John C. Thomas, John M. Carroll
Design Studies
A scenario machine limits the user to a single action path through system functions and procedures. Four scenario machines were designed to embody different approaches to prompting, feedback, and automatic error correction for a “learning-by-doing” training simulator for a commercial, menu-based word processor. Compared with users trained directly on the commercial system, scenario machine users demonstrated an overall advantage in the “getting started” stage of learning. Initial training on a “prompting + automatic correction” system was particularly efficient, encouraging a DWIM (or “do what I mean”) approach to training system design. Curiously, training on a “prompting + feedback” system led to relatively impaired performance on a set of transfer of learning tasks. It was suggested that too much training information support may obscure the task coherence of the action scenario itself relative to a design that provides less explicit direction. © 1988, Academic Press Limited. All rights reserved.
John C. Thomas, John M. Carroll
Design Studies
Jiawei Chen, Anbang Xu, et al.
CHI EA 2020
Rachel K.E. Bellamy, John M. Carroll
International Journal of Man-Machine Studies
John M. Carroll
Lingua