Intelligent eLearning EnvironmentsPaul Dan CristeaPolitehnica University of Bucharest Spl. Independentei 313, 77206 Bucharest, Romania, Phone: +40 -21- 411 44 37, Fax: +40 -21- 410 44 14e-mail: pcristea@dsp.pub.roE-Learning FORUM 21 Martie 2003

Artificial Intelligence and Neural Network Tools for Innovative ODLCoordinator : Politehnica University of Bucharest E-Learning FORUM 21 Martie 2003SOCRATES - MINERVA PROJECT87574-CP-1-2000-1-RO-MINERVA-ODL

Vrije Universiteit Brussels, BE Prof. Jan Cornelis, Head of Electronics & Digital Signal Processing Department Prof. Edgard Nyssen, Prof. Rudi Deklerck Universitat Erlangen - Nrnberg, DE Prof. Manfred Kessler, Director of Institute fur Physiologie und Kardiologie

University of La Rochelle, FR Prof. Patrice Bourcier, 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

Patras University, GR Prof. Nicolas Pallikarakis, Coordinator of BioMedical Engineering Scool

Global One Communications Romania, RO Dr. Pavel Budiu, Strategy ManagerPartners

ObjectivesMain goal : develop and use a set of innovative ODL tools for on-line and Internet-based learning, using the methods and techniques of artificial intelligence and neural networks. O1. Provide a model of the collaborative learning process involving human and artificial intelligent agents;O2. Provide a set of tools based on AI&NN techniques to develop innovative ODL systems;O3. Carry out pilot implementations of ODL systems;O4. Develop a methodology for intelligent ODL production and performance evaluation;O5. Evaluate and disseminate the outcomes of the project for future developments.

Contractual Time Table

Start of eligibility period1 October 2000Submission of 1st Interim Report1 June 2001Submission of 2ndInterim Report1 June 2002End of Eligibility Period1 September 2003Submission of Final Report1 November 2003

WP0: Project Management, Monitoring and Reporting (PMMR) PUB + PMGWP1: Collaborative Learning Model (CLM) ULR + PUB + UPWP2: Learners Profile Eliciting Tool (LPET) EPCC + PUB + GOCWP3: Automatic Tutoring Tool (ATT) UNL + ULR + PUB + VUBWP4: Learners Personal Assistant (LPA) PUB + UNL + UEN + GOCWP5: ODL courses on Bio-Medical Data Processing and Visualisation (BMDPV) using the new AI&NN tools BMDPV M1: Medical visualisation UEN + PUB + VUB BMDPV M2: Cortical brain anatomy VUB + PUB + UP + UENWP6: Elaboration of Instructions, Guidelines, and Examples of integrating the AI&NN tools with existent ODL materials (IGE) UP + UPB + EPCC + allWP7: Testing, evaluation, assessment and dissemination (TEAD) of AI&NN tools for innovative ODL PUB + allWorkpackages and Responsabilities

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.

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

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

Knowledge transfer

Skill development

Learning by being told

Solution analysis

Problem solving

Creative learning

Level of learners active participation

Problem solving demo

Module for accessing learning resources and managing interactions:

- Problem Solving KB

- Selection of relevant knowledge

- Coordination activities

Module to respond to learner requests and needs

Module to select learning modalities and to adapt to learner profile

Control Module

Communication Module

Tutor Agent KB:

Knowledge to access PSKB

Methodological

Knowledge on how to adjust to learner profile

Problem Solving Knowledge Base (PSKB)

Tutor Agent

Other agents

Module for accessing learning resources and managing interactions:

- Problem Solving KB

- Coordination activities

Module responsible for monitoring tutor actions and guiding

Module to extract:

- tutoring knowldege

- tutoring strategies

- creative learning

experiences

Control Module

Communication Module

Tutor KB:

Knowledge to retreive elements from PSKB

Training history

Elicited tutoring knowledge

Problem Solving Knowledge Base (PSKB)

Tutor Assistant Agent

Other agents

Module for accessing learning resources and managing interactions:

- Problem Solving KB

- Coordination activities

Module responsible for monitoring learner actions and requests

Module to develop the learner profile

Control Module

Communication Module

Learner KB: learner history and learner profile

Problem Solving KB

Learner Personal Agent

Other agents

Learning ObjectivesControl ModuleCommunication ModuleLearners Profile Eliciting ToolStudent inputRegistration formQuestionnairesLearning ModalitiesKnowledgeWatch Curricular study for a diploma Complementary study Executive up-dating Specialist up-dating Problem centered Test orientedPreferredly / 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

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 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

ContentManagementMandatoryTestingContribution to Collaborative LearningTutor inputOn-line students monitoringValidation of students proposalsSelfTesting

Student Tracking Tool

No purely empirical approach to modelling. Even the definition of attributes/features & the selection of the relevant ones in a given context are actually theory driven, explicitly or not.

Prototype model of the learner Encodes general theoretical knowledge in the field of learning. Can not be used directly in practice - rigid and biased: Large variability in human personality and in human behaviour, The essential traits are context-dependent.

Customised model by using empirical data - sets of examples collected for the given user, while interacting with the system.

New refined theory If tuning parameters can not adapt the model to user's profile, new features are extracted from data and added to the 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.

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.

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.

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.

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 for ODL at Politehnica University of Bucharest. The system is currently under development; several components written in Java are already functional.

To test the system, we are concurrently developing learning materials on: Sorting Algorithms, Resolution Theorem Proving, Neural Networks, Advanced Digital Signal Processing.

The distributed solution has the advantage of creating an ODL environmentthat can be joined by any interested learner.

The system is an effective response to the the increased demand for cooperation and learning in today's open environments, academic and economic, the 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 such an approach to generate 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 of understanding or the ability to creatively use the acquired knowledge.