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<p>Vocational education often faces a mismatch between curriculum design and evolving enterprise skill demands. This paper proposes RMSD, an interpretable framework for streaming-compatible multi-source data fusion, student–job matching, and skill-gap diagnosis. RMSD integrates student records, behavioral logs, project and internship information, job descriptions, and enterprise feedback. It combines BERT-based semantic encoding, temporal self-attention, CFN-based multimodal fusion, and hierarchical skill-tree context to estimate matching scores and node-level skill gaps. Experiments on student–enterprise interaction data show that RMSD outperforms representative baselines, including DeepFM, NCF, BERT-Dual Encoder, Transformer-Seq, and Skill-KG Matching. Compared with DeepFM, RMSD improves HR@5 and MRR@10 by 5.7 and 4.7 percentage points, respectively. It also reduces the average skill-gap score from 0.42 to 0.30 during the observed curriculum-intervention period. System-level evaluation shows that RMSD supports near-real-time updates under the tested institutional workload. These results suggest that RMSD offers a practical and interpretable approach for data-driven vocational education analytics.</p>

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RMSD: an interpretable framework for streaming-compatible multi-source data fusion and skill-gap diagnosis in vocational education

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月28日

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  • Kuihua Li,
  • Pennee Narot,
  • Saowanee Sirisooksilp,
  • Ying Tang,
  • Hanwen Zhang

摘要

Vocational education often faces a mismatch between curriculum design and evolving enterprise skill demands. This paper proposes RMSD, an interpretable framework for streaming-compatible multi-source data fusion, student–job matching, and skill-gap diagnosis. RMSD integrates student records, behavioral logs, project and internship information, job descriptions, and enterprise feedback. It combines BERT-based semantic encoding, temporal self-attention, CFN-based multimodal fusion, and hierarchical skill-tree context to estimate matching scores and node-level skill gaps. Experiments on student–enterprise interaction data show that RMSD outperforms representative baselines, including DeepFM, NCF, BERT-Dual Encoder, Transformer-Seq, and Skill-KG Matching. Compared with DeepFM, RMSD improves HR@5 and MRR@10 by 5.7 and 4.7 percentage points, respectively. It also reduces the average skill-gap score from 0.42 to 0.30 during the observed curriculum-intervention period. System-level evaluation shows that RMSD supports near-real-time updates under the tested institutional workload. These results suggest that RMSD offers a practical and interpretable approach for data-driven vocational education analytics.