<p>This paper presents an innovative combination of Apriori-based frequent itemset mining, hypergraph modeling, and federated MapReduce to nonlinear literacy measurement in vocational education. In the context of increasing emphasis on comprehensive talent cultivation, assessing core literacy in vocational education requires advanced modeling approaches that can capture the nonlinear, dynamic, and multidimensional characteristics of student competencies. This study proposes a federated nonlinear dynamic modeling framework for vocational core literacy assessment by integrating frequent itemset mining, hypergraph representation, and matrix-based structural analysis. Leveraging an optimized Apriori algorithm, we extract high-confidence competency co-occurrence patterns from distributed educational data while preserving privacy. These itemsets are encoded as hypergraphs to model higher-order interactions, and then transformed into incidence and co-occurrence matrices for graph-based clustering and trajectory tracking. The proposed framework is implemented in a real-world college physics experiment setting involving 312 students across five majors. Eight literacy traits–ranging from communication to emotional regulation–are binarized and analyzed across time using a federated MapReduce architecture. Empirical analysis of a sample of 312 vocational students indicated a 14.8 percent increase in clustering consistency, a 0.12 % increase in predictive R2, and a 9.6 % decrease in relative RMSE relative to baseline models. These findings support the framework’s predictive accuracy, interpretability, and strength. In the experiments conducted for 312 students across five majors, the framework gave an improvement of 14.8% in clustering consistency, 0.12 in predictive accuracy (R<InlineEquation ID="IEq1"> <EquationSource Format="TEX">$^2$</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mrow /> <mn>2</mn> </mmultiscripts> </math></EquationSource> </InlineEquation>), and 11.5% in the reduction of RMSE with respect to the baseline methods. Unlike the previous centralized approach, this study unifies frequent itemset mining, hypergraph modeling, and federated MapReduce within a single framework, providing a new, privacy-assured, and interpretable assessment solution for vocational literacy. Experimental results demonstrate the framework’s capacity to reveal latent competency clusters, monitor their temporal evolution, and offer interpretable insights for personalized instruction. Comparative evaluations show that our model outperforms traditional methods in clustering consistency, interpretability, and predictive power.</p>

Nonlinear Dynamic Modeling for the Assessment of Core Literacy in Vocational Education

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发表日期：

2026年3月11日

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Ji Zhao

摘要

This paper presents an innovative combination of Apriori-based frequent itemset mining, hypergraph modeling, and federated MapReduce to nonlinear literacy measurement in vocational education. In the context of increasing emphasis on comprehensive talent cultivation, assessing core literacy in vocational education requires advanced modeling approaches that can capture the nonlinear, dynamic, and multidimensional characteristics of student competencies. This study proposes a federated nonlinear dynamic modeling framework for vocational core literacy assessment by integrating frequent itemset mining, hypergraph representation, and matrix-based structural analysis. Leveraging an optimized Apriori algorithm, we extract high-confidence competency co-occurrence patterns from distributed educational data while preserving privacy. These itemsets are encoded as hypergraphs to model higher-order interactions, and then transformed into incidence and co-occurrence matrices for graph-based clustering and trajectory tracking. The proposed framework is implemented in a real-world college physics experiment setting involving 312 students across five majors. Eight literacy traits–ranging from communication to emotional regulation–are binarized and analyzed across time using a federated MapReduce architecture. Empirical analysis of a sample of 312 vocational students indicated a 14.8 percent increase in clustering consistency, a 0.12 % increase in predictive R2, and a 9.6 % decrease in relative RMSE relative to baseline models. These findings support the framework’s predictive accuracy, interpretability, and strength. In the experiments conducted for 312 students across five majors, the framework gave an improvement of 14.8% in clustering consistency, 0.12 in predictive accuracy (R $^2$ $^{2}$ ), and 11.5% in the reduction of RMSE with respect to the baseline methods. Unlike the previous centralized approach, this study unifies frequent itemset mining, hypergraph modeling, and federated MapReduce within a single framework, providing a new, privacy-assured, and interpretable assessment solution for vocational literacy. Experimental results demonstrate the framework’s capacity to reveal latent competency clusters, monitor their temporal evolution, and offer interpretable insights for personalized instruction. Comparative evaluations show that our model outperforms traditional methods in clustering consistency, interpretability, and predictive power.