Triple Dimensions of Precision Teaching in Physical Education under the “Four New” Initiatives Framework: Mechanism Development, Current Bottlenecks, and Pathway Innovations

To accelerate educational modernization and address unprecedented global challenges, the Central Committee of the Communist Party of China has introduced the “Four New” policy framework. In physical education, this translates to leveraging multimodal data and advanced technologies to achieve precision teaching, a critical strategy for advancing national education and sports development. This study employs a problem-cause-solution analytical framework to systematically explore the mechanism construction, practical constraints, and innovative pathways of data-driven precision teaching in physical education. Key findings indicate: (1) Mechanism construction requires establishing an integrated data-driven platform that enables closed-loop workflows spanning real-time data collection, AI-powered analytics, and adaptive feedback delivery; (2) Practical constraints stem from multifaceted challenges including poor data quality (low signal-to-noise ratios), resistance to technology-integrated pedagogy, inadequate teacher technological competency, ethical dilemmas in sensitive data handling, and fragmented data ecosystems—all systematically deconstructed through causal analysis; (3) Innovative pathways propose a four-pillar solution framework: technological augmentation (e.g., multi-camera AI vision, edge computing), pedagogical transformation (e.g., dynamic grouping, competency-based progression models), systemic resource orchestration (e.g., federated learning platforms, interoperable cloud architectures), and institutional safeguards (e.g., tiered data governance protocols, AI ethics guidelines). This multidimensional approach not only addresses current implementation barriers but also provides a scalable model for aligning precision teaching with the strategic objectives of the “Four New” initiatives, ensuring both educational efficacy and technological sustainability in the digital era.

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Publication Details

Journal
Educational Innovation Research
Published
2025-04-26
DOI
https://doi.org/10.18063/eir.v3i3.849
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
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article

Triple Dimensions of Precision Teaching in Physical Education under the “Four New” Initiatives Framework: Mechanism Development, Current Bottlenecks, and Pathway Innovations

Wensu Dong, Shuyong Liu, Rui Liu
Educational Innovation Research
Online Learning and Analytics
article

Triple Dimensions of Precision Teaching in Physical Education under the “Four New” Initiatives Framework: Mechanism Development, Current Bottlenecks, and Pathway Innovations

Wensu Dong, Shuyong Liu, Rui Liu
article en

Abstract

To accelerate educational modernization and address unprecedented global challenges, the Central Committee of the Communist Party of China has introduced the “Four New” policy framework. In physical education, this translates to leveraging multimodal data and advanced technologies to achieve precision teaching, a critical strategy for advancing national education and sports development. This study employs a problem-cause-solution analytical framework to systematically explore the mechanism construction, practical constraints, and innovative pathways of data-driven precision teaching in physical education. Key findings indicate: (1) Mechanism construction requires establishing an integrated data-driven platform that enables closed-loop workflows spanning real-time data collection, AI-powered analytics, and adaptive feedback delivery; (2) Practical constraints stem from multifaceted challenges including poor data quality (low signal-to-noise ratios), resistance to technology-integrated pedagogy, inadequate teacher technological competency, ethical dilemmas in sensitive data handling, and fragmented data ecosystems—all systematically deconstructed through causal analysis; (3) Innovative pathways propose a four-pillar solution framework: technological augmentation (e.g., multi-camera AI vision, edge computing), pedagogical transformation (e.g., dynamic grouping, competency-based progression models), systemic resource orchestration (e.g., federated learning platforms, interoperable cloud architectures), and institutional safeguards (e.g., tiered data governance protocols, AI ethics guidelines). This multidimensional approach not only addresses current implementation barriers but also provides a scalable model for aligning precision teaching with the strategic objectives of the “Four New” initiatives, ensuring both educational efficacy and technological sustainability in the digital era.

Educational Innovation ResearchVol. 3(3)
Harbin Normal University (CN), Lingnan Normal University (CN)
Openalex Percentile: Top 85%
Online Learning and Analytics
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