{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Faculty of Education, Khon Kaen University",
    "Faculty of Education, Southwest University",
    "The Network and Data Security Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Kuihua Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Pennee Narot"
    },
    {
      "affs": [
        0
      ],
      "name": "Saowanee Sirisooksilp"
    },
    {
      "affs": [
        1
      ],
      "name": "Ying Tang"
    },
    {
      "affs": [
        2
      ],
      "name": "Hanwen Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f55d88dtw5t8k.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-63794-4",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-63794-4",
  "id": "f55d88dtw5t8k",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-63794-4",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "1706|Online Learning and Analytics"
    ]
  ],
  "openalex": "W7171519711",
  "pdf_url": "https://www.nature.com/articles/s41598-026-63794-4_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/f55d88dtw5t8k.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:58Z",
      "native_id": "a96973a23311484cab3a9da454adc6c4",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-63794-4"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "RMSD: an interpretable framework for streaming-compatible multi-source data fusion and skill-gap diagnosis in vocational education",
  "title_text": "RMSD: an interpretable framework for streaming-compatible multi-source data fusion and skill-gap diagnosis in vocational education",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:58Z",
  "url": "https://research.draconitic.com/paper/f55d88dtw5t8k",
  "venue": "Scientific Reports",
  "versions": []
}