Qualitative Student Models.

Autor/in	Clancey, William J.
Institution	Stanford Univ., CA. Dept. of Computer Science.
Titel	Qualitative Student Models.
Quelle	(1986), (84 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Artificial Intelligence; Cognitive Structures; Computer Assisted Instruction; Computer System Design; Educational Diagnosis; Higher Education; Instructional Design; Man Machine Systems; Models; Programing; Qualitative Research + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Cognitive structure; Kognitive Struktur; Computer based training; Computerunterstützter Unterricht; Pedagogical diagnostics; Pädagogische Diagnostik; Hochschulbildung; Hochschulsystem; Hochschulwesen; Lesson concept; Lessonplan; Unterrichtsentwurf; Mensch-Maschine-System; Analogiemodell; Qualitative Forschung
Abstract	The concept of a qualitative model is used as the focus of this review of qualitative student models in order to compare alternative computational models and to contrast domain requirements. The report is divided into eight sections: (1) Origins and Goals (adaptive instruction, qualitative models of processes, components of an artificial intelligence based instructional program, contrast with traditional computer assisted instruction, historical progression, and the range of existing programs); (2) Scope of the Review (review topics, important distinctions, relation to cognitive psychology, relation to other areas of artificial intelligence, and special emphases); (3) The Role of Qualitative Models in Instruction (explanation of qualitative models, situation-specific models, simulation or executable models, representation requirements, and the central role of diagnosis); (4) Student Model Assessments; (5) Contrast between Formal and Physical Domains (written notation and operators, subtraction compared to medical diagnosis, artifactual vs. natural functionality, and algorithmic vs. heuristic inference); (6) Types of Qualitative Process Models (process models of reasoning, behavioral vs. functional process models, levels of abstraction, idealized competence vs. individual models, computational representations for qualitative models, classification vs. simulation models of bugs, overlay vs. bug models, and pragmatic considerations); (7) Constructing Situation-Specific Process Models (simulate variations of the inference procedure, simulate variations in the program's general domain model, and derivation of the inference procedure); and (8) Conclusions (recapitulation, methodology, state of the art, and trends). A brief guide to the literature is included. (191 references) (MES)
Erfasst von	ERIC (Education Resources Information Center), Washington, DC