Modified Fuzzy Rule-Based Classification System for Early Warning of Student Learning

Abstract

This study uses the log data from Moodle learning management system for predicting student learning performance in the first third of a semester. Since the quality of the data has great influence on the accuracy of machine learning, five major data transmission methods are used to enhance data quality of log file in the data preprocessing stage. Furthermore, the modified FRBCS-CHI (fuzzy rule-based classification system using Chi's technique) algorithm, based on the weighted consequence, is proposed to improve the prediction accuracy of classification. Thereafter, the confusion matrix with two dimensions is employed to illustrate the prediction results, such as false positives, false negatives, true positives, and true negatives, which are further used to produce the parameters of prediction performance, including the precision rate, the recall rate, and the F-measure. From the results of experiment, the proposed modified FRBCS-CHI method will have higher prediction accuracy than the original FRBCS-CHI method.