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Article Excerpt
This article discusses some important aspects of Web Intelligence (WI) in the context of educational applications. Some of the key components of WI have already attracted developers of web-based educational systems for quite some time--ontologies, adaptivity and personalization, and agents. The paper focuses on the application of Computational Intelligence (CI) in Intelligent Web-Based Education (IWBE), such as intelligent web services and their potential in developing service-oriented architecture of web-based educational systems.
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Web-based education (WBE) has become a very important branch of educational technology. For learners, it provides access to information and knowledge sources that are practically unlimited, enabling a number of opportunities for personalized learning, telelearning, distance-learning, and collaboration, with clear advantages of classroom independence and platform independence. On the other hand, teachers and authors of educational material can use numerous possibilities for web-based course offerings and teleteaching, availability of authoring tools for developing web-based courseware, and cheap and efficient storage and distribution of course materials, hyperlinks to suggested readings, digital libraries, and other sources of references relevant for the course.
Abundant developments in the field of Computational Intelligence (CI) during the 1990s have made the CI technologies potentially a comprehensive and effective algorithmic platform for supporting education processes. CI encompasses several important technologies aimed at the development of intelligent systems, that is fuzzy systems, granular computing, neural networks, and evolutionary optimization. What is also very characteristic for CI today is a broad array of hybrid systems, such as neuro-fuzzy systems, neuro-evolutionary systems, and genetic fuzzy systems. They emerge as a result of an indepth understanding of the benefits of individual technologies and their genuine complementarity. When applied to the development of WBE systems, CI technologies bring about important improvements that make the resulting WBE systems more flexible, more user-friendly, and better understood intuitively.
The purpose of this article is to introduce the synergy of WBE and CI for the benefit of the learners, teachers, and authors of educational material on the Web. The next section covers the basics of CI and its issues relevant for WBE. The following section surveys important components of intelligent WBE technology in the context of knowledge representation, knowledge processing, and ontological support for the learning, teaching, and authoring processes. In the final sections, the principles of applying CI to developing WBE applications and tools are discussed, and examples of successful CI-supported WBE systems are indicated.
COMPUTATIONAL INTELLIGENCE (CI)
The development framework considered in the context of this study is Computational Intelligence (CI). CI (Pedrycz, 1997; Pedrycz & Vasilakos, 2000) is a well-established paradigm that seamlessly combines three main technologies aimed at the development of intelligent systems, that is granular computing, neural networks, and evolutionary optimization. As in the design of such systems, we have to address various challenging issues such as knowledge representation, adaptive properties and learning abilities, and structural developments--CI has to a cope with each of them. With regard to the properties of intelligent systems being supported by CI, we can envision two general points of view. These properties can be sought as intrinsic to any intelligent systems or they can be extrinsic to them. In the first case, we are concerned with the features that are crucial to the design of the systems, which usually do not manifest externally so by analyzing the performance of the system we cannot say whether a specific technology has been used. Essentially, we are not concerned about that. The extrinsic properties are dominant and become of a paramount relevance when dealing with communication of intelligent systems with others or facilitating an effective interaction with human users. This aspect is extremely relevant in providing the user a sense of intelligent and user-friendly capabilities of the systems. Here we can stress that these capabilities are very diversified and could cover a vast territory. For instance, one can envision several interesting scenarios:
* Coping with heterogeneous information. Quite often, in intelligent systems we may encounter information coming not only from sensors (in which case these are numeric readings) but also from users (in the form of linguistic evaluations) or being a result of some initial aggregation or summarization. Interestingly, these inputs are essential to the functioning of a system and cannot be ignored or downplayed. The heterogeneity of information requires special attention in the sense of the use of more advanced mechanisms of processing and representing such a mix of various pieces of evidence
* Establishing an effective, transparent, and customized communication with the end user when presenting the results of processing completed by a system. Here the notion of generality (abstraction) or granulation of information plays a pivotal role. A suitable level of granulation of information is essential to the effective communication and acceptance of a system (in whichever role we can envision the system to be utilized). This immediately leads us to the concept of adaptive and user-driven interfaces which become an essence to most interactive and human centric systems including tutoring architectures, decision-support systems, and knowledge-based architectures (including expert-like systems and their more advanced topologies).
The term of CI being coined in the 1990s (quite commonly viewed as a synonym of soft computing) helps us establish a sound mapping between the technologies and their dominant role in meeting some specific requests of the domain. What is also very characteristic for CI today is a broad array of hybrid systems (called neurofuzzy systems, neuro-evolutionary systems, genetic fuzzy systems). They emerge as a result of an indepth understanding of the benefits of individual technologies and their genuine complementarity.
In what follows, we briefly highlight the essence of the contributing technologies of CI, discuss their synergies and elaborate on the resulting architectures
Granular computing. Granular information is everywhere. We granulate information all the time. We rarely reason on a basis of numbers. Our judgment is often triggered by some aggregates which in a nutshell are a result of abstraction.
Originally, CI embraced fuzzy sets as the key vehicle of information granulation. It is worth stressing that the other fundamental environments for describing granular information are readily available and...
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