By Konstantina Chrysafiadi, Maria Virvou
This ebook goals to supply vital information regarding adaptivity in computer-based and/or web-based academic platforms. with a purpose to make the coed modeling procedure transparent, a literature evaluation pertaining to scholar modeling ideas and methods up to now decade is gifted in a unique bankruptcy. a singular pupil modeling procedure together with fuzzy good judgment suggestions is gifted. Fuzzy good judgment is used to instantly version the educational or forgetting strategy of a scholar. The provided novel pupil version is liable for monitoring cognitive kingdom transitions of novices with admire to their growth or non-progress. It maximizes the effectiveness of studying and contributes, considerably, to the variation of the educational procedure to the training velocity of every person learner. consequently the e-book offers vital 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 by myself academic purposes and clever tutoring systems.
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Extra info for Advances in Personalized Web-Based Education
A solution to this is the use of fuzzy logic, which is able to deal with uncertainty and inaccurate data. This chapter explains how fuzzy logic can be used to automatically model the learning or forgetting process of a student, offering adaptation and increasing the learning effectiveness in Intelligent Tutoring Systems. In particular, it presents a novel rule-based fuzzy logic system, which models the cognitive state transitions of learners, such as forgetting, learning or assimilating. The operation of the presented approach is based on a Fuzzy Network of Related-Concepts (FNR-C), which is a combination of a network of concepts and fuzzy logic.
7. Hierarchies give information about the order in which the learning material should be taught, but they do not clearly depict the relations among the domain concepts. The network of concepts gives this kind of information. 8). Many adaptive tutoring systems, such as Web-PTV (Tsiriga and Virvou 2003a, 2003b), DEPTHS (Jeremic´ et al. 2009) and IDEAL (Khamis 2011) use a network of concepts for representing the knowledge domain. However, in a network of concepts the relations between concepts are restricted to “part-of”, “is-a” and prerequisite relations.
2012) as well as Zatarain-Cabada et al. (2010) have used artificial neural networks (learning machine) to identify the student’s cognitive and learning styles correspondingly. Finally, the student’s preferences in Personal Reader (Dolog et al. 2004) and in the tutoring system of Pramitasari et al. (2009) have been modeled by using ontologies. Many attempts to model other cognitive characteristics of students except of learning styles have, also, made. Conati et al. (2002) have tried to model in Andes cognitive aspects like long-term knowledge assessment, plan recognition, ability to solve problems and reading latency using Bayesian Networks.