By Konstantina Chrysafiadi, Maria Virvou
This publication goals to supply vital information regarding adaptivity in computer-based and/or web-based academic platforms. which will make the scholar modeling strategy transparent, a literature assessment bearing on scholar modeling ideas and techniques prior to now decade is gifted in a unique bankruptcy. a singular scholar modeling technique together with fuzzy good judgment recommendations is gifted. Fuzzy common sense is used to immediately version the training or forgetting means of a scholar. The offered novel pupil version is answerable for monitoring cognitive kingdom transitions of inexperienced persons with admire to their development or non-progress. It maximizes the effectiveness of studying and contributes, considerably, to the difference of the educational strategy to the training velocity of every person learner. for that reason the ebook presents very important info to researchers, educators and software program builders of computer-based academic software program starting from e-learning and cellular studying structures to academic video games together with stand on my own academic purposes and clever tutoring systems.
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Additional resources for Advances in Personalized Web-Based Education
2008), who have used machine learning techniques in combination with fuzzy techniques; Al-Hmouz et al. (2010, 2011), who have combined stereotypes with machine learning techniques, and Viccari et al. (2008), who have built a student model based on cognitive theories and Bayesian networks. Therefore, there are a variety of student modeling techniques that can be used to model the learner’s cognitive features. 3, the percentages of preferences for each one of the student modeling techniques for modeling the student’s learning styles and preferences are presented considering the above literature review.
5 Meta-Cognitive Features Meta-cognitive features allow the student to be aware of her/his knowledge and abilities and make her/him able to monitor and direct her/his own learning processes. 3 Student’s Characteristics to Model 21 controlling, regulation and orchestration (Flavell 1976). For example, a student has meta-cognitive features when s/he is aware of and controls their own thinking; s/he is able to select her/his own learning goal; s/he can use properly the obtained and prior knowledge; s/he can choose the appropriate each time problem-solving strategy (Mitrovic and Martin 2006; Barak 2010).
Similarly, Oscal CITS adapts to the student’s learning styles incorporated a fuzzy mechanism (Latham et al. 2014). Also, TADV (Kosba et al. 2003, 2005) includes a student model, which combines overlay with fuzzy logic, to represent communication styles of individual students, except of their knowledge. Moreover, in GIAS (Castillo et al. 2009) the appropriate selection of the course’s topics and learning resources are based not only on the student’s goals and knowledge level but also on the student’s learning style that is modeled using stereotypes and machine learning techniques.