Design of a Simulation Framework for Model-based Learning

Sarah Zribi

, Antonello Calabr

, Francesca Lonetti

, Eda Marchetti

Tom Jorquera

and Jean-Pierre Lorr

Linagora, 75 route de revel 31400, Toulouse, France

Istituto di Scienza e Tecnologie dell’Informazione “A. Faedo”, CNR, Pisa, Italy

Keywords:

Model-based Learning, Simulation, Business Process.

Abstract:

In model-driven learning, simulation of Business Process is a key step for improving the learners’s skills and

enhancing teaching performance. In this paper, we provide the architectural design and the main functionali-

ties of a model-based learning simulation framework. The main objectives of the proposed framework are: i)

enable user-friendly learning simulation with a strong support for collaboration and social interactions; ii) pro-

vide a process-driven learning environment that allows emulation of the learner behavior when no learners are

available to be involved on the simulation; iii) include event-based monitoring aiming at providing feedbacks

for the learner assessment.

1 INTRODUCTION

In recent years, inside many industrial contexts and

application domains, Business Process (BP) models

have increased their importance due mainly to the

possibility to easily allow formal speciﬁcation, pro-

vide accepted and concise deﬁnitions and taxonomies,

and develop an executable framework for overall

managing of the process itself.

Speciﬁcally, in learning environment the availabil-

ity of this kind of models makes easier the adoption

of Business Process simulation for teaching purposes.

Usually, a simulation attempts to mimic real-life or

hypothetical behavior to see how processes or systems

can be improved and to predict their performance un-

der different circumstances. In learning environment,

commonly BP simulation enhances student learning

and problem-solving so to improve students’s knowl-

edge. Thus, models and simulations become a foun-

dation for improving the learners’s skill, enhancing

teaching performance and providing a comprehensive

framework. Different conceptual and mathematical

models have been proposed for model-based learning

and several types of simulations, including discrete

event and continuous process simulations, have been

considered (Blumschein et al., 2009). However, the

main challenges of existing learning simulation pro-

posals are about collaborative simulation and the de-

rived learning beneﬁts.

In this paper, we present a new simulation frame-

work providing a ﬂexible simulation environment

with a strong support for collaboration and social in-

teractions, as well as process visualization and moni-

toring. Model-based learning simulation can be com-

pared to a collaborative game where a team of players

composed of one coach and any number of learners

work together in order to achieve a common goal. The

main objective is consequently to provide an easy to

use and user-friendly environment for the learners in

order to let them take part of the process when their

turn comes, assuming different roles according to the

content they have to learn. The principal contribu-

tion of this paper is the design of an infrastructure

for simulation of model-based learning. The simu-

lation environment includes an event-based monitor-

ing framework aiming at providing feedbacks for the

learner evaluation and allows multi-sessions as well

as collaborative BP simulation. In the rest of the paper

we ﬁrst brieﬂy introduce some background concepts

(Section 2), then is Section 3 we present the main

components of the simulation framework architecture

whereas in Section 4 we describe its main functional-

ities. Finally, conclusion concludes the paper.

2 BACKGROUND

The proposal of a simulation framework for model-

based learning originated in the context of the Model-

Based Social Learning for Public Administrations

(Learn PAd) project (LearnPAd, 2016). The devel-

oped Learn PAd platform will support an informa-

tive learning approach based on enriched BP mod-

els, as well as a procedural learning approach based

Zribi, S., Calabrò, A., Lonetti, F., Marchetti, E., Jorquera, T. and Lorré, J-P.

Design of a Simulation Framework for Model-based Learning.

DOI: 10.5220/0005688606310639

In Proceedings of the 4th International Conference on Model-Driven Engineering and Software Development (MODELSWARD 2016), pages 631-639

ISBN: 978-989-758-168-7

631

on simulation and monitoring that will allow users

to learn by doing. In learning context, BP simula-

tion approaches are very popular since learners pre-

fer simulation exercises to either lectures or discus-

sions (Anderson and Lawton, 2008). Simulations

have been used to teach procedural skills and for

training of software applications and industrial con-

trol operations as well as for learning domain speciﬁc

concepts and knowledge, such as business manage-

ment strategies (Clark and Mayer, 2011). Nowadays,

more attention is given to business process oriented

analysis and simulation (Jansen-Vullers and Netjes,

2006). Studies have shown that the global purpose of

these existing business process simulation platforms

is to evaluate BPs and redesign them, whereas in the

last years simulation/gaming is establishing as a dis-

cipline (Crookall, 2010). However, these platforms

present several shortcomings regarding their applica-

bility to a collaborative learning approach. Namely,

no existing platform regroups all of the main func-

tionalities of a learning simulation solution such as

facilities for providing a controlled and ﬂexible sim-

ulated environment (for example allowing to switch

between possible outcomes of a task, in order to ex-

plore the different paths of a process), good visualiza-

tion and monitoring of a process execution ﬂow (in or-

der both to assist and evaluate the learners) (Crookall,

2010). The main challenges of a learning simulation

are about collaborative simulation and the derived

learning beneﬁts. To answer all of these concerns

a new learning simulation framework is designed in

this paper, providing a ﬂexible simulation environ-

ment with a strong support for collaboration and so-

cial interactions, as well as process visualization and

monitoring. The important objective is consequently

to provide an easy to use and user-friendly environ-

ment for the learners in order to let them take part of

the learning process assuming different roles.

3 SIMULATION FRAMEWORK

ARCHITECTURE

In this section, we describe the high level architecture

of the proposed simulation framework, its main com-

ponents, their purpose, the interfaces they expose, and

how they interact with each others. In particular, as

depicted in Figure 1 each component is exposed as

a service and provides an API as a unique point of

access. Inside the Learn PAd infrastructure, the pro-

posed simulation framework interacts with the Learn

PAd components by means of the Learn PAd Core

Platform and speciﬁcally through the Bridge and the

Core Facade interfaces. Moreover, in the Learn PAd

vision two levels of learners have been considered:

the civil servant who is the standard learner, and the

civil servant coordinator who is a generalization of the

civil servant who is in charge to activate and manage

a simulation session.

The simulation framework components are:

SimulationGUI: it is in charge of the interactions

between learners and simulator’s components.

PersistenceLayer: it stores the status of the sim-

ulation at each step in order to give to the civil servant

the ability to stop it and restart when needed.

RobotFramework: it allows to simulate the be-

havior of civil servants by means of robots.

SimulationEngine: this is the core component

of the simulation framework. It enacts business pro-

cesses and links activities with corresponding civil

servants or robots.

Monitoring: it collects the events occurred during

the simulation and infers rules related to the business

process execution.

Communication middleware: it provides event-

based communication facilities between the simula-

tion components according to the publish/subscribe

paradigm.

UserFacade: it is in charge of encapsulating real

or simulated civil servants (i.e. robots) in order to

make the learner interaction transparent to the other

components of the architecture.

In the following more details are provided.

3.1 GUI

As depicted in Figure 2, the main features of the sim-

ulation GUI are: the possibility of chatting and noti-

ﬁcation, the mutual interaction by input/output panel,

the visualization of simulation members, the possibil-

ity of reading and searching for documents and addi-

tional information useful during the simulation activi-

ties (Context Area) and the usage of standard facilities

as for instance Play, Save, Pause, Stop (Simulation

lifecycle menu).

During a simulation, the civil servant coordinator

selects the difﬁculty level of the simulation that can

be elementary, intermediate or advanced. For each

task of the BP and until the end, the simulation frame-

work notiﬁes the civil servant when his/her turn to

learn comes, so that the civil servant ﬁlls the required

forms and submits them. Moreover, the simulator of-

fers the possibility to the civil servant, when he/she

needs some help, to chat with online experts, or to

consult recommended resources and FAQ that ﬁt for

the current context. Finally, when all tasks of the BP

are simulated, the simulator provides an evaluation of

the simulation.

MODELSWARD 2016 - 4th International Conference on Model-Driven Engineering and Software Development

632

Figure 1: Simulation Framework Architecture.

Figure 2: Main User Interface.

3.2 Persistency Layer

The Persistency Layer component is in charge to store

simulation logs and business process states. Its main

sub-components are:

Logger: It is in charge of storing time-stamped

event data coming from the simulation engine. These

event data are produced each time a treatment is trig-

gered by the engine: BP activity invocation, user in-

put/output, etc.

BPStateStorage: This component allows to

store/retrieve/delete/update the state of a given sim-

ulation associated to a BP. This is a key feature that

allows the user to freeze a simulation, log out and

come back to the simulation later on. The compo-

nent manages all the necessary data and stores them

into a database.

TestDataRepository: This component collects

the historical data that relate to the simulations ex-

ecutions. Historical data specify input and output in-

formation/values corresponding to paths (or subpaths)

and activities of the BP and are inserted and validated

Design of a Simulation Framework for Model-based Learning

633

Figure 3: Simulation Environment Functionalities (Civil Servant Coordinator).

Figure 4: Simulation Environment Functionalities (Civil Servant).

by experts. These data can be used for verifying the

correctness of the information provided by a civil ser-

vant during a simulation session as well as for infer-

ring the robot behavior.

3.3 Robots Framework

During a simulation some of the required civil ser-

vants may not be present, therefore robots mimic

missing learners behavior. They are aimed to provide

outputs that would have been produced by a human

with the same input in the same context. The Robots

are implemented on the basis of the availability of his-

torical data, i.e. the data saved in the TestDataRepos-

itory during a previous simulation session and pro-

vided by an expert who takes the role of the civil

servant. In case of unavailability or incompleteness

of these data, either an expert may be asked to pro-

MODELSWARD 2016 - 4th International Conference on Model-Driven Engineering and Software Development

634

vide historical data manually or a knowledge-based

approach able to deduce output from previously col-

lected answers provided by learners, is applied.

3.4 Simulation Engine

Simulation engine takes in charge the simulation of a

given business process instance. It takes the form of

an orchestration engine that invokes treatments asso-

ciated to each activity of the current process. Such

workﬂow may involve multiple civil servants taking

different roles that may be present or not. For those

that are not available, robots are used in order to

mimic their behavior. A simulation manager is pro-

vided in order to manage BP lifecycle according to

the current context (create, stop, resume, kill, etc.).

Business processes are made of two kinds of activ-

ities: i) Human activities involve civil servants who

should provide information in order to complete the

task. The concept of human activity is used to spec-

ify work which has to be accomplished by people; ii)

Mocked activities involve robots to compute the treat-

ment associated to the activity. When the simulation

engine invokes a human activity the corresponding

civil servant is asked to provide input through a form.

Those forms are managed by a form engine that dele-

gates task to a robot if necessary. All the state infor-

mation necessary to restart a speciﬁc simulation are

stored “on the ﬂy”. The civil servant may decide to

freeze a running simulation, to store it, to backtrack

to a previous stored state and to logout. He/she will

be able to resume it later.

Business Process orchestrator takes in charge the

step by step execution of a given BP instance. Such

BP instance is made of a BPMN description enriched

with necessary run-time information such as end-

points of software applications mocks, user id, etc.

The BP engine is connected with the Forms Engine in

order to take in charge users and robots input/output.

As reported in Figure 5, during the simulation pro-

cess, the input generated by the civil servant (mes-

sages 1-4) can be validated on line by means of data

stored on TestDataRepository component.

In particular the validation process is divided into

three main sub-cases:

• generic case: it occurs when the simulation en-

gine have to validate the learner input. In this case

the simulation engine will check through the Test-

DataRepository if the input can be considered cor-

rect or not and provides a feedback to the Learner

(messages 5,6 of Figure 5).

• extra-evaluation required: this sub-case is acti-

vated when the learner (Civil Servant) input and

the validation obtained by the simulation frame-

work do not match each other. In case the learner

is sure of the correctness of his/her reply, he/she

has the possibility to open a dispute requesting an

extra evaluation from one or more experts (mes-

sage 7 of Figure 5).

• validation missed: in this last case the simulation

framework has insufﬁcient information to evalu-

ate the learners input. This could happen because

Test Data Repository does not contain a valid and

certiﬁed by expert data (messages 8,9 of Figure

5).

In both second and third case, the simulation engine

will raise an alert to an expert and let the learner con-

tinue the simulation (if experts are not available on-

line), and the result of that validation will be provided

to the learner as soon as possible.

3.5 Monitoring

The simulation framework is equipped with a mon-

itoring facility that allows to provide feedbacks on

the business process execution and learning activi-

ties. This monitoring allows to collect data of in-

terest during the run-time business process execution

and is based on an event based monitoring infrastruc-

ture (Bertolino et al., 2011) which has the peculiar-

ity of decoupling the events speciﬁcation from their

collection and processing. This monitoring facility

can be independent from any speciﬁc business pro-

cess modeling notation and execution engine. Data

collected during monitoring of business process exe-

cution are used for deriving the key performance indi-

cators (KPI) that allow continuous tracking of the pro-

cess behavior and measurement of learning-speciﬁc

goals. We refer to (Calabr

o et al., 2015b) for a de-

tailed description of the monitoring components.

4 FUNCTIONAL SPECIFICATION

OF THE LEARNING

SIMULATION FRAMEWORK

The simulation framework provides the subsystem

where learners can simulate BP interactively and is

used by one or multiple civil servant(s) in order to

learn processes. Figures 3 and 4 show the main func-

tionalities that the Simulation Framework provides at

the stakeholders. As mentioned in Section 3, the sim-

ulation framework distinguishes between the two fol-

lowing actors: the civil servant coordinator who is in

charge of starting a simulation session and the civil

Design of a Simulation Framework for Model-based Learning

635

Figure 5: Simulation activity diagram.

servant who represents a generic participant to a sim-

ulation session. In particular, as in Figure 3, the civil

servant coordinator can request to start a new simu-

lation execution of a Public Administration business

process or he/she can manage an ongoing one by for

instance inviting/cancelling other civil servants. The

civil servant coordinator can also restart/stop a current

simulation session and redeﬁne a new coordinator. On

its turn, each civil servant has different possibilities

(as listed in Figure 4) like for instance joining, dis-

connecting or pausing a simulation session, chatting,

asking for evaluation/help, or managing his/her own

proﬁle.

The simulation framework functionalities have

been split into three different phases: i) Initialization

in which the simulation environment is set up; ii) Ac-

tivation in which the participants to the simulation are

invited; iii) Execution in which the participants effec-

tively collaborate each other during a learning ses-

sion. During the Activation phase, the civil servant

can select the type of simulation he/she wants to exe-

cute. Speciﬁcally, three different types of simulation

are provided:

Individual Simulation. The civil servant decides to

execute the simulation without interacting with other

human participants. In this case the other participants

are emulated by means of Robots (see section 3.3 for

more details). The creation of robots instances is per-

formed before the simulation execution.

Collaborative Simulation. This option of simula-

tion involves the collaboration of several human par-

ticipants (no robots instances are involved). During

the collaborative simulation, users can interact be-

tween them using chat instruments. This will improve

performances of the overall learning session due to

the possibility to rapidly share experience between

human participants. This kind of simulation can

be considered the most interesting from the learning

point of view, because cooperation can make learn-

ing procedures more intensive and productive. Di-

versities will raise up and the opportunity to reﬂect

upon encountered issues will help learners to improve

MODELSWARD 2016 - 4th International Conference on Model-Driven Engineering and Software Development

636

Figure 6: Simulation execution.

Design of a Simulation Framework for Model-based Learning

637

their knowledge and better understand the problem.

For activating a simulation, the system requires that

all the civil servants involved have joined the session

in order to provide an online collaborative environ-

ment. Moreover, the simulation framework also sup-

ports also some asynchronous tasks execution among

simulation participants. If a civil servant does not sat-

isfy the simulation requirements or time constraints,

the civil servant coordinator may decide either to kick

the civil servant, or to swap him with another one

among those available, or replace him with a Robot.

Mixed Simulation. This type of simulation re-

quires the participation of both humans and robots.

This usually happens when there are not enough civil

servants to cover all the necessary roles to execute a

BP or if one or more civil servants leave the ongoing

simulation (disconnection or kick). The activation of

a mixed simulation can be done only if the following

two constraints are met: i) the required instances of

robots are ready; ii) all the invited civil servants have

completed the connection procedures.

The sequence diagram presented in Figure 6 is one

of the realizations of the use case “Manage own sim-

ulation session” of Figure 3 and shows the main ac-

tivities of the simulation execution. This sequence

diagram refers to simulation activation sequence di-

agram as a precondition, not presented here for space

limitations. Once simulation activation phase is com-

pleted the Simulation Engine generates the events re-

lated to the execution of tasks of the BP (message

1) and sends them to the Monitor for their evalua-

tion (message 2). The simulation execution contin-

ues managing the BP tasks and sending notiﬁcation to

the civil servants by means of SimulationGuiAPI till a

special kind of event is received. Four types of special

events are managed: END rises when the BP execu-

tion reaches the natural end of the model; STOP rises

when the civil servant coordinator decides to early ter-

minate a simulation execution; PAUSE rises when a

civil servant decides to pause the simulation execu-

tion; PLAY rises when a civil servant decides to re-

cover a paused simulation. During the execution the

simulation engine, continuously generates events and

forwards them to the CepAPI. According to the event

executed the civil servants participating to the simu-

lation receive through the SimulationGUI, the docu-

ments and suggestions related to each task. If the BP

execution has reached the end, an end event is sent

to the CepAPI in order to stop the monitoring session

and unload resources.

5 CONCLUSIONS

In this paper, the architectural design of the simula-

tion and monitoring framework is presented with a

particular focus on the deﬁnition of the functionalities

and interactions among its components. The frame-

work is currently under evaluation inside the Learn

Pad project. However, the preliminary results pro-

vided in (Calabr

o et al., 2015b; Calabr

o et al., 2015a;

Calabr

o et al., 2016) evidenced positive feedbacks,

especially concerning the possibility of executing col-

laborative simulation and providing learners assess-

ment. Moreover, the design of some parts of the archi-

tecture, such as the Test Data Repository, will be re-

ﬁned and improved through comments and hints that

will be collected over the project duration.

ACKNOWLEDGEMENTS

This work has been partially funded by the Model-

Based Social Learning for Public Administrations

project (EU FP7-ICT-2013-11/619583).

REFERENCES

Anderson, P. H. and Lawton, L. (2008). Business simula-

tions and cognitive learning: Developments, desires,

and future directions. Simulation & Gaming.

Bertolino, A., Calabr

o, A., Lonetti, F., and Sabetta, A.

(2011). Glimpse: A generic and ﬂexible monitoring

infrastructure. In Proc. of the 13th EWDC, pages 73–

78.

Blumschein, P., Hung, W., and Jonassen, D. H. (2009).

Model-based approaches to learning: Using systems

models and simulations to improve understanding and

problem solving in complex domains. Sense Publish-

ers.

Calabr

o, A., Lonetti, F., and Marchetti, E. (2015a). Kpi

evaluation of the business process execution through

event monitoring activity. In Proc. of Third Interna-

tional Conference on Enterprise Systems.

Calabr

o, A., Lonetti, F., and Marchetti, E. (2015b). Moni-

toring of business process execution based on perfor-

mance indicators. In Proc. of Euromicro-SEAA.

Calabr

o, A., Lonetti, F., Marchetti, E., Zribi, S., and Jor-

quera, T. (2016). Model-based learning assessment

management. In Proc. of MODELSWARD.

Clark, R. C. and Mayer, R. E. (2011). E-learning and

the science of instruction: Proven guidelines for con-

sumers and designers of multimedia learning. John

Wiley & Sons.

Crookall, D. (2010). Serious games, debrieﬁng, and simu-

lation/gaming as a discipline. Simulation & gaming,

41(6):898–920.

MODELSWARD 2016 - 4th International Conference on Model-Driven Engineering and Software Development

638

Jansen-Vullers, M. and Netjes, M. (2006). Business process

simulation–a tool survey. In Workshop and Tutorial

on Practical Use of Coloured Petri Nets and the CPN

Tools, Aarhus, Denmark, volume 38.

LearnPAd (2014-2016). Model-Based Social Learning for

Public Administrations European Project (EU FP7-

ICT-2013-11/619583). http://www.learnpad.eu/.

Design of a Simulation Framework for Model-based Learning

639