Fuzzy logic based associative classifier for slow learners prediction.

Education is a collective intelligence system where a group of persons ranging from students to management thinks and work together to achieve institutions' goals. The primary goal of every institution is to accomplish excellent end-semester examination results. A good result is achieved through proper training given by the educators and in response to the performance of students in the examination. Training is cost accounting, whereas students' performance is unpredictable. Outlier analysis in the education system has been stipulated in recent decades to predict the students' uncertain behavior in learning activities which are utilized to alert the education systems. Fuzzy Logic System can handle such uncertainties in learning activities. The major issues that affect the accuracy of fuzzy based outlier detection methods are fixing appropriate membership function and validating the fuzzy rules before extracting outliers. To remedy these issues the proposed Fuzzy Temporal Outlier Detection (FTOD) method detects outliers from mid-semester examination results using fuzzy logic based associative classifier with optimal membership functions. The resultant outliers distinguish the slow learners from spurious-slow learners with high accuracy than the existing FARIM and modified-FARIM algorithms. Thus, educators can provide cost-effective training to enrich the slow learners' cognition to score high in end-semester examinations. [ABSTRACT FROM AUTHOR]

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