Theory and Practice of Constructing College Physical Education Curricula Based on Immersive Multimedia Technology

Theory and Practice of Constructing College Physical Education Curricula Based on Immersive Multimedia Technology

Jinhua Ouyang
DOI: 10.4018/IJWLTT.333629
Article PDF Download
Open access articles are freely available for download

Abstract

The overload of sports information resources has greatly increased the difficulty for users to choose resources. To solve this problem, this paper proposes a sports digital teaching resource recommendation model based on collaborative filtering algorithm to achieve the optimization of sports teaching mode in the context of big data and multimedia. Based on the detailed analysis of the teaching resource construction platform, the improved algorithm is applied to the teaching resource recommendation platform, and the algorithm effect is verified based on the collected data. The experimental data shows that the error of this method is significantly better than ID3 algorithm, the error is reduced by 26.55%, and the recall rate is 95.72%, which is 12.76% higher than ID3 algorithm. The introduction of personalized recommendation technology into the utilization of sports information resources can improve the efficiency and accuracy of users' access to resources and strengthen the adaptability to the continuous deconstruction and new integration of the higher education system.
Article Preview
Top

Introduction

The digitalization of sports is fundamental for the execution of national fitness initiatives. It fosters a robust sports service infrastructure, catering to the rising demands of organizing large-scale sports events. This not only elevates the competitive edge in sports but also bolsters their administrative oversight. Given the nature of sports, its informational assets encompass a plethora of semi-structured and unstructured data types, such as videos, images, athlete profiles, event details, and a myriad of multimedia elements. These resources, being multifaceted and intricate, present classification challenges. Users often find it daunting to efficiently pinpoint relevant and valuable content within this expansive data ecosystem. Such a vast resource pool means users may overlook essential information while their engagement wanes, leading to suboptimal utilization of these resources (Varela et al., 2020). Traditional search methodologies often fall short in accuracy, delivering redundant results. Ideally, users should have a clear intent when searching, but they often struggle to articulate or even identify their exact needs, rendering conventional search tools less effective.

Amidst educational evolutions, physical education (PE) must proactively tackle the challenges ushered in by the big data era to foster its growth (Zhu, 2021). Historically sidelined and encumbered by outdated teaching models, PE now needs a fresh, modern approach. Multimedia, a cornerstone of contemporary education, has revolutionized classroom experiences. In the digital age, it has seamlessly integrated into PE classes, enhancing the teaching milieu. Such integration invigorates student engagement and amplifies their motivation in PE, paving the way for elevated instructional quality (Grunspan et al., 2020). Universities, capitalizing on the digital era’s advantages, must overhaul their PE teaching techniques. Recognizing the pivotal role of PE in academic curricula, they should strive for enhanced teaching outcomes (Zhang & Min, 2020). Embracing multimedia teaching approaches can expedite student immersion in lessons. Nonetheless, given PE’s unique characteristics, meticulous planning is paramount—spanning physiological, functional, athletic, neural, and skill-based facets—to achieve desired learning outcomes. Furthermore, fostering positive student sentiment and a fervent passion for sports is integral to optimizing their learning experience.

Effective physical education teaching techniques not only bolster students’ physical health but also contribute to their psychological well-being. However, imparting such methods in primary school settings remains a significant challenge for PE teachers. It is imperative for educators to meticulously manage every aspect of the lesson to ensure each student derives joy from the learning experience. Yet, PE classes are not without unforeseen challenges, such as accidents leading to injuries or lack of student engagement, which can be problematic for instructors (Bai & Zhang, 2020). As the educational landscape shifts towards digitization, physical education is delving into the realm of digital resource creation. While this journey has led to the accumulation of some noteworthy insights, there’s still a gap in aligning these digital resources with the unique essence of physical education, and a dearth of established models for reference. This paper introduces a recommendation system for digital PE instructional resources, leveraging the Collaborative Filtering (CF) algorithm. This model aims to pave the way for a data-driven, multimedia-rich smart campus, thereby enhancing the physical education instructional framework.

Utilizing online resources in physical education maximizes student engagement, transforming them from mere recipients of knowledge to proactive seekers. This shift facilitates a transition from passive to active learning (Liu et al., 2022). Such an adaptable learning process fosters students’ abilities in self-management and independent learning. Traditional physical education methods in universities tend to be repetitive and lack diversity, often leading to diminished student interest. However, integrating multimedia and Information Technology (IT) enhances the presentation of PE content through visuals and videos, sparking student interest and elevating the overall teaching quality.

Complete Article List

Search this Journal:
Reset
Volume 19: 1 Issue (2024)
Volume 18: 2 Issues (2023)
Volume 17: 8 Issues (2022)
Volume 16: 6 Issues (2021)
Volume 15: 4 Issues (2020)
Volume 14: 4 Issues (2019)
Volume 13: 4 Issues (2018)
Volume 12: 4 Issues (2017)
Volume 11: 4 Issues (2016)
Volume 10: 4 Issues (2015)
Volume 9: 4 Issues (2014)
Volume 8: 4 Issues (2013)
Volume 7: 4 Issues (2012)
Volume 6: 4 Issues (2011)
Volume 5: 4 Issues (2010)
Volume 4: 4 Issues (2009)
Volume 3: 4 Issues (2008)
Volume 2: 4 Issues (2007)
Volume 1: 4 Issues (2006)
View Complete Journal Contents Listing