{
  "abstract": "Using science data from the 2022 Programme for International Student Assessment (PISA), we examined survey-weighted descriptive association and model-attribution patterns across a fixed, non-probability cohort of nine education systems (78,685 students). Because PISA science plausible values are drawn from a latent population distribution generated with a conditioning model that uses background information, the analysis describes survey-weighted descriptive associations rather than independent individual predictions. Seven machine-learning pipelines were evaluated descriptively across 20 repeated school-grouped held-out partitions in each system. The genetic algorithm-optimized backpropagation neural network (GA-BPNN) was retained as the focal architecture for Shapley additive explanations (SHAP), without claiming systematic or universal superiority. Alternative training-only median/mode imputation produced system- and metric-specific performance changes. The four sensitivity-supported variables—ESCS, HOMEPOS, MATHEFF, and ST255Q01JA—formed a conservative subset of the eight variables that recurred under the primary top-35 screening procedure. Feature-screening stability was assessed under the high-missingness quartile stress test, whereas held-out performance and SHAP magnitude/rank variation were assessed only under the full alternative median/mode imputation workflow; the sample-restriction analysis was not used to assess changes in SHAP ranks. A cross-architecture check showed substantial but incomplete agreement between GA-BPNN SHAP and XGBoost TreeSHAP. These results describe recurrent and system-specific attribution patterns across the nine education systems. They do not support causal inference, individual diagnosis, or generalization to all PISA systems; missing-data analyses did not resolve missing-not-at-random (MNAR) mechanisms or residual selection bias.",
  "abstract_shown": true,
  "affiliations": [
    "School of Artificial Intelligence in Traditional Chinese Medicine, Zhejiang Chinese Medical University, Hangzhou, China",
    "School of Medical Technology, Zhejiang Chinese Medical University, Hangzhou, China",
    "School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
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        0
      ],
      "name": "Meng Liu"
    },
    {
      "affs": [
        1
      ],
      "name": "Xin Bai"
    },
    {
      "affs": [
        2
      ],
      "name": "Xiaohong Li"
    },
    {
      "name": "Wei Jin"
    },
    {
      "name": "Weifeng Jin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/79bpmbf01vwfg.bib",
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  "doi": "10.1371/journal.pone.0351534",
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    "1932-6203"
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  "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": [
    [
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      "3320|Global Educational Policies and Reforms"
    ],
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      "plos",
      "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive psychology/Learning/Human learning"
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      "/Research and analysis methods/Research design/Survey research/Questionnaires"
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      "/Social sciences/Psychology/Behavior/Recreation/Sports"
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      "/Social sciences/Psychology/Cognitive psychology/Learning/Human learning"
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      "/Social sciences/Sociology/Education/Schools"
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  "openalex": "W7213550004",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0351534&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-18",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/79bpmbf01vwfg.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:49Z",
      "native_id": "10.1371/journal.pone.0351534",
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      "url": "https://doi.org/10.1371/journal.pone.0351534"
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  "subjects": [
    {
      "field": "biology",
      "origin": "plos:neuroscience",
      "subfield": "neuroscience"
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  "title": "Recurrent and system-specific model-attribution patterns in PISA 2022 science performance across nine education systems",
  "title_text": "Recurrent and system-specific model-attribution patterns in PISA 2022 science performance across nine education systems",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:49Z",
  "url": "https://research.draconitic.com/paper/79bpmbf01vwfg",
  "venue": "PLOS One",
  "versions": []
}