Content

• 1. Subject urgency
• 2. Aim and research tasks
• 3. Supposed scientific novelty
• 4. Planned practical results
• 5. Review of research-and-developments on the topic. Global level
• 6. Review of research-and-developments on the topic. National level
• 7. Review of research-and-developments on the topic. Local level
• 8. Development of a model of the adaptive structuring of informative resources of the intellectual teaching system
• 8.1 Informative resources in ITS
• 8.2 Raising of task
• 8.3 Choice of tool for development of model
• 9. Model structuring and process of adaptive learning and testing
• 10. Directions of perfection of model
• Conclusions
• Literature

1. Subject urgency

It is now widely works on creation of intelligent tutoring systems (ITS), adaptive Web-based educational systems have emerged as an alternative and complement to the traditional approach in the development of computer training course[1]. To improve the efficiency of the educational process in such systems should be supported individual educational trajectory.

Existing systems use intelligent adaptations that do not provide the individual trajectories and in the best case focused on a group of students: best, average, worst. Customize to meet the requirements necessary to create training and testing of information resources, which are able to take into account both the needs and abilities of students, which would allow to classify the training materials on selecting a discipline, would indicate the direct and indirect connections between the individual modules would contain a set of tasks with indicating their complexity and execution time. For the use of such resources in ITS should include a means of structuring an adaptive learning content for a particular user. Adaptation to the characteristics of users are of vital importance.

Relevance of the study determined that under conditions of intensive dissemination of information systems in the world educational space increases the need to develop intelligent information systems educational purposes with the ability to adapt content.

2. Aim and research tasks

The aim is to create on the basis of colored Petri nets model of structuring information learning resources in intelligent tutoring systems.

To achieve this goal requires the following tasks:

1. Evaluate existing adaptive educational systems and means for presenting the content in them.
2. Analysis of possibilities of colored Petri nets to represent resources and learning processes.
3. Formalize the processes of adaptive learning and testing based on a structured representation of information resources using the apparatus of colored Petri nets.

The object of this study are intelligent learning systems and means of structuring educational resources to customize instruction.

Subject of research are model structure and adapt information resources in intelligent learning systems.

3. Supposed scientific novelty

Model based on colored Petri nets, adaptive structuring educational content in intelligent tutoring system.

4. Planned practical results

The developed model can be used to build intelligent learning system, allowing more efficient use of information resources of the educational purpose by adapting the content to the individual needs and capabilities of students.

5. Review of research-and-developments on the topic. Global level

The first publications on development of the intellectual teaching systems (ITS) appeared in 70 20 centuries in works o J. Carbonell [2], the first classifications of these systems offered in works of P. Brusilovsky [3]. Approaches to implementation of intellectual technologies of training on Web-platform discussed in [4]. The objective of this research areas — to include in the distance learning system customization options [5]. With the help of adaptive and intelligent tutoring system technology allows for individual student's ability, his previous knowledge, skills. On the basis of these data about the student, the learning process takes place for him the best way [6]. In developing the training systems used adaptive hypermedia and neural network technologies, machine learning, genetic algorithms, evolutionary modeling methods, and others.

The problem of constructing individual learning paths in adaptive systems considered in [7]. Trajectory based on the simulation model estimates the level of competence. Simulation modeling was based on the simple device of Petri nets. The proposed model takes into account the individual set of competencies for the implementation of individual student-directed learning.

6. Review of research-and-developments on the topic. National level

Problems of intellectual learning technologies in Ukraine are resolved in the leading universities of Lvov, Kiev, Kharkov, a National Academy of Sciences. In work [8] based on an integrated use of the technology and methods, application of intelligent Internet technologies, proposed adaptive remote learning and knowledge control EduPro and considered its introduction in the educational process. The efficacy of the proposed methods by experimental studies the functioning of the developed system. It is shown that the use of distance learning systems can not only maintain the quality of the traditional technology transfer of knowledge, but in some cases through the use of adaptive algorithms to achieve a noticeable increase in student learning outcomes.

The system of adaptive hypermedia authors use different types of user models to adapt the content automated training system (EPA) and the links within it to the level of knowledge and interests of the user, using techniques that allow developers to define navigation rules for the movement of students for content EPA [9].

In work [10] proposed information technology design adaptive learning systems, yaks based on models of students based on the parameters and the level of training cognitive features using maps the knowledge gaps in the study of educational material. Process modeling of adaptive learning and testing proposed multilevel Petri net which is used as a functional model and generates a unique scenario learning for each student.

7. Review of research-and-developments on the topic. Local level

In work [11] an approach which implements the principles of adaptive learning programmable, allowing to carry out in Intranet/Internet, as the delivery of educational methodological information and intensive remote adaptive learning. Adaptive learning management model based on the theory of finite automata.

In work [12] consider the organization of the system of formation of learning content in the form of ontological knowledge base of intellectual training system. An algorithm and composition software tools automate association of domain ontologies for filling this database.

8. Development of a model of the adaptive structuring of informative resources of the intellectual teaching system

8.1 Informative resources in ITS

Modern technologies allow to provide the student in learning the necessary information for the development of the subject in a diverse way.

One way of representing knowledge is implemented using the following principles:

1. Domain knowledge represented modularly.
2. Each chapter corresponds to the area of expertise of several modules, possibly overlapping.
3. Different modules:
   1. Way, the level and depth of presentation.
   2. Necessary for the development of advanced knowledge.
   3. List of competencies acquired during the development of the module.

Educational content of the module is a set of information resources — training and test items presented in the form of text pages, web pages, links to files, tests, questions.

8.2 Raising of task

Consider a training course consisting of m modules, each module corresponds to a single subject course.

This course provides an adaptation to the volume of the material under study and its complexity. Adaptation procedure creates a sequence of each student passing sections of modules, ie its trajectory study course. Below the path of learning students will understand the passage sections of the course with the ability to change the level of difficulty depending on the evaluation of the tests in the previous step.

Each module contains theoretical and reference material for homework assignments, as well as a set of reference materials for self-examination and obtain an estimate.

The process of passing students training module is as follows. Of base training modules retrieved another piece of theoretical material, which is proposed to master student. Once the student graduated from the study of this material, the system proceeds to testing. Test base of the test material is selected and presented the student who prepares and introduces in the answers to the test items. These responses are evaluated by the evaluation, which will decide on the adjustment of learning paths.

Need to develop a model for structuring the course of training resources and the management of individualized student learning.

8.3 Choice of tool for development of model

One convenient process modeling tools to interact with the learning resources in teaching methodology is colored (colored) Petri nets — Coloured Petri Net (CPN). Feature of this methodology is that it simulates the system in terms of conditions-event, which allows to study the dynamics of the system. Application of Petri nets allows to visualize the use of information resources in the dynamics of the training course, and also serves as a basis for studying the properties of the simulated system.

In the colored Petri nets are special designations for chips for different purposes, which are called flowers. For this class of Petri nets in the classical definition of [13] further input function color C:P→Σ, which Σ is a finite set of non-empty types. Introduction of a new type of chips occurs when declaring new types of variables (or constants).

9. Model structuring and process of adaptive learning and testing

Consider the example of building a model curriculum of five modules M1-M5. Fig. 1 shows a model of the course in the form of colored Petri net top level. Each module is represented as the transition and the corresponding position.

Dynamic objects are model students of course. Petri net nodes are interpreted as learning resources. Transitions markers show progress in the user study course. Each transition corresponds to a particular stage of the educational process — working with the resource, such as: learning, testing. Triggering the transition is interpreted as doing some training mission. Learning resource is any type of problems which the student must complete during the study.

Determination of color network objects defined as follows: c_j = <Id,b,l>, where Id = {l,...,m} — color component for the identification of the student; b — color component to count the total number of points a student; l — color component to account for the level of training of the student.

Each transition corresponds to a second-level Petri net (Pic. 2), which describes the performance of the relevant module, ie each of the five modules contains theoretical material, which is divided into blocks — separate independent parts (M1.1,..., M1.N) where N — number of blocks (M1.1 — the first unit the first module)), and tests on each module (MK1,..., MK5).

Module M1 contains 6 theoretical units (M1.1,..., M1.6) and test control MK1. Transitions are deterministic and depend on the amount of educational material tem.Set Petri second level module.

After passing those (M1.1,..., M1.6) student must pass the test unit MK1, which is made in a way that includes the task of each block (M1.1,..., M1.6). On the basis of test results MK1 determined the level of achievement of each theoretical unit. With insufficient mastering each theoretical unit (M1.1,..., M1.6) student returns to the beginning of the module 1 to block M1.1 and begins studying this module first. With insufficient assimilation of certain topics (M1.1,..., M1.6) for the student formed individual scenario further study with the addition of the following theme blocks repetition enough lessons from (M1.1,..., M1.6). Transitions from one stroke in Pic. 2 represent the addition of the corresponding theoretical block specifying information for a better understanding not learned when studying the topic of knowledge. Junctions with two touches are included in the appropriate unit theoretical training elements to repeat in the new topic is not learned knowledge in the previous topic. Likewise, all other modules are presented discipline. Current unit tests allow verification of the student's knowledge of blocks of theoretical material and form individual scenario learning for each student, based on which the adaptation of new material feed system and block repetition.

10. Directions of perfection of model

Model as a second-level Petri nets for the module is built for one level of complexity of the representation of the course materials.

To account for the adaptation process, which provides for the student with the same level of difficulty learning the material on the other, it is expected to extend the model by creating subnets structuring of educational material for each difficulty level. Adaptation in this case will be implemented by the organization transitions between networks of different levels of difficulty based on the results of testing the student's knowledge level.

Conclusions

The proposed model can be used to improve the quality of individual training in the ITS. Application of Petri nets allows us to represent a hierarchy of resources with different levels of difficulty and use the model in the management of education. Using this model will automatically generate a sequence providing educational material — the main program and re-training, according to the current results of learning trainees.

Literature

Comment

The master's work is not completed yet. Final completion is on December 2014. Full work text and subject materials can be obtained from the author or her adviser after this date.