Learning Operator Semantics by Analogy

Sarah A. Douglas, Thomas P. Moran

This paper proposes a cognitive model for human procedural skill acquisition based on problem solving in problem spaces and the use of analogy for building the representation of operator semantics. Protocol data of computer-naive subjects learning the EMACS text editor suggests that they use their knowledge of typewriting to decide which commands to use in performing editing tasks. We propose a formal method of analysis that compares operators in two problem spaces (based on posrcondirion similarity) and generates misconceptions (based on pre- and postcondition differences). Comparing these predicted misconceptions with error data and verbal comments in problem solving episodes validates this analysis.

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