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

Autor/inn/en	Hafidi, Mohamed; Bensebaa, Tahar
Titel	Developing Adaptive and Intelligent Tutoring Systems (AITS): A General Framework and Its Implementations
Quelle	In: International Journal of Information and Communication Technology Education, 10 (2014) 4, S.70-85, Artikel 6 (16 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1550-1876
DOI	10.4018/ijicte.2014100106
Schlagwörter	Intelligent Tutoring Systems; Models; Foreign Countries; Pretests Posttests; Control Groups; Experimental Groups; Multiple Intelligences; Electronic Learning; Hypermedia; Cognitive Processes; Difficulty Level; College Students; Questionnaires; Gender Differences; Algeria + Suchen Sie Ihr Suchwort? Intelligentes Tutorsystem; Analogiemodell; Ausland; Intelligenz (Psy); Cognitive process; Kognitiver Prozess; Schwierigkeitsgrad; Collegestudent; Fragebogen; Geschlechterkonflikt; Algerien
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 the learner's multiple intelligences, the learner's skill level and the 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 the learner's skill level and the learner's multiple intelligences can promote personalized learning performance. The learner's skill level is obtained from pre-test result analysis, while the 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. The 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." (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01