Fuzzy evaluation model for physical education teaching methods in colleges and universities using artificial intelligence

Abstract

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.

Design and study of mine silo drainage method based on fuzzy control and Avoiding Peak Filling Valley strategy

“…Canadian scientists first put forward the concept of a "digital mine", which will be intelligent, uncrewed aerial vehicles, information technology and coal mine production process data collection and monitoring combined through a large number of sensors, detection and PLC automatic control to achieve many sensors. Through many sensors and PLC automatic control, real-time monitoring of mine drainage equipment and remote automation control 19 – 21 . Finland's Coal Mining Industry Association put forward the concept of "intelligent mine technology", mainly relying on wireless information and network technology to achieve all aspects of coal mine data collection and transmission through PLC automatic control to achieve coal mine production automation and mining real-time monitoring purposes 22 .…”