Article Instance

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{
    "pk": 31342,
    "title": "Using Theory Revision to Model Students and Acquire Stereotypical Errors",
    "subtitle": null,
    "abstract": "Student modeling has been identified as an important component to the long term development of Intelligent Computer-Aided Instruction (ICAI) systems. Two basic approaches have evolved to model student misconceptions. One uses a static, predefined library of user bugs which contains the misconceptions modeled by the system. The other uses induction to learn student misconceptions from scratch. Here, we present a third approach that uses a machine learning technique called theory revision. Using theory revision allows the system to automatically construct a bug library for use in modeling while retaining the flexibility to address novel errors.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [],
    "section": "Talks",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/7pj183nd",
    "frozenauthors": [
        {
            "first_name": "Paul",
            "middle_name": "T.",
            "last_name": "Baffes",
            "name_suffix": "",
            "institution": "University of Texas at Austin",
            "department": ""
        },
        {
            "first_name": "Raymond",
            "middle_name": "J.",
            "last_name": "Mooney",
            "name_suffix": "",
            "institution": "University of Texas at Austin",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "1992-01-01T18:00:00Z",
    "render_galley": null,
    "galleys": [
        {
            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/31342/galley/22411/download/"
        }
    ]
}