Artificial Instruction. A Method for Relating Learning Theory to Instructional Design.

Autor/in	Ohlsson, Stellan
Institution	Pittsburgh Univ., PA. Learning Research and Development Center.
Titel	Artificial Instruction. A Method for Relating Learning Theory to Instructional Design.
Quelle	(1990), (57 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Arithmetic; Artificial Intelligence; Cognitive Processes; Computer Simulation; Elementary Education; Instructional Design; Learning Processes; Learning Strategies; Learning Theories; Mathematics Education; Mathematics Instruction; Mathematics Skills; Research Design; Research Methodology; Subtraction; Teaching Methods + Suchen Sie Ihr Suchwort? Addition; Arithmetik; Arithmetikunterricht; Rechnen; Künstliche Intelligenz; Cognitive process; Kognitiver Prozess; Computergrafik; Computersimulation; Elementarunterricht; Lesson concept; Lessonplan; Unterrichtsentwurf; Learning process; Lernprozess; Learning methode; Learning techniques; Lernmethode; Lernstrategie; Learning theory; Lerntheorie; Mathematische Bildung; Mathematics lessons; Mathematikunterricht; Mathmatics achievement; Mathematics ability; Mathematische Kompetenz; Forschungsdesign; Research method; Forschungsmethode; Subtraktion; Teaching method; Lehrmethode; Unterrichtsmethode
Abstract	Prior research on learning has been linked to instruction by the derivation of general principles of instructional design from learning theories. However, such design principles are often difficult to apply to particular instructional issues. A new method for relating research on learning to instructional design is proposed: Different ways of teaching a particular topic can be evaluated by teaching that topic to a simulation model of learning and recording the complexity of the resulting learning processes. A study to compare two mathematically correct algorithms for computing the difference between two multi-digit numbers from a conceptual or mechanical perspective was designed for both methodological and substantive purposes. The algorithms chosen to model were "regrouping" and"augmenting". Explanations of the architecture of simulation system production are provided. Learning difficulty is determined by the number of states and cycles that the simulation system carries out to learn the method carried out over all the training problems. Results of the learning runs imply that regrouping is more difficult that augmenting, and that learning subtraction conceptually is more difficult than learning it mechanically, a conclusion that would seem to contradict widely held beliefs in the mathematics education community. The presuppositions that accurate simulation models can be developed are discussed and the advantages and disadvantages of the general method of simulation use are evaluated. (MDH)
Erfasst von	ERIC (Education Resources Information Center), Washington, DC