Application of improved clustering algorithm in mixed teaching of modern educational technology
Abstract This study explores the application of an improved clustering algorithm in blended teaching with modern educational technology. It utilizes data analysis to enhance teaching processes and outcomes. As information technology rapidly advances, traditional teaching methods are increasingly una...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-08-01
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| Series: | Discover Artificial Intelligence |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44163-025-00393-8 |
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| Summary: | Abstract This study explores the application of an improved clustering algorithm in blended teaching with modern educational technology. It utilizes data analysis to enhance teaching processes and outcomes. As information technology rapidly advances, traditional teaching methods are increasingly unable to meet the diverse learning needs of students, prompting a shift to blended teaching. This research employs cluster analysis to categorise student data based on specific characteristics, facilitating the design of personalised teaching paths. It also discusses optimizing teaching resources and dynamic adjustment mechanisms to meet real-world teaching demands. The improved clustering algorithm demonstrates high precision and flexibility, enabling accurate resource allocation, effective teaching content, and targeted pacing adjustments based on students’ learning progress. Results indicate that cluster analysis enhances resource allocation, improves learning outcomes, and supports the personalisation and accuracy of teaching methods. With advances in algorithm optimization and computational capabilities, cluster analysis holds broad potential for educational applications. Specifically, the improved clustering algorithm increased the silhouette score from 0.51 (K-means) and 0.58 (DBSCAN) to 0.68 and reduced the clustering error from 0.27 to 0.18. This resulted in an average gain of 4.5 points in student performance and a 10% improvement in personalised learning progress. |
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| ISSN: | 2731-0809 |