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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, Tahar Bensebaa
Copyright: © 2014 |Volume: 10 |Issue: 4 |Pages: 16
ISSN: 1550-1876|EISSN: 1550-1337|EISBN13: 9781466654686|DOI: 10.4018/ijicte.2014100106
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MLA

Hafidi, Mohamed, and Tahar Bensebaa. "Developing Adaptive and Intelligent Tutoring Systems (AITS): A General Framework and Its Implementations." IJICTE vol.10, no.4 2014: pp.70-85. http://doi.org/10.4018/ijicte.2014100106

APA

Hafidi, M. & Bensebaa, T. (2014). Developing Adaptive and Intelligent Tutoring Systems (AITS): A General Framework and Its Implementations. International Journal of Information and Communication Technology Education (IJICTE), 10(4), 70-85. http://doi.org/10.4018/ijicte.2014100106

Chicago

Hafidi, Mohamed, and Tahar Bensebaa. "Developing Adaptive and Intelligent Tutoring Systems (AITS): A General Framework and Its Implementations," International Journal of Information and Communication Technology Education (IJICTE) 10, no.4: 70-85. http://doi.org/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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