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doi:10.1080/17543266.2026.2699601

July 9, 2026International Journal of Fashion Design Technology and EducationOpen Access

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Key Points

The central aim is to develop a framework that enhances fashion education using data-driven approaches.
Utilized principal component analysis (PCA) to identify key competencies in fashion skills.
Implemented neural networks for pattern recognition in learner data.
Conducted learner clustering to tailor educational approaches.
A significant improvement in skill proficiency was observed among students engaged with the framework (mean increase 25%).
The neural network model achieved an accuracy of 85% in predicting learner outcomes.
Cluster analysis revealed three distinct learner profiles for targeted educational strategies.

Implication

Reveals a data-driven framework for fashion education, suggesting improved skill acquisition.

Abstract

Article title: Developing a data-driven digital fashion skills framework for fashion education: evidence from PCA, neural networks, and learner clusteringAuthors: Sholikhah, R., Triyono, M. B., Suk...

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Cite This Study

A 2026 study studied this question.

synapsesocial.com/papers/6a532e624f7abc118adecb6b https://doi.org/10.1080/17543266.2026.2699601

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Key Points

Implication

Abstract

Discussion

Cite This Study

Correction

Key Points

Implication

Abstract

Discussion

Cite This Study

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