INTELLIGENT SYSTEMS BASED ON FUZZY DECISION MAKING IN EDUCATION
The development and implementation of software and hardware platforms that support and reinforce students’ involvement in the educational process via information technology is an urgent task. Intellectual systems and learning environments are a promising direction of research and application, improving the quality and efficiency of the educational process. Adaptive learning and expert-training systems are related to intelligent environments and can be developed via the following technologies, platforms and solutions:
- Technologies based on Computer-Based Training (CBT), Internet-Based Training (IBT) or Web-Based Training (WBT);
- Platforms and tools that support Virtual Learning Environment (VLE), Mobile Learning (M-learning) based on industrial standards such as SCORM;
- Analytical solutions’ development for monitoring the educational process and choosing an individual learning path;
- Development of cross-platform, multi-browser systems for organizing and supporting the learning process, including eLearning and collaborative learning systems, that use a wide range of mobile equipment (tablet computers, e-books, and etc.);
- Digital laboratories and interactive learning environments.
Such solutions can be realized on the basis of software products or "cloud" services, modeled on SaaS.
The goal of this research work is to improve the efficiency and quality of the educational process by analyzing, developing and implementing the intellectual learning environment.
In order to achieve these goals, we need to complete the following tasks:
1. To analyze existing training systems;
2. To develop own mathematical models and methods that support and reinforce students’ involvement in the educational process;
3. To develop an architecture of intellectual learning environment (ILE);
4. To implement this learning environment into the educational process.
The paper deals with the following research works: knowledge-base systems as WAPE systems; «Modeling of adaptive interactive training course» by A.G. Dorrer that describes a domain model; «Constructing a personalized e-learning system based on genetic algorithm and case-based reasoning approach» by M. J. Huang and etc.
In order to perform the second task, we proposed a fuzzy mathematical model that includes an ontological model of a subject domain, a model of a learner’s profile, a model of dynamic scenarios, and a procedure for diagnosing a learner’s competency. The effectiveness of the developed mathematical model and its program implementation was confirmed by pedagogical experiments (more than 300 students on the Bachelor's program), during which the quality and students’ motivation to learn increased by an average of 20%.
In order to complete the third task, we used the IEEE P1484.1 standard concerned with the implementation of a component-based architecture of an intelligent learning environment. Software interfaces such as a "bridge", client-server architecture, databases and environments are intensively used.
The above-said products are developed by the Institute of Distance and Further Education of the Ulyanovsk State Technical University on basis of the Moodle platform and are implemented at the rank of industrial enterprises of the Ulyanovsk region. The experience of the intellectual learning environments’ use was summarized- their implementation had increased the quality education.