In
retrieval and modification, pre-existing examples are used,
either directly or with modification, that have been precompl led
by the system bui Ider. It Is only In the construction phase that
an example is actually generated from domain first principles,
and even here the construction may instead be done by combining
existing examples to create a larger example. The work on CEG is
currently focused only on the retrieval and modification aspects
of this method and has yet to address the construction aspect of
generation from domain first principles [4]. This paper presents
a method which Includes that aspect.
222
2 Methodology
A typical situation where Instructional systems would be
useful in space related operations involves the learning of a
command and control system. Learning these systems is similar to
learning a computer operating system. There are numerous commands
with various effects, possibly different In different contexts,
and during the use of these commands the user must have an
understanding of the overall view of things. Because of this
similarity, and for practical testing reasons, this paper
presents the proposed method In the domain of Instruction about
the UNIX operating system.
Appendix A shows a portion of the semantic net of basic
domain primitive concepts. These form a framework for talking
about types of objects. Note that some of these types, such as
entity and Information, are domain Independent whi le others, such
as command, are domain dependent.
Appendix B shows portions of the set of conceptual graphs
comprising the extensional knowledge about the domain [3] and the
user environment. It Is there that methods would be represented
for obtaining Information about actual flies and for interpreting
that Information, about a directory for example.
The best way to I I lustrate the proposed method Is, of
course, with some examples. In these examples the concept to be
exempl ified is first presented textual ly as might be composed by
an Instructional designer. Next Is presented a conceptual graph
for that concept, followed by a description of the processing
Involved and the resultant exemplifying conceptual graph.
Example 1
"The UNIX Programmer's Manual is kept on-I ine. You can
use the 'man' command to print the manual pages for a system
command."
COMMAND
COMMAND-NAME: man
COMMAND-ARGUMENT: _command-name
This conceptual graph (CG) would be matched on the command
name field to the CG for the 'man' command (refer to Appendix B).
Since no options are specified In the example CG, they would not
be Included in the match result. This resultant CG would specify
that the command argument must be In the Intersection of the set
of manual titles and the set of command names. An entry from that
Intersection would be randomly chosen and Inserted into the
command argument field resulting in the following exemplifying
graph:
COMMAND
COMMAND-NAME: man
COMMAND-ARGUMENT: sort
which an English generator would render as "man sort"
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Example 2
The previous
compl Icated example
meta-characters.
example was relatively simple. A more
Involves the explanation of the use of shell
"The * can be used in conjunction with ?, as In
match multiple fl lenames."
??b = , to
SET
SET-SIZE: >=2
SET-ELEMENTS:
FILE-NAME
LENGTH: >=3
PATTERN: ??b =
First we match this CG with the FILE-NAME CG in the semantic
net. This sets the length field to 3-14. At this point we can
either match against the user environment or generate fl lenames
directly. If we choose to match against the user environment, we
would call a routine to return all fl lenames in the user's
current directory and attempt to match those against the
requirements. If we matched enough of those fi lenames, we would
return that set. Alternatively, generating the fl lenames would
involve Instantlatlng patterns that met both the length and
pattern constraints. In either case an actor CG for matching
would be Invoked which knew about character patterns and
meta-characters.
A generated result (compressed for brevity) might be
SET
SET-SIZE: 4
SET-ELEMENTS:
FILE-NAME: { aab, 23bso, aabredor, debar }
3. Conclusions and Further Directions
This outlines the concept of system-generated examples and a
method for achieving them. The present work has focused only on
the representational and generational problems. Before generated
examples can be ful ly used In computer-based instructional
systems, some manner of generating English from the resultant
conceptual graph Is also needed. Additionally, It would be
convenient to have a parser generate the Initial conceptual graph
from the English statement of the concept to be exemplified.
However, these are separate and further areas for research. That
examples are useful in regular Instructional materials cannot be
denied. Further work will allow them to benefit computer-based
Instructional systems as well.
224
REFERENCES
1) Blenkowskl, M.A., R.E. Culllngford and M.W. Krueger,
Generating Natural Language Explanations In a Computer-Aided
Design System, Technical Report CS83-1, Laboratory of Computer
Science Research, The University of Connecticut, 1983
2) Mandl, H., W. Schnotz and S. Tergan, On the Function of
Examples in Instructional Texts, Presented at the 1984 AERA
Annual Meeting
3) McGI Iton, H.
McGraw-Hill, 1983
and R. Morgan, Introducing the UNIX System,
4) Rlssland, E.L., Constrained Example Generation, COINS
Technical Report 81-24, University of Massachusetts at Amherst
5) Schank, R.G. and C.J Rleger III, Inference and
Understanding of Natural Language, in Readings
Representation, R.J. Brachman and H.J. Levesque
Kaufman, 1985, pp. 119-139
the Computer
in Knowledge
(eds), Morgan
6) Slnnot,
Artificial
1976
L.T., Generative Computer-Assisted Instruction and
Intelligence, Educational Testing Service, October,
7) Sowa, J.F., Conceptual Structures: Information Processing
Mind and Machine, Addison-Wesley, 1984
In
8) Barr, A. and E.A. Felgenbaum (eds), The Handbook of Artificial
Intelligence, V2, William Kaufman, 1982, pp. 225-8
9) Barr, A. and E.A. Feigenbaum (eds), The Handbook of Artificial
Intelligence, V2, William Kaufman, 1982, pp. 254-60
225
APPENDIX A - Intensional Knowledge
This Is a slmpl Ifled representation showing only the concept
types and not the relations linking the concepts involved. All
relation types in this example are characteristics and are
indicated by Indentation.
ENTITY • (Is a subtype of) UST (the universal subtype)
Includes physical objects as well as abstractions
INFORMATION • UST Anything that can be communicated
FILE-NAME • ENTITY, INFORMATION
LENGTH: 1-14
PATTERN: { valid-characters }
COMMAND-NAME • FILE-NAME
COMMAND-DESCRIPTION • TEXT
COMMAND-OPTION • INFORMATION
OPTION-LETTER
OPTION-DESCRIPTION
OPTION-ARGUMENT
COMMAND • ENTITY
COMMAND-NAME
COMMAND-OPTION
COMMAND-ARGUMENT
COMMAND-DESCRIPTION
The entity used to access a fi
Performs an operation
e
APPENDIX B - Extensional Knowledge
COMMAND
COMMAND-NAME:
LENGTH: 3
PATTERN: man
COMMAND-DESCRIPTION: "finds Information by keywords; prints
selected manual pages"
COMMAND-OPTION: {
OPTION-LETTER: k
-DESCRIPTION: "prints a 1-1 ine synopsis of each
manual section whose I istlng
in the table of contents
contains one of the keywords"
-ARGUMENT: { keywords },
OPTION-LETTER: t
-DESCRIPTION: "forces use of TROFF format"
}
COMMAND-ARGUMENT: { manual-titles }
manual-titles: {at,awk,cat,cc .... Is,man .... sort,tal I,wc }
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The use and generation of illustrative examples in computer-based instructional systems

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