Paul Dan Cristea, Rodica Tuduce, Cosmin Popa, Razvan Popescu

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4-th EUROPEAN CONFERENCE ON E-COMMERCE /
E-ACTIVITIES / E-WORKING / E-BUSINESS, ON-LINE
SERVICES, VIRTUAL INSTITUTES AND THEIR INFLUENCES
ON THE ECONOMIC AND SOCIAL ENVIRONMENT E-COMM-LINE
2003 Bucharest, ROMANIA, September 25-26, 2003
Artificial Intelligence and Neural Network Tools
for e-Learning environments
Paul Dan Cristea, Rodica Tuduce, Cosmin Popa,
Razvan Popescu Politehnica University of
Bucharest Spl. Independentei 313, 77206
Bucharest, Romania, Phone [phone],
Fax [phone] e-mail pcristea_at_dsp.pub.ro
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1. Introduction 2. Cooperative Distance
Learning 3. Learning modalities 4. System
architecture 5. Learner Profile Eliciting Tool 6.
Keywords 7. Actors of the system New
user, Learner, Tutor, Administrator 8. User
model 9. Conclusions
Outline
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1. Introduction
Need for Intelligent Tools

• Professional qualification is no longer a
  life-long achievement
• Complex knowledge and skills have to be
  transmitted and acquired efficiently
• E- Learning will play a continuously increasing
  role.
• Intelligent educational tools can bring the
  flexibility and adaptability required to actively
  support the learner.

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2 . Cooperative Distance Learning

• Basic paradigms
• Intelligent Human-Computer Interaction
• Computer-Supported Cooperative Work (CSCW)
• Learning approach Cooperative learning by
  interaction between student and tutor/expert or
  inside the group of learners
• Organization Group of learners assisted by
  artificial agents with active role in the
  learning process.
• Tutor Human or artificial agent
• Structural features
• Set of tools to assist the learner at several
  levels of the knowledge acquisition process.
• Personalised model of the trainee

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3. Learning Modalities

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

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4. System Architecture
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5. Learner Profile Eliciting Tool
Learners Profile Eliciting Tool
Control Module
Communication Module
Student input Registration form Questionnaires
Learning Modalities
Student Tracking Tool
Learning Objectives
Knowledge Watch
Content Management
Self Testing

• 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
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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 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
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1.2.1. Paragraph xxxxxxxxxxxxxxxxxxxx
1.2.2. Paragraph xxxxxxxxxxxxxxxxxxxxx
1.2.3. Paragraph
xxxxxxxxxxxxxxxxxxxxx 1.3 Section 1.3
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1.3.1. Paragraph xxxxxxxxxxxxxxxxxxxx
1.3.2. Paragraph xxxxxxxxxxxxxxxxxxxxx
1.3.3. Paragraph
xxxxxxxxxxxxxxxxxxxx 2 Second Chapter
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Section 2.1 xxxxxxxxxxxxxxxxxxxxxxxxxx
2.1.1. Paragraph xxxxxxxxxxxxxxxxxxxxx
2.1.2. Paragraph
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Paragraph xxxxxxxxxxxxxxxxxxxxx
Tutor input On-line students monitoring Validatio
n of students proposals
Mandatory Testing
Contribution to Collaborative Learning
Standard Path
Recommended Path
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6. Keywords
Keywords allow a flexible structuring of the
material, not only according to the initial
structural division in sections, chapters,
paragraphs, and atoms, but also in
accordance with freely chosen conceptual
criteria. Keywords are used for each unit of
the course content, question or answer
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7. Actors of the system

• New user
• Person that must register by providing personal
  data to enter the system
• First name,
• Last name,
• Affiliation,
• Address,
• Email,
• Suggested acount name,
• Password.
• After submission and data verification, the
  registration request is approved by the
  SuperAdmin.
• Only approved users may access the system.

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• Learner
• The target user of the system.
• Specific functionalities
• select courses from the available ones,
• access and read course material according
  to a suggested or chosen road-map,
• test acquired knowledge by taking quizes,
• engage in active learning by synchronously or
  asynchronously providing complementary material
  and/or quizes, interact with peer learners in a
  co-operative learning approach.
• The suggested study road-map takes into account
  the previously read course material and the
  results at the quizes. It is presented as a tree
  with changing color branches, the color
  corresponding to the current status
  (not-yet-accessed / browsed / learned-and-assessed
  / recommended sections).

Actors of the system
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• Tutor
• Main tasks
• prepare and up-date the course content,
• provide quizes,
• establish the conditions for the acceptance
  of any taken section (point for good/wrong
  answers, threshholds to pass/reject),
• validate the course material/ exercizes/
  quizes, proposed by the students,
• answer to students question,
• supervize the didactic process,
• attach and edit keywords.
• Tools are provided to support all activities,
  e.g., to help organizing the structure, to add
  text and graphics, to edit quizes, to set points
  and thresholds, etc

Actors of the system
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Administrator In charge of system technical
monitoring and maintenance. Especially important
in the development stage of the system. Tasks
visible to the user adding / removing user
rights assisting users in running the
system Invisible tasks monitoring the stored
information, up-dating system logs, a.s.o.
Actors of the system
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8. User Model

• No systematic way to empirically identify the
  domains of the
• feature space that are not properly represented
  in a set of examples.
• The available collection of examples is never
  large enough to cover
• all the possible classes in an unbiased
  manner, to avoid spurious
• correlation when elaborating a model.
• Small sets of exceptions may be poorly
  represented or even ignored.
• The underlying theory
• helps eliminate irrelevant features,
• guides the selection of relevant examples to
  scan of the input space,
• gives confidence in the solutions produced.
• A purely theoretical approach may be brittle,
  i.e.,
• can yield dramatically incorrect results for
  exceptions,
• scores of instances that fall in the limits of
  validity domain are
• 
  treated correctly (abrupt degradation).
• Exhaustive theories may become intractable
• The domain of validity must be restricted.
• Compromise scope - accuracy.

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User Model

• Combined use of theoretical knowledge and
  experimental results allows
• Incomplete and/or incorrect theoretic
  knowledge,
• keeps the model in the range of an
  acceptable approximation.
• Incomplete or noisy experimental data
• inherent ability to recover from
  errors.
• The user model being developed uses a hybrid
  approach
• Artificial Intelligence (AI) -- symbolic
  representation of theory,
• Neural network (NN) -- sub-symbolic
  representation of data.
• NN has the ability to represent "empirical
  knowledge",
• but behaves almost like a black box
• Information expressed in sub-symbolic form,
• not directly readable for the human
  user
• No explanation to justify the decisions in
  various instances,
• forbids the direct usage of NNs in
  learning/teaching and
• 
  safety critical areas
• Difficult to verify and debug software that
  includes NNs.

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User Model
Extraction of the knowledge contained in an NN
allows the portability to other systems in
symbolic (AI) and sub-symbolic (NN) forms,
towards human users. AI and NN approaches are
complementary in many aspects can mutually
offset weaknesses and alleviate inherent
problems, able to exploit both theoretical
and empirical data - hybrid aproach,
efficient to build a fault tolerant and adaptive
model, help discover salient features in
the input data. First phase. The system operates
using statistics about which buttons were
selected by the lerner when using the system,
in which order, which error messages
have been generated. The system is trained
to use this input to offer advice in the form of
access to some additional data and
information, additional reading,
recommend or trigger an interaction with the
human tutor.
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User Model
Subsequent phase. The system uses
error databases, special interest
databases, preference
databases, including the input from a human
tutor. The output helps identifying some profile
of the user, defined roughly
by the set of classes the user belongs to. This
influences the future interaction of the system
with the user, e.g., changing the type and level
of the exercises presented to the user. Next
step. The system includes some voluntary
feedback learners, offered to all the other
learners, to help conveying original ideas and
generate groups of interest. Increase of tutor
"productivity. The system is a useful
assistant, not a replacement
of the human tutor. The work done traditionally
by two or three tutors could be
accomplished in this approach by only one
assisted tutor.
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9. Conclusions

• The basic contribution of this research is
  twofold
• Identification of several Learning Modalities
  that combine
• traditional teaching with problem-centred
  learning
• to better motivate the student and to
  increase the efficiency
• of the learning process,
• Conception of a Collaborative Distance Learning
  System in which
• human and artificial agents collaborate to
  achieve a learning task.
• The Tutor Agent tries to replace partially the
  human teacher, in
• assisting the learners at any time of their
  convenience.
• The development of the learning system is a
  collaborative effort
• to develop a novel intelligent virtual
  environment in the
• framework of an European project.
• The system is currently under development most
  components
• written in Java are already functional.

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Conclusions

• To test the system, we have developed learning
  materials on
• Java Fundamentals,
• Neural Networks,
• Electrical Engineering,
• Advanced Digital Signal Processing.
• The distributed solution has the advantage of
  creating an ODL environment
• that can be joined by any interested learner.
• The system is an effective response to the
• increased demand for cooperation and learning in
  today's open
• environments, academic and economic,
• necessity of developing effective learning tools
  that can be smoothly
• integrated in the professional development
  process and with company work.
• Care is taken to prevent generating an "elitist"
  system.
• The system is designed to enhance the specific
  features of each user,
• without increasing the differences between users
  in what concerns the level