A special structure is examined for evolving a detached model of the user of an intelligent tutoring system. Tutoring is used in the context of education and training devices. A detached approach to populating the student model data structure is examined in the context of the need for time dependent reasoning about what the student knows about a particular concept in the domain of interest. This approach, to generating a data structure for the student model, allows an inference engine separate from the tutoring strategy determination to be used. This methodology has advantages in environments requiring real-time operation

Holmes, Willard M.

English

NASA Technical Reports Server

N90-27320
A STRUCTURE FOR MATURING INTELLIGENT TUTORING
SYSTEM STUDENT MODELS
Dr. Willard M. Holmes
Systems Simulation and Development Directorate
Research, Development, and Engineering Center
U.S. Army Missile Command
Redstone Arsenal, Alabama 35898-5252
Phone 876-1048
ABSTRACT
In this paper, a special structure is examined for evolving a "Detached"
model of the user of an intelligent tutoring system. Tutoring is used here in
the context of education and training devices. A "Detached" approach to popu-
lating the student model data structure is examined in the context of the need
for time dependent reasoning about what the student knows about a particular
concept in the domain of interest. This approach, to generating a data struc-
ture for the student model, allows an inference engine separate from the
tutoring strategy determination to be used. This methodology has advantages
in environments requiring real-time operation.
INTRODUCTION
During the past decade, a considerable increase in research on
Intelligent Tutor[ng Systems (ITS) has resulted in an expanded body of
knowledge about computer based tutoring systems. ITS are sharply contrasted
with what is traditionally identified as Computer Aided Instructions (CAI).
Early research on the distinguishing characteristics of ITS and differences
compared with CAI are reported in a reference text "Intelligent Tutoring
Systems" edited by Sleeman and Brown (1982)[7]. Wolf and McDonald (1984)[9]
emphasizes the importance of student modeling in developing an effective
tutoring system. A general state of technology development in the emerging
technology of ITS is reported on by Clancy (1987)[1], Wenger (1987)[8],
Kearsley (1987)[5], Poison and Richardson (1988)[6].
In addition to the man-machine interface, the classic model of an ITS
includes a teaching module, an expert problem solving module, and a student
model. A student model is an essential component of ITS. However, a student
model in general cannot be developed entirely independent of the domain in
which the model will be used. The vast majority of student model development
efforts focus on the "Classic" tutoring problem, i.e., duplicating the
tutoring function that takes place in a classroom. Another area of interest
in the use of tutoring and student models is related to training and job aids.
A mental model for identifying some differences between "Classic" instruc-
tional strategies, training, and job aids are reported on by Harman and King
(1985)[2].
The use of a student model in context of a specif[c application domain is
described by Holmes (1988)[3] and Holmes and Cha,_berlain (1988)[4]. For pur-
poses here, the student model is defined as that component of an ITS that
collects student model performance information to be used to make inferences
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about what the student knows and does not know about a particular concept or
required training task. Before the student model can be used to draw conclu-
sions about the state of knowledge possessed by the student, the model must
first be initialized or have results available from previous tutoring opera-
tions. More precisely, the student model must have a specified structure and
a defined process for populating the structure. With the populated structure,
inferences can be made about the knowledge state of the student.
By using an ITS in the context of a Training System (TS), it can be [den-
tilled as an Intelligent Tutoring-Tralning System (ITTS). The task to be pre-
sented to the student in an ITTS application is similar in nature to the basic
principles that would be used for an ITS for the "Classic" knowledge tutoring
problem associated with classroom settings. In the ITTS operation, simulation
systems are frequently included to support the exercise of both knowledge and
skills in the tutoring operation.
HIERARCHICAL DECOMPOSITION OF TUTORING TASK
Given that a main level concept with specific performance objectives has
been identified as an element to be used in a tutoring operation. A series of
steps must be completed before the tutoring function can be implemented. An
initial step is to perform hierarchical decomposition of the main concept into
subconcepts. The decomposition continues until desired fidelity level is
acquired. The fidelity of the decomposed task is related to the number of
levels in the hierarchy. The lowest level subtask in the hierarchy is
defined here as the component subtask. Skills and knowledge associated with
the component subtask is identified as the primitives of the task. This
decomposition process is typically an element that is part of a total task
analysis effort. In conjunction with the task analysis is the skill analysis
to identify requisite skill associated with the task elements. A conceptual
model for identifying the task analysis components is shown in Figure I.
!iiic
OBJECTIVES
Figure I. Conceptual model for identifyin_ task analysis components.
422
The hierarchy of skill components indicated in Figure 1 is a simplifica-
tion of the many subtask levels that can be associated with a task. A major
point here is to focus on the desirability of having associated with each
subtask a concept that is an element in the major task. The knowledge and
skill components associated with the subtask is the knowledge necessary to
master the concept and the skill to demonstrate the operation associated with
the subtask. Knowledge is defined here to include what the student or opera-
tor needs to know (principles, concepts, facts, etc.) about the subtask to be
accomplished. A skill is defined as having the requisite kaowledge and the
ability to apply that knowledge effectively.
SKILL BASED TASK ANALYSIS
An example emphasizing the use of information at the component subtask
level with associated knowledge component and skill component is instructive.
Consider this over simplified example of teaching a student how to fly a small
airplane as indicated in the following table.
The observed performance with resulting conclusion and recommendation is
typical of that made by a human tutor. The knowledge component is information
the student can obtain in classroom sessions, books, and discussion with an
experienced pilot. The measure of the student's knowledge can be a series of
questions. The skill component is developed and tested either with a training
device or the actual airplane. The performance measure can be an observation
and measurement of action taken in response to a given stimuli. As can be
observed, the same knowledge and skill component can appear in more than one
subtask, i.e., adjust controls in ST.-I and ST.-2. This is the same skill
but the student must have the knowledge of the context of using that skill,
i.e., in landing or stall situation. Hence, the ability to use the knowledge
correctly.
The expert, in expressing his rule-of-thumb, may use terms and expressions
not used at the knowledge component or the skill component level. However,
the student's action can be compared with the expert's at the subtask level.
Tutoring operation to improve deficiencies can use student performance at the
subtask level and the related information at the knowledge component and
skill component level.
STRUCTURE FOR STUDENT MODEL DATA GENERATION
For purposes of prototype model development, the structure shown in
Figure 2 will be used to identify a procedure for developing student model
data. As indicated, the basic structure includes a main concept, one or more
sun-concepts, and primitive element. The primitive elements have context sen-
sitive Primary (P) and Alternate (A) question associated with each primitive.
423
TABLE. Learn to fly a small airplane.
COMPONMENT SUBTASKS:
SUBTASK.I
ST.I
Learn how
to land
SUBTASK.2
ST.2
Learn how
to take off
SUBTASK.3
ST.3
Learn how to
handle stalls
KNOWLEDGE COMPONENTS
Head winds
Cross winds
Flight path angle
Landing speed
Plane load
Air temperature
Take off speed
Aerodynamic lift
Get nose down
Increase speed
Sufficient speed
Control settings
SKILL COMPONENTS
Adjust controls
Observe airport
wind indicator
Compare approach
Angle with horizon
Adjust air speed
Set throttle
Observe cross
wind indicator
Changes controls
with correct
air speed
Adjust controls
Observe air speed
Level off
OBSERVED MEASURE OF PERFORMANCE FOR SUBTASK.I
Approach angle too high, hard landing
Landing speed too high, ran off runway
CONCLUSION
Student and plane survived, but the student need more practice.
RECOMMENDATIONS
The student engage in extended practice session of touch-and-go landings
with emphasis on control surface adjustment and speed adjustments.
424
LEARNING
LO (OBJECTIVE)
To
S
MAIN |CENARIO
._ONCEPT NO. K --
U
11mESHOU) (TH)
S¢ORI_. M
--®
LO {LEARNING)/ \ PRIMITIVE PRIMARy ALTERNATE
_/OBJECTIVE ELEMENTS QUESTIONS QUESTIONS
_,OBJC-CnVE/
- -".'ONCEPT SET-1 --
K, U
SCORE • N
_ DYNAMICQUES'nOM8
EXPLANATIONS
MAIN
CONCEPT
I l PRIMITIVE I PRIMARY AND
SUB-CONCEPTS/SUB-ELEMENT ELEMENTS ALTERNATE QUESTIONS
Figure 2. Basic structure for developing student model information.
The main concept is considered to be a particular scenario operation. A
Learning Objective (LO) is identified for the main concept. The LO will be
dependent on concepts included in the scenario. The Tutoring Objective (TO)
will be dependent on the particular student being tutored, i.e., student model
information and capabilities required to solve the scenario. As used here, a
TO is related to the process of establishing the number of chunks of knowledge
to be presented to the student. In turn, a LO is related to the process of
tesing to determine if sufficient knowledge has been mastered. Obtaining stu-
dent model information at the main concept level may include presenting the
student with a set of Dynamic Questions (DQ) involving both knowledge and
skill or presenting static Question with Explanation (QE) as required.
The student may not be required to master i00 percent of the knowledge
and concepts contained in this main event (scenario) Rule Set (RS) before
advancing to the next main concept (scenario). A Threshold (TH) of perfor-
mance is established for the main concept. An indication that the LO has been
Satisfied (S) or Unsatisfied (U) is indicated by setting the appropriate per-
formance indicator. The TO can be satisified even if the LO was not sati-
sified. Results of the student's performance at the main concept level is
added to the student model knowledge base.
Entering into a particular subconcept operation can be accomplished by
one of three approaches. The learning objectives associated with the main
concept was not satislfed and the tutoring strategy directed that a subconcept
of the main concept be explored, (top down approach). Second, the tutoring
strategy and student model contents indicate that the bottom line component
subconcept be explored before advancing to a higher level concept (bottoms up
approach). The bottom line subconcept level is defined as the level directly
connected to the primitive elements.
425
The third approach to subconcept operation is associated with paths that
includes several subconcept levels between the main concept and the primitive
level. Under these conditions, the tutoring strategy can require that opera-
tions proceed to a particular subconcept to satisfy certain TO (arbitrary
approach). This approach would be applicable to an expansion of the basic
structure as shown in Figure 2. The subconcept levels directly associated
with the primitive elements are identified as the subele,neflt level.
The student model contains results of the student performance at all
subtask levels, including the component subtask level. Any interaction with
the primitive elements during a TO is not recorded in the student model. This
requirement is tied to the fact that knowledge about the student's performance
consist of two parts: the student's performance during Past Tutoring Efforts
(PTE); and the students performance in the Present Tutoring Operation (PTO).
The student's performance is not considered to be a past performance until the
PTO for a subtask is satisifed.
Consider the option that while achieving a TO it is necessary to cuter
the subelement level of operation. Also, consider _lile satisfying the LO of
a particular subelement it is necessary to interact with the associated pri-
mitive elements. The P question and A questions are used to infor_Q tTle
student about the characteristics of the primitive elements. Primitive ele-
ment level interaction with the student continues until the threshold level of
that particular subelement is achieved. When the performance threshold level
of the subelement is achieved, the results at the subelement level is recorded
in the student model. This gives information on the student's performance in
relation to particular LO associated with the subelement and not in relation
to the primitive ele_leats. At this point, the results are based on past per-
formance since the LO has been achieved. The interaction with the primitive
elements occurred before the TO was achieved, i.e., in a present scenario
operation mode.
This requirement, that the threshold level of the subelement be
satisfied before continuing the TO with higher level concepts, can be used to
establish lower bounds on the tutoring and training operation. An ideal
structure would have a small number of primitive elements associated with each
subelement, i.e., three. If the threshold performance level is set at I00
percent and the student cannot achieve the LO of the component subelement,
then the student should receive training of a more fundamental nature than the
particular ITTS can provide.
However, other optic),is exist if the performance threshold level of the
subelements are set at some value other than I00 percent. No record is kept
of the order the PEs are presented to the student during the first tutoring
session using the particular subelement. Since a flag was set indicating
results of the last LO, at least one pass has been made using the subelement.
In the event a second pass is req_ired to meet a TO, the order of presenting
the PEs on a second pass through ca,_ he reversed to make it interesting for
the student. Shown in Figure 3 is a conceptual model of an expanded structure
for student model data development.
426
OE
LO, TO
OQ
LO, TO
QE
7 \
QE ....
MAINCONCEPerl SUBCONCEP'rs I SUL-i_LE_EN,'S 1 l
Figure 3. Expanded structure for student model development.
CONCLUSION
An initial implemeatation on the prototype structure, as described here,
is effective in providing required information to a student model for a
special application of tutoring systems. The implementation of the structure,
described here, involved an initial prototype. The next step is to investi-
gate the effectiveness of applying the process to larger tasks with an
increased number of subtasks.
ORIGINAL PY.'_ °
OF POOR QUALITY
427
•1
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,
•
•
•
o
REFERENCES
Clancy, William J., Knowledge-Based Tutoring: The GUIDON Program, The MIT
Press, Cambridge, MA, (1987).
Harmon, Paul, and King, David, Artificial Intelligence in Business, John
Wiley and Son, Inc., New York, NY, (1985).
Holmes, Willard M., and Chamberlain, David, "A Scenario Generator for an
Intelligent Tutoring Device used in Maintaining Operator Skill Levels,"
Proceedings of the 1988 Summer Simulation Conference, Seattle, WA,
pp. 613-619, July 1988.
Holmes, Willard M., "A Structure for Developing a Domain Specific
Intelligent Tutoring System for Maintaining Proficient Skill Levels for
Weapon System Operation," 2nd Annual Conference on Advancement in
Training Technology, National Science Center for Communications and
Electronics, Augusta, GA, 26-18 April 1988.
Kearsley, Greg, Artificial Intelligence and Instructions, Addison-Wesley
Publishing Company, Menlo Park, CA (1987).
Polson, Martha C., and Richardson, Jeffrey J., (Eds.) Foundations of
Intelligent Tutoring Systems, Lawrence Erlbaum Associates, Inc., (1988).
Sleeman, D., and Drown, J. S., (Eds.) Intelligent Tutoring Systems,
Academic Press, Inc., New York, NY, (1982).
Wenger, Etienne, Artificial Intelligence and Tutoring Systems, Morgan
Kaufman Publishers, Inc., 95 First Street, Los Alto, CA, (1987).
Wolf, Beverly, and McDonald, David D., "Building a Computer Tutor: Design
Issues," Computer, IEEE Publication, pp. 67-73, September 1984.
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A structure for maturing intelligent tutoring system student models

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