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类型学
计算机科学
培训(气象学)
软件
软件包
心理学
人工智能
物理
考古
数据库
气象学
历史
程序设计语言
作者
Steven J. Lorenzet,Eduardo Salas,Scott I. Tannenbaum
摘要
We conducted an experiment using training in a software package for presentations. Ninety undergraduate students with no previous experience received either training that guided them to commit common errors or alternatively training that sought to prevent errors from occurring. From previous research and relevant theory, a typology for manipulating errors is presented. In addition, we offer and test a new way of using errors in training, based on guided errors. Before training, a subject matter expert identified common errors that occur when first learning the software package. Trainees in the guided-errors condition were then guided into and out of mistakes during training. Findings revealed superior performance (accuracy and speed) and self-efficacy associated with using guided errors during training. Study limitations and implications for research and practice are also discussed.
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