基于人工智能的在线学习满意度分析与评价

Huang Li
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引用次数: 0

摘要

以人工智能为代表的创新技术推动着教育理念和实践的变革,学习环境和教学方式向智能化转变,在线学习进入学习者主权时代。本文采用粗糙集算法构建在线学习质量评价指标体系,基于人工智能对在线学习质量和满意度进行评价分析。结果表明,粗糙集算法的准确率最高,不同数据集的召回率最高,整体呈上升趋势,最高召回率为93.58%。一级指标权重百分比分别为课程环境体验(15%)、课程内容体验(38%)、课程活动体验(26%)、课程互动体验(6%)和学习效果体验(15%)。相应的评价分数也相应反映出来,能够客观地描述在线质量评价。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis and Satisfaction Evaluation of Online Learning Based on Artificial Intelligence
Innovative technology represented by artificial intelligence drives the change of educational concept and practice, the transformation of learning environment and teaching methods to intelligence, and online learning enters the era of learner sovereignty. In this paper, rough set algorithm is used to build an online learning quality evaluation index system, and online learning quality and satisfaction are evaluated and analyzed based on artificial intelligence. The results show that the accuracy of rough set algorithm is the highest, and the recall rate of rough set algorithm is the highest in different data sets, showing an overall upward trend, the highest recall rate is 93.58%. The weight percentages of the first-level indicators are curriculum environment experience (15%), of curriculum content experience (38%), of curriculum activity experience (26%), curriculum interaction experience (6%) and learning effect experience(15%). The corresponding evaluation scores are reflected accordingly, which can objectively describe the online quality evaluation.
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