Proc. of the 2005 Westen Simulation Multiconference
SIMCHI 2005, International Conference on Human-Computer Interface Advances for Modeling and Simulation,
January 2005
Combining Intelligent Tutoring and Simulation Systems
Alke Martens
University of Rostock
Albert-Einstein-Str. 21
alke.martens@informatik.uni-rostock.de
Keywords
educational computer simulation, intelligent tutoring
system
Abstract
Intelligent Tutoring Systems (ITS) are well established in educational settings for more than 30 years.
To use simulation systems for training is also standard
in a lot of domains. Usually, the purpose of simulation in educational settings is the training of certain aspects of an application domain. The system described
in this paper combines ITS and simulation in a casebased training environment. In contrast to traditional
ITS or simulation systems, the purpose of the described
approach is to help the students to learn modeling and
simulation in exemplary training cases. The so called
Intelligent Tutoring System for Modeling and Simulation, abbreviated as TutMoSi, can be perceived as an
extension of JAMES II (Java Agent Modeling Environment for Simulation). JAMES II allows the usage of
different modeling formalisms. Extended with an ITS
interface, JAMES II shall allow for teaching and training of modeling and simulation in a case-based manner.
In TutMoSi Students should interact with a customer,
who provides some aspects of the model to develop. After the acquisition of the required facts, the student has
to choose an adequate modeling formalism and to construct the model. After creating the model the student
has to validate the model by making simulation runs
and by discussing the evaluated results with the customer, to check whether he has met the requirements.
The result of this may be that the student has to refine
or change his model. Thereby the ITS shall support the
student with feedback, correction, and help in each of
the described steps. Application domain of the training
cases is life science.
Jan Himmelspach
University of Rostock
Albert-Einstein-Str. 21
jan.himmelspach@informatik.uni-rostock.de
1
INTRODUCTION
Intelligent Tutoring Systems (ITSs) can be traced
back to the early 1970s, when the upcoming Cognitive Science in combination with Artificial Intelligence
has led to a new type of teaching and training system
(e.g. [4]). In contrast to the traditional Computer Based
Training Systems (CBT), these ITSs are based on expertsystems, containing the knowledge of the application domain. Moreover, nowadays ITSs usually contain
a learner model. The system’s flexibility in combination with the learner model allows for adaptation to the
learner’s performance, the learner’s expertise and the
learner’s needs. For more information about ITS see
e.g. [5, 15].
Simulation systems have always been used to support teaching and training, e.g. flight simulators. Here,
the simulation itself is the medium to teach and train.
To use simulation systems in ITS is also not a new
approach. Here, the simulation takes over one of two
roles. On one hand the simulation is used to steer a
certain component of the ITS, e.g. the simulation of
a virtual tutor. On the other hand, the simulation is
used to train a certain aspect of the application domain,
e.g. simulation of electrical circuits or medical simulations. Here, additional to the ”pure” simulation, the
ITS provides knowledge about pedagogical approaches
and about problem solving strategies.
In contrast to this usage of simulation, almost no systems actually train modeling, simulation and evaluation
of the simulation’s result. Thus, whereas simulation is
a good medium for teaching and training, the teaching and training of modeling and simulation has seldom
been realized. Case based training, where the learner
takes over a role in a training case, provides the adequate environment for establishing teaching and training of modeling and simulation.
To develop an ITS, which allows for simulating models the learner has modeled, techniques of ITS have to
be combined with a simulation system. Here, we use
the system JAMES II, which provides the basis to simulate models based on different modeling approaches.
For example, JAMES II is able to run simulations in a
discrete event, and even in a discrete time stepped way.
It soon will be possible to provide support for continuous simulations as well. Thus, the system is able to
support every model the learner thinks is appropriate
for the given task.
In the following, we will start with a description of
the different ways of using simulation in ITS. We will
throw a short glance at how case based training can be
realized to support teaching and training of modeling
and simulation. After that, an introduction into JAMES
II will be given. Finally, we will describe, how JAMES
II can be used as a constituent of an ITS.
2
SIMULATION IN ITS
Simulation systems have always been used for teaching and training. However, the usage of the simulation
in a training environment is quite heterogeneous. In the
field of educational computer simulations, we can distinguish between at least three different ways:
1. Interactive training simulations
2. Demonstrative training simulations
3. Character simulations
In interactive training simulations, the learner uses the
simulation system to train a certain aspect of the application domain. The learner is not necessarily aware of
the simulation itself. He interacts with an offered environment and e.g. solves the described problems (e.g.
[19] and [14]). One of the first interactive training simulations is the system SOPHIE (SOPHIsticated Instructional Environment), developed by Brown, Burton and
Bell [2] and Burton, Brown and de Kleer [3]. Using SOPHIE, the learner shall learn the functionality of electrical circuits. SOPHIE offers an electrical circuit containing a bug. This bug shall be located and repaired
by the learner. Via continuously simulating the circuit,
SOPHIE checks the repair strategy of the learner. Also
the well-known flight simulators are part of the category of interactive training simulations. Sometimes, as
described in [7], the interactive training simulation can
also be used to learn something about the learner himself. Thus, it can provide the basis of psychological and
cognitive research.
In the demonstrative training simulation, the learner
has no possibility to interact. Here, the visualization of
aspects of the training domain is the focus of interest.
Simulations can for example easily be embedded as subprograms in case based ITS in the application domain
of clinical medicine, like [6].
To embed a character simulation in an ITS most of
the time means to embed a virtual persona in the ITS.
The aim of such an approach is to support the learner
and to personalize the training with the ITS. The virtual
persona is often realized as a virtual tutor, offering feedback and help on demand or proactively. Two virtual
tutors are well known in this area: STEVE and ADELE
[13, 12, 17]. STEVE is the ”Soar Training Expert for
Virtual Environments”. Realized as a three dimensional
agent, STEVE is capable to virtually interact with the
training environment. STEVE is not only based on a
simulation, moreover it is embedded in an interactive
training simulation. ADELE, the ”Agent for Distance
lEarning, Light Edition”, is the two dimensional pendant of STEVE. In contrast to STEVE, ADELE acts as
virtual tutor in a case based medical ITS, and not in an
additional simulation environment.
3
CASE-BASED TRAINING FOR
MODELING AND SIMULATION
The aim of the system described in the following is
to teach and train modeling, simulation and to evaluate
the results of the simulation. The system intended is
a combination of a classical ITS based on a formal tutoring process model [15, 16] and the simulation system
JAMES II which is able to run models of different types.
Result of this combination is an interactive training simulation, referring to the categories described in the prior
section. In contrast to the categories described above,
the simulation is not only the medium but the subject
of teaching and training.
Case-based training is famous in a lot of domains like
clinical medicine, education in law, and in business education. Classroom teaching of modeling and simulation often takes place in a case-based or problem-based
manner. Students learn modeling and simulation techniques based on examples. However, there seem to be no
systems yet, which allow for web-based and case-based
training of different modeling and simulation techniques
in an ITS. In [1], an approach of using a multimedia ITS
for simulation modeling has been described. The paper
focuses on the multimedia aspects of simulation modeling, thus it lacks a description of how modeling and simulation should be trained with the system. In the casebased approach, pursued in our system, the learner takes
over the role of a computer scientist in a scenario, where
a client wants to have a simulation. Via text bricks,
the client ”tells” the computer scientist, which kind of
system he wants to have simulated. Thus, our system
starts with the data collection, given an unordered and
not scientifically specified amount of information. The
learner has to decide, which kind of modeling approach
is appropriate for the system described. He must decide whether information is missing and how to structure the information given. After the learner chose a
modeling formalism, he has to develop a model. The
ITS, equipped with an expert knowledge base, can compare the learner’s modeling approach with an expert’s
solution. If the model is completed, the learner starts
the simulation and can have a close look at the simulation results. He can decide whether the simulation is
appropriate to the client’s demands, or whether some
re-modeling has to be done. The simulation run and
the simulation results can be evaluated by the ITS.
The graphical user interface of the ITS should allow
for different modeling techniques. For example, modeling of UML diagrams, and of Petri nets should be possible, as well as modeling of data based only on XML or
on formal descriptions. However, for the first system developed, a simple modeling dialog has been constructed.
The learner can decide, which parts of the information
given in the text shall constitute a model. He can decide, which properties are required and how these properties shall develop over time. Furthermore, the learner
must decide about the models’ coupling structure. For
this purpose, in the first training case realized, a simple
grid of atomic models is displayed to the learner. Using
this grid, the learner can define model positions, neighborhoods, and influences. This data is stored in XML
and used to run the simulation. Via the interface, the
learner can pursue the simulation, stop it and view the
simulation results. The initially developed models are
re-visitable and correctable.
4
JAMES II
JAMES is a modeling and simulation framework created for being applicable to a broad range of diverse
applications. For meeting this challenge JAMES had
to be designed in a highly flexible manner. Thus, everything in JAMES is considered to be a component.
The component design includes communication via interfaces and allows for exchangeability. The design of
the system resembles the idea that a modeling and simulation framework should clearly separate between the
modeling and the simulation layer. The advantages of
this separation are the following.
• There may be more than one simulator for a particular model, which adds to the system’s flexibility.
• There are no side effects between model and simulator.
The current version of JAMES, i.e. JAMES II, supports
any number of modeling formalisms and any number of
execution mechanisms. So far we have realized support
for PDEVS, dynPDEVS, epiPDEVS, epiDynPDEVS
and CA (cellular automatons). Thereby the PDEVS
formalisms can be executed either in a sequential, parallel or in a combined parallel sequential manner. In
addition they can be executed paced, i.e. the simulation time relates to wallclock time during execution, or
unpaced, i.e. as fast as possible [20, 9, 18, 10, 11]. The
epi-formalisms provide support for the integration of external processes, the dyn-formalisms provide support for
Design
Visualization
Runtime
Data
Analysis
Model
access
data flow
Simulator
Figure 1: JAMES
dynamic structures [11]. We are currently planning to
create a set of components to support pure continuous
ITS
Design
as wellVisualization
as hybrid models.
Runtime
Figure 1 shows a top level view of the Analysis
components of
JAMES. These top level components are the simulator,
the model, the visualization, and the data.
Data
Model
Simulator
The simulator package contains simulator components. access
A simulator maybe specific for one model
Simulator
type or it may be able to compute
a broader range
datatypes.
flow In addition there may be more
of model
than one simulator per model type. Furthermore
the simulator package contains components for supporting distributed model execution (sever management, partitioning). A concrete simulator component for running a simulation is build based on the
components of this package.
Model
The model package contains the basic executable
model components. It also contains the items which
are required in each model, e.g. couplings for DEVS
models. Each model to be executed within the
framework must support the basic interfaces defined in the corresponding base model package. The
model components provide support for the observer
pattern [8], i.e. data produced by models is not directly stored by the models themselves. Observers
are listening for changes and forward them to the
data component. A concrete model component (a
model) is build based on the components defined in
here.
Visualization
The visualization component is currently under development, but it already provides a basic GUI
(graphical user interface) which allows to parameterize and start a model. Currently the focus for
further development is on how to model which formalism visually, e.g. with statecharts. In principle
a GUI should provide support for modeling, runtime (i.e. control, visualization) and analysis.
access
Simulator
data flow
ITS
Visualization
Data
access
data flow
simulation run, the user has access to the data component’s entries, which are displayed in the ITS. Thus, he
has information about the simulation run available, and
can revisit and correct his model, if required.
Design
Runtime
Analysis
Model
6
Simulator
Figure 2: ITS and JAMES II
Data
The data component is used to provide support
for storing the data generated by a simulation run.
There is no restriction on how the data has to be
stored - the data will be delivered by observers and
can be written into plain ASCII files, XML files or
even a database (as long as a proper storage component exists).
5
COMBINING
JAMES II
THE
ITS
AND
Figure 2 shows how JAMES II can be integrated into
the ITS system by adding a ITS layer to the JAMES II
architecture.
Combining the ITS system with JAMES II is a rather
simple task due to the component oriented design of
JAMES II. Each part of the framework is a component
composed of other components. Thus, exchanging complete parts of the framework or reusing any combination
of parts in any other system is possible. From the framework’s point of view the ITS will simply replace the visualization part. From the ITS’s point of view the components of the visualization component are reused. The
reminder of the framework is just used as it is. Thus,
given the description of JAMES II in the former section,
the ITS uses the simulation package, the model package
and the data component.
The ITS is responsible to display information to the
learner. The learner abstracts the information of a
model, i.e. he uses the modeling component of the ITS.
The information recorded by the learner is internally
transformed to an XML model. Also the model structure, the neighborhood and the couplings are stored in
XML. These learner-made models must meet the requirements for executable models, defined by JAMES
II. Thus, if information is missing, the ITS will correct and help the learner to complete his model. If the
learner decides to start the simulation execution (and if
the models are ready for simulation), he selects a simulator component of the simulation package. After the
FIRST IMPLEMENTATION
In our first implementation, the learner is confronted
with a classical modeling and simulation training case:
he should develop a model and run a simulation of the
”forest fire” scenario. In our training case, the client
provides background information, like: ”There is a river
crossing the forest. The trees in the forest are quite
old, but there are some younger birches at the river
bank.” The learner has to decide, which parts of information he will use to develop an appropriate model. He
uses a modeling editor to note each model’s attributes
and properties. The transfer of the learner’s model into
JAMES is realized with a model description in XML.
This XML description is also used to compare an optimal ”expert-”model with the learner’s model. The
model itself is distinguished in essential parts, which
must be part of the resulting model, and optional parts,
which might be embedded. Thus, the learner will be
immediately corrected if he wants to start a model with
missing essential parts. The result of the missing optional parts might influence the simulation, but will not
be immediately commented by the program. The first
implementation of our teaching and training program
uses a simple grid as basis for the spatial distribution
of the different models. Via marking regions in the grid
and associating models to the regions, the learner models the forest with the parts necessary for simulating the
described training case. If the modeling is finished, the
learner can run a simulation, and have a look at the
results. If he feels that the model is not appropriate,
he might revisit the modeling part of the program and
make model adjustments.
7
CONCLUSION
To combine simulation and ITS has been realized for
quite a long time. The combined systems can be categorized in three groups, i.e. interactive training simulations, demonstrative training simulations and character
simulations. In the first kind of teaching and training
simulation, the simulation steers the interactive environment of the training domain, e.g. a flight simulator or
a simulation of an electrical circuit. The second kind of
simulation is only used for demonstration purposes - the
learner has no possibility to interact. In the third kind
of simulation, a virtual persona is simulated. This kind
of simulation is an add-on to an ITS. Thus, a character
simulation can be part of an interactive training simulation, like STEVE. Whereas the learner steers the sim-
ulation in the first group, in the third group the learner
interacts with the virtual character, but not with the
simulation itself.
Another kind of interactive training simulation is described in this paper. Here the learner starts his work
with an ITS without simulation. He has to solve the
task to develop a model, given a large amount of unstructured, relevant and irrelevant knowledge. After the
learner has finished the model development, he has to
start a simulation. This simulation of his model can
be interrupted by the learner at any point in time. The
learner can revisit his model, make corrections, and start
the simulation again. He also has the possibility to completely change between modeling formalisms. For example, the learner might start with a discrete-step wise
model, and learns that this does not really meet the requirements of the client, then he might decide to develop
a discrete event model and re-run the simulation. After
the simulation run, the learner has to interpret the results. This kind of interactive training simulation does
not simulate a certain aspect of the training domain. In
the system type described in the categorization above,
the learner has to interact and the simulation steers the
training environment. In the system TutMoSi, simulation is one embedded part of an ITS. The simulation
itself is steered by the learner. The system TutMoSi
that is based on a combination of an Intelligent Tutoring System (ITS) and a Modeling and Simulation environment, JAMES II, allows for interactive training of
modeling and simulation, based on simulation.
8
OUTLOOK
The system described in this paper is work in
progress. Whereas the ITS and the system JAMES
II exist and provide full functionality, the combination
of both is currently realized. Our first training case
is the classical ”‘forest fire”’ simulation. Other exemplary training cases, e.g. in systembiology and medicine,
are currently developed. However, the main problem
in developing an ITS for Modeling and Simulation can
be seen in the interpretation and the validation of the
learner’s modeling success. One big problem is for example to decide what makes a model optimal. Here, a
didactial solution is necessary. Development of training
cases in this domain is a very demanding task. Thus,
the first application realized is on a rather coarse level,
regarding the potential abilities of an ITS.
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AUTHOR BIOGRAPHIES
Alke Martens is Dr.-Ing. of computer science. She
made her phd in the area of formal methods for intelligent tutoring systems. Her research areas are modeling
and simulation, teaching and training systems, and a
combination thereof. Current application domains of
her research are medicine and education of computer
scientists. She is currently a research scientist at the
Modeling and Simulation Group at the University of
Rostock.
JAN HIMMELSPACH holds an MSc in Computer
Science from the University of Koblenz. His research
interests are developing methods for modeling and simulation, with a focus on effective and efficient simulation
mechanisms. He is currently a research scientist at the
Modeling and Simulation Group at the University of
Rostock.