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首页> 外文期刊>Ergonomics >Evaluating a range of learning schedules: hybrid training schedules may be as good as or better than distributed practice for some tasks
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Evaluating a range of learning schedules: hybrid training schedules may be as good as or better than distributed practice for some tasks

机译：评估一系列学习计划：对于某些任务，混合训练计划可能比分布式练习好或更好

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摘要

We investigated theoretically and empirically a range of training schedules on tasks with three knowledge types: declarative, procedural, and perceptual-motor. We predicted performance for 6435 potential eight-block training schedules with ACT-R's declarative memory equations. Hybrid training schedules (schedules consisting of distributed and massed practice) were predicted to produce better performance than purely distributed or massed training schedules. The results of an empirical study (N=40) testing four exemplar schedules indicated a more complex picture. There were no statistical differences among the groups in the declarative and procedural tasks. We also found that participants in the hybrid practice groups produced reliably better performance than ones in the distributed practice group for the perceptual-motor task - the results indicate training schedules with some spacing and some intensiveness may lead to better performance, particularly for perceptual-motor tasks, and that tasks with mixed types of knowledge might be better taught with a hybrid schedule. Practitioner Summary: We explored distributed and massed training schedules as well as hybrids between them with respect to three knowledge types based on theories and an empirical study. The results suggest that industrial and operator training in complex tasks need not and probably should not be done on a distributed training schedule.
机译：我们在理论上和经验上研究了一系列有关三种知识类型的任务的培训计划：声明性，过程性和感知性运动。我们使用ACT-R的声明性记忆方程式预测了6435个潜在的8块训练时间表的性能。预计混合训练时间表（由分布式和大规模练习组成的时间表）会比纯分布式或大规模训练时间表产生更好的性能。对四个示例性时间表进行实证研究（N = 40）的结果表明情况更为复杂。在声明性和程序性任务中，各组之间没有统计学差异。我们还发现，混合练习组的参与者在感觉运动任务方面的表现比分布式练习组的参与者可靠地更好-结果表明，训练间隔一定的间隔和一定的强度可能会导致更好的表现，特别是对于感觉运动任务，以及具有混合知识类型的任务可能会通过混合时间表更好地教授。从业者摘要：基于理论和实证研究，我们针对三种知识类型探索了分布式和大规模的培训计划以及它们之间的混合。结果表明，在复杂任务中的工业和操作员培训不需要并且可能不应该按照分布式培训计划进行。

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  • 来源
    《Ergonomics》 |2016年第2期|276-290|共15页
  • 作者

    Paik Jaehyon; Ritter Frank E.;

  • 作者单位

    LG Elect, UX Lab, Seoul, South Korea;

    Penn State Univ, Coll Informat Sci & Technol, University Pk, PA 16802 USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    learning; retention; training schedules; knowledge types;

    机译：学习;保留;培训时间表;知识类型;

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