A computational model for developing semantic web-based educational systems
Introduction
Current artificial intelligence in education (AIED) systems have tried to incorporate semantic web resources to their design and architecture. The main idea behind this purpose is the attempt to represent information on the Web so that computers can understand and manipulate it, leading to more adaptable, personalized and intelligent learning environments. In fact, there is a significant interest within the AIED community on the evolution of e-learning systems in this direction. The new generation of AIED systems comes from the combination of two broad modalities of web-based educational systems: (i) e-learning systems (or learning management systems – LMS), which provide interaction between students and teachers through the use of information technology (by using synchronous and asynchronous tools) to ensure this communication, and (ii) AIED systems, which use artificial intelligence techniques to provide personalized interactions, aiming to improve the learning and problem solving processes. The result of such combination are the so-called semantic web-based educational systems (SWBES).
The construction of SWBES, however, is a rather complex task which faces challenges in terms of software engineering and artificial intelligence aspects, such as: extensibility, interoperability, contextualization and consistence of metadata, dynamic sequence of learning and contents, integration and reuse of content and artificial intelligence techniques, distribution of services and new models of learning [15]. Such issues have been influenced by the aim of representing information on the Web in a way computers can understand and manipulate it. Therefore, SWBES are assumed as the new generation of Web-based educational systems that uses semantic web technologies to generate more personalized, adaptable and intelligent educational systems [13].
This paper presents a computational model for the development of SWBES focusing on the problem of how to make the development easier and more useful for both developers and authors. Before, a reference model is introduced in order to be used in the definition of the computational model. In order to illustrate the features of the proposed model, a case study is presented. Furthermore, a discussion about the results regarding the computational model construction is available.
The remainder of this paper is structured as follows. An overview about the evolution of intelligent educational systems and some problems for building them are presented in Section 2. Section 3 presents a reference model adopted as the next generation of educational systems, here assumed as the semantic web-based educational systems. Section 4 presents a computational model for developing SWBES. A case study to illustrate the computational model is presented in Section 5. Section 6 presents a discussion about the results regarding the computational model construction. Related Work on SWBES is detailed in Section 7. Conclusions and future work are presented in the final section.
Section snippets
Overview of educational systems and research problems
This section provides a review of the computer-based educational systems, aiming to present sufficient background and open issues to understand the proposed model. It starts with a discussion about classical approaches to these systems before embarking on Adaptive e-learning environments and problems for building them.
Reference model
The semantic web (SW) extends the classical web in the sense that it allows a semantic structure of web pages, giving support to humans as well as artificial agents to understand the content inside the web applications. As a result, Semantic Web provides an environment that allows software agents to navigate through web documents and execute sophisticated tasks. SW itself offers numerous improvements in the context of Web-based educational systems contributing to the upgrade of learning quality.
Computational model
This section describes a computational model, called e-Mathema, for building semantic web-based educational systems based on the proposed reference model. Some applications have been created by using e-Mathema, such as [9], [7], [10], [5]. This model has three layers which are presented in Fig. 2 followed by the description of each layer.
The architecture was developed as a multi-layer architecture whose layers are described below [14]:
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Framework: it is maintained by developers who can add new
Case study
The aim of this section is to illustrate the features of the proposed computational model through the development of an intelligent tutoring system.
The system is applied at legal domain, providing Law students with real cases, rules and different viewpoints of a given body of knowledge. The main idea is to engage Law students into interactions with the system based on the resolution of Legal problems and their consequences on other tutorial activities, concerning the penal Law. The main
Discussion of the addressed problems
This Section aims at discussing how each problem stated in Section 2.3 was addressed by the proposed computational model. During the system implementation and evaluation in a real scenario, the solutions adopted to these problems are discussed as follows.
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High development cost: it was decreased through the reuse of an agent-based architecture, ontologies, and a methodology that specifies the main aspects of a SWBES;
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Complexity to develop AI algorithms: it was solved through the reuse of some
Related work
Some tools for building educational systems have been created. A relevant analysis of the state of the art can be viewed in [23] in a traditional perspective and other proposals were developed under the perspective of semantic web technologies [2], [31], [33]. However, recently, some new environments have been developed. Some of them, considering the proposals closely related to the presented proposal here are described below.
Millard [29] proposes the use of semantic web services for building
Conclusions
In this paper a computational model for developing semantic web-based educational systems was described. The approach introduced in this work promotes an easy and efficient way to build such systems for both authors and developers. This computational model is characterized by offering low development costs, scalability, extensibility, interoperability, and low maintenance costs. Moreover, with this approach it is also possible to deal with the development of artificial intelligence, interactive
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