modeling tutoring as a dynamic process - a discrete

MODELING TUTORING AS A DYNAMIC PROCESS - A DISCRETE EVENT SIMULATION APPROACH A. Martens A.M. Uhrmacher Department of Computer Science Fachhochschule Ulm Universit
at Ulm D-89069 Ulm Germany e-mail: [email protected], [email protected]

KEYWORDS

and can be treated as such - a perspective we wish to persue in this paper. Typically the tutoring process is strongly guided and the student solves the teaching tasks step by step. In interacting with the system each student reproduces more or less the same process since the system hardly provides any freedom of choice. A diversity of authoring systems provide the functionality to design this kind of tutoring systems 7] which produce an alternating sequence of informations and questions to the student. Thus, the students activities are preconstrained. After each step, the students answers are corrected. Due to the xed course of a guided tutoring, corrections and further explanations can be given in a goal directed and context dependent manner. However, a guided tutoring does not reect reality, neither in general nor in the medical domain, we are interested in particular. E.g. at the emergency admission physicians have to decide on the y whether to do further anamnesis or to order laboratory tests. Thus, this kind of tutoring systems are not suitable to train students to master independently complex decision situations. Also this kind of tutoring implies that complete separate tutoring processes have to be developed according to the knowledge and experience of the student. An alternative approach provides a complete free access to the dierent elements of the learning process 10, 13]. What is presented to a student depends only on his or her own decisions. Tutoring takes on the shape of a rather strange dynamic process in which the student interacts with a set of unrelated teaching units. The teaching units do not take the individual history of students navigating through the tutoring process into account. Help and explanation are from a pedagogical point of view rather coarse since the

Tutoring System, Medical Application, AgentOriented Simulation, Discrete Event Simulation, Java

ABSTRACT Most of the work in the area of simulation and education is dedicated to the process of teaching students the essentials of dynamic systems by simulation. Thereby, the system to be taught becomes the subject to be modeled and simulated. However, the process of tutoring can be perceived itself as a dynamic process and can be treated as such. To support a exible and intelligent tutoring process we distinguish between model and simulation level. At the model level we employ a state-based, modular and hierarchical agent-oriented model design. The user is represented as an agent which moves through the tutoring process accessing its structure. Thus, agents and their changing interaction structure becomes central.

1 INTRODUCTION Most of the work in the area of simulation and education is dedicated to the process of teaching students the essentials of dynamic systems by simulation. Simulation contributes to the development of tutoring systems as a source that allows students to conceptually interact with the domain. It helps students to envision possible eects of their activity and to grasp part of the underlying causal structure 8, 6, 4]. Thereby, the system to be taught becomes the subject to be modeled and simulated. However, the process of tutoring in which the student interacts with interrelated teaching units can be perceived itself as a dynamic process, 1

author of the tutoring system has no possibility to foresee the navigation route which has lead the student to that point. Unguided tutoring systems ought to be employed under the supervision of experts, if they cannot directly access a vast expert knowledge base 9]. To realize a exible learning environment that combines free decisions with unsupervised learning, we choose an approach which lays in between. This implies a variety of possible navigation routes through a network of interrelated teaching units. The dynamic is caused by the student's interaction with the teaching process. Thus, the tutoring process evolves as a dynamic and complex process of a student interacting with teaching units and teaching units interacting among each other. The realization of the tutoring process requires description and controlling strategies as provided by modeling and simulation methodologies. With respect to modeling and simulation methodology the problem becomes that of controlling a discrete event system with discrete, typically qualitatively scaled variables, where time ows in discrete time steps.

2 THE APPLICATION AREA According to the German "Approbationsordnung", medical education should provide practice-relevant training in medical issues with emphasis on patient contact to complement the systematic knowledge provided by text books and lectures. A patient-centered education however requires a large investment of time and human resources and therefore is hardly realized. The collaborative project Docs 'n Drugs is aimed at developing a web-based multimedia framework to teach medical students in a case-based manner 1, 5]. At the core of the project are the case-based knowledge model 12] and the tutoring process model. The medical case can be roughly divided into a set of situations. A situation reects a part of medical reality where physician and patient do interact, e.g. this can be an emergency or an in-patient situation in hospital. Situations structure the medical treatment process beginning with the rst contact with the patient and ending with a situation which represents a certain goal in the entire medical case. E.g. the correct treatment, the correct nal diagnosis or the discharge of the patient might complete the tutoring process. Each situation consists of a description, a tutoring goal and a situation navigation. One situation comprises several tasks, which can be distinguished in information and decision tasks. The only purpose of an information

task is to present information to the student, typically as multi-media data. A decision task contains a question or an interaction request and an interaction part. The latter can be a multiple choice question or a picture in which the student is asked to mark specic regions, for example. Furthermore the decision task holds typically an information block. Part of the information which the student has achieved by visiting a certain task is stored in the medical ndings folder. The system shall be able to adapt the tutoring process dynamically, according to the actions and the learning progress of the student and the overall teaching goal, and to produce student sensitive and pedagogical explanations and corrections. They both require that the system is able to reason about the tutoring process and the achievements of the student, which necessitates a declarative model design. The sketched medical tutoring process comprises dierent situations, each of which consists of dierent entities. This overall structure lends itself for a hierarchical model design. Tasks and situations can be perceived as entities with a clear boundary to their environment. Their classication implies certain properties and interaction possibilities. An object-oriented model design seems therefore most suitable. Since the tutoring system shall be executed on the Internet where it is supposed to interact with external resources, e.g. with medical online databases and the project's own case based knowledge model, it is mandatory that the system allows to start and to communicate with other processes over the net. A platform independence shall support the use of the system in dierent training laboratories and at the student's home, as well. Given the nature of the tutoring process we expect the structure of the tutoring process to change, as we already mentioned. Certain answers or diculties of the student might cause the tutoring system to present additional tasks and to skip others. It might also inuence the order in which certain tasks are visited. Concludingly, a hierarchical, modular and object-oriented model design is required which supports a exible handling of variable structures, in terms of composition and interaction. Since the student and his or her interaction with the tutoring system is of central interest the user should uniquely be represented and identied on the model level of the system. This facilitates the maintainance of user-specic and learning relevant information and a user-specic adaptation of the tutoring process, particularly if more than one student is active. Therefore, the model design should allow the embeddence of agents which reect the user's knowledge, experiences,

and options of activities, and which are equipped with capabilities to steer and control the tutoring process. Thus, an agent-oriented model design is required 15].

3 MODEL DESIGN IN JAMES JAMES, a Java-Based Agent Modeling Environment for Simulation, realizes variable structure models including mobility from the perspective of single autonomous agents. JAMES constitutes a discrete event simulation system which is based on parallel DEVS (Discrete Event System Specication) and adopts its abstract simulator model. It inherits from parallel DEVS its modular, hierarchical and declarative model design 17, 3]. JAMES distinguishes between atomic and coupled models. An atomic model is described by a set of input and output events, a state set, an internal, external, and conuent transition, an output and time advance function. The internal transition function dictates state transitions due to internal events, the time of which is determined by the time advance function. The external transition function is triggered by external inputs which are dened as bags over simultaneously arriving input events. If an external and internal event coincide, the behavior of a model is dened by the conuent transition function 3]. Each model communicates with the external "world" through its input and output events. Its state is described by a set of variables. The state space of an atomic model can easily be structured to reect aspects of deliberative agents, e.g desires, intentions, and beliefs 16]. In our scenario the state of an agent captures e.g. the teaching goal, structure and constitutive components of the current situation, the student's experience level, errors so far produced, and the medical ndings folder. The attributes which describe the information and decision tasks, e.g. the structure of the screen, multi-media elements to be displayed, and navigation options, can be encoded in the state of an atomic model equally easy. A coupled model is a model consisting of dierent components and specifying the coupling of its components. Its interface to its environment is given by a set of external input and output events, respectively. Coupled models allow modular and hierarchical modeling. As in DEVS, coupled models have no behavior, i.e. transition functions, of their own. Unlike other approaches, which equip DEVS coupled models with an own behavior to eectuate structural changes 2], JAMES provides mechanisms which empower the atomic model to change the structure of the model. Structural changes, e.g. the deletion and creation of

models and couplings, are the building blocks to capture the phenomenon of mobile agents. The ability of a model to change the structure is locally restricted, it is allowed to delete itself, to change its interaction with other models and to add a model into the coupled model it belongs to. Atomic models rely on the cooperation of other models to delete other models or to arrive at a remote location. For that purpose, they have to send structural change requests to other models, which are cooperative by default 14].

4 MODELING THE TUTORING PROCESS - A FIRST STEP Originally, JAMES does not allow the user to interact on the model level JAMES only supports interactions with the simulation run during execution. For tutoring the user interaction determines the behavior of the model, driving and steering the execution of the tutoring process. To include \humans in the loop", we introduce an atomic model, i.e. a mobile agent for each student, which moves through dierent situations and is responsible for the interaction with the student. To represent a situation we introduce a coupled model whose role it is to comprise and interrelate atomic models which function as information and decision tasks, respectively. Additionally, each coupled model is equipped with a specic atomic model, which is called the concierge. In the moment an agent enters a new situation the concierge embeds the agent in the current situation and forwards all situation specic information to it. The concierge contains the tutoring goal of the situation and the restrictions that apply in the situation, e.g. during night laboratory examinations are not possible. In the moment an agent wishes to move into a situation it directs its requests to the concierge, which will add it to the current situation and forwards the situation specic information, e.g. it will send the agent the learning goals of the situation and where to start within the current situation. Accordingly, the agent will trigger the information task or the decision task model by forwarding knowledge about the student, e.g. his or her experience level. An agent will be only connected to one task model. The task model will answer the request by sending a bunch of information back to the agent. This includes the information to be shown on the screen, the navigation possibilities, and an abstract description of the content, to name only a few. Based on this abstract description the agent collects and interpretes the knowledge gained by the

Situation A

Situation B

D Task A

Task A

D Task B

Task B

Agent Concierge

Concierge

Figure 1: The Situation Model - One situation follows the other and each situation comprises interconnected information and decision tasks. student. E.g. on a medical ndings folder it documents the information gain and learning progress of the student. This information is transported from one situation to the next through the entire tutoring process, since the medical ndings folder presents part of an agent's internal state. In our rst approach a situation accommodates one atomic model producing step by step the information and one atomic model producing step by step the decisions (Fig. 1). The rst is called the situation's information task, the latter the situation's decision task. Both are responsible for modeling the behavior and causal connections between single information and decision steps which belong to the given situation. Temporarily, also an agent might be on visit, as in situation A. The information task displays information according to the student's navigation, the student's level of experience and the student's prior activities. The latter is presented locally in the task's state. E.g. during examination in one situation anamnesis might occur several times, producing successively a comprehensive description of the patient's situation and disease. The information task might be visited alternated with decision tasks. Transition functions are responsible for selecting the information which shall be displayed. This can be plain text, references to pictures, animations, web-pages, and sounds, or a combination thereof. Their being shown is recorded in the internal state of the information task. Afterwards the control is passed on to the user agent, which receives from the information task the information and the user's navigation options and is also sent an abstract, declarative representation of the information which is presented to the user. This abstract declarative representation conforms to the terminology used in the overall tutoring and is cemented in the case based knowledge model 12]. The information is stored in the medical ndings folder.

Part of the decision task is equivalent to the information task including major parts of its interaction with the agent. As do information tasks, decision tasks decide which multimedia information to present to the student. In addition, they require an action of the student, e.g. answering questions, and marking regions of an x-ray image. A student might move to another task without responding to the requests of a decision task, or a student might answer the questions, and afterwards ask for corrections to his or her answers. At a certain point of time he or she might even wish to move on to the next situation. Each interaction with the user requires an interaction with the agent model. As does the information task, the decision task sends the information and options of a user to the agent model via its output functions and updates its own internal state. According to the information received, the external transition function of the agent will display information and decision requests on the screen. The agent model waits for the input of the user and processes it, e.g. sending the reactions of the user back to the decision or information task. If the learning goals of a situation are achieved, e.g. by answering the question or activating the required correction, which typically completes a decision step, the agent signalizes the decision task that the situation navigation shall be enabled. If the student now wishes to leave the current situation he or she is allowed to do so. Otherwise, depending on the internal structure and the chosen activity of the student, the decision task presents the next decision step or activates the information task by charging its corresponding output port. We will illustrate the general procedure (Fig. 2) by a concrete example which is part of a medical case about a patient who suers from a complete right artery stenosis. Firstly an emergency situation was shown to the student. It described an old man coming to the hospital with the following symptoms: oedema in the legs, high blood pressure and a red face. The goal of the rst situation was to diagnose a serious nephrological problem and to recommend a hospitalization. In the current situation, the man is an inpatient in the nephrological ward. The student has just entered the second situation in the tutoring case. The information that is shown to the student in the decision task is an angiography, which the student ordered in the rst situation but which took some time to proceed. The system presents a multiple choice question about what can be seen in the picture, and an interaction request, which invites the student to mark the pathological area in the picture (Fig. 2).

In-patient situation: Yesterday, you have chosen to admit Hubert M. stationary to the nephrological ward.

Situation Solution Next Situation

You have requested an angiography, which has just arrived. Before reading the finding description, written down by your collegue, look at the picture and try to answer the following question. What kind of finding can you see in the picutre? No pathological finding

Navigation Anamnesis Physical Examination

Renal artery stenosis, right, incomplete

Technical Examination

Renal artery stenosis, left, incomplete

Laboratory

Renal artery stenosis, right, complete Renal artery stenosis, left, complete Differential Diagnosis Findings Folder Mark the right renal artery in the picture! Next Solve the requests and afterwards click one of the Navigation Buttons to choose your following step.

Quit

Figure 2: A Screen Shot Sketching a Decision Task in the Tutoring Process The goal of the current situation is the correct diagnosis of the complete renal artery stenosis on the right side. After the student has identied this diagnosis, he or she is allowed to leave the situation assuming that the diagnosis was the only teaching goal of the situation. In a following situation, the student has to decide how to treat the patient. All information steps of a situation have been modelled within one atomic model and all decision steps in another one. This allows an abstract and compact description of the tutoring process. However, it makes reasoning about the process more dicult, since the ow of the tutoring and dependencies between tasks are hidden in the transition functions. The transition functions determine which decision step follows which other step (Fig. 3). Thus, tasks constitute \black boxes" for the agent. It is the responsibility of the agent to construct and adapt during run time the structure of the tutoring model by determining, according to the experience level of the student, situations, tasks, and their interrelation 11]. If the agent decides that additional steps are required, either the decision task has somehow to foresee this possibility or the agent has to replace the entire decision task by another one. Encoding information in transition functions deprives the agent of the possibility to reason about single steps within the decision and information tasks. Therefore, we revise our rst approach.

5 A FINER GRAINED APPROACH Now, each information step and decision step is represented as an atomic model. The couplings between information and decision tasks make the sequences in which the student can navigate through the tutoring process visible and accessible. Otherwise hardly anything changes.

Situation A

D D

Information In Decision In Agent In

D

Information Out Decision Out Agent Out

Agent Concierge

Figure 3: The Finer Grained Approach - Each Information and Decision Step is represented as an independent task, i.e. an atomic model.

class DecisionTask extends AtomicModel f ... State deltaExt(State state, double elapsedTime) f ... // updates state, sets phase variables, and // welcomes the agent state.interpretInputAndUpdateState() state.setDefaultAdvanceTime() if (state.agent == null) f addCoupling(this outAgent state.agent inDec) state.setAgent = agent

g

if (state.phase == "decision") f // selects and displays the corrections step state.selectNextDecisionStep() state.nextDisplay.update() // fills a slot with the options, the information // to be displayed, and an declarative shortform state.setUseroptionsAndHandOnInformation() g else f if (state.phase == "movedOn") // at the moment finished with the situation state.setInfinityTimeAdvance()

g

g

void lambda(State state) f ... if (state.phase == "moreInformation") outPortPut("informationOut", (state.addressee, state.agent)) else if (state.phase == "moreDecisions") outPortPut("decisionOut", (state.addressee, state.agent)) else outPortPut("agentOut", state.userOptionsAndHandOnInformation)

g

g

State deltaInt(State state) f // update state state.update() // wait for next input state.setInfinityTimeAdvance() // if agent moved on to another decision or // information task in the situation delete // coupling if (state.phase == "moreInformation" || state.phase == "moreDecisions") delCoupling(self outAgent state.agent inDec) return state

g

g

Figure 4: Abstract Description of a Decision Task The code illustrates the typical three fold structure of DEVS models. The agent still has the same role as described in the rst version. The concierge informs the agent at its arrival about the current components and their coupling structure, and where to start according to the situation. So the agent knows about its environment, the components and how they are interrelated. The agent connects to the information or decision task it has been informed to start with. Throughout its being in the situation it is only connected to the currently

active task. At the rst arrival it connects to a certain task and sends this task also its name. If a task is left it sends the name of the agent to the new task which will install the connection to the agent by adding the required coupling (Fig. 3). The prior task dissolves the connection by invocing the corresponding JAMES function. If an agent decides to change the interaction structure between information and decision tasks, to delete or add tasks, it connects to the tasks involved and sends them the corresponding \variable structure requests". These requests are interpreted by the model. All JAMES models that do not represent autonomous agents will obey those requests by default. The causal and temporal relations between dierent information and decision steps are visible as the components and the interactions of the coupled model. Each navigation choice leads to charging the corresponding output port. Each task is equipped with ports to communicate with other information tasks, other decision tasks and the agent. Since at each output port dierent informationtasks might be listening, the agent identies the correct addressee. The agent interprets the activity of the user given the options of the tasks and its information about the environment. Figures 4 sketches the structure of the generic decision task at a high level. The functionality is hidden behind the functions interpretInputAndUpdateState(), selectNextDecisionStep() and setUseroptionsAndHandOnInformation(). This abstract description will work independently whether a ne grained or a course grained scenario is chosen. However, a decision task that represents more than one decision step has to carefully decide when to go into the phase \moreDecisions". In this phase the agent will move on to the next decision task. With this approach it is possible to model explicitly the guided case 9], the unguided case 13, 10], and the case of moderate guidance. The latter of which we are particular interested in (Figure 5).

6 STEERING THE TUTORING PROCESS - THE SIMULATION Specic JAVA libraries provide the functionality for modeling and simulation in JAMES. The purpose of JAMES is to support experiments with multi agent systems, which might be composed of several concurrently deliberating, e.g. planning or learning, agents. Therefore, models in JAMES are executed concurrently in a distributed environment 16]. JAMES sup-

D

D D

Agent Concierge

Agent Concierge

Figure 5: Unguided and Partly Guided Tutoring ports a continuous time model which for our application reduces itself to a discrete time model. Most time steps are by default \1", which means one step follows simply the other. To signalize that a communication, between agent and tasks, or between tasks, has been completed for now, the time advance function is set to innity. Thus, the tasks are put to sleep waiting to be triggered by incoming events. If only one student works with the system, only one entity will be active most of the time. One exception is if certain information is requested but shall be presented to the student after a certain delay, in one of the following situations. In this case, the time advance function has a value that is dierent from \one" or \innity". The activity of the task will be triggered by the ow of time. Since the agent is the only point where an interaction with the user takes place, a throughout testing of the tutoring system and its parts including the intelligent agent can easily be achieved by equipping the agent with an atomatic procedure to simulate the choices of the student. During testing the interactive simulation turns into a non-interactive one: not only a tutor's but also a student's activities being now simulated.

7 CONCLUSION In the chosen design, the agent represents the connection of the system to the world around it. It is responsible for user interaction and also for launching requests to external resources. A student can also announce that he or she wishes to cooperate with another student, at that point a second agent will enter the situation. It is the point for synchronisation real-time ac-

tivities, e.g. decisions of the student, search results of virtual agents, or collaborative eorts, with the simulation environment. The autonomy of the agent allows to mimic at the model level the autonomy of the student in choosing between dierent tasks and moving on to dierent situations. Although the tutoring process is only realized in fragments and most of the models are still skeletons compared to what they are supposed to be, the statebased modular model design of JAMES provides a suitable and suciently exible platform to structure and to embed the heterogeneous knowledge sources required by the tutoring process. Each task can easily be identied and all the information necessary is locally available. Future work will be dedicated to ll in the skeletons, e.g. to connect the tutoring process model with the case-based knowledge model, and to support several students doing collaborative work on one medical case. The agent will be equipped successively with a variety of deliberative skills, e.g. reasoning, collaboration, and producing student sensitive explanations 11]. As does DEVS, JAMES supports situation-based and time-based events equally. However, the tutoring process has been driven mainly by situation-based rather than time-based events so far. Only a few times, a delayed display of information and decisions is triggered by time-based events. Nor have we utilized the ability of the system to synchronize distributed concurrent activities so far. Thus, the synchronization mechanism of JAMES will probably require some tailoring to t the needs of the application more tightly. Due to its modular, hierarchical model design combined with exible mechanisms for variable structure models and a distributed execution environ-

ment, JAMES constitutes a suitable basic layer and a promising frame for developing a web-based multimedia tutoring system pinpointing tutoring as a dynamic process.

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