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Peer-reviewedCC BYScientific Reports

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

  • Kuihua Li1
  • Pennee Narot1
  • Saowanee Sirisooksilp1
  • Ying Tang2
  • Hanwen Zhang3
  1. 1Faculty of Education, Khon Kaen University
  2. 2Faculty of Education, Southwest University
  3. 3The Network and Data Security Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China
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Abstract

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.

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Cite

BibTeXRIS
BibTeX
@article{li2026rmsd,
  title = {{RMSD: an interpretable framework for streaming-compatible multi-source data fusion and skill-gap diagnosis in vocational education}},
  author = {Kuihua Li and Pennee Narot and Saowanee Sirisooksilp and Ying Tang and Hanwen Zhang},
  journal = {Scientific Reports},
  year = {2026},
  month = jul,
  doi = {10.1038/s41598-026-63794-4},
  url = {https://doi.org/10.1038/s41598-026-63794-4},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - RMSD: an interpretable framework for streaming-compatible multi-source data fusion and skill-gap diagnosis in vocational education
AU  - Kuihua Li
AU  - Pennee Narot
AU  - Saowanee Sirisooksilp
AU  - Ying Tang
AU  - Hanwen Zhang
PY  - 2026
DA  - 2026/07/01
JO  - Scientific Reports
DO  - 10.1038/s41598-026-63794-4
UR  - https://doi.org/10.1038/s41598-026-63794-4
AB  - 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.
SN  - 2045-2322
ER  -