Information processing of novice and expert in Bayes' Theorem problem-solving
Abstract
This dissertation presents a framework for coginitive mechanism of learning in computational problem solving developed through the investigation of experts and novices solving quantitative and qualitative Bayes' Theorem problems. It is proposed that there are two different kinds of knowledge compilation processes involved in mastering a class of computational problems which requires an application of a series of discrete steps to solve a problem. The first-stage knowledge compilation (proceduralization and composition) works on basic or micro skills as described in Anderson's theory of learning. The second-stage knowledge compilation is viewed as an important cognitive mechanism for the class of computational problems and takes the form of chunking or schema creation. Economy of cognitive efforts is explored as an inhibiting factor for the composition of production rules for this class of problems. Chunk or schema is further explored in solving qualitative Bayes' Theorem problems. The relationships between this framework and findings from the related previous research are discussed, and the implications of our study for theory of learning and design of intelligent tutoring system are presented.
Degree
Ph.D.
Advisors
Wong, Purdue University.
Subject Area
Management
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