{
  "abstract": "Academic early-warning systems are commonly evaluated as prediction tools, although their institutional value depends on how risk estimates are translated into support decisions. We developed and retrospectively evaluated a decision-aware framework for pre-regulatory academic warning using longitudinal administrative records spanning six academic years and twelve standardised semesters at a regional public university. Condition-specific risks of violating formal academic-warning regulations were estimated using three predictive backbones and evaluated through semester-based rolling-origin testing. Calibrated probabilities were converted into support priorities using context-sensitive expected regulatory violation costs under a fixed semester-level support budget. At 20% intervention coverage, the proposed policy reduced the missed intervention rate to 0.447, compared with 0.514 for the strongest non-ERVC comparator, while increasing positive predictive value from 0.377 to 0.429. The estimated equity gap in missed intervention was smaller under ERVC (0.052 versus 0.081 for the strongest non-ERVC comparator), but the paired difference did not reach conventional statistical significance ( $$p=0.06$$ ), indicating a favourable but statistically uncertain equity pattern. These findings support evaluating early-warning systems through decision quality, resource use, and equity, while preserving human oversight and formal academic governance.",
  "abstract_shown": true,
  "affiliations": [
    "Faculty of Arts and Communications, Thai Nguyen University of Information and Communication Technology",
    "School of Computer Science and AI, Duy Tan University",
    "Faculty of Information Technology, Hai Phong University",
    "Faculty of Information Technology, Thai Nguyen University of Information and Communication Technology"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Trung-Nghia Phung"
    },
    {
      "affs": [
        1
      ],
      "name": "Gia-Nhu Nguyen"
    },
    {
      "affs": [
        2
      ],
      "name": "Dac-Nhuong Le"
    },
    {
      "affs": [
        3
      ],
      "name": "The-Vinh Nguyen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/12rbaczg62zqm.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-72395-0",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-72395-0",
  "id": "12rbaczg62zqm",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-72395-0",
  "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": "W7213986760",
  "pdf_url": "https://www.nature.com/articles/s41598-026-72395-0_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/12rbaczg62zqm.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:52Z",
      "native_id": "0e2cf957af97438ba876ddaf628d4afa",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-72395-0"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "A decision-aware framework for pre-regulatory academic early warning under resource constraints",
  "title_text": "A decision-aware framework for pre-regulatory academic early warning under resource constraints",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T14:20:43Z",
  "url": "https://research.draconitic.com/paper/12rbaczg62zqm",
  "venue": "Scientific Reports",
  "versions": []
}