1. 1 Paul Dan Cristea, Rodica Tuduce, Cosmin Popa, Razvan Popescu “Politehnica” University of Bucharest Spl. Independentei 313, 77206 Bucharest, Romania, Phone: +40 -1- 411 44 37, Fax: +40 -1- 410 44 14 e-mail: pcristea@dsp.pub.ro Artificial Intelligence and Neural Network Tools for e-Learning environments 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

2. 2 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 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

3. 3 Professional qualification is no longer a life-long achievement Professional qualification is no longer a life-long achievement Complex knowledge and skills have to be transmitted and acquired efficiently Complex knowledge and skills have to be transmitted and acquired efficiently E- Learning will play a continuously increasing role. E- Learning will play a continuously increasing role. Intelligent educational tools can bring the flexibility and adaptability required to actively support the learner. Intelligent educational tools can bring the flexibility and adaptability required to actively support the learner.

4. 4 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

5. 5 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

6. 6

7. 7 Learning Objectives Control ModuleCommunication 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

8. 8 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

9. 9 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.

10. 10 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).

11. 11 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

12. 12 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.

13. 13 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.

14. 14 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 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.

15. 15 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.

16. 16 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.

17. 17 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.

18. 18 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 of understanding or the ability to creatively use the acquired knowledge.