An evaluation of smart learning approach using bloom taxonomy based neuro-fuzzy system
An evaluation of smart learning approach using bloom taxonomy based neuro-fuzzy system
复制标题
使用基于布卢姆分类法的神经模糊系统评估智能学习方法
DOI：
10.3233/jifs-219299
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
I. Memon
中科院分区：
文献类型：
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作者：
S. Soomro;A. H. Jalbani;M. Channa;Shamshad Lakho;I. Memon
世界卫生组织已将新型冠状病毒（COVID-19）列为一种对人类构成当前危害的大流行病。2019冠状病毒病大流行迫使全球停止了几项活动，包括教育活动。这导致了大量的危机应对移民的教育机构与在线智能学习帮助作为教育平台。智能学习的目标是为使用现代技术的学生提供普遍学习，使他们为世界各地快速变化的世界做好充分准备。在这项研究中，本文已经开发了一个评价系统，是基于开花分类。用于智能和传统学习结果的数据训练和测试的神经模糊系统已应用于收集的数据。对于这项研究工作，我们选择了计算学科的学生，并专注于核心计算科目。这项研究工作的这一发现表明了智能学习的重要性及其对学生学习成果的积极影响。评价标准基于修订的水华分类水平，因此涵盖了所有六个水平。与地面实况值相比，学生的表现非常令人鼓舞，并报告了收集样本的建议模型的91.2%的整体准确度。
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. This finding 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.