Intelligent Learning Environment for Acquiring Knowledge
Application Ability
Akira Takeuchi,1 Hiroyuki Yoshida,
2 Tomoyuki Fujita,
1 and Kazuko Ishibashi
1Department of Artificial Intelligence, Kyushu Institute of Technology, Iizuka, 820-8502 Japan
2Kyushu Multi-Media System Lab., Matsushita Electric Industrial Co., Ltd., Iizuka, 820-0067 Japan
SUMMARY
This paper proposes an architecture of intelligent
learning environments that aims at assisting students in
acquiring the ability to apply physical laws to complex
systems. This paper also presents its application to a spring-
system learning environment. The learning process in the
architecture consists of three steps: to interpret data col-
lected by experiment, to predict results of experiment, and
to verify the prediction by experiment. If a student encoun-
ters difficulties, the intelligent learning environment pro-
vides assistance such as prompting to collect additional data
that are useful for interpretation, or providing a new physi-
cal system that is suitable for the student�s state of under-
standing. To realize such tailored assistance for each
individual student, we need a method to model a student�s
ability to apply rules. This paper proposes a new student
model represented by a multidimensional space. Axes of
the space correspond to measures of generality of a subject
domain. We implemented an intelligent learning environ-
ment called �SpringMaster� to validate the proposed archi-
tecture. The result of its evaluation shows that students who
learned by SpringMaster had better understanding than
students who learned by real instruments. © 2001 Scripta
Technica, Syst Comp Jpn, 32(8): 1�9, 2001
Key words: Intelligent learning environment
(ILE); knowledge application; physical law; complex sys-
tem; student model; intelligent CAI.
1. Introduction
In physics learning, reading textbooks or attending
lectures is not sufficient to obtain deep understanding;
experimentation is important, especially for the elementary
level. Students are not familiar with the symbolic repre-
sentations of physics that they learn from textbooks or
lectures. They need to make correspondences between sym-
bolic knowledge and objects by experiment. In fact, they
spend a certain amount of time on experiments in class. But
teachers cannot take care of all students at the same time,
because students encounter different problems and the
speed of progress is different. One of the solutions of this
problem is a simulation-based learning environment with
an intelligent advisor. The intelligent advisor observes the
states of the experiments and provides advice. It also infers
the student�s states of understanding, and decides both the
learning objectives and the contents of the experiments
according to the state of understanding.
Even a student who has fundamental knowledge may
not be able to apply the knowledge to complex problems
during the process of learning new concepts. For example,
even if the student knows Ohm�s law, he or she may not
understand the nature of electric circuits composed of re-
sistances. To assist the student in such situations, intelligent
learning environments should recognize the level of com-
plexity that the student is able to understand. This is an issue
of student models. Most student models of previous work
represent the student�s state of understanding of each piece
© 2001 Scripta Technica
Systems and Computers in Japan, Vol. 32, No. 8, 2001Translated from Denshi Joho Tsushin Gakkai Ronbunshi, Vol. J83-D-I, No. 6, June 2000, pp. 523�530
1
of knowledge. Few studies consider the situation in which
the knowledge is used.
This paper proposes an architecture of intelligent
learning environments and a representation method of stu-
dent models. Learning environments in this architecture
support students who learn by doing experiments. The
learning objective of the experiments is to acquire the
ability to apply physical laws to complex systems. In order
to provide students with adaptive assistance, the student�s
ability to apply knowledge is represented by a multidimen-
sional space. Each coordinate corresponds to a measure of
generality of the target domain of learning.
Section 2 describes the architecture of intelligent
learning environments. Section 3 provides an implementa-
tion of the architecture for spring-system learning, and
Section 4 describes its empirical evaluation. Section 5
discusses characteristics of our proposal, and Section 6
provides conclusions.
2. Framework of Intelligent Learning
Environments for Learning by Experiment
2.1. Learning process in intelligent learning
environments
The process of acquiring new knowledge by discov-
ery learning and acquiring skills to use the knowledge
consists of the following three stages.
(1) The inductive knowledge acquisition stage, where
students handle individual cases.
(2) The generalization/formalization stage, where
multiple cases are taken into account.
(3) The knowledge mastery stage, where students
solve problems by applying knowledge deductively.
The first and second stages of learning are effectively
supported by ILEs (Interactive Learning Environments)
that support students in recognizing situations, collecting
information, and managing collected information. The stu-
dents acquire new knowledge through activities in a learn-
ing environment, and verify the knowledge by applying it
in the environment. The third stage of learning is effectively
supported by ITSs (Intelligent Tutoring Systems), which
support students in assimilating domain knowledge [1].
The architecture presented in this paper is for ILEs
that aim to assist students in acquiring the ability to under-
stand complex physical systems by applying physical laws
to them. It supports the second stage of learning. It also
supports learning to apply knowledge when a student fails
to solve a problem at the third stage. The student ascertains
the status of the problem situation by experiment. In order
to support these two kinds of learning, the architecture has
two learning modes, called �stepwise learning mode� and
�training mode.�
The user interface of the architecture consists of a
parts box that stores instruments for both building physical
systems and measurement, a workbench, and a notebook to
record results of experiments. Students carry out experi-
ments and collect data in both learning modes. The stu-
dent�s activities in the environment are as follows.
(a) Stepwise learning mode
The stepwise learning mode is designed to develop
the ability to understand the nature of complex physical
systems step by step. The learning objectives of experi-
ments are ordered from simple to complex. Learning in the
stepwise learning mode proceeds as follows.
(1) A learning objective is given to a student.
(2) The student builds a physical system by assem-
bling given components according to the learning objective.
(3) The student decides the physical parameters to
observe and writes their names in a notebook.
(4) The student carries out experiment by changing
the values of the independent parameters.
(5) The student measures the values of physical pa-
rameters and records them in the notebook.
(6) After repeating steps 4 and 5 several times, the
student sets up a hypothesis about the nature of the physical
system, and predicts the values of physical parameters
without doing experiments.
(7) The student carries out an experiment and verifies
his or her hypothesis.
(8) If the expected values do not coincide with the
experimental values, the student goes back to step 4 and
collects more data. If the expected values agree with experi-
mental values, the learning objective has been achieved.
Students try to interpret collected data by applying
physical laws in the stepwise learning mode. We expect that
students will acquire the ability to understand complex
physical systems by repeating this process from simple
systems to complex systems step by step.
(b) Training mode
The training mode is an advanced learning mode.
Students are allowed to build physical systems of arbitrary
complexity. In this learning mode, questions about a physi-
cal system that the student has built or that the intelligent
learning environment provides are posed to the student.
Learning in the training mode starts at step 6, where the
student answers questions without doing experiments, and
then verifies the result by experiment. If the student�s
answer does not agree with the experimental values, the
student goes back to step 4 and examines the states of the
physical system. This learning mode is provided for stu-
dents to practice applying fundamental physical laws to
complex systems.
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2.2. Assistance in intelligent learning
environments
Students repeat experimentation, prediction, and
verification in the architecture. They need trial and error to
achieve learning objectives. It is, therefore, not appropriate
to give immediate assistance when the student�s behavior is
not adequate. However, when students cannot achieve
learning objectives after several trials, intelligent learning
environments provide the following assistance.
(1) Proposal of a physical system: If the physical
system that the student builds does not fit the given learning
objective at step 1, intelligent learning environments indi-
cate the desirable structure of the physical system. In order
to implement this function, a function to recognize struc-
tures of physical systems is required.
(2) Proposal of physical parameters to observe: If the
student repeats experimentation and data collection without
selecting physical parameters that are relevant to the learn-
ing objective, it proposes parameters to observe. If the
student fails to predict the values of parameters, it also
provides the advice to observe additional parameters that
are useful in interpreting the physical system. In order to
identify physical parameters that are relevant to learning
objectives, a function for recognizing the structures of
physical systems is required. In order to choose additional
parameters to observe, a function for recognizing depend-
encies between physical parameters and a function for
identifying causes of student�s errors are also required.
(3) Pointing out erroneous data: If values that the
student has recorded in the notebook are wrong, intelligent
learning environments point out errors. Errors are mainly
caused by misreading measurement tools, measuring an
incorrect parameter other than a target parameter, or record-
ing data at a wrong place in the notebook. Unexpected
values are written into the notebook in the case of the first
type of error, while values that exist somewhere in the
physical system are written into the notebook in the case of
the second and third types of error. We distinguish these two
kinds of causes by this phenomenon.
(4) Asking questions: If the student does not proceed to
step 6 after repeating steps 4 and 5 several times, the intelligent
learning environment gives values of independent parameters
and urges the student to predict values of dependent parame-
ters without experimentation. In the training mode, it asks a
question to start the lesson. In order to implement this assis-
tance, a function for recognizing structures of physical systems
and a function for generating questions according to the
student�s understanding are required.
(5) Suggestion of a physical system: In the training
mode, the student is allowed to construct physical systems
of arbitrary complexity to attempt to achieve what he or she
wants. However, if the constructed system is too complex
judging from the student�s understanding state, and if the
student fails to predict correct values, intelligent learning
environments propose a simplified system. The purpose of
the simplified system is to fill the conceptual gap between
what the student has already understood and what he or she
has not. In order to achieve this educational objective,
intelligent learning environments generate a simplified
physical system that has the same nature as the original
system and that the student is expected to understand. On
the other hand, if the student is expected to understand the
constructed system and the result is as expected, intelligent
learning environments provide a more complex system for
learning of more general cases. In order to implement this
assistance, a function for recognizing the structures of
physical systems and a student model that is able to repre-
sent the extent of complexity of physical systems that the
student understands are required.
2.3. Architecture of intelligent learning
environment
2.3.1. Components of intelligent learning
environment
Figure 1 shows the architecture of an intelligent
learning environment that has the capabilities presented in
Fig. 1. Structure of intelligent learning environment for
learning by experiment.
3
Section 2.2. The functions of the primary modules are as
follows.
(1) Parts library
The parts library is a container of components for
building physical systems and measurement instruments.
The student builds a physical system by connecting com-
ponents on the workbench. Each component is accompa-
nied by functional relations between physical parameters,
which are used by the physical system model generator to
recognize dependencies among parameters in the physical
system.
(2) Physical system model generator
The physical system model generator generates a
physical system model, which represents functional rela-
tions among the physical parameters of a physical system,
from both connection relations between components and
the functional relations defined for each component. It also
evaluates the complexity of the physical system. A defini-
tion of complexity will be presented in the next section.
(3) Notebook manager
The notebook manager judges whether the values of
physical parameters recorded in the notebook are obtained
by experiment or by prediction from the state of the physi-
cal system. If a recorded value is wrong, it judges whether
the error is caused by measurement error or by confusion
of physical parameters. If the wrong value agrees with one
of the physical parameters in the system, the notebook
manager judges that it is the latter case.
(4) Error origin analyzer
The error origin analyzer identifies the causes of the
student�s errors. We adopted the same technique as intelli-
gent tutoring systems.
(5) Problem generator
The problem generator realizes two assistance meth-
ods: suggestion of physical parameter to observe, and ask-
ing questions. It chooses the physical parameters to which
it draws the student�s attention according to the physical
system model, the student model, and the learning objectives.
(6) Student model
The student model represents the extent of complex-
ity at which the student understands physical systems by
applying physical laws. Its details are described in the next
section.
(7) Physical system generator
The physical system generator generates physical
systems by simplifying or complicating the original system
and sets up learning objectives according to both the physi-
cal system model and the student model.
2.3.2. Student model based on generalization
space
Students sometimes cannot apply fundamental laws
to complex systems even if they know the laws. Because
the purpose of the architecture for intelligent learning envi-
ronments presented in this paper is to train students to
understand complex systems by using fundamental knowl-
edge, the student model should represent not only how a
student understands each piece of knowledge, but also to
how general a situation the student can apply the knowl-
edge.
In order to model the applicability of knowledge, we
propose a student model representation in a multidimen-
sional space, the axes of which correspond to measures of
generality of a subject domain. We call the multidimen-
sional space the �generalization space.� In physics learning,
we consider the applicability of knowledge to complex
physical systems as the applicability of knowledge to gen-
eral situations. We therefore assign ways of combining
components to build physical systems to the axes of the
generalization space. For example, in the case of the domain
of a spring system where springs and masses are connected
vertically, the student model is represented by a three-di-
mensional space as shown in Fig. 2. The axes are the
number of springs connected in series, the number of
springs connected in parallel, and the number of interme-
diate weights. When the student learns a physical system,
its coordinates are calculated from its complexity and the
student�s state of understanding of the system is recorded
at the point in the generalization space. Because the func-
tion of each component is defined by functional relations
between physical parameters, the state of understanding of
each component is represented by the state of under-
standing of functional relations. The state of understanding
of functional relations is inferred by the same method as in
intelligent tutoring systems. That is, if the student obtains
the correct value of a physical parameter, it is inferred that
the student understands the functional relations involved in
working out the value. On the other hand, if the student
Fig. 2. An example of student model representation by
generalization space.
4
makes mistakes, the causes are inferred by a method of error
origin identification.
In the student model based on the generalization
space, if a student can understand a complex system by
applying knowledge, the student is expected to understand
simpler systems. That is, if the student understands system
�a� whose complexity is Xa = (Xa1, Xa2, . . . , Xan), the
student is expected to understand system �b� whose com-
plexity is Xb �Xb1, Xb2, . . . , Xbn�, where Xbi d Xai for all
i. For example, the student�s understanding system 1 in Fig.
2 implies an understanding of system 2. On the other hand,
the state of understanding of system 3 is unpredictable from
the state of understanding of the system 1, because system
3 includes an element of complexity not previously encoun-
tered. Physical systems are, therefore, partially ordered in
regard to their complexity in our student model.
3. SpringMaster: An Intelligent Learning
Environment for Spring Systems
3.1. Structure of SpringMaster
We implemented an intelligent learning environment
for spring systems based on the architecture presented in
Section 2. The domain of SpringMaster is spring systems
in which springs are connected in series and/or in parallel.
The learning objectives in the stepwise learning mode are
designed to proceed from a one-spring system to multi-
spring systems. The students learn the nature of spring
systems step by step.
Figure 3 shows a screenshot of SpringMaster. There
are springs, metal fittings for parallel connection, weights,
and tools for measurement of length and force in the parts
library. There is a table in the notebook, and the physical
parameters to be observed are selected from the constructed
system in the workbench.
3.2. Domain knowledge and student model
Table 1 shows some of the component definitions.
Constant values such as the mass of the weights and the
natural length of the spring are predefined for each compo-
nent. The physical system model is generated from the
functional relations of each component and the connection
relations of the components. Figure 4 shows an example of
a physical system model. The unknown values of the physi-
cal parameters in the physical system model are calculated
by propagating known values according to functional rela-
tions.
When a student predicts the values of the parameters
without performing an experiment, their correctness is
judged by comparing them with the values in the physical
system model. If a value is wrong, the causes of the error
are inferred by the bug model. Each functional relation
defined for each component is accompanied by debugging
Fig. 3. SpringMaster�s user interface. Fig. 4. Example of a physical system model.
Table 1. Excerpt of component definitions
5
rules. The causes of a student�s error are identified by trying
to reproduce the error by replacing correct functional rela-
tions with invalid functional relations. The inferred state of
the student�s understanding is recorded in the student model
represented by the three-dimensional generalization space
as shown in Fig. 1.
4. Empirical Evaluation
4.1. Method of evaluation
We evaluated the effectiveness of SpringMaster at a
junior high school. The subjects were 82 first-year students
in three classes. Three school hours of 45 minutes were
assigned for the evaluation. After a pretest, 53 students in
two classes used SpringMaster, while 29 students in one
class used real instruments for learning by experiment.
After that, they took a follow-up test. The pretest and the
follow-up test were the same problems.
4.2. Results and discussion
Figure 5 shows the results of the evaluation. The
students were divided into four groups according to the
result of the pretest and the follow-up test. The graphs in
Fig. 5 show the percentages of students in four groups. On
the left of the arrow �->� is the result of the pretest and on
the right, the result of the follow-up test. �o� means correct
and �x� incorrect. For example, students who solved a
problem correctly in both the pretest and follow-up test
were categorized as �o->o,� and students who solved a
problem correctly only in the pretest were categorized as
�o->x.� The results indicate the following.
(1) Most students solved problem 1 correctly in both
the pretest and follow-up test. They therefore knew Hooke�s
law before the pretest. However, half of the students could
not apply Hooke�s law correctly to physical systems in
problems 2 and 3, and few students could solve problem 4
at the pretest.
(2) In regard to category �x->o� for problems 2, 3,
and 4, percentages of students who learned with Spring-
Master are higher than those who learned with real instru-
ments. Table 2 shows the numbers of students who could
not solve problems 2, 3 or 4 in the pretest in each category.
A non-parametric test of independency reveals that the two
categories of problem 2 and 3 are not independent at the 5%
significance level. This means that the learning methods of
Fig. 5. Results of evaluation.
6
the two groups made the results of the follow-up test
different.
The conclusion derived from these two facts is that
use of SpringMaster was effective for learning whose ob-
jective is to be able to apply fundamental knowledge to
complex systems.
5. Related Work
Many studies have been done on simulation-based
learning assistance systems. Ueno and colleagues [3] pro-
posed a method of generating explanations from different
viewpoints in order to make students pay attention to im-
portant phenomena of kinetic systems in an interactive
learning environment. Forbus and Falkenhainer [4] and
Amador and colleagues [5] proposed methods of generating
accurate explanations of physical phenomena by combin-
ing qualitative and quantitative simulation. Providing ex-
planations is an effective way to help students understand
system behavior or system structures. The domain of these
studies is dynamic systems, while our domain is static
systems. Although the domain is different, our approach
does not provide explanations but provides a chance to
interpret the results of experiments and suggestions to
collect data that are helpful for interpretation. We believe
that it is important for students to discover ways of using
knowledge by themselves in order to be able to understand
complex systems by applying physical laws.
Reimann [6] also focused on students� hypothesis
generation. He proposed a method of assisting hypothesis
generation and providing feedback by using a graphical
user interface. Because his system did not diagnose the
students� hypotheses, it could not provide tailored feedback
depending on each student�s understanding. On the other
hand, we have introduced a student model that takes the
complexity of systems into account, and provides tailored
feedback depending on both the student�s understanding
and the complexity of the system.
White and Frederiksen [7] proposed a method of
progressive learning of new concepts of physical systems
in a study of intelligent learning environments. This study
focused on stepwise refinement of physical phenomena,
while we focused on the refinement of meta-knowledge for
the application of domain knowledge. Their model did not
distinguish what a student knows from what the student can
do. One of the important points of our study is a student
model that represents the student�s ability to apply knowl-
edge.
Shingae and colleagues [8] proposed a sophisticated
method of intelligent assistance for discovery learning.
Their intelligent learning environment is equipped with
tools for assisting experimentation, data collection, and
hypothesis generation. The ILE monitors all trial-and-error
processes through tool operations, and infers the student�s
intentions by the plan recognition technique. If the student
is at an impasse, it provides subgoals of learning, or indica-
tions of what to do next. Our ILE cannot infer the student�s
intention from his or her behavior in the environment.
However, a characteristic of our method is to propose both
suitable physical systems to be learned and suitable learn-
ing goals by recognizing system structures and the student�s
learning progress.
Student models allowing for the fact that a student
can use knowledge in some cases but not in other cases are
approached in different ways. Torres and co-workers [9]
tried to model the student�s inconsistent behavior in concept
learning by introducing fuzzy logic. Yacef and Allen [10]
modeled the student�s skill by a set of achievement levels
measured for each task in order to represent skill levels for
different situations. Vallano [11] and Conati and VanLehn
[12] proposed a method for evaluating the uncertainty of a
student�s behavior by means of a Bayesian network. These
methods do not model the student�s behavior or knowledge
in different situations, and relations between different situ-
ations are not defined. As a result, it is impossible to predict
the student�s behavior in a situation from a model of some
other situation. On the other hand, our student model,
represented by the generalization space, specifies relations
between situations in terms of partial ordering by general-
ity, and the student�s understanding state is represented by
a rule base. This enables us to predict the student�s behavior
in a different situation.
Table 2. Results of evaluation: transition of students
who failed pretests
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6. Discussion and Conclusions
This paper has presented an architecture of an intel-
ligent learning environment whose learning objective is to
assist students to understand the nature of complex systems
by experiment. It has also presented an application of the
architecture to spring-system learning for junior high
school students. We modeled the student�s state of under-
standing in different situations by a multidimensional space
representing the generality of the target domain. The stu-
dent model contributes to the implementation of learning
environments providing tailored assistance depending on
the complexity of the system. The definition of partial
ordering with respect to system complexity is essential to
the ability to suggest a similar system, when complexity is
different from the original system, depending on the stu-
dent�s state of understanding.
The results of SpringMaster�s evaluation show that it
is effective for students who understand the fundamental
nature of a spring but cannot understand the nature of
systems composed of springs. In a questionnaire, the stu-
dents expressed their impressions that SpringMaster
aroused feelings of achieving goals and that they experi-
enced the satisfaction of accomplishment.
We used the same technique as intelligent tutoring
systems to infer the causes of the students� errors in domain
knowledge. However, the student model based on the gen-
eralization space does not explicitly represent meta-knowl-
edge on how to use domain knowledge. It only records the
degree of mastery for each situation in regard to the ability
to apply domain knowledge. It therefore is different from
rule-based student models of intelligent tutoring systems at
this point. Andriessen and Sandberg [13] classified learning
scenarios as transmission, studio, and negotiation in their
future view of artificial intelligence in education. They held
that student modeling in the studio, where learning goals
are fixed but the processes for reaching goals are not enu-
merable, would shift from process evaluation to product
evaluation. The student model of generalization space fol-
lows the same line in regard to the ability to apply rules,
and it is a suggestion of a definite method.
In this paper, we have applied the proposed architec-
ture of intelligent learning environments to learning spring
systems in junior high schools. It is possible to apply it to
other domains of static systems in the same manner. The
representation of the student model based on the generali-
zation space and the definition of partial ordering on a
problem space are also applicable to areas other than phys-
ics where difficulty or generality is defined by independent
concepts or skills.
Our method has some limitations. To apply the
architecture to dynamic systems, we need to consider
methods of generating the physical system model. The
current definition of SpringMaster�s generalization space
maps physical systems that have the same complexity but
differ in appearance onto the same point in the generaliza-
tion space. It is not clear that students can apply rules on
these systems in the same way. We need further evaluation
of this issue.
Acknowledgments. We would like to thank Seiichi
Takei for conducting the evaluation of SpringMaster at
Kobukuro Junior High School. We thank Naomi Sasao and
Kouichi Fukuda for help in implementing SpringMaster.
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AUTHORS (from left to right)
Akira Takeuchi is a professor of artificial intelligence at Kyushu Institute of Technology. His research interests include
intelligent tutoring systems, human computer interface, and natural language processing.
Hiroyuki Yoshida is with Kyushu Multi-Media System Lab. of Matsushita Electric Industrial Co. He is engaged in
research and development of intelligent tutoring systems.
Tomoyuki Fujita received his master�s degree from Kyushu Institute of Technology. His research theme was intelligent
tutoring systems. He is currently engaged in developing network instruments at Mitsubishi Electric Co.
Kazuko Ishibashi worked at Kyushu Multi-Media System Lab. of Matsushita Electric Industrial Co. She engaged in
research and development of intelligent tutoring systems and natural language processing.
9
