Developing Adaptive and Intelligent Tutoring Systems (AITS): A General Framework and Its Implementations

Developing Adaptive and Intelligent Tutoring Systems (AITS): A General Framework and Its Implementations

Mohamed Hafidi (LRS Laboratory, University of Badji Moktar, Annaba, Algeria) and Tahar Bensebaa (LRI Laboratory, University of Badji Moktar, Annaba, Algeria)
DOI: 10.4018/ijicte.2014100106
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Abstract

Several adaptive and intelligent tutoring systems (AITS) have been developed with different variables. These variables were the cognitive traits, cognitive styles, and learning behavior. However, these systems neglect the importance of learner's multiple intelligences, learner's skill level and learner's feedback when implementing personalized mechanisms. In this paper, the authors propose AITS based not only on the learner's multiple intelligences, but also the changing learning performance of the individual learner during the learning process. Therefore, considering learner's skill level and learner's multiple intelligences can promote personalized learning performance. Learner's skill level is obtained from pre-test result analysis, while learner's multiple intelligences are obtained from the analysis of questionnaire. After computing learning success rate of an activity, the system then modifies the difficulty level or the presentation of the corresponding activity to update courseware material sequencing. Learning process in this system is as follows. First, the system determines learning style and characteristics of the learner by an MI-Test and then makes the model. After that it plans a pre-evaluation and then calculates the score. If the learner gets the required score, the activities will be trained. Then the learner will be evaluated by a post-evaluation. Finally the system offers guidance in learning other activities. The proposed system covers all important properties such as hypertext component, adaptive sequencing, problem- solving support, intelligent solution analysis and adaptive presentation while available systems have only some of them. It can significantly improve the learning result. In other words, it helps learners to study in “the best way.”
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Introduction

Contemporary e-Learning systems provide useful tools for computer-supported learning, such as forums, chat rooms, discussion groups and email. However, most of them display content and educational material in the same way to all learners, allowing them to choose their own learning pathway through the course, which is not necessarily the most effective one in terms of their previous knowledge or needs. One possible solution to this problem is to use adaptive and intelligent tutoring systems (AITS). These systems can use different techniques in order to suggest online learning activities or optimal browsing pathways to learners, based on their preferences, knowledge and the browsing history of other learners with similar characteristics. These systems provide intelligence and student adaptability, inheriting properties from Intelligent Tutoring Systems (ITS) and Adaptive Hypermedia Systems (AHS) (Koutsojannis et al., 2007; Giotopoulos et al., 2010; Dabbagh et al., 2007; Cristea et al., 2003 ; Hong et al., 2007 ; Huang et al., 2007 ; Bai et al., 2008 ; Chen, 2008 ; Bhaskar et al., 2010 ; Chu et al., 2011). Intelligent Tutoring Systems (ITS) are computer-aided instructional systems with models of instructional content that specify what to teach, and teaching strategies that specify how to teach. Adaptive Hypermedia Systems adapt the content of a hypermedia page to the user’s goals, knowledge, preferences and other user’s information for each individual user interacting with the system These systems neglect the importance of learner’s multiple intelligences, learner’s feedback and learner’s skill level when implementing personalized mechanisms. Our objective was to develop an (AITS):

  • Adapted for letting the learners work in several disciplinary fields.

  • Based not only on the learner’s multiple intelligences, but also the changing learning performance of the individual learner during the learning process.

The rest of the paper is organized as follows: the next section provides the literature review on individual trait differences and adaptive educational systems. In the section after, we will give an overview on the overall architecture of the intelligent e-learning system. The section following that will describe the scope of our approach. The experiments that have been conducted will be presented in the section after. Following that will be a section that will discuss the results of the experiment. We will conclude the paper in the last section along with the further works of the study.

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