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Education

Published on January 6, 2009

Author: aSGuest9331

Source: authorstream.com

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Slide 1: Paul Dan Cristea and Rodica Tuduce “Politehnica” University of Bucharest Spl. Independentei 313, 77206 Bucharest, Romania, Phone: +40 -21- 411 44 37, Fax: +40 -21- 410 44 14 e-mail: {pcristea, trodica}@dsp.pub.ro Intelligent eLearning Environments e-medicine e-le@rning the near future ICT from the Alps to the Mediterranean 1st Conference & Expo Naples, 5th – 8th June 2003 – Castel dell’Ovo Istituto Nazionale per la Ricerca Scientifica e Tecnologica sulla Montagna Seconda Università degli Studi di Napoli Facoltà di Medicina e Chirurgia Slide 2: 1. Introduction 2. Learning modalities 3. System architecture 4. Agent specification 4.1. Tutor Agent 4.2. Tutor Personal Agent 4.3. Learner Personal Agent 4.4. Learner Profile Eliciting Tool 5. Towards a user comprehensive model 6. AI&NN tools for Innovative ODL Socrates - Minerva project 7. FP6 ILE Network of Excellence 8. Conclusions Outline Slide 3: Essential changes of contemporary training with respect to the training a generation ago Largely increased volume of knowledge for both professional work and every-day-life; High mobility and short validity duration of information and knowledge; Change of the target public from the full time young learners, to mature and senior life-long learners; Economic globalization implies the globalization of the labour market which brings the globalization of training in a multi-lingual and multi-cultural context. Slide 4: Main requirements of the current professional and every-day-life training Specialized training is needed for almost all societal activities; Professional training is no longer a youth achievement lasting for entire life; Availability and efficiency are basic requests of the learning process; Asynchronicity, flexibility and adaptivity to cover a broad spectrum of sometimes divergent learner’s demands. Slide 5: Role of e-Learning in the teaching/learning process Avoiding or reducing tedious and repetitive intellectual work, Mastering large volume of finely structured and highly organized information, Empowering or replacement of the traditional style of tutorial learning by the learner-centered, problem-based or creative learning approaches Slide 6: Drawbacks of the currently available e-Learning tools and environments that make them less appealing than the traditional teaching methods Lack the "human understanding and intuition of the learner” that professor-to-student or peer-to-peer face-to-face interaction normally provides; Insufficient stress on “learner-centered”, “problem-based” or “creative learning” approaches; To little provision for collaborative learning and participative learning, stimulating the mutual synchronouus and asynchronous exchange of ideas between the learners. Slide 7: Potential of Intelligent e-Learning Environments. Increase efficiency of learning and further motivate learners in a problem solving and learner centered approach; Promote participative and collaborative learning; Offer learners individualized learning according to their profiles. Slide 8: Basic requirements for an efficient and attractive Intelligent e-Learning Environment Web-based learning environment, complementary to face-to-face teaching; Non-intrusive automatic tool to guide students through the learning process; Provide the tutor with an objective feed-back from learners, to monitor their progress and to continually appraise the course efficiency; Recommend on-line activities and use of resources to improve the learning process; Adapted guidance by taking into account the paths followed by previously successful learners and the current learner profile. Slide 9: 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. Need for Intelligent Tools Slide 10: 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 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 Cooperative Distance Learning System Slide 11: 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 Slide 12: System Architecture Slide 13: Agent specification Tutor Agent Slide 14: Agent specification Tutor Personal Agent Slide 15: Agent specification Learner Personal Agent Slide 16: Agent specification Learner Profile Eliciting Tool Learning Objectives Control Module Communication Module Learner’s Profile Eliciting Tool Student input Registration form Questionnaires Learning Modalities Knowledge Watch 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 ………………………………… 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 ………………………………… ? Standard Path Recommended Path Content Management Mandatory Testing Contribution to Collaborative Learning Tutor input On-line students monitoring Validation of students proposals Self Testing Student Tracking Tool Slide 17: User Comprehensive Model Steps towards a 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. Slide 18: 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. User Comprehensive Model Slide 19: 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. User Comprehensive Model Slide 20: 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. User Comprehensive Model Slide 21: 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. User Comprehensive Model Slide 22: Artificial Intelligence and Neural Network Tools for Innovative ODL Coordinator : “Politehnica” University of Bucharest SOCRATES - MINERVA PROJECT 87574-CP-1-2000-1-RO-MINERVA-ODL Slide 23: Vrije Universiteit Brussels, BE Prof. Jan Cornelis, Head of Electronics & Digital Signal Processing Department Prof. Edgard Nyssen, Prof. Rudi Deklerck Universitat Erlangen - Nürnberg, 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 Manager Partners Slide 24: Objectives Main 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. Slide 25: Contractual Time Table Slide 26: WP0: Project Management, Monitoring and Reporting (PMMR) PUB + PMG WP1: Collaborative Learning Model (CLM) ULR + PUB + UP WP2: Learner’s Profile Eliciting Tool (LPET) EPCC + PUB + GOC WP3: Automatic Tutoring Tool (ATT) UNL + ULR + PUB + VUB WP4: Learner’s Personal Assistant (LPA) PUB + UNL + UEN + GOC WP5: 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 + UEN WP6: Elaboration of Instructions, Guidelines, and Examples of integrating the AI&NN tools with existent ODL materials (IGE) UP + UPB + EPCC + all WP7: Testing, evaluation, assessment and dissemination (TEAD) of AI&NN tools for innovative ODL PUB + all Workpackages and Responsabilities Slide 27: Network of Excellence Intelligent E-learning Environments Coordinator : Eindhoven University of Technology 6TH Framework Programme 1. Specific programme: Integrating and strengthening the European Research Area. 2. Activity: Thematic priority area: information society technologies. 3. Call title: Call 1 of the IST priority. 4. Call identifier: FP6-2002-IST-1. Slide 28: 1. Integration activities 1.1. Management & Organization 1.1.1. Network management and administration 1.1.2. Joint research communication infrastructure 1.2. Legal tasks 1.2.1. IPR management 1.2.2. EU/national legal requirements 1.3. Evaluation tasks 1.3.1. Quality criteria/ensurance 1.3.2. Assessment Slide 29: 2. Research and development activities 2.1. Research 2.1.1. Requirement analysis 2.1.2. Learning marketplace analysis 2.1.3. User modeling 2.1.4. Didactic aspects 2.1.5. Innovative tools, interworking, convergence 2.1.6. Intelligent e-Learning Environments 2.1.7. New system architecture 2.1.8. Interfaces and support agents 2.1.9. Standardization 2.1.10. Innovative e-learning services 2.1.11. Knowledge management 2.2. e-Learning software integration and development 2.2.1. Adaptation of existing e-learning platforms to: · Intelligent e-learning environments functionality · Participative & Proactive e-learning · Collaborative e-learning · Organizational, national & EU legal requirements 2.2.2. Implementation of new modules / autonomous agents 2.2.3. Multilingual support : 2.2. e-Learning software integration and development 2.2.1. Adaptation of existing e-learning platforms to: · Intelligent e-learning environments functionality · Participative & Proactive e-learning · Collaborative e-learning · Organizational, national & EU legal requirements 2.2.2. Implementation of new modules / autonomous agents 2.2.3. Multilingual support Slide 31: 3. Dissemination activities 3.1. Dissemination 3.1.1. Workshops, conferences 3.1.2. Exchange of researchers 3.1.3. Fellowship program 3.1.4. Implementation of an electronic and hard copy publishing system 3.1.5. Involvement of commercial e-learning providers 3.1.6. Contacts to multiplier/distributors 3.2. Support activities 3.2.1. Support for development 3.2.2. Support for setup, administration, operation 3.2.3. E-learning and face-to-face training courses Slide 32: 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 for ODL at “Politehnica” University of Bucharest. The system is currently under development; several components written in Java are already functional. Slide 33: 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 environment that 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. Conclusions

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