{
  "abstract": "The quality of graduate education in mining engineering is important for safeguarding national energy security and supporting the green and intelligent transformation of the mining industry. This study developed an evaluation system comprising five criterion-level dimensions, 12 sub-criteria, and 26 indicators. Interpretive Structural Modeling (ISM) was used to identify the hierarchical relationships among the indicators, and an expert-informed Bayesian network was subsequently established for probabilistic evaluation. The results showed that the target node had a positive-state probability of 47%, indicating an overall medium level of development. Curriculum quality exhibited the highest positive-state probability among the sub-criteria. When the target node was set to the positive state, the core curriculum renewal cycle, supervision frequency in practical training, and supervisors’ technological updating capability showed comparatively high posterior probabilities, whereas research platform support emerged as a relatively weak node requiring further attention. These findings provide structured decision-support evidence for education administrators and policymakers seeking to improve the quality of graduate education in mining engineering in China.",
  "abstract_shown": true,
  "affiliations": [
    "School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Liyun Wu"
    },
    {
      "name": "Zhipeng Sun"
    },
    {
      "name": "Yuzhong Yang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3gajqqdq03bwd.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0359427",
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  "id": "3gajqqdq03bwd",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0359427",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2214|Engineering Education and Curriculum Development"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive psychology/Intelligence"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive psychology/Learning/Human learning"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Learning and memory/Learning/Human learning"
    ],
    [
      "plos",
      "/Biology and life sciences/Psychology/Cognitive psychology/Intelligence"
    ],
    [
      "plos",
      "/Biology and life sciences/Psychology/Cognitive psychology/Learning/Human learning"
    ],
    [
      "plos",
      "/Computer and information sciences/Network analysis"
    ],
    [
      "plos",
      "/Engineering and technology/Energy and power/Fuels/Fossil fuels/Coal"
    ],
    [
      "plos",
      "/Engineering and technology/Industrial engineering/Mining engineering"
    ],
    [
      "plos",
      "/People and places/Population groupings/Educational status/Graduates"
    ],
    [
      "plos",
      "/People and places/Population groupings/Professions/Supervisors"
    ],
    [
      "plos",
      "/Physical sciences/Materials science/Materials/Fuels/Fossil fuels/Coal"
    ],
    [
      "plos",
      "/Physical sciences/Materials science/Materials/Organic materials/Coal"
    ],
    [
      "plos",
      "/Physical sciences/Physics/Thermodynamics/Entropy"
    ],
    [
      "plos",
      "/Social sciences/Psychology/Cognitive psychology/Intelligence"
    ],
    [
      "plos",
      "/Social sciences/Psychology/Cognitive psychology/Learning/Human learning"
    ]
  ],
  "openalex": "W7215083553",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0359427&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/3gajqqdq03bwd.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:37Z",
      "native_id": "10.1371/journal.pone.0359427",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0359427"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "plos:neuroscience",
      "subfield": "neuroscience"
    },
    {
      "field": "chemistry",
      "origin": "plos:materials science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Evaluation of the high-quality development of graduate education in mining engineering based on ISM and Bayesian networks",
  "title_text": "Evaluation of the high-quality development of graduate education in mining engineering based on ISM and Bayesian networks",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:37Z",
  "url": "https://research.draconitic.com/paper/3gajqqdq03bwd",
  "venue": "PLOS One",
  "versions": []
}