An evaluation of smart learning approach using bloom taxonomy based neuro-fuzzy system

Abstract

The World Health Organization has stated Covid-19 as a pandemic that has posture a current hazard to humanity. Covid-19 pandemic has magnificently forced global shutdown of several events, including educational activities. This has caused in tremendous crisis-response immigration of educational institutes with online smart learning helping as the educational platform. Smart learning targets at providing universal learning to students consuming modern technology to completely prepare them for a fast-changing world everywhere. In this research paper an evaluation system has been developed that is based on bloom taxonomy. A Neuro-fuzzy system for the training and testing of the data for smart and traditional learning outcomes has been applied on collected data. For this research work, we have selected students of the computing discipline and focus on core-computing subjects. The findings of this research work shows the importance of smart learning and its positive impact on student learning outcomes. The evaluation criteria are based on revised bloom taxonomy levels, such that all six levels have been covered. The students’ performance are very much encouraging when compared with ground truth values and reported 91.2% overall accuracy of proposed model on collected samples.