By Konstantina Chrysafiadi, Maria Virvou
This ebook goals to supply very important information regarding adaptivity in computer-based and/or web-based academic platforms. which will make the scholar modeling approach transparent, a literature overview referring to scholar modeling suggestions and methods in the past decade is gifted in a unique bankruptcy. a unique scholar modeling process together with fuzzy common sense strategies is gifted. Fuzzy good judgment is used to instantly version the training or forgetting strategy of a scholar. The offered novel pupil version is chargeable for monitoring cognitive country transitions of rookies with recognize to their development or non-progress. It maximizes the effectiveness of studying and contributes, considerably, to the variation of the educational procedure to the educational speed of every person learner. consequently the booklet offers very important info to researchers, educators and software program builders of computer-based academic software program starting from e-learning and cellular studying platforms to academic video games together with stand on my own academic functions and clever tutoring systems.
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Additional resources for Advances in Personalized Web-Based Education
Moreover, Baschera and Gross (2010) have represented through the perturbation approach the student’s strength and weaknesses, in order to allow for appropriate remediation actions to adapt to students’ needs. A perturbation student model for detecting the student’s errors has been used in AUTO-COLLEGE (Tourtoglou and Virvou 2012). Furthermore, there are adaptive tutoring systems that have used other techniques than perturbation to model the student’s errors and misconceptions. In particular, Virvou and Kabassi (2002) have added more “human” reasoning to F-SMILE by using stereotypes and cognitive theory of Human Plausible Reasoning (HPR) (Collins and Michalski 1989).
Type-1 fuzzy sets used in conventional fuzzy systems cannot fully handle the uncertainties that are present in intelligent systems (Castillo and Melin 2008). To handle these uncertainties, Lotfi Zadeh (1975) proposed a more sophisticated kind of fuzzy sets theory that is called type-2 fuzzy sets (Mizumoto and Tanaka 1976; Mendel 2001). 2 Interval Type-2 Fuzzy Sets The concept of a type-2 fuzzy set was introduced first by Zadeh (1975) as an extension of the type-1 fuzzy set. g. age, height, grade, temperature) • 2nd dimension: the primary membership function (PMF), which is a function and not just a value between 0 and 1.
1 Knowledge Domain Representation Using a Fuzzy Related-Concept Network A Fuzzy Related-Concepts Network is a network of concepts, which depicts, also, the knowledge dependencies that exist between the domain concepts of the learning material. Therefore, it illustrates so the structure of the learning material, as the concepts’ knowledge dependencies. Particularly, it represents the fact that the knowledge level of a domain concept is increased when the knowledge level of a related topic improves, as well as the fact that the knowledge level of a domain concept is decreased when the knowledge level of a depended topic is not satisfactory.