S.A. Cerri, G. Gouardères, and F. Paraguaçu (Eds.): ITS 2002, LNCS 2363, pp. 671–680, 2002.© Springer-Verlag Berlin Heidelberg 2002

Collaborative Discovery Learning of Model Design

Crescencio Bravo1, Miguel A. Redondo1, Manuel Ortega1, and M. Felisa Verdejo2

1Dpto. de Informática. Universidad de Castilla - La Mancha.Paseo de la Universidad, 4. 13061 Ciudad Real (Spain)

{cbravo,mredondo,mortega}@inf-cr.uclm.es2Dpto. de Lenguajes y Sistemas Informáticos. Universidad Nacional de Educación a

Distancia. Ciudad Universitaria, s/n. 28040 Madrid (Spain)felisa@lsi.uned.es

Abstract. Design and simulation environments are tools offering contrastedbenefits for discovery learning in multiple domains. However, when thesedomains are complex the problems that the students have to solve during theirlearning justify the necessity to carry out the design activity itself in groups.From the perspective of modelling and simulation in collaboration, we presentDomoSim-TPC, a system supporting collaborative learning in Domotics1. Thesystem provides shared workspaces integrating tools for domain problemsolving with generic but customized tools to support collaborative decisions anddiscussions. The potential of this synthesis to support learning tasks inmodelling is described. We outline the mechanisms offered by the system anddiscuss our solution compared to other relevant systems.

1 Introduction

Computer simulation tools allows nowadays the modelling and simulation of complexproblems in very diverse fields such as Engineering, Experimental Sciences,Economy, Statistics, or Sociology. Scientific discovery learning is an approach whichconsiders the learner as an active agent in the knowledge acquisition process. Indiscovery environments learners are engaged in constructivist activities, and theyacquire knowledge through a process of subjective construction starting from theexperience rather than through the discovery of an ontological reality [6]. In 1997 acommittee of scientific advisers handed the President of the United States of Americaa list of the most promising constructivist applications and simulations were on top ofthe list [18]; this gives an idea of the importance of this discipline.

When we face domains requiring modelling and simulation techniques,collaboration is usually necessary [9], this is the case to solve complex designproblems. According to Guzdial et al [8], collaboration in problem solving activitiesprovides not only an appropriate performance but also promotes reflection, amechanism enhancing learning processes. Thus, using this approach: (1) groups ofstudents can solve more interesting and more complex problems than individualstudents and (2) the students that work in groups need to communicate, make 1 Domotics: It is the technical discipline that studies housing automation. It is also called

intelligent building design.

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arguments and give opinions to other members of the group, encouraging the kind ofreflection that leads to learning.

We have built an environment for collaborative learning of model design that usessimulation. Using this environment the students, organized in groups, can solveproblems selected from a collection. We consider design as a discipline that requiresthe construction of some scenario under a variety of constraints. This scenario is themodel that the students will be able to test on a simulation tool. They will obtain asolution to a proposed problem in a refinement process involving guided discussion.The environment itself allows the teachers to manage the problem collection, thedefinition of activities, the monitoring of the learning process, the tutoring of studentsand the analysis of the activities.

The domain in which we have materialized our theoretical positions is Domotics.This discipline is taught as a subject in Secondary Education (Technical Training) andin Technical University Colleges of Industrial Engineering in Spain, and it covers thesubject of house automation or intelligent buildings design. We have developed amodelling tool including (1) a set of operators relative to different management areas(activators, receivers and systems that have to be placed on the layout of a house), (2)a set of parameters and (3) some relations between the operators [5].

This paper describes our collaborative simulation environment. In section 2 we willrefer to simulation applications in which groups of people take part and some systemsthat make use of simulation in collaboration situations. Then, we will define in detailour environment and the principles under which it has been developed. Finally, someconclusions will be drawn and future work will be outlined.

2 Collaboration and Simulation

CSCL (Computer-Supported Collaborative Learning) is a paradigm that focuses onthe social dimension of learning processes and promotes the use of the technology asa tool mediating [11] and facilitating co-constructive activities. It seems interesting toapply CSCL with simulation scenarios. We call this symbiosis Computer-SupportedCollaborative Simulation (CSCS).

First of all, let us mention some kinds of simulations that can be confused with thisnew term or that have similarities with it. Interactive simulation is a reproduction ofreality, which stimulates the scientific curiosity and research practice. In distributedsimulation different simulators act as elements of a distributed simulation at a greaterscale [12]. A very similar simulation type to the one we propose is participativesimulation, in which the participants are agents in a simulation such as players in awide range of computer-supported games [19].

A number of systems exploring the idea of collaborative simulation have beenimplemented. Following the participative approach we can mention SESAM(Software Engineering by Simulation of Animated Models) [17], created at theUniversity of Stuttgart. It has been developed to study the concepts of SoftwareEngineering and how to apply these concepts to practical situations. The systemsupports the simulation of the development cycle of a software project, representingsoftware products, documents of diverse types, fictitious participants (clients, projectmembers, etc.) and the evolution of the project in the time line based on a model. Amodel builder and a player (student) collaboratively analyse the phenomenon of

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Software Engineering by means of the construction, validation and refinement of asoftware project model.

ERCIS is a system of participative and distributed simulation based on DIS(Distributed-Interactive Simulation). DIS is a training system for militaryapplications. It can be used to create a Group-Distance Exercise (GDE), in whichparticipants are distributed in different locations. ERCIS [3] is a system of GDE thatsupports the teaching in the handling of the RBS-70 missile unit. ERCIS has two maincomponents: the team simulators and the simulation server. The simulation servercontrols a microworld of the group’s environment, including simulation of aircrafts,the exercise scenario and some geographical information. ERCIS has an importantpedagogic value. The GDE systems can automate the registration and evaluation ofexecution statistics. It is also possible to record the sessions and to reproduce them inorder to improve the effectiveness in post-action revisions.

Other systems do not follow any of the aforementioned approaches, but they allowsome kind of collaboration among the users. Two examples are SIMPLE and LESP.

VacSim [14] is a simulation system designed to learn the basic principles ofvacuum-pump technology for semiconductors manufacturing, and it is framed in anenvironment of engineering learning based on simulation called SIMPLE (SimulatedProcesses in a Learning Environment). It is based on learning traces, which seemspecially interesting in simulations, for example in manufacturing and chemicalprocesses where the sequence and time of the actions can have a dramatic effect in thesimulated world. The collaboration is reflected by facilitating the communicationbetween the teacher and the student and among students by sharing execution tracesof the simulation.

The LESP2 environment (Learning Environment for Simulation of ParticulateModels of Matter) allows the study of matter as a set of particles. It is based on thecombination of multiple representations (macroscopic phenomena in form of particlemodels, combined with text, animation and videotape) with constructive activities(the learners being creators of theoretical models of particles). LESP combines theeasiness of use of a visual learning environment with a complex physical simulationand a modelling program. Its e-mail module allows sending and receiving messagesabout the built models, enabling the attachment of LESP simulation files; this allowsthe student-student and teacher-student discussion about the models.

3 Collaborative Simulation for the Learning of Design

We have built a Computer-Supported Collaborative Simulation (CSCS) environ-ment,called DomoSim-TPC, for learning domotic design. It is based on the CSCLprinciples, developing ideas of Problem-Based Learning [2] and Learning by Design[10]. Its characteristics are described next.

3.1 Description of the Learning Situation

With the use of DomoSim-TPC, teaching Domotics in Secondary Education andUniversity involves also a modification of the educational protocol. First, the teacher

2 http://www.uni-essen.de/chemiedidaktik/LESP/bestellen.html, visited in Jan. 2002.

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carries out a presentation of basic theoretical contents. Next, students are organized insmall groups whom the teacher assigns the resolution of design problems selectedfrom an available library of problems. The problems have a characterization andinclude specific didactic objectives according to the criterion of the teachers. Thestudents use DomoSim-TPC to design the models that, they consider, satisfy therequirements of the proposed problem. During this process the learners discuss andjustify the design decisions that they have taken, building a common knowledge base.In addition, they can carry out a simulation of the model to test its behaviour inextreme situations and this way to check the fulfilments of the expected requirements.Students work in collaboration and at distance, taking advantage of the enrichingaspect provided by the different visions that the students from different colleges have.Finally, teachers and students discuss and reach conclusions about the experience.

3.2 Modular Architecture: Workspaces

The system has three different workspaces with diverse tools. Teachers use the firstone, and students use the second one. The third one is a workspace for student-studentand teacher-student coordination. In the student’s workspace there are two importanttools for collaborative problem-solving: (1) the Planning of Model Design, thatfacilitates that the student builds a plan -a set of abstract design actions- by means ofan intermediate representation language [15], and (2) the Model Design andSimulation, that allows them to refine the previous plan to build a model solution andto test the model by simulation [4]. In the following we focus on the Model Designand Simulation Tool.

3.3 Activities and Problems

The aim students have to achieve is the design of a model solving a particularproblem. A problem is described by a formulation, some restrictions, requirements,and the house or building plan to “domotize”.

Students are organized in groups and the teacher proposes the realization ofproblem solving activities to these groups. There is a collection of generic problems,created by experts in the domain and teachers by means of authoring tools. Thecollection is organized in three complexity levels: high, medium and low. Thisparameter together with a configurable help level allows the system to offerscaffolding [16] in the learning process. Thus, students will gradually solve problemsstarting with the ones with smaller complexity to reinforce the intrinsic structure ofthe problem resolution process. In this way knowledge is built by means of a leverageprocess of re-elaboration and integration. For difficult problems less detailed help isprovided while for easy problems greater help is offered because problems are solvedfrom small to big complexity and when students face a difficult problem they havealready solved ones which are easier. Thus, help fades along the learning process.

3.4 Coordination and Communication Tools

In the Coordination space there is a variety of tools allowing the communication andthe coordination among the participants: Chat, Recent News Board, Electronic Mail

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and Session Agenda. During the model design and simulation stage two extrasynchronous tools can also be used: the Guided Chat and the Decision Making Tool.

Collaborative interactions for problem solving include two interdependentcognitive tasks: solving the problem (model design) and collaborating. Besides task-oriented action, collaboration requires communication to exchange informationrelative to the domain, to coordinate actions and to reach agreements. Collaborativeinteractions involve explanation, reflection, verification, discussion, argumentation,etc. For this reason we have decided to include a chat linked to the design tools. Thischat is defined as guided because it does not allow the introduction of free text.Students have to select from a pre-set of goal-oriented communicative acts. Thissolution offers two advantages, on one hand students clearly express in a formunderstandable for everybody their personal perspective, on the other the systemprovides a structure for the conversation. This approach has been explored in anumber of proposals such as the one in Baker & Lund [1].

The Decision-Making Tool proposes a question to the group to reach a decisionabout some topic. There are three kinds of questions: those that have an affirmative ornegative answer, those that have a real value as an answer and those that have analternative between a set of possible answers. When all the students have respondedto the question, or there is a configurable timeout, the pooling result is shown in anew window.

3.5 Design and Simulation of the Problem Solution

The Model Design and Simulation Tool accessible from the student’s workspace isused to build a solution to a domotic problem. To carry out this process the studentsof the work group must follow a collaboration protocol [13], used in order tocoordinate the phases in a problem solving session. A protocol is described as a state-transition diagram in which each node of the diagram represents a state in theprotocol. Each state represents a workspace in which to perform a kind of task. Theprotocol we propose (fig. 1) presents three nodes matching three different workspacesnamed “subspaces”.

Fig. 1. State diagram of the collaboration protocol.

The first subspace to be accessed when an activity is selected is the Designsubspace. Here, students carry out the sub-task of designing a model. When theyconsider they have solved this phase, they can move to the Simulation Cases andHypothesis subspace, in which they outline hypothesis and check whether these areverified and they can also propose new test-cases and select another in the case libraryto run a simulation. Once a case has been chosen, the group can move to theSimulation subspace (fig. 2) to check and evaluate the quality of their solution byobserving the behaviour of their model.

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Fig. 2. Simulation case selection and hypothesis verification.

Figure 2 shows the different areas of the window corresponding to the Case andHypothesis subspace:

− Work area: It is a shared electronic whiteboard where the designed model isdisplayed; it contains the house plan and the operators. In figure 2 we can see asingle room (a lounge) with four subsystems.

− House plan: It is the representation of the plan on which the problem is defined.− The user and activity identification area.− Session Panel: It contains the list of participants in the work session.− Selection and proposal of simulation cases: In this area simulation cases related to

the problem and the new ones proposed by the students are shown.− Outlining and verification of simulation hypothesis: Hypothesis are essential to

deeply understand the mechanisms that govern the simulation. The hypothesis areexpressed as sentences in natural language. In this area the incorporated hypothesisof the problem and the ones proposed by the students are displayed.

To materialize both the processes of proposition and selection of cases and theproposition and verification of hypothesis we have taken into account theLanguage/Action Perspective [20]. Communicative acts are carried by Propose(Proponer), OK, NO OK, Simulate (Simular) and Confirm (Confirma) buttons thatrepresent speech acts. Once the group have reached consensus about a certain case to

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simulate, the users can access the Simulation subspace. The Case and Hypothesispanels disappear and the Environment and Guided Chat panels (fig. 3) became visibleso that the simulation can start.

Fig. 3. Collaborative session of simulation in real time.

The system provides for different roles. The student who has proposed the case tosimulate leads the participation and other students observe and communicate. Theleader student can alter the automatic behaviour of the model, that is to say, of theautomatized house, switching operators on or off, opening or closing doors, causingaccidents, etc. To do this, he/she will select an object and will press a button toperform a simulation action (see fig. 3). The teacher role can control the simulationsession by stopping it temporarily, for example, to propose a question, to promote areflection, etc. The interactions carried out by the system and the interactionsperformed by the student are displayed on the whiteboard in a graphic way and in thelist of interactions in a textual way (see fig. 3). This way students will check if themodel behaviour is the one expected. The students can talk and collaborate by meansof the communication and coordination tools. When the simulation finishes the Caseand Hypothesis subspace is accessible again.

From the Case and Hypothesis subspace the group can return to the Designsubspace to make changes in the model, motivated by the observation andexperimentation carried out in the simulation phase, so that a problem is solved insuccessive refinements combining design and simulation.

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3.6 Collaborative Support

The previous subspaces integrate different mechanisms that support the design andsimulation process in collaboration:

− Mechanisms of direct manipulation: Students design and simulate the model bymeans of elementary actions of direct manipulation. The outstanding feature of thisparadigm is the acceleration of learning, facilitating the intuitive and effective useof the systems and reducing and preventing errors. The metaphor of thecollaborative electronic whiteboard based on the object-action model is used. Thismodel provides a structure to increase the user’s control. There are tele-pointingcapabilities too, that allow users to show ideas by pointing, crossing out, etc.

− Support for communication and coordination: When the students go designing andsimulating the solution they can make use of the Guided Chat and the Making-Decision Tool to exchange information, to coordinate actions and to reachagreements.

− Awareness techniques: To facilitate the collaboration in real time a greatimportance has been given to awareness. Awareness is the perception andknowledge of the interaction that other people make in a shared workspace [7].This is reflected in the following aspects: (1) the students can see the mousepointers of the rest of group members all the time, each one with each participant’srepresentative colour; (2) each student’s state is maintained and represented(editing, parameterisation, selecting, linking, designing, drawing and com-municating); (3) the interactions made by other students, besides being visualizedon screen in real time, are reflected in the message area like a history of the designsession (it is also possible to activate an alert sound). All this allows users to knowwhat other students are doing, what intentions they have, etc.

In table 1 the different mechanisms that are offered in the three subspaces for theexecution of the corresponding tasks are summarised and compared.

The design and simulation tasks are carried out by means of direct manipulationfollowing the electronic whiteboard metaphor and the object-action model. Theobjects are the domain operators (activators, sensors, systems and links) and theactions that are applied on them are insertion, selection, deletion, movement, linkbetween operators and parameterisation. During the realization of both tasks thestudent can use communication and coordination tools to facilitate collaborationamong the participants. Taking awareness into account, the following functionalitiesare offered: teledata (shared data model), a list of the interactions performed and theparticipants’ name and photo; in the Design subspace, telepointers and eachparticipant’s state (that indicates what he/she is doing: edit, select, communicate, etc.)are also shown. The Case and Hypothesis subspace is used for a more specific taskand there are not communication and coordination tools. It is also based on the object-action model but in this case the student does not work with a whiteboard and iconsbut with forms, which represent objects, and buttons, which represent actions.

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Table 1. Collaboration Support Mechanisms to the collaboration in the different subspaces.

Collaboration Support MechanismsSubspac

e Direct Manipulation Communicationand Coordination

Awareness

Design

Electronic WhiteboardObject-Action ModelObjects: domain operatorsActions: edition,parameterisation and link

Guided ChatDecision-MakingTool

TeledataTelepointersInteraction ListSession Panel: photo, name andstate

Casesand

Hypothesis

Object-Action ModelObjects: cases and hypothesisActions: propose, ok, not ok

---TeledataInteraction ListSession Panel: photo and name

Simulation

Electronic WhiteboardObject-Action ModelObjects: Domain operatorsActions: on/off, open/close,manual/automatic ...

Guided ChatDecision-MakingTool

TeledataInteraction ListSession Panel: photo and name

4 Conclusions and Future Work

In this paper we have presented a synchronous collaborative environment for complexdomain learning based on modelling and simulation. The system offers differentshared workspaces to the users according to their role (student and teacher). The mainworkspace, in which problem solving activities are carried out, is structured through acollaborative protocol identifying three subspaces: model design, outlining of casesand hypothesis, and model simulation. These subspaces integrate tools for the domainproblem solving with generic but customized tools to support collaborative decisionsand discussions. The tools use different mechanisms for the realization of the tasks:direct manipulation based on the object-action model, communication andcoordination support, and awareness techniques to facilitate real time collaboration.

We have applied our research to the Domotics domain. The environment has beenevaluated by experts in this domain and by teachers, and it has been tested in realsituations of experimentation in Technical Training Schools. This way, its practicalutility and the validity of the proposals of this work have been demonstrated. This isthe formative evaluation.

Our oncoming goal is to perform the summative evaluation, i.e., to obtainquantitative and qualitative data from the experiences in order to draw conclusionsabout this approach and to extend our positions to other domains in which the designand simulation are justified.

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