Combining Expert Systems and Multiple Intelligences in an Adaptive and Intelligent Tutoring System

Combining Expert Systems and Multiple Intelligences in an Adaptive and Intelligent Tutoring System

Hafidi Mohamed (Laboratoire de Recherche en Informatique (LRI), University of Badji Moktar, Annaba, Algeria) and Bensebaa Taher (Laboratoire de Recherche en Informatique (LRI), University of Badji Moktar, Annaba, Algeria)
DOI: 10.4018/ijitwe.2013070102
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Abstract

This paper describes an adaptive and intelligent tutoring system (AITS) based on multiple intelligences and expert system. Most of adaptive and intelligent tutoring systems based their adaptation to user’s skill level. Other learner features taken into account are background, hyperspace experience, preferences and interests. However, less attention was paid to multiple intelligences and their effects on learning. Moreover, to design AITS which can manage both different disciplinary domains and a guide for the learner is difficult. The specialization of the analysis treatments is responsible for the loss of reusability for the other disciplinary domains. To overcome these limitations, the authors will try to combine the benefits of paradigms (adaptive hypermedia, intelligent tutoring system, multiple intelligences) in order to adapt the course to the needs and intellectual abilities of each learner.
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Multiple Intelligences And Learning Styles

The study of individual differences is central to understanding how some students perform better than others. Two main categories of individual traits in learning that are consistent over the long term can be identified: intelligences and style. Comparing intelligences to style, individual differences in intelligence refer to the ability with which one can do something, whereas styles refer to preferences in the use of abilities.

Much research has been conducted on the integration of learning styles in the design of adaptive educational systems. However, it has been difficult to demonstrate conclusively how the concept of learning style can be supported and how it can improve learning outcomes. Some reasons for this include (Riding & Rayner, 1998):

The lack of a unifying framework or organising theory to understand different styles in relation to each other, difficulty in developing valid methods for objectively assessing dimensions of style, Classification of individuals into categories, theories classify people but people are flexible and do not fit neatly in predefined types,

Questions around the construct validity of style with statistical analyses providing mixed support.

In contrast, there is much evidence to support the concept of intelligence as a predictor of learning performance. Instead with intelligence, there is much debate about how intelligence can be measured and on the concept of a single general intelligence level where all abilities are correlated. Critics argue that good or poor performance in one area in no way guarantees similar performance in another and that the full range of intelligent behaviour is not completely captured by any single general ability (Snow, 1992; Sternberg, 1996).

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