Goal and plan knowledge representations: from stories to text editors and programs
Interfacing thought: cognitive aspects of human-computer interaction
CHI '88 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A quantitative model of the learning and performance of text editing knowledge
CHI '85 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Learning to use word processors: problems and prospects
ACM Transactions on Information Systems (TOIS)
The Architecture of Cognition
CHI '90 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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A good deal of research in cognitive psychology has demonstrated that, although learners can solve problems that are just like the ones they have been trained on, they often have great difficulty solving new types of problems. People also have difficulty trying to understand instructions or training materials that try to teach a procedure at a level that is general enough to apply to many different kinds of cases. These two findings lead to a quandary for people designing instructions for procedural tasks such as operating computer software: Instructions should be written with a good deal of specificity so that new users can understand and use them right away, but at the same time the user will have great difficulty generalizing what they have learned to novel cases. Experiment 1 seems to echo this quandary. Computer novices, in this study, were able to follow specific instructions for using a word processor more easily than general instructions. However, they had great difficulty generalizing the specific instructions to novel tasks. Experiment 2 demonstrates that when specific instructions are rewritten to help users form a more general procedure, novices can easily do new tasks and still maintain their initial quality of performance. A production rule formalism is used to represent the knowledge users obtain from instructions and to explore the conditions under which these productions can be generalized. Experiment 2 suggests that this knowledge can be used to improve the generalizability of instructions.