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  "Title": "Computerized Adaptive Testing with Cognitive Diagnostic Models",
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  "Description": "A session-based engine for cognitive diagnostic\ncomputerized adaptive testing (CD-CAT), the application of\nadaptive testing to cognitive diagnosis models. Three models\nare supported: the deterministic inputs, noisy \"and\" gate\n(DINA), the deterministic inputs, noisy \"or\" gate (DINO), and\nthe generalized DINA (GDINA) model. Item selection criteria\ninclude Kullback-Leibler (KL) information, posterior-weighted\nKullback-Leibler (PWKL), modified posterior-weighted\nKullback-Leibler (MPWKL), and Shannon entropy (SHE). Latent\nattribute profiles are estimated by maximum likelihood\nestimation (MLE), maximum a posteriori (MAP), or expected a\nposteriori (EAP). Content balancing, item exposure control, and\nshadow testing are configurable through constraint functions.\nThe implemented methods follow Cheng (2009)\n<doi:10.1007/s11336-009-9123-2> and de la Torre (2011)\n<doi:10.1007/s11336-011-9207-7>. Designed for real-time,\nitem-by-item adaptive applications.",
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  "Encoding": "UTF-8",
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  "Date/Publication": "2026-06-07 22:54:41 UTC",
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      "page": "cdcat_items",
      "title": "Create a cdCAT Item Bank",
      "topics": [
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    },
    {
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      "topics": [
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    {
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      "title": "Simulated CD-CAT datasets",
      "topics": [
        "cdcat_sim"
      ]
    },
    {
      "page": "CdcatSession",
      "title": "CD-CAT Session Object",
      "topics": [
        "CdcatSession"
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    {
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      "title": "Evaluate CD-CAT Stopping Criteria",
      "topics": [
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    {
      "page": "estimate_alpha",
      "title": "Estimate Latent Attribute Profiles",
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