Transcript Corporate Profile

AIED 2005
12th International Conference on
Artificial Intelligence in Education
Third International Workshop on Authoring of
Adaptive and Adaptable Educational Hypermedia
Amsterdam, The Netherlands, July 19th, 2005
Automatic Generation of Exercises for
Self-testing in Adaptive E-Learning
Systems: Exercises on AC Circuits
Paul Dan Cristea, Aurora Rodica Tuduce
University POLITEHNICA of Bucharest
Spl. Independentei 313, 060042 Bucharest, Romania,
Phone/Fax : +40 - 21- 316 95 68, 694
e-mail:[email protected]
1
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
2
Learning Modalities
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
Learning
by being
told
Problem
solving
demo
Skill development
Solution
analysis
Problem
solving
Creative
learning
Level of learner’s active participation
3
e-Learning
• Dramatic change of the target public for training
• Professional qualification is no longer a life-long
achievement
• Complex knowledge and skills have to be
transmitted and acquired efficiently
• Open and Distance Learning play a continuously
increasing role
4
Intelligent e-Learning
• Intelligent educational tools can bring the flexibility and
adaptability required to actively support the learner;
• Increase efficiency of learning and further motivate
learners by giving them a set of intelligent tools that will
actively support them in the learning endeavour;
• Promote participative and collaborative learning;
• Offer learners individualised learning according to
elicited learner profiles.
5
Intelligent e-Learning (cont)
• 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.
6
Intelligent e-Learning (cont)
• 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.
7
Pilot System Structure
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
8
Architecture of ILE pilot
9
Learner Profile Eliciting Tool
LPET
Student
Student
Initial
Input
Input
Student
Tracking
Tool
Tutor
Input
Course
Presentation
Learning
Objectives
Learning
Modalities
Knowledge
Watch
Engine
Adaptive
Testing
10
Learner Profile Eliciting Tool (details)
Learner’s Profile Eliciting Tool
Control Module
Student
input
Registration
form
Learning
Objectives
Communication Module
Learning
Modalities
Student
Tracking
Tool
Knowledge
Watch
Questionnaires
Content
Management
Tutor
input
On-line
students
monitoring
Validation of
students
proposals
• 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 study
1 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
…………………………………
Standard
Path
?
Studied
material
1 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 xxxxxxxxxxxxxxxxxxxx
2 Second Chapter xxxxxxxxxxxxxxxxxxxxxxxxxxx
2.1 Section 2.1 xxxxxxxxxxxxxxxxxxxxxxxxxx
2.1.1. Paragraph xxxxxxxxxxxxxxxxxxxxx
2.1.2. Paragraph xxxxxxxxxxxxxxxxxxxxx
2.1.3. Paragraph xxxxxxxxxxxxxxxxxxxxx
…………………………………
Recommended
Path
Self
Testing
Mandatory
Testing
Contribution to
Collaborative
Learning
11
Learner's test window
Test for
section
5.1.
Number of
questions
10
Question
#5
Time
Text for
question # 5
Figure for
question # 5
Submit
12
Question appraisal
Sum of points for a question Q
SP(Q) 
 P(C)
CS ( Q )
S (Q)  O (Q) - the set of selected options at question Q.
Correct choices  positive points, Wrong answers  negative points.
Assigning negative points to wrong choices discourages guessing.
Points acknowledged for question Q
 SP(Q), if SP(Q)  0,

P(Q)   0, if 0  SP(Q)  T (Q),
 SP(Q), if SP(Q)  T (Q),

T(Q) - the threshold for the acceptance of the reply to Q
13
Learning item appraisal
Sum of points for a learning item LI
SP( LI ) 
 P(C)   P(LI )
QLI
LI C ( LI )
C (LI) – the children of LI.
The points obtained for LI are transferred upwards
Points acknowledged for a learning item LI
 SP( LI ), if SP( LI )  T ( LI ),
P( LI )  
 SP( LI )  A( LI ), if SP( LI )  T ( LI ).
T(LI) - threshold
A(LI) - award for the successful completion of the study of LI
14
Learning item status
Status of the learning item LI
 0, if SP( LI )  T ( LI )  and


S ( LI )  0, LI  C ( LI ),
S ( LI )  
1, if SP( LI )  T ( LI )  or


( S ( LI )  1, LI  C ( LI )),

0 – pending, 1 – studied,
Down-propagation of the acquired knowledge confirmation
15
Point Transfer
Level 1
Level 2
Level 3
Learning item a
(chapter)
T(a)
P(a)
Learning item a.b
(sub-chapter)
T(a.b)
P(a.b)
Acceptance Transfer
Point and acceptance propagation
Learning item a.b.c T(a.b.c)
(paragraph)
P(a.b.c)
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.
16
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
17
OBJECTIVES
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.
18
Problem Set Description
Choosing the parameters of the set of AC problems to generate.
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param = 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' ...
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}, ...
'Set Parameters');
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Choosing Variables &
Independent Parameters
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param = query({'4','7','4','4','1', '4', '4', '0', '1', '4', '4', '2', 'Y'}, ...
{
'Nnodes
- number of nodes', ...
'Nbranches
- number of branches', ...
'I_chord_a_max - maximum absolute value of chord current active components [A]', ...
'I_chord_r_max - maximum absolute value of chord current reactive components [A]', ...
'R_twig_min
- minimum value of twig resistences [Ohms]', ...
'R_twig_max
- maximum value of twig resistences [Ohms]', ...
'X_twig_max
- maximum absolute value of twig reactance [Ohms]', ...
'E_twig_max
- maximum absolute value of twig Re & Im emf-s [V]', ...
'R_chord_min
- minimum value of chord resistences [Ohms]', ...
'R_chord_max
- maximum value of chord resistences [Ohms]', ...
'X_chord_max
- maximum absolute value of chord reactance [Ohms]', ...
'nJ
- number of branches with current sources', ...
'CrossLinks
- Y/N - mutual inductances and controlled sources'...
}, ...
'Circuit Variables & Independent Parameters');
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Mutual inductive couplings and
controlled source parameters
if strcmp(lower(CrossLinks), 'y')
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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',...
'nJU - number of voltage controlled current sources J = Yt * U', ...
'nEU - number of voltage controlled voltage sources E = A * U', ...
'nJI - number of current controlled current sources J = B * I', ...
'nM - number of mutual inductive couplings', ...
['Zta_max - maximum absolute value of transfer resistance [Ohms]' char(10) ...
'
Ea + j.Er = (Zta + j.Ztr) (Ia + j.Ir)'], ...
'Ztr_max - maximum absolute value of transfer reactance [Ohms]', ...
['Yta_max - maximum absolute value of transfer conductance [Siemens]' char(10) ...
'
Ja + j.Jr = (Yta + j.Ytr) (Ua + j.Ur)'], ...
'Ytr_max - maximum absolute value of transfer susceptance [Siemens]', ...
['Aa_max - maximum absolute value of voltage gain active component' char(10) ...
'
Ea + j.Er = (Aa + j.Ar) (Ua + j.Ur)'], ...
'Ar_max - maximum absolute value of voltage gain reactive component', ...
['Ba_max - maximum absolute value of current gain active component' char(10) ...
'
Ja + j.Jr = (Ba + j.Br) (Ia + j.Ir)'], ...
'Br_max - maximum absolute value of current gain reactive component', ...
'XM_max - maximum value of mutual inductive reactance [Ohms]' ...
}, ...
'Selection of mutual inductive couplings and controlled source parameters');
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21
Circuit Topology
C_nodes_twigs = GenerateTree(Ntwigs, mode)
ShowTree(C_nodes_twigs, SetID, k)
C_nodes_chords = GenerateCoTree(C_nodes_twigs, Nchords)
ShowGraphNet(C_nodes_twigs, C_nodes_chords, SetID, k)
C_twigs_chords = EssIncid(C_nodes_twigs, C_nodes_chords)
22
Tree Generation
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4
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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; end
end
23
Examples
C_nodes_twigs =
C_nodes_twigs =
-1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
-1
0
0
0
0
0
0
0
-1
1
0
0
0
0
0
1
0
0
-1
0
0
0
0
0
0
-1
0
1
0
0
0
0
0
0
1
0
-1
0
0
0
0
0
0
0
1
-1
1
0
0
0
0
0
0
0
-1
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
0
0
0
0
C_nodes_twigs =
0
-1
0
0
1
0
0
0
0
0
-1
0
0
1
0
0
-1
0
0
0
0
0
1
0
0
0
0
0
-1
0
0
1
-1
0
0
0
0
0
0
0
0
-1
0
0
0
0
0
0
1
0
-1
0
0
0
0
0
0
0
1
-1
0
0
0
0
1
0
0
0
-1
0
0
0
0
0
0
0
1
-1
0
0
0
1
0
0
0
0
-1
0
0
0
0
0
0
0
0
-1
24
Cotree Generation
Starts from the chosen tree
1
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.
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Examples
C_nodes_twigs =
-1
0
0
0
0
0
0
0
0
-1
0
0
0
0
0
0
1
0
-1
0
0
0
0
0
0
0
1
-1
0
0
0
0
1
0
0
0
-1
0
0
0
0
0
0
0
1
-1
0
0
0
1
0
0
0
0
-1
0
0
0
0
0
0
0
0
-1
0
0
0
0
0
-1
1
0
0
0
0
0
0
0
0
1
1
-1
0
0
0
0
0
0
0
0
-1
1
0
0
0
0
C_nodes_chords =
0
0
0
-1
0
1
0
0
0
0
0
0
0
0
-1
1
0
-1
1
0
0
0
0
0
0
0
0
1
-1
0
0
0
0
0
0
0
1
-1
0
0
0
0
0
0
0
0
1
-1
-1
0
0
0
0
0
0
0
0
1
-1
0
0
0
0
0
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Tree Plot
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Circuit Parameter and Variable Generation
Et 
Chord currents
 1
T



tc
Twig currents
Ut 
Uc 
voltages Yc 
ZTwig
t
IChord
voltages I c 
t
 tc 
1
Chord emf’s
Ec 
Jc 
Y 
c
Ic 
It   tc  I c 
U t   Zt  I t   Et 
Uc   tc  Ut
T

Ec   Zc  I c   Uc 
Et 
Chord currents
 1
T



tc
Uc 
Twig currents
Ut 
voltages
ZTwig
t
 1
Ic 
IChord
voltages
t
 tc 
Zc 
Chord emf’s
Jc 
Yc  Ec 
28
Global Circuit Variables
Concatenate the matrices for tree & cotree
U t 
U
U c 
I t 
I 
I c 
Et 
E 
Ec 
J t 
J
J c 
29
Converting Voltage Sources to Current Sources
Current sources (change of independent voltage source emf’s)

E new  E  Z  J 
Z
Z
J
E
Enew =E - Z J
Convert nJ voltage sources to current sources
[E, J] = ConvertE2J_AC(E, Z, nJ);
30
Cross Parameters
[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);
Controlled sources (change of independent voltage source emf’s)

  Y  U 
  A U 
  B I 

  E Z  Y  U 
  E  A U 
  E Z  B I 
E controlled  Zt  I 
E new  E  Zt  I 
J controlled
E new
E controlled
J controlled
t
Mutual reactances

E induced  - j XM  I 
E new
E new
t

E new  E  E induced

31
Web 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
32
Platform
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/
33
Conclusions
• 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 describing
the 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
34
Acknowledgment and disclaimer
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.
35
Partners
• 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
36