Li Siyuan, Wang Chao, Wang Ying
Graduate School, Adamson University, 0900, Manila, Metro Manila, Philippines.
Sci Rep. 2024 Feb 27;14(1):4788. doi: 10.1038/s41598-024-53177-y.
The evaluation of Physical Education Teaching Methods in Colleges and Universities faces two main challenges: an excess of evaluating elements and a lack of assessment framework. Hence, the research proposes the multi-feature fuzzy evaluation model based on artificial intelligence to streamline the evaluation process and provide an efficient framework for accessing teaching methods. The framework integrates natural/human language using fuzzy instructions considering three evaluation perspectives, including the management stage, instructors, and students and employs the enhanced cuckoo search optimization algorithm. After the teaching expert has determined each parameter's ratings, they are fed into the improved cuckoo search algorithm and solved using an unbiased function to assess the assessment's final result. It incorporates the students' mobility mechanism and movement vector deconstruction designed based on functional criteria. A system for evaluating the quality of instruction has been developed utilizing the proposed model with enhanced cuckoo search optimization. The results indicate that the proposed algorithm has achieved the highest scores across multiple assessment categories, average skill performances of 97.01%, learning progress of 87.36%, physical fitness of 93.49%, participation rate of 95.04%, student satisfaction of 95.49%, and physical education of 96.8% teaching efficiency. The usefulness of the proposed framework in enhancing physical education teaching methods has been demonstrated by comparing the results with traditional methods. It contributes to advancing pedagogical practices in the field.
评估要素过多和缺乏评估框架。因此，本研究提出了基于人工智能的多特征模糊评估模型，以简化评估过程，并为评估教学方法提供一个有效的框架。该框架使用模糊指令整合自然/人类语言，考虑管理阶段、教师和学生三个评估视角，并采用增强型布谷鸟搜索优化算法。在教学专家确定每个参数的评分后，将其输入改进的布谷鸟搜索算法，并使用无偏函数求解以评估评估的最终结果。它纳入了基于功能标准设计的学生移动机制和运动矢量解构。利用所提出的具有增强型布谷鸟搜索优化的模型开发了一个教学质量评估系统。结果表明，所提出的算法在多个评估类别中取得了最高分，平均技能表现为97.01%，学习进度为87.36%，身体素质为93.49%，参与率为95.04%，学生满意度为95.49%，体育教学效率为96.8%。通过将结果与传统方法进行比较，证明了所提出框架在改进体育教学方法方面的有用性。它有助于推动该领域的教学实践。
