1 Automatic Generation of Exercises for Self-testing in Adaptive E-Learning Systems: Exercises on AC...

1

Automatic Generation of Exercises for Self-testing in Adaptive E-Learning Systems: Exercises on AC Circuits

Third International Workshop on Authoring of Adaptive and Adaptable Educational HypermediaAmsterdam, The Netherlands, July 19th, 2005

Paul Dan Cristea, Aurora Rodica TuduceUniversity POLITEHNICA of Bucharest

Spl. Independentei 313, 060042 Bucharest, Romania, Phone/Fax : +40 - 21- 316 95 68, 694

e-mail:pcristea@dsp.pub.ro

AIED 2005 12th International Conference on Artificial Intelligence in Education

2

1. Introduction Learning Modalities

Need for Intelligent e-Learning Systems

2. System architecture Pilot System Multiagent Structure

Architecture of ILE Pilot

3. Basic Tools Learner Profile Eliciting Tool

Question Apprisal

Learning Item Apprisal & Status

Point and Acceptance Propagation

4. Automatic Generation of AC

Electric Circuit Problems

5. Implementation & Web Accessibility

6. Conclusions

3

Combine the traditional style of teaching with the problem-based style:• learning by being told, • problem solving demonstration, • problem solution analysis, • problem solving, • creative learning

Knowledge transfer Skill development

Learning by being

told

Problem solving demo

Solution analysis

Problem solving

Creative learning

Level of learner’s active participation

Learning ModalitiesLearning Modalities

4

• Dramatic change of the target public for trainingDramatic change of the target public for training

• Professional qualification is no longer a life-long Professional qualification is no longer a life-long achievementachievement

• Complex knowledge and skills have to beComplex knowledge and skills have to be

transmitted and acquired efficientlytransmitted and acquired efficiently

• Open and Distance Learning play a continuously Open and Distance Learning play a continuously increasing roleincreasing role

e-Learninge-Learning

5

• Intelligent educational tools can bring the flexibility and Intelligent educational tools can bring the flexibility and adaptability required to actively support the learner;adaptability required to actively support the learner;

• Increase efficiency of learning and further motivate Increase efficiency of learning and further motivate learners by giving them a set of intelligent tools that will learners by giving them a set of intelligent tools that will actively support them in the learning endeavour;actively support them in the learning endeavour;

• Promote participative and collaborative learningPromote participative and collaborative learning ;;• Offer learners individualised learning according to Offer learners individualised learning according to

elicited learner profiles.elicited learner profiles.

Intelligent e-LearningIntelligent e-Learning

6

• Significant research and implementation effort has been dedicated to develop Intelligent Tutoring Systems and Adaptive Hypermedia, able to adapt to learner’s objectives, interests, and preferences, i.e., to a Learner Profile (LP).

• To implement adaptivity, an ILE needs a quite complex structure, with several parallel version of the same learning item (LI), allowing many different learning paths to be selected in accordance with the LPs.

• Considerable additional effort in elaborating teaching materials, might require several authors and might need institutional support, but brings the advantage of real flexibility and adaptability.

• A course is not a flat juxtaposition of learning items, but a multilevel structure with many branches, along which the ILE recommends an optimal path for a user or for a class of users.

Intelligent e-Learning (cont)Intelligent e-Learning (cont)

7

• Authoring learning material and building the structure of adaptive systems tends to become too complicated for the average teacher.

• Portability – the ability to deploy the content of a system on any other system,

Reusability – the ability to store, search and retrieve LIs, including lessons, modules, exercises, activities for reusing,

are strictly necessary for an efficient implementation and for a wide scale acceptance of the concept.

Intelligent e-Learning (cont)Intelligent e-Learning (cont)

8

The system is learner centred, all human and artificial agents being focused on achieving the learning-training tasks.

Human agents:• students, • authors of teaching materials, • tutors, • course administrators, • system administrator(s).

The pilot web oriented ILE has a server-client distributed multiagent hybrid architecture

Pilot System StructurePilot System Structure

9

Architecture of ILE pilotArchitecture of ILE pilotClient

Student admin. data

Server(s)

Client

INTRANET / INTERNETINTRANET / INTERNET

Learner Learner

System Admin.System Admin.

Author Author

WebBrowser

WebBrowser

Client

Tutor Tutor

Client

Course Admin.

Course Admin.

LOAuthoring

Tools

LOAuthoring

Tools

TestsAuthoring

Tools

TestsAuthoring

Tools

Reg. &Pers. Data

Manag.

Reg. &Pers. Data

Manag.

CourseAdmin. Tools

CourseAdmin. Tools

AutomaticTutoring

Tool

AutomaticTutoring

Tool

Student Tracking

Tool

Student Tracking

ToolLearner’s Profile

Eliciting ToolLearner’s Profile

Eliciting Tool

Course DB

Test DB

Comm. Tools

Comm. Tools

Pers. Assist. Pers.

Assist. Comm. Tools

Comm. Tools

Pers. Assist. Pers.

Assist. Comm. Tools

Comm. Tools

Pers. Assist. Pers.

Assist. Comm. Tools

Comm. Tools

Pers. Assist. Pers.

Assist.

LOAuthoring

Tools

LOAuthoring

Tools

TestsAuthoring

Tools

TestsAuthoring

Tools

LO FilesLO Files

Aux.FilesAux.Files

BufferBuffer

BufferBufferStudent

Evaluation Tool

Student Evaluation

Tool

Evaluationresults

Student profiles

Comm. Tools

Comm. Tools

SystemAdmin. Tools

SystemAdmin. Tools

WebBrowser

WebBrowser

WebBrowser

WebBrowser Web

BrowserWeb

Browser

Client

Student admin. data

Server(s)

Client

INTRANET / INTERNETINTRANET / INTERNET

Learner Learner

System Admin.System Admin.

Author Author

WebBrowser

WebBrowser

Client

Author Author

WebBrowser

WebBrowser

WebBrowser

WebBrowser

Client

Tutor Tutor

Client

Course Admin.

Course Admin.

LOAuthoring

Tools

LOAuthoring

Tools

TestsAuthoring

Tools

TestsAuthoring

Tools

Reg. &Pers. Data

Manag.

Reg. &Pers. Data

Manag.

CourseAdmin. Tools

CourseAdmin. Tools

AutomaticTutoring

Tool

AutomaticTutoring

Tool

Student Tracking

Tool

Student Tracking

ToolLearner’s Profile

Eliciting ToolLearner’s Profile

Eliciting Tool

Course DB

Test DB

Comm. Tools

Comm. Tools

Pers. Assist. Pers.

Assist. Comm. Tools

Comm. Tools

Pers. Assist. Pers.

Assist. Comm. Tools

Comm. Tools

Pers. Assist. Pers.

Assist. Comm. Tools

Comm. Tools

Pers. Assist. Pers.

Assist. Comm. Tools

Comm. Tools

Pers. Assist. Pers.

Assist. Comm. Tools

Comm. Tools

Pers. Assist. Pers.

Assist. Comm. Tools

Comm. Tools

Pers. Assist. Pers.

Assist. Comm. Tools

Comm. Tools

Pers. Assist. Pers.

Assist.

LOAuthoring

Tools

LOAuthoring

Tools

TestsAuthoring

Tools

TestsAuthoring

Tools

LO FilesLO Files

Aux.FilesAux.Files

BufferBuffer

BufferBufferStudent

Evaluation Tool

Student Evaluation

Tool

Evaluationresults

Student profiles

Comm. Tools

Comm. Tools

SystemAdmin. Tools

SystemAdmin. Tools

WebBrowser

WebBrowser

WebBrowser

WebBrowser

WebBrowser

WebBrowser

WebBrowser

WebBrowser Web

BrowserWeb

BrowserWeb

BrowserWeb

Browser

10

Learner Profile Eliciting ToolLearner Profile Eliciting Tool

Student Input

LearningObjectives

LearningModalities

Student Tracking

Tool

KnowledgeWatch

StudentInitial Input

Tutor Input

Engine

CoursePresentation

AdaptiveTesting

LPET

11

Learning Objectives

Control Module Communication Module

Learner’s Profile Eliciting Tool

Student input

Registration form

Questionnaires

Learning Modalities

KnowledgeWatch

• Curricular study for a diploma• Complementary study• Executive up-dating• Specialist up-dating• Problem centered• Test oriented

Preferredly / Predominantly:

• Descriptive• Demo• Analytical details• Practical aspects• Examples• Multimedia / Text

Material to study1 First Chapter xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 1.1 Section 1.1 xxxxxxxxxxxxxxxxxxxxxxxxxxx 1.1.1. Paragraph xxxxxxxxxxxxxxxxxxxxxX 1.1.2. Paragraph xxxxxxxxxxxxxxxxxxxxxX 1.1.3. Paragraph xxxxxxxxxxxxxxxxxxxxxX 1.2 Section 1.2 xxxxxxxxxxxxxxxxxxxxxxxxxx 1.2.1. Paragraph xxxxxxxxxxxxxxxxxxxxxx 1.2.2. Paragraph xxxxxxxxxxxxxxxxxxxxxX 1.2.3. Paragraph xxxxxxxxxxxxxxxxxxxxxX 1.3 Section 1.3 xxxxxxxxxxxxxxxxxxxxxxxxxxx 1.3.1. Paragraph xxxxxxxxxxxxxxxxxxxxxx 1.3.2. Paragraph xxxxxxxxxxxxxxxxxxxxxx 1.3.3. Paragraph xxxxxxxxxxxxxxxxxxxxxx 2 Second Chapter xxxxxxxxxxxxxxxxxxxxxxxxxxxx 2.1 Section 2.1 xxxxxxxxxxxxxxxxxxxxxxxxxxx 2.1.1. Paragraph xxxxxxxxxxxxxxxxxxxxxx 2.1.2. Paragraph xxxxxxxxxxxxxxxxxxxxxX 2.1.3. Paragraph xxxxxxxxxxxxxxxxxxxxxx …………………………………

Studied material1 First Chapter xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 1.1 Section 1.1 xxxxxxxxxxxxxxxxxxxxxxxxxx 1.1.1. Paragraph xxxxxxxxxxxxxxxxxxxxxxxxxxxxx 1.1.2. Paragraph xxxxxxxxxxxxxxxxxxxxx 1.1.3. Paragraph xxxxxxxxxxxxxxxxxxxx 1.2 Section 1.2 xxxxxxxxxxxxxxxxxxxxxxxxxx 1.2.1. Paragraph xxxxxxxxxxxxxxxxxxxx 1.2.2. Paragraph xxxxxxxxxxxxxxxxxxxxx 1.2.3. Paragraph xxxxxxxxxxxxxxxxxxxxx 1.3 Section 1.3 xxxxxxxxxxxxxxxxxxxxxxxxxx 1.3.1. Paragraph xxxxxxxxxxxxxxxxxxxx 1.3.2. Paragraph xxxxxxxxxxxxxxxxxxxxx 1.3.3. Paragraph xxxxxxxxxxxxxxxxxxxx2 Second Chapter xxxxxxxxxxxxxxxxxxxxxxxxxxx 2.1 Section 2.1 xxxxxxxxxxxxxxxxxxxxxxxxxx 2.1.1. Paragraph xxxxxxxxxxxxxxxxxxxxx 2.1.2. Paragraph xxxxxxxxxxxxxxxxxxxxx 2.1.3. Paragraph xxxxxxxxxxxxxxxxxxxxx…………………………………

?

Standard Path

Recommended Path

ContentManagement

Mandatory

Testing

Contribution to Collaborative Learning

Tutor input

On-line students monitoring

Validation of students proposals

Self

Testing

Student Tracking

Tool

Learner Profile Eliciting Tool (details)Learner Profile Eliciting Tool (details)

12

Question # 5

Text for question # 5

Figure for question # 5

Test forsection

5.1.

Number ofquestions

10

Time

Submit

Learner's test windowLearner's test window

13

Question appraisalQuestion appraisal

Sum of points for a question Q  

)(

)()(QSC

CPQSP

- the set of selected options at question Q.

Correct choices positive points, Wrong answers negative points.

Assigning negative points to wrong choices discourages guessing.

)()( QOQS

Points acknowledged for question Q

),()( if),(

),()(0 if,0

,0)( if),(

)(

QTQSPQSP

QTQSP

QSPQSP

QP

T(Q) - the threshold for the acceptance of the reply to Q

14

Sum of points for a learning item LI

)(

)()()(LICILLIQ

ILPCPLISP

C (LI) – the children of LI. The points obtained for LI are transferred upwards

Points acknowledged for a learning item LI

).()( if),()(

),()( if),()(

LITLISPLIALISP

LITLISPLISPLIP

T(LI) - thresholdA(LI) - award for the successful completion of the study of LI

Learning item appraisalLearning item appraisal

15

Status of the learning item LI

)),(,1)((

or )()( if,1,)(,0)(

and )()( if ,0

)(

ILCLIILS

LITLISPILCLIILS

LITLISP

LIS

0 – pending, 1 – studied,

Down-propagation of the acquired knowledge confirmation

Learning item statusLearning item status

16

Points obtained for choices C from the set of options O(Q) pertinent to a certain question Q are recorded at the LI to which the question is attached and transferred upwards.

Point and acceptance propagationPoint and acceptance propagation

17

Automatic Generation of AC Electric Circuit Problems

Automatic Generation of AC Electric Circuit Problems

1. Problem set description

2. Tree generation

3. Cotree generation

4. Tree plot

5. Graph plot

6. Circuit parameters and variables generation

7. Converting voltage sources to curent sources

8. Introducing controlled sources

18

OBJECTIVESOBJECTIVESOBJECTIVESOBJECTIVES

Design and develop a software able to automatically generate large sets of circuit analysis problems, all with the same general features, but having different topological structures and parameters of the circuits.

Conditions:• The problems are for use both during the tutorials and for examinations, thus -- despite the inherent risk for an engineering perception of reality -- all parameters and variables describing.the circuits should be integers to facilitate the computational task.

• Problems and solutions should be stored automatically on disk in distinct directories.

• Files referring to the same problem (text, graphics, etc) will have related labels.

• The system will be developed for making it accessible on the web.

19

Problem Set DescriptionProblem Set DescriptionChoosing the parameters of the set of AC problems to generate.

Problem Set DescriptionProblem Set DescriptionChoosing the parameters of the set of AC problems to generate.

% 1 2 3 4 5 6 7param = query({'311_CA_21.11.2004', '30','1','RO', 'd', 'g', 'no'}, ... { 'SetID - problem set label (Year of Study/Group ID/Date)', ... % 1 'Nproblems - number of problems', ... % 2 'StartID - ID of the first problem', ... % 3 'Language - RO/EN', ... % 4 'Out_medium - s = save on hard, d = display' ... % 5 'Represent - t = tree, g = graph, b = both, other char = none' ... % 6 'Entropy - yes/no = compute and display graph entropy' ... % 7 }, ... 'Set Parameters');

20

CChoosing Variables & hoosing Variables & Independent ParametersIndependent ParametersCChoosing Variables & hoosing Variables & Independent ParametersIndependent Parameters

% 1 2 3 4 5 6 7 8 9 10 11 12 13param = query({'4','7','4','4','1', '4', '4', '0', '1', '4', '4', '2', 'Y'}, ... { 'Nnodes - number of nodes', ... % 1 'Nbranches - number of branches', ... % 2 'I_chord_a_max - maximum absolute value of chord current active components [A]', ... % 3 'I_chord_r_max - maximum absolute value of chord current reactive components [A]', ... % 4 'R_twig_min - minimum value of twig resistences [Ohms]', ... % 5 'R_twig_max - maximum value of twig resistences [Ohms]', ... % 6 'X_twig_max - maximum absolute value of twig reactance [Ohms]', ... % 7 'E_twig_max - maximum absolute value of twig Re & Im emf-s [V]', ... % 8 'R_chord_min - minimum value of chord resistences [Ohms]', ... % 9 'R_chord_max - maximum value of chord resistences [Ohms]', ... %10 'X_chord_max - maximum absolute value of chord reactance [Ohms]', ... %11 'nJ - number of branches with current sources', ... %12 'CrossLinks - Y/N - mutual inductances and controlled sources'... %13 }, ... 'Circuit Variables & Independent Parameters');

21

MMutual inductive couplings and utual inductive couplings and controlled source parameterscontrolled source parametersMMutual inductive couplings and utual inductive couplings and controlled source parameterscontrolled source parameters

if strcmp(lower(CrossLinks), 'y') % 1 2 3 4 5 6 7 8 9 10 11 12 13 14 param = query({'0', '0', '0', '0', '0', '3', '0', '3', '0', '5', '0', '5', '0', '4'}, ... { 'nEI - number of current controlled voltage sources E = Zt * I',... %1 'nJU - number of voltage controlled current sources J = Yt * U', ... %2 'nEU - number of voltage controlled voltage sources E = A * U', ... %3 'nJI - number of current controlled current sources J = B * I', ... %4 'nM - number of mutual inductive couplings', ... %5 ['Zta_max - maximum absolute value of transfer resistance [Ohms]' char(10) ... ' Ea + j.Er = (Zta + j.Ztr) (Ia + j.Ir)'], ... %6 'Ztr_max - maximum absolute value of transfer reactance [Ohms]', ... %7 ['Yta_max - maximum absolute value of transfer conductance [Siemens]' char(10) ... ' Ja + j.Jr = (Yta + j.Ytr) (Ua + j.Ur)'], ... %8 'Ytr_max - maximum absolute value of transfer susceptance [Siemens]', ... %9 ['Aa_max - maximum absolute value of voltage gain active component' char(10) ... ' Ea + j.Er = (Aa + j.Ar) (Ua + j.Ur)'], ... %10 'Ar_max - maximum absolute value of voltage gain reactive component', ... %11 ['Ba_max - maximum absolute value of current gain active component' char(10) ... ' Ja + j.Jr = (Ba + j.Br) (Ia + j.Ir)'], ... %12 'Br_max - maximum absolute value of current gain reactive component', ... %13 'XM_max - maximum value of mutual inductive reactance [Ohms]' ... %14 }, ... 'Selection of mutual inductive couplings and controlled source parameters');

22

Circuit TopologCircuit TopologyyCircuit TopologCircuit Topologyy

C_nodes_twigs = GenerateTree(Ntwigs, mode)

ShowTree(C_nodes_twigs, SetID, k)

ShowGraphNet(C_nodes_twigs, C_nodes_chords, SetID, k)

C_nodes_chords = GenerateCoTree(C_nodes_twigs, Nchords)

C_twigs_chords = EssIncid(C_nodes_twigs, C_nodes_chords)

23

Tree GenerationTree GenerationTree GenerationTree Generation

31 20 54

function C_nodes_twigs = GenerateTree(n, mode)C_nodes_twigs = zeros(n, n);rand('state',sum(100*clock));r = rand(2,n);c = 2 * ( r(2, :) >= 0.5 ) - 1;m = 0;for k = 1:n s = ceil( (k-m)* r(1, k) + m-1 ); f = k; if s>0, C_nodes_twigs(s, k) = c(k); end C_nodes_twigs(f, k) = - c(k); if mode == 's', m = s; endend

24

C_nodes_twigs =

-1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 -1 -1 0 0 0 0 0 0 0 1 0 -1 0 0 0 0 0 0 -1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 -1 1 0 0 0 0 0 0 0 -1

C_nodes_twigs =

1 -1 0 0 0 0 -1 0 0 1 0 0 -1 0 0 0 0 0 1 0 0 -1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 -1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1

C_nodes_twigs =

-1 0 1 0 1 0 0 0 0 -1 0 0 0 0 1 0 0 0 -1 1 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 -1 1 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 -1

ExamplesExamples

25

Cotree GenerationCotree GenerationCotree GenerationCotree Generation

1

2 10

3

4 5

6

7

8

9

11

12

Starts from the chosen tree

Chords are introduced between nodes chosen randomly from the class of nodes with the lowest rank (lowest number of connected branches).

This order assures the best connectivity of the circuit for a given number of chords.

26

ExamplesExamples

C_nodes_chords =

0 0 0 0 0 0 1 0 0 0 -1 0 0 0 -1 0 0 0 -1 0 0 0 0 1 0 0 1 0 0 0 0 -1 0 0 0 -1 -1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 -1 0 0 0 0 1 0 0 0 1 0 0 0 -1 0 0 0 -1 0 0 0 0 -1 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 -1 0 0

C_nodes_twigs =

-1 0 1 0 1 0 0 0 0 -1 0 0 0 0 1 0 0 0 -1 1 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 -1 1 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 -1

27

Tree PlotTree Plot

28

Circuit Parameter and Variable GenerationCircuit Parameter and Variable Generation

Chord currents Twig currents Twig voltages Chord voltages  Chord emf’s 

cI cI

ctct II ctct II

tttt EIZU tttt EIZU

ttcc UU T ttcc UU T

cccc UIZE cccc UIZE

TtctU cU

tI cI tc

1

tE

tZ

1

cY

cJ

Chord currents Twig currents Twig voltages Chord voltages  Chord emf’s 

TtctU

cU

tIcI

tc

1

tE

tZ 1

cZ

cEcE cJ cY cY

29

Global Circuit Variables Global Circuit Variables

c

t

E

EE

c

t

J

JJ

c

t

U

UU

c

t

I

II

Concatenate the matrices for tree & cotree

30

Current sources (change of independent voltage source emf’s)

JZEE new

Converting Voltage Sources to Current SourcesConverting Voltage Sources to Current Sources

ZZ

E Enew =E - Z J

J

Convert nJ voltage sources to current sources

[E, J] = ConvertE2J_AC(E, Z, nJ);

31

Controlled sources (change of independent voltage source emf’s)

IZEE tnew

UYZEE tnew

UAEE new

IBZEE new

IZE tcontrolled

UYJ tcontrolled

UAE controlled

IBJ controlled

Cross ParametersCross Parameters

Mutual reactances

[E, J, Zt, Yt, A, B, XM] = ControlledSources_AC(E, J, I, U, Z, ... nControl, nEI, nJU, nEU, nJI, nM, ... Zta_max, Ztr_max, Yta_max, Ytr_max, … Aa_max, Ar_max, Ba_max, Br_max, XM_max);

inducednew EEE IXE Minduced j-

32

Web AccessibilityWeb AccessibilityWeb AccessibilityWeb Accessibility

The system will be accessible on the INTERNET, to allow remote use, for both professors and students

Partial examination of problems will be done on the computer,In a face-to-face or remote setting.

The web accessibility is currently partially functional and partially under development

33

PlatformPlatformPlatformPlatform

Web server:Tomcat 4.1.29 - http://jakarta.apache.org/tomcat

DB server:MySQL 3.2x - http://www.mysql.com/

Scripts tool:Apache ANT - http://ant.apache.org/

Versioning server:

CVS - http://www.cvshome.org/, http://www.wincvs.org/

34

ConclusionsConclusionsConclusionsConclusions

• A specialized e-learning system able to automatically generate large sets of circuit analysis problems, all with the same difficulty,but having different topological structures and parameters of the Circuits, has been designed, implemented and experimented.

• The problems are for use both during the tutorials and for examinations, thus -- despite the inherent risk for an engineer understanding of reality -- all parameters and variables describingthe circuits should be integers to facilitate the computational task.

• Problems and solutions should be stored automatically on disk in distinct directories, with files referring to the same problem having related labels

• The system will be developed for making it accessible on the web

35

COMMISSION OF THE EUROPEAN COMMUNITIES EDUCATION AND CULTURE DIRECTORATE - GENERAL SOCRATES - Minerva Transnational Projects in the field of Information and Communication Technology and Open and Distance Learning in Education

This work has been partially supported by the Socrates Minerva Project 87574-CP-1-2000-1-RO-MINERVA- ODL

Artificial Intelligence and Neural Network Tools for Innovative ODL

(http://www.dsp.pub.ro/)

This product does not necessarily represent the Commission's official position.

Acknowledgment and disclaimerAcknowledgment and disclaimer

36

• Vrije Universiteit Brussels, BE Prof. Jan Cornelis, Vice-Rector

Prof. Edgard Nyssen, Prof. Rudi Deklerck

• Universität Erlangen-Nürenberg Prof. Manfred Kessler, Director Institute für Physiologie und Kardiologie

• Université de la Rochelle , FR Prof. Michel Eboueya, Assistant Director of Information and Industrial Imaging Lab.

• Universidade Nova de Lisboa, PT Prof. Adolfo Steiger Garcao, President of UNINOVA Prof. Jose Manuel Fonseca

• University of Edinburgh, UK Dr. Judy Hardy, Applications Consultant at EPCC Dr. Mario Antonioletti

• Patras University, GR Prof. Nicolas Pallikarakis, Coordinator of BioMedical Engineering Scool Res. Cristian Badea

• Equant Romania, RO Dr. Pavel Budiu, Strategy Manager

PartnersPartners