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The Effects of Memory Structure on Using Rule‐Based Expert Systems for Training: A Framework and an Empirical Test *
Author(s) -
Pei Buck K.W.,
Reneau J. Hal
Publication year - 1990
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1990.tb01685.x
Subject(s) - computer science , task (project management) , consistency (knowledge bases) , congruence (geometry) , test (biology) , representation (politics) , cognitive psychology , artificial intelligence , mental representation , control (management) , machine learning , cognition , psychology , social psychology , management , neuroscience , politics , political science , law , economics , paleontology , biology
One justification for eliciting and representing the judgment strategy of an expert in a rule‐based expert system (RBES) is to facilitate knowledge transfer to individuals with less expertise. However, prior research suggests complexities and potential problems when using RBESs for training. In this paper, a conceptual framework of user learning from RBESs is presented. It is proposed that learning may be ineffective when the problem representation of the RBES is inconsistent with the user's mental representation of the task‐domain knowledge. An experiment was conducted to examine the effects of consistency (inconsistency) between the problem‐solving strategy of RBESs and individuals' memory structures. Groups of subjects whose memory structure either matched or did not match two RBESs' problem‐solving strategies were examined using an internal control evaluation task. The results suggest that learning was facilitated only for groups with congruence between the RBES's problem‐solving strategy and a subject's memory structure.