Attentiveness assessment in learning based on fuzzy logic analysis
Learner attention affects learning efficiency. However, in many classes, teachers cannot assess the degree of attention of every learner. When a teacher is capable of addressing inattentive learners immediately, he can avoid situations in which learners are inattentive. Many studies have analyzed driver attentiveness by the applying of image detection technologies. If this mechanism can be applied to in-class learning, it will help teachers keep learners attentive, and reduce teacher load during class. This study mainly applies fuzzy logic analysis of learner facial images when participating in class. Two fuzzy logic algorithms are proposed to determine the level of inattention by measuring the leaving, drowsiness, head turning and no motion. Applying fuzzy logic can prevent erroneous judgments associated with a single term, and help teachers deal with learner attentiveness. The simulation works are carried to evaluate the effect of the proposed system under various conditions. The simulation results indicated that the proposed system is effective for detecting of learner attentiveness in class.
fuzzy logic learner attention analysis image processing