A multi-Feature-based 3D Model for Personalized Teaching in Higher Education
Wei Yue
Journal of Information Organization, Vol. 15, No. 4, 2025.
DOI: https://doi.org/10.6025/jio/2025/15/4/179-186
Link: https://www.dline.info/jio/fulltext/v15n4/jiov15n4_3.pdf
Abstract:
The paper titled "Table Tennis Teaching 3D Model Based on Hierarchical Clustering Intelligent Algorithm"
by Wei Zeng proposes an innovative approach to enhance university level table tennis instruction by leveraging
a hierarchical clustering intelligent algorithm. Recognizing the limitations of traditional teaching
methods particularly their inability to accommodate student's diverse skill levels the study introduces a
data driven, layered teaching model. The algorithm preprocesses player movement data using backgrounddifference
and shadow removal techniques, then extracts features via Radon transformation and wavelet
analysis. A multi feature fusion strategy enables precise posture recognition. The core of the method involves
a two layer hierarchical clustering process that separates data points by density, removes outliers
(~10% of low density points), and organizes the remaining data into cohesive clusters, improving classification
accuracy. Experimental results from a PDCA cycle based teaching intervention show that this approach
outperforms traditional algorithms like k-means++, CURE, and CBDP especially in handling complex, nonspherical
cluster shapes. The study concludes that hierarchical clustering not only enhances teaching personalization
and student engagement but also offers a robust framework for skill assessment and instructional
design in physical education, particularly for sports like table tennis that require fine motor control
and rapid decision making. This intelligent model supports adaptive, student centered pedagogy and paves
the way for technology integrated sports education reforms