Surpassing Online Learning Obstacles

Surpassing Online Learning Obstacles

Dilermando Piva Jr. (Faculty of Technology of Indaiatuba, Brazil), Ricardo Luís de Freitas (Catholic University of Campinas, Brazil) and Gilberto S. Nakamiti (Catholic University of Campinas, Brazil)
Copyright: © 2009 |Pages: 8
DOI: 10.4018/978-1-60566-198-8.ch295
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Abstract

In the last decade, the use of Internet and wide-band access have supported the spread of distance learning through the Web, which we will call Online Teaching. Schools and universities have been rethinking their teaching practices and educational policies in establishing online teaching programs. Online Teaching becomes not only a new pedagogical model or an educational technology, but also a new social model and technology, gathering each time more students at decreasing costs. Nevertheless, Online Teaching is far from achieving its maximum potential. Personal, methodological, technological, and institutional restrictions are usually associated with important limitations. As an example, teachers may not attend many students without affecting the quality of the online learning process. Clearly, this may impose serious restrictions to a major growth of the number of students attended and/or the quality of the learning process.
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The Teaching Model

In AUXILIAR, concepts may be exposed through different instructional materials and have their own evaluation method. A simplified model of contents organization is shown in Figure 1. It presents synthetically how the system inferences kernel conducts the evaluation process for each studied concept. As shown, each concept is composed of three basic parts: the Pedagogical Proposal; the Pedagogical Contents and Media; and Questions for Apprenticeship Checking.

Figure 1.

Pedagogical contents and questions for each concept

All concepts are stored in Knowledge Bases, making their maintenance and retrieval easier. The retrieval process, based on the student profile, uses a Case-Based System. This facilitates the symmetric migration among concepts in a given apprenticeship problem (Nakamiti, 2000; Piva Jr., 2006).

Key Terms in this Chapter

Case: An occurrence that is relevant to the system and can be represented through a set of attributes.

Online Teaching: Distance learning conducted through the use of the internet and wide band access.

Similarity Degree: A measure of how similar two cases are, one related to the other.

Semiotic: Is a formal doctrine of the signs, and a sign is something that represents something to someone, under specific aspects or capacities (according to Sanders Peirce).

Cases Retrieval: A process conducted by a case-based system that consists in retrieving the most similar cases from a case-base.

Case-Based Systems: Artificial Intelligence technique that stores past cases characteristics and solutions, reusing and readapting them to new situations.

Similarity: Closeness of two or more cases. It is measured by a similarity degree.

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