By Konstantina Chrysafiadi, Maria Virvou
This ebook goals to supply vital information regarding adaptivity in computer-based and/or web-based academic platforms. so as to make the coed modeling strategy transparent, a literature assessment touching on pupil modeling options and techniques prior to now decade is gifted in a unique bankruptcy. a singular pupil modeling method together with fuzzy common sense suggestions is gifted. Fuzzy common sense is used to instantly version the training or forgetting means of a scholar. The awarded novel pupil version is liable for monitoring cognitive nation 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 technique to the educational velocity of every person learner. hence the booklet presents 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 by myself academic functions and clever tutoring systems.
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Extra info for Advances in Personalized Web-Based Education
2013) have modeled student engagement in a computer-based scientific inquiry learning environment using a Bayesian Networks model. Also, Liaw and Huang (2013) and Cho and Kin (2013) have investigated learner self-regulation in e-learning environment. They have tried to define the learner’s characteristics that affect the self-regulation. Meta-cognitive features are very sophisticated and constitute a new field of research. Their identification is difficult and their definition is complex. There is not adequate literature review to be able to draw conclusions about either the learner’s characteristics that determine a meta-cognitive factor or the technique that is suitable for modeling a particular meta-cognitive feature.
The emotional state can have a negative or positive effect on learning. That is the reason why in real classroom settings, experienced teachers and professors observe and react accordingly to the emotional state of the students in order to motivate them and improve their learning process (Johnson et al. 2000; Lehman et al. 2008). Therefore, adaptive and/or personalized educational systems should detect the emotional state of students and adapt its behavior to their needs, giving an appropriate response for those emotions (Katsionis and Virvou 2004).
For example, ‘novice’ is a fuzzy set that describes the student’s knowledge level, ‘young’ is a fuzzy set that describes the person’s age, ‘cold’ is a fuzzy set that describes the environment’s temperature, ‘tall’ is a fuzzy set that describes the person’s height, ‘loud’ is a fuzzy set that describes the sound’s intensity, ‘close’ is a fuzzy set that describes the distance between two objects. 1). 1 Fuzzy sets and their partitions. a Fuzzy sets for age; b Fuzzy sets for knowledge level; c Fuzzy sets for height Fuzzy logic variables have a truth-value that ranges in degree between 0 and 1.