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1 Intellectual Control Algorithm Interaction improvement by the Users Education Process of the Automation Education Systems Uglev V. A. Abstract – The paper considers an integrated approach to trajectory learning control algorithms for individualizing a person in the training systems based on Artificial Intelligence methods, Learning Computer Testing and Cognitive Maps of Diagnosis Knowledge. Index Terms – Intellectual Control, Automation Educated System, Learning Computer Testing, Cognitive Maps of Diagnosis Knowledge, Artificial Intelligence. I. INTRODUCTION Modern conditions of civilization require the development of innovative, controlled and effective forms of learning: the old forms of verbal-logical learning start to show its limitations brighter, which leads to the need for a transition to new forms of imagelogical Dialectical Learning [1]. On the one hand, education clearly dominate the requirements of standardization processes of control and mass training of specialists, and on the other hand -the role of the principles of individualization of trajectory learning, of student-centered approach, transparency and openness of the educational process. Let us turn to the topic in terms of its automation, using a high-tech approach to intellectual interaction Control process of the users with Educated Systems. Automated Educated Systems (AES), as the most advanced interactive training tools, are designed to solve complex pedagogical tasks: transition of educational material, knowledge control, preparation of consolidated financial statements and making decisions on the control of the educational process. The effectiveness of these operations each year is growing, but it still can not be compared with the work of human teachers. The main reason for difficulty - a small amount of information about the user for control algorithms. The strategy of the AES work may be based on different approaches: - as a frame - when the response of the system is Manuscript received April 92, 2011. V. A. Uglev is with the Center for Applied Research of Siberian Federal University, Zheleznogorsk, 662971 Russia (corresponding author to e-mail: [email protected]). firmly described in a set pattern of operations; - in the form of statistical and probabilistic model when the response of the system depends on the hypothesis state about the level of training of the user (based on a probabilistic automaton, Petri net, Markov chain, etc.); - as an intellectual system - when in the basis for decision-making is laid one or more methods of Artificial Intelligence (neural networks, expert systems, etc.). It is obvious that the choice of approach depends significantly on the effectiveness of the AES. We consider possible mechanisms of the advancement of the AES by integrating into it the methods of Artificial Intelligence (AI) as the most prospective one in the automation of the study. II. BASE MODEL OF ELECTRONIC EDUCATION COURSE The composition of ATS, traditionally, includes such subsystem as a transmitter of Electronic Education Course (EEC), a system of Computer Testing (CT), knowledge base and intellectual planner. controlling, in accordance with the canons of the cybernetic approach [2], is done by the scheduler, who should be responsible for the effective adoption of the following decisions: - about the composition of the teaching material according to the user’ s objectives and the material developers of EEC; - about the sequence of presentation of teaching material and adapt it to the current state of knowledge; -about the importance of the evaluation score successes of the user based on the results of the control measure taking (the scoring for the solution of the tests and tasks); - about the contents of the recommendations, given to the user in natural language form to control his trajectory when working with EEC. All it requires special rules in the storage and processing of information, based on statistical methods, data analysis and Artificial Intelligence. Assume that the developer of EEC, working with services AES, sets educational material in the hierarchy of the type a tree on the following levels: subjects sections of the course - didactic units- a set of tests, 2 tasks and terms. In addition, in the AES it should be included some meta-information, which should form a kind of ontology learning process, including statistics data on the educational process and a number of models: - a model of organization of educational material; - a model of a student; - a model of learning process and control. In order to allow a flexible layout of the course and its intellectual analysis it will be necessary to enter the assembly level, which forms the implementation course for a particular user according to his goals. This can be achieved by applying the notation of semantic networks (see Fig. 1): each node is a unit of study (including the characteristics of the type of material, its being wanted, importance, complexity, developed competencies as well as storing references to test items and terms), and edge - its ties within the educational material (which determines the relationships of the sequence of study, the logic of presentation, the child and parent relation) [3]. Thus, the original tree structure can be displayed in a new structure having an individual configuration and described significant for AES properties (metainformation). Part 1 Part 4 Part 3 Part 2 Fig. 1 Example of semantic links organization among the elements of EEC in the course model It is obvious that under certain assumptions, the proposed structure can be reduced to any developed standard in the field of e-learning (IMS, CMI, SCORM, etc.). But, in our opinion, the closest in philosophy of data is a standard Learning Objects Metadata [4]. In this case, no restrictions on the presentation of material: Therefore, we turn to a discussion the following points: what methods of AI can be efficiently used to operate the structure described above. III. INTELLECTUAL ALGORITHM IN AES The idea of a comprehensive user support during the entire period of study in an environment AES involves developing a set of solutions to optimize the interaction between man and machine. Criteria for optimizing act simultaneously as a goal of the authors of the course (government or industry standards, such as [5]), and the purpose of the student, expressed in a set of these competencies - all are static parameters that define the standard of education [6]. In addition, the data with the work time of the user with the individual subsystems AES (including module CT), expressed in the form of quantitative and qualitative indicators are available for the training system for fixation. Quantitative indicators include the following: the test results, rates of change of test scores in a cut section of the course, frequency of treatment to the elements of EEC, the time of the test, etc. The quality indicators are not so obvious: the systematic work with EEC, the adequacy of user response to prompts and recommendations of AES , the reliability of testing results, etc. All this requires an integrated intellectual analysis of information about the learning process. Integration into the AES of the following AI technologies allows us to find a balanced solution of many problems with automated path control of the educational process [7]: - Semantic networks - the basic element on which it is necessary to build a model of the course, and the algorithms of individualization and adaptation; - Expert systems - the main instrument for integrated multi-criteria analysis of quantitative and qualitative information; - Methods of Data Mining - mechanisms for identifying and updating the expert system for problematic moments in the learning process; - Fuzzy logic - a basic method for transforming quantitative data into qualitative. Systematic coverage of the factors of the learning process of the expert system is due to processing of semantic relations elements EEC and statistics about the learning process. This allows a reasonable approach to implementing the algorithm of intellectual knowledge test, then move to the algorithm to identify the problems of developing training material and as a result of formalizing a mechanism of synthesis of individual trajectories of learning. IV. INTELLECTUAL LEARNING CONTROL Teachers test, as is known, can be conducted in the learning process, and then he called an intermediate or a boundary, and on its completion, and then it is called the ultimate control. Current AES, based on this approach to testing, is in deep crisis. This is due to the fact that learning processes and controls are artificially isolated. From systems theory and systems analysis it is known that any subsystem must operate not only relying on its local goal, but take into account the objectives of the whole system. Final testing, as well as intermediate, is created solely for the sake of control and it has limited impact on the learning process (see, eg, [8]). Therefore, having changed the purpose of testing, we must move to a Learning Test, which functions as appreciation of the level of knowledge and training assistance. For the first 3 time the two approaches to the formation of an individual trajectory of the material EEC (direct and indirect), based on the results of Learning Computer Testing have been proposed by the author in [9]. To do this quiz, expert systems are entered, involved in the implementation of three algorithms: - the adaptation of the test on the thematic content; - multicriteria evaluation of test results; - developing a set of recommendations to the user for further work with the material EEC. When integrated in the AES of Learning Computer Testing method it is assumed that the user himself and repeatedly refers to the subsystem CT. It should be emphasized that the adaptation mechanism operates only in training mode, while in other cases, implementing a standardized control. The process of analyzing the semantic relationships and their correlation to learning objectives and learner's current rates provide broad opportunity for the development of control information. This allows us to concentrate all data on the learning process in a single result set of information, having got a name the Cognitive Maps of Diagnostic Knowledge. V. COGNITIVE MAPS OF DIAGNOSTIC KNOWLEDGE Cognitive Maps of Diagnostic Knowledge (CMDK) are designed for solution the intermediate data mining tasks on the learning process in the AES. CMDK should not be equated with the theory of Mind Maps [10]: although this method is the concentration of information, but it relies on theiry of statistical analysis, cognitive computer graphics [11] and Data Mining. The main problem to be solved by CMDK can be expressed as follows [12]: - to concentrate the information about the learning process within a single service; - visualize the current state of the learning process, taking into account the history of education and the outcome standards (dialectic); - to implement a centralized preprocessing of data for submission to the expert system for estimating the next session CT or in the synthesis of complex prompt. As a result of CMDK expert system receives a vector of the following questions, important to control: - Does the user get recommendations of AES? - Is training progressing successfully? - Does the user work with the AES enough? - What is most important to study at the current level of knowledge and error? Thus, the cognitive maps of diagnostic knowledge form the basis for the control decisions in the algorithms for adaptation and personalization. VI. PROVIDING INDIVIDUALISATION IN AES Processing of information about all the parameters of educational process, as was shown above, is as follows: learning – control – analysis of the results – control – training... Therefore, in all phases of AES it should be a process of gathering information about the user's learning activities and, where it is possible, to implement intellectual control. The trajectory of learning, which was originally defined in the synthesis of course, depends on the speed and efficiency of the process of development of material, estimated largely by the tests and training problems. But in the process of learning from time to time a situation arises that requires a return to the previously studied materials, which requires individualized providing of material or to do the next stage of controlling [13]. The idea of individualization in automated learning is expressed in the concept of learner-centered approach. Even with the work of B.F. Skinner, N.A. Crowder, W.P. Bespalko of programmed instruction was established such a strategy knowledge test, when a wrong decision of individual test items the user was directed to the teaching material, which contains their theoretical basis [14]. On this principle now work almost all the systems of pedagogical CT and AES. It gives the results only when in front of student there is a simple course with a consistent presentation of educational material, in order to "learn everything" (emphasis on short-term memory). When learning a modern engineering disciplines work with the material becomes more complicated: there is a non-linear movement of educational materials, especially when the individual elements of knowledge were previously obtained outside of the courses, misunderstanding and / or partially forgotten. Moreover, the adaptation of the EEC and test control with the user's purpose can significantly modify the initial tree structure display of educational material (see Section II). On this basis, at the present level of technology development of AI and EEC it is not allowed to take each training unit in isolation. Consequently, each element of EEC should be analyzed in the context of educational material, goals, and the user's knowledge, not forgetting about the standardization [15]. In the new generation of AES process of individuation should cover all stages of learning, which requires the use of AI techniques in the analysis of available factors, revealing the features of the learning process. Manifestation of individualization should be rationally organized to: - Selection of logic and layout of EEC, when the goals of the authors of course do not match with the goals of the student (there selected list of individual competencies and indicates the depth of learning material); 4 VII. SOME RESULTS Approbation of the control ideas outlined in the learning process (special courses for senior students)on the experienced patterns of intellectual software has demonstrated a positive effect .There greatly increased self-restraint of students to independent learning, reduced the number of calls to the tutor for help, selfassessment of learning outcomes has become realistic. However, it was possible to increase the degree of personalization algorithms work with the AES of the user. To illustrate this, we give a generalized diagram of achievement of students using an experimental learning system (Fig. 2): averages show a steady positive dynamics in the training of user interaction with the automated system. This confirms the promise of this research in improving the efficiency of learning control algorithms of interaction of AES with human beings. CONCLUSION Learning human-computer interaction should be effective and controlled as a temporary aspect and the aspect of development of required information amounts. Artificial Intelligence techniques, in conjunction with a set of innovative teaching methods (the Dialectical Learning, the Learning Computer Testing, the Cognitive Maps of Diagnostic Knowledge) should accelerate the emergence of Automated Education Systems of the new generation. Mark (%) - Selection of problems for the consolidation of knowledge; - Selection of tests for the subsystem Learning Computer Testing in training mode; - Grading based on the results of the test; - Synthesis in prompts calling for help in the training mode bye Learning Computer Testing; - Synthesis of complex recommendations to the user on the result of performing the next stage of controlling (or a random request); - Engaging in dialogue with the electronic assistant in natural language form. All these aspects of individualization , as it was shown in Sections III-V, could be realized by combining Artificial Intelligence techniques and Learning Computer Testing methods and CMDK. An important feature of this combination of technologies is that at any stage of training, you can get not only a comprehensive diagnosis of the level of studying of the material, but also a set of specific recommendations. At the same time the proposals that haves the ability to synthesize EEC, will not only define current difficulties of the user but they will explain their reason taking an information from CMDK and a structure of database of expert system. The unit of explanation is responsible for it given to any expert system with production output knowledge). Information provided to the user will be synthesized from the fact that the current appreciation of knowledge, identified the cause-effect relationships and the facts supporting the assessment. Number of test Fig. 2 Generalization of statistics marks in the student group on the results of work with intellectual learning system REFERENCES [1] M. Botov, “Aboute the Dialectical Learning method”, Vocational education (application to journal), no. 7, 2007, pp. 27-37. [2] N. Wiener, Cybernetics or Control and Communication in the Animal and the Machine, New York: The Technology Press and John Wiley & Sons, Inc. – Paris: Hermann et Cie, 1948. [3] V. Uglev, V. Ustinov, F. Samrina, “Approach to the Semantic Links organization about the education material structure in Automated Education Systems”, Neuroinformatic, her application and data analysis, Krasnoyarsk: IVM SO RAN, 2010, pp. 171-175. [4] IEEE 1484.12.1-2002/ Learning Objects Metadata standard. – New York: IEEE, 2002. [5] Software Engineering 2004: Curriculum Guidelines for Undergraduate Degree Programs in Software Engineering, New York: ACM and IEEE, 2006. [6] V. Uglev, V Ustinov, B. 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