CITS – C++ Intelligent Tutoring System:
A Domain Independent User Centered Curriculum Approach
1
Hassan Zia, 2Qaiser S. Durrani, 3 Rana Adnan Farrakh,
4
Amir Riaz, 5Farhan Ahmed
FAST Institute of Computer Science
852-B, Faisal Town, Lahore-54700, Pakistan
1
HZ_Hassan@hotmail.com, 2Durrani@fast.edu.pk, 5Raf@Truesoft.com.pk,
4
Amir_Riaz@Netsolpk.com, 5Farhan_Adil@hotmail.com
Abstract
Most of the existing Intelligent Tutoring Systems
(ITS) teach students based on their domain
knowledge. Explicit cognitive skills of the
students are generally not considered before
delivering any lesson. This paper discusses an
application of ITS employing cognitive
modeling as a main tool to understand learner
and correspondingly impart teaching. Here, in
addition to the domain knowledge, cognitive
skills of the student (which are domain
independent) are also considered before
delivering the lesson. The system teaches C++
language to the students. A number of lessons
were designed in C++ to see the effects of
cognitive measures like verbal, mathematical,
and abstract reasoning on students’ learning. The
results revealed a positive feedback as far as the
mapping of numerical reasoning with
mathematical contents and abstract reasoning
with the corresponding lesson contents in
abstract categories were concerned. However, no
positive results could be generated regarding
mapping of user verbal reasoning with lesson
contents belonging to verbal category. The
graphical presentation of the lessons was found
to be more effective than textual presentation,
especially in case the students had corresponding
graphical reasoning abilities.
1. Introduction
The idea in this paper is to build such a real-life
teaching environment which not only caters the
domain skills but also looks into an individual’s
problem solving skills, learning habits, abstract
reasoning, verbal skills, and so on. We believe
that these skills are part of in an individual and
have developed over a long life process. These
are, thus, more important to explore because they
can help to solve problems in any domain than
those specific skills which are simply learned to
counter a particular problem.
To test this idea we are proposing three
hypotheses. The paper is regarding the
implementation
of
domain
independent
intelligent tutoring system to test these
hypotheses and see if building of such systems
can improve the user interfaces and eventually
improve the user learning process. The
hypotheses are:
H1:
Students with high abstract reasoning
will perform better if the lesson contents belong
to an abstract category and are presented using
graphical media.
H2:
Students with high numerical reasoning
will perform better with mathematical contents
of the lessons.
H3:
Students with high verbal reasoning will
perform better if the lesson contents belong to
verbal category and are presented using textual
mode.
2. Background
Before we get to the working of the existing
system let us clarify few terms like ITS, User
Modeling, Stereotypes, Domain Dependent and
Domain Independent User Modeling.
Intelligent Tutoring System (ITS) is a kind of
tutorial system in which the contents of the
lesson are taught in an intelligent way. It asks
questions from the user and accordingly updates
its teaching strategies for that particular user.
These tutoring systems customize their
presentation of knowledge to the individual
needs of the student [Eliot and Park, 1995]. An
intelligent tutor takes Computer Based Training
(CBT) and customizes it to the needs of each
individual student, just like a real human tutor
would do [Chan et al., 1998].
The basic structure of an ITS is often described
by modules representing knowledge of four areas
as shown in Figure 1. The domain expert module
(also commonly known as domain module or
domain knowledge base) contains the knowledge
of the subject matter to be taught. The tutoring
module (or instructional module) represents the
rules, strategies and processes involved to
communicate with students (i.e. instructional
planning). The student module contains student
knowledge about the domain and is accessed by
the system to ascertain the thinking and
strategies of an individual student on the specific
subject matter that he is learning. The
communication module provides the system
interface to the student [Wong et al., 1998].
A User Model is a knowledge source, which
contains explicit assumptions on all aspects of
the user that may be relevant to the system
[Kosba and Wahlster, 1993]. All interactive
systems contain one of three types of model of
users (canonical, implicit, and automatic). Our
primary concern in this paper is the last type, i.e.
automatic or dynamic model which is built by
the system on the basis of its interaction with the
user [Kosba, 1993].
Traditional user modeling systems mainly utilize
user’s knowledge about the application domain
like user’s expertise in that area and user’s usage
history with the system. The models build with
such domain knowledge can be used with the
Student
Intelligent Tutoring System
Communication
Module
Domain
Module
Tutor
Module
Student
Module
Figure 1: Basic architecture of ITS
specific application only, thus called Domain
Dependent User Models (DDUM) [Durrani,
1997]. The deficiency with such systems is that
they do not model the cognitive attributes or
processes of the user, which are involved in all
sort of cognitive activities taking place in
humans. A cognitive model build with such
atomic level cognitive processes (processes that
remain same over all domains) behaves similarly
irrespective of any domain. Such models will be
applicable in virtually all domains, hence called
Domain Independent User Models (DIUM)
[Durrani, 1997].
The problem with traditional user modeling
systems is that, before the user model stabilizes,
they require considerable amount of time. The
system tries to tune itself with the user based on
the user’s interaction and feedback. It learns the
behavior and attitude of the user and adapts itself
accordingly. This problem can partially be
solved using stereotypes approach.
User modeling systems can enhance their
performance using stereotyping technique [Kay,
1994] [Kosba and Wahlster, 1993]. A Stereotype
is a knowledge structure, which contains
information about a group of users, i.e., a
collection of attributes that often co-occur in
people. Stereotypes give the system a predictive
power by capturing naturally occurring
regularities in human characteristics. They can
play an important role in a user modeling system
because they enable the system to make a large
number of plausible inferences on the basis of a
substantially smaller number of observations.
The major advantage that stereotype based
systems have over non-stereotype systems is that
they can fill in missing information about a user
from the stereotype the user falls in.
3. Proposed Solution
C++ Intelligent Tutoring System (CITS) is an
effort to develop a domain independent
intelligent tutoring system that teaches C++
language depending upon the cognitive skills and
knowledge level of C++ of the student. The tutor
works with a student model, a teaching model
and the lesson contents. The main task is to build
an individual user model for each student by
taking some psychological tests. We used a
standard culture free psychological test called
Differential Aptitude Test (DAT) [Hyde and
Tricky, 1995] and a self-made test to check the
C++ knowledge of the student. The result of
these tests assesses students’ characteristics.
Based on these assessments the system picks up
one of the stereotypes from the set of stereotypes
and eventually initializes a user model for the
student. Based on the user model, the system
starts teaching the student by presenting C++
lessons of various complexities (easy, normal
and difficult). Apart from this, the system keeps
track of the interaction of the student during each
lesson like his attention span and memory. The
system also takes feedback at the end of each
lesson to observe the student behavior. Based on
results of the feedback the system updates the
user model and plans future schedule for the
particular student.
3.1. User Modeling in CITS
Our user model consists of domain independent
and domain dependent attributes. The domain
independent attributes that we have selected for
our user model are:
 Verbal Reasoning
 Graphical Reasoning
 Mathematical Reasoning
To measure the above-mentioned cognitive
attributes of the user a sub-set of DAT
psychological test [Hyde and Tricky, 1995] is
used.
3.3. Lesson Designing
After studying the language theories of C++ we
believe that this language can be divided into
verbal, abstract, and mathematical categories.
The verbal category includes the topics like data
types, control structures, functions, pointers,
arrays etc. The abstract category includes
classes,
inheritance,
objects
etc.
The
mathematical category includes operators (unary,
binary etc.), operator-overloading etc.
Each topic of C++ has its own complexity. So
before presenting any lesson we determine the
media required (depending upon user model and
feed back from the user) for teaching that
particular lesson. For example, if a lesson lies in
1
Verbal
ART 2
Abstract
2
Numerical
n
Figure 2: Formation of stereotypes
There is only one domain dependent attribute in
CITS, that is, C++ knowledge level of the
student. For measuring this domain knowledge
of the student C++ tests were designed.
3.2. Formation of Stereotypes
Formation of stereotypes is one of the key
operations of CITS. We are using ART2 Neural
Networks for this purpose. These nets cluster
inputs by using unsupervised learning. Each time
an input pattern is presented an appropriate
cluster unit is chosen [Kosko, 1995].
ART 2 clusters the students on the basis of their
cognitive attributes. Three inputs (verbal
reasoning, abstract reasoning & numerical
reasoning) are fed to ART 2. The output we get
from ART 2 is a cluster number (1, 2 or n, where
n is any arbitrary number). Thus the students
with similar cognitive properties are classified in
the same stereotype or cluster as shown in Figure
2. Whenever a new user gets registered in the
system, he/she is mapped into an already existing
stereotype or a new stereotype is created (if the
user does not map on any of the existing
stereotypes) [Ata et al., 1998].
textual category and the student learning that
particular lesson has high verbal reasoning
ability, then we do not need much more media
help to make him understand that lesson. But
when considering the other case, that is, the
student has low textual reasoning ability, then we
certainly require more media help to teach him
that particular lesson.
The way in which a media or system interface is
designed can effect performance of users in
terms of two aspects: semantic distance and
articulatory distance. Semantic distance depends
on the relationship between what the user wants
to say and the capabilities of the input language.
Articulatory distance depends on the relationship
between the meaning of an expression in the
interface language and its form. A greater
distance means that users must spend more
cognitive efforts to translate from their own
formulation of their behavior to the input
language or from the output language into their
own understanding [Hutchins., 1986]. But here
we will consider the former case, as we are not
dealing with the semantic distance. So if the
articulatory distance is greater, then we have to
spend more efforts (i.e. more media help) to
make the user understand that particular lesson.
For example, if a lesson lies in textual category
and the user who is learning that lesson has low
textual reasoning, the articulatory distance will
be high. So in order to reduce the distance we
require more media help to present that particular
lesson.
User
Attributes
Lesson
Attributes
Attributes
Levels
Verbal
Textual
Easy, Normal,
Difficult
Abstract
Graphical
Easy, Normal
Numerical
Mathematical
Easy, Normal
Figure 3: Mapping of user attributes
After measuring the cognitive attributes of a
user, we map them to the attributes of C++
lesson, which combines to depict the complexity
of the over all lesson. The mapping is on one to
one bases as shown in Figure 3. The verbal
reasoning, abstract reasoning and numerical
reasoning of students are mapped on the textual,
graphical and mathematical contents of the
presented to the user based on his/her model.
After presentation of each lesson, a feedback is
taken from the user about its contents. If the user
gives correct feedback then he/she is moved to
the next lesson. On the other hand, if the user
gives incorrect answers then he/she is asked to
give comments about the presentation (Text,
Mathematics and Graphics) of the lesson. On the
basis of the feedback of the user, the model of
the user is updated and revised lesson is
presented.
For example, a lesson is presented to the user
with textual, mathematical and graphical
complexities as difficult, normal & normal
respectively as shown in Figure 4. After the
presentation of lesson, feedback about the
contents of lesson is taken from the user. If the
user gives incorrect feedback then he/she is
asked to comment about the presentation of
lesson. With this feed back the same lesson but
with different complexity of text, mathematics
and graphics is presented to the user. The lesson
updation continues until the user is satisfied or
system exhausts all available possibilities. We
have assumed here that whenever the user gives
at least three consecutive correct responses then
his/her model is stable and he/she is satisfied
(Text t, Math m Graphics g)
Initial Lesson Attributes
difficult, normal, normal
Feedback
normal, easy, normal
Intermediate Lesson Attributes
(Text t, Math m, Graphics g)
Final Lesson Attributes
Figure 4: Learning of the System
lesson respectively. We have three versions of
text (Easy, Normal and Difficult) used in the
lessons. Similarly, there are two versions (Easy
and Normal) of mathematical examples and
graphics used in the lessons [Zia et al., 1998].
3.4. Learning of the System
For learning purposes we are using Back
Propagation Neural Network. A lesson is
with the presentation of lessons. When a user
model gets stable, it is added in a contradiction
list. When five users are added in the
contradiction list and a new user comes in, with
the same stereotype attributes, then the lesson
presented to that user is of the complexity where
the last five users got stable. In this way the
system has learned from the experience of
previous users and fresh experimentation is not
required for the new user.
verbal may be attributable to the fact that the
students who were taking these tests were mostly
studying in technical schools. Thus, the training
and emphasis of their studies and work was
mostly on the technical or numerical matters
rather towards verbal or English comprehension.
4. Results and Analysis
Initially 40 students volunteered to experiment
and learn C++. However, during one month’s
period of testing and learning only 20 students
Final Graphic
Initial Graphic
Final Math
Initial Math
Final Textual
Initial textual
Pair 1
Pair 2
Pair 3
Mean
N
5.00
4.55
5.60
6.70
4.00
3.65
20
20
20
20
20
20
Standard
Deviation
1.6222
1.9595
1.1425
1.6255
1.6543
1.7852
Standard
Error Mean
0.3627
0.4381
0.2555
0.3635
0.3699
0.3992
Table 1: Paired Samples Statistics
completed both DAT and C++ testing. The result
of students in Differential Aptitude Test is
Pair 1
Pair 2
Pair 3
Table 1 shows the Paired Sample Statistics.
Initial Graphic, Initial Math and Initial Textual
N
20
20
20
Final Graphic & Initial Graphic
Final Math & Initial Math
Final Textual & Initial Textual
Correlation
0.464
0.669
0.927
Sigmoid
0.040
0.001
0.000
Table 2: Paired Samples Correlation
shown in Figure 5 (see on next page). The
number of students are along the x-axis and
score of students in scale of 1-10 in three subtests (Verbal Reasoning - VR, Abstract
Mean
Pair 1
Pair 2
Pair 3
Final &
Graphic
Final &
Math
Final &
Textual
are the initial attributes of the C++ lesson. These
values are gathered from one-to-one mapping of
cognitive attributes of user (from DAT test) and
attributes of lesson as shown in Figure 3. Final
Paired Differences
Std.
Std.
95% Confidence
Deviation
Error
Interval of the
Mean
Difference
Lower
Upper
1.877
0.420
-0.428
1.323
t
df
Sig. 2tailed
1.072
19
0.297
Initial
0.45
Initial
-1.10
1.209
0.270
-1.666
-0.534
-4.067
19
0.001
Initial
0.35
0.671
0.150
3.60E-02
0.664
2.333
19
0.031
Table 3: Paired Samples Test
Reasoning – AR & Numerical Reasoning – NR)
of DAT is along y-axis.
The results of the DAT test shows that the
students generally scored well in abstract and
numerical reasoning and performed rather poorly
in verbal test. The better performance in
numerical and abstract reasoning as compared to
Graphic, Final Math and Final Textual are the
attributes of the lesson where the users learning
got stable after working on the system for about
one month. Initial attributes of the lesson
changes after the feedback of users about the
presentation of the lesson. N is the total number
of students.
Table 2 shows the Paired Samples Correlation.
The Correlation shows how the two variables
(for example, Final Graphic & Initial Graphic)
high verbal reasoning ability is rejected. There
may be many reasons for the rejection of this
hypothesis. One major reason could be that the
12
10
8
6
S
C
O
R
SEu
m
4
AR
2
NR
0
1 .0 0
VR
5 .0 0
3 .0 0
9 .0 0
7 .0 0
NO. OF STUDENTS
STUD ENT
1 3 .0 0
1 1 .0 0
1 7 .0 0
1 5 .0 0
2 1 .0 0
1 9 .0 0
2 5 .0 0
2 3 .0 0
2 9 .0 0
2 7 .0 0
3 3 .0 0
3 1 .0 0
3 7 .0 0
3 5 .0 0
Figure 5: Results of DAT
are correlated with each other. The calculation in
Table 2 is used to calculate value of t in Table 3.
Since the number of cases in consideration were
less or equal to 20 we used T- Test instead of Ztest. The tabulated value of t0.05 is 1.729. The
calculated value of t is 1.072, -4.4067 and 2.333
for graphical, mathematical and textual lessons
respectively as shown in Table 3. Since the
calculated value of t for graphics and
mathematics used in the lessons is less then the
tabulated value of t, so our hypotheses for
graphical and mathematical contents of lessons
are accepted. Also note that the calculated value
of t for text used in the lessons is greater than the
tabulated value of t, so our hypothesis against the
textual lessons is rejected.
From the above results we concluded that there
is a positive feedback in the case of mathematics
and Graphic attributes since they have not
changed. So our hypothesis are accepted that the
graphical contents and presentation of lessons
would enhance the performance of users with
high abstract reasoning ability and mathematical
lessons would enhance the performance of users
with high mathematical reasoning ability.
While considering the case of textual lessons, as
there is negative feedback so our hypothesis that
textual contents and representation of lessons
would enhance the performance of users with
textual lessons could not be prepared correctly,
that is, we could not handle or understand the
complexity of text used in the lessons. Secondly,
since most of the students had in general low
verbal skills (see Figure 5) so they were unable
to properly comprehend textual contents of the
lessons.
5. Conclusion and Future Work
CITS is a domain independent intelligent
tutoring system that teaches C++ language
depending upon the cognitive skills and C++
knowledge of the student. The results shows that
there is a potential of building such training
systems which can train users in relatively short
period of time not just based on their domain
skills. The training thus given should not be just
based on the designers’ approach for how to
learn and solve problem but should be focusing
on the strengths and weaknesses of an individual
trying to learn the system. So while designing
systems care needs to be taken to know an
individual (i.e., to capture the personal attributes
of the user’s) and then generate response to suite
an individual’s requirements.
We have made the application domain
independent by using the cognitive skills of the
students (created an intelligent agent), so it can
be modified for teaching some other domains
like Java, English Language etc.
Key benefits of CITS includes:
 Can easily be modified to teach some other
domain.
 Customizes to the cognitive skills of a
student.
 Compresses learning time.
 Simulates the behavior of a human tutor.
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