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深度学习在翻转课堂下的大学体育教学设计中的应用。

Application of Deep Learning in College Physical Education Design under Flipped Classroom.

机构信息

School of Competitive Sports, Shandong Sport University, Rizhao 276827, Shandong, China.

出版信息

Comput Intell Neurosci. 2022 Sep 16;2022:7368771. doi: 10.1155/2022/7368771. eCollection 2022.

Abstract

With the development of information technology, teaching reform has also undergone major changes. The traditional college physical education teaching method cannot meet the needs of the majority of students, and the physical education teaching mode continues to be reformed. Microcourse is the most intuitive form of deep integration of information technology and physical education. From the perspective of the flipped classroom (FC), the physical education model has gradually changed from teacher centered to student centered. Deep learning (DL) emphasizes that learners have the ability to actively construct knowledge, effectively transfer knowledge, and solve real problems. This design applies DL and convolutional neural network to the teaching design of physical gymnastics in colleges and universities. The application of the DL teaching model based on FC in the microcourse teaching of gymnastics in colleges and universities is studied and evaluated. The results show that the current utilization of microcourse teaching resources is too low. Interest-oriented teaching microcourses cannot improve students' interests. The proportion of students who are interested is relatively small, and more than 50% of students are not interested. Teachers generally believe that the current gymnastics microcourse needs further optimization and improvement. The poor quality of microvideos and the lack of supervision and reward mechanism in the course are the main reasons for the insufficient students' interest. The complexity of the videos and the liveliness of the discussions are the main problems of low resource utilization. The student's interest in learning is greatly improved after the application of the designed model, and the proportion increases to 82.4%. The effect on ordinary college students is the most obvious, and the effect of microvideo learning has been significantly promoted. Design mode has the most obvious improvement in improving learning efficiency and autonomous learning ability. The improvement of learning ability has increased from 18% to 72%, and the improvement of learning efficiency has increased from 39% to 82%. Meanwhile, students' interest in learning is stimulated, and the utilization of resources is improved.

摘要

随着信息技术的发展,教学改革也发生了重大变化。传统的高校体育教学方法已经不能满足广大学生的需求,体育教学模式不断改革。微课是信息技术与体育教学深度融合最直观的形式。从翻转课堂(FC)的角度来看,体育教学模式逐渐从以教师为中心转变为以学生为中心。深度学习(DL)强调学习者有能力主动构建知识,有效地转移知识并解决实际问题。本设计将 DL 和卷积神经网络应用于高校体操体育教学的教学设计中。研究和评估了基于 FC 的 DL 教学模式在高校体操微课教学中的应用。结果表明,目前微课教学资源的利用率过低。以兴趣为导向的教学微课并不能提高学生的兴趣。对学生的兴趣比例较小,超过 50%的学生不感兴趣。教师普遍认为,目前的体操微课需要进一步优化和改进。微课质量差,课程中缺乏监督和奖励机制是学生兴趣不足的主要原因。视频的复杂性和讨论的生动性是资源利用率低的主要问题。应用设计模型后,学生的学习兴趣大大提高,比例增加到 82.4%。对普通大学生的效果最为明显,微视频学习效果明显提升。设计模式在提高学习效率和自主学习能力方面的改进最为明显。学习能力的提高从 18%增加到 72%,学习效率的提高从 39%增加到 82%。同时,激发了学生的学习兴趣,提高了资源利用率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0688/9507692/f81e555241c9/CIN2022-7368771.001.jpg

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