Innovation Literacy Assessment in Vocational Education: A Data Analytics Approach

DOI: https://doi.org/10.62517/jike.202504414

Author(s)

Meide Xu, Jingyi Wang, Yuanfeng Li, Xinyu Xiao, Longying Zhao, Fei Peng*

Affiliation(s)

School of Automotive Engineering, Beijing Polytechnic University, Beijing, China *Corresponding Author

Abstract

Cultivating innovation literacy has become a critical mission for vocational education in the era of digital transformation. This study addresses the challenge of assessing this multifaceted competency by developing a data-driven analytical framework. Using a 7-point Likert scale questionnaire survey conducted at a vocational university, we applied a suite of unsupervised machine learning techniques-including correlation analysis, Principal Component Analysis (PCA), and K-means clustering-to analyze responses from 20 variables. The analysis reveals three distinct student profiles with characteristic innovation competency patterns. The first two principal components were found to explain 54.5% of the total variance, with the first component alone accounting for 43.3%, indicating a strong underlying factor structure. Visualizations through nine complementary charts provide comprehensive insights into variable relationships, data structure, and group characteristics. The results demonstrate that integrated machine learning approaches can effectively decode complex assessment data, revealing meaningful patterns beyond conventional statistical methods. This research offers both methodological contributions for educational data analysis and practical implications for designing targeted interventions to enhance innovation capabilities in vocational education contexts.

Keywords

Innovation Literacy; Vocational Education; Principal Component Analysis; K-Means Clustering; Data Visualization; Unsupervised Machine Learning

References

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