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Adapting training in real time: An empirical test of adaptive difficulty schedules
Military Psychology ( IF 1.270 ) Pub Date : 2021-04-14 , DOI: 10.1080/08995605.2021.1897451
Matthew D. Marraffino 1 , Bradford L. Schroeder 1 , Nicholas W. Fraulini 2 , Wendi L. Van Buskirk 1 , Cheryl I. Johnson 1
Affiliation  

ABSTRACT

Adaptive Training (AT) has been shown to be an effective technique for training tasks in multiple domains. Despite the promise AT has shown as a training technique, researchers remain unsure of the specific qualities that improve learning. In this experiment, we examined how adaptation schedule affects the efficacy and efficiency of difficulty adaptation in computer-based training. We used Cognitive Load Theory to guide our predictions about performance gains. In the reported study, we hypothesized that an adaptation schedule that adapts more frequently would lead to superior performance. To test this, we examined two types of difficulty adaptation (i.e., Within-Adaptive & Between-Adaptive) schedules using an audio-visual change detection task over five 10-minute scenarios. The Within-Adaptive condition adapted difficulty throughout the scenario based on trainee performance in real time. The Between-Adaptive condition adapted difficulty of subsequent scenarios based on previous scenario performance. We compared these two conditions to a Control condition, which maintained a constant difficulty throughout the experiment. We identified performance benefits for the Within-Adaptive condition, particularly for individuals whose performance was initially poor. A closer examination of the results suggested that average difficulty was the driving factor for performance gains in the Between-Adaptive condition. The data support that a within-scenario adaptation schedule effectively manages cognitive load to facilitate learning gains.



中文翻译:

实时调整训练:适应性难度时间表的实证检验

摘要

自适应训练 (AT) 已被证明是一种用于多领域训练任务的有效技术。尽管 AT 已显示出作为一种培训技术的前景,但研究人员仍然不确定可以改善学习的具体品质。在这个实验中,我们研究了适应计划如何影响基于计算机的训练中难度适应的功效和效率。我们使用认知负荷理论来指导我们对性能提升的预测。在报告的研究中,我们假设更频繁地适应的适应时间表将导致卓越的表现。为了测试这一点,我们在五个 10 分钟的场景中使用视听变化检测任务检查了两种类型的难度适应(即自适应内和自适应之间)时间表。内部自适应条件根据学员的实时表现在整个场景中调整难度。自适应条件之间根据之前的场景表现调整了后续场景的难度。我们将这两种条件与在整个实验过程中保持恒定难度的控制条件进行了比较。我们确定了内适应条件的性能优势,特别是对于最初表现不佳的个人。对结果进行更仔细的检查后发现,平均难度是在“间自适应”条件下获得性能提升的驱动因素。数据支持情景内适应计划有效管理认知负荷,以促进学习成果。自适应条件之间根据之前的场景表现调整了后续场景的难度。我们将这两种条件与在整个实验过程中保持恒定难度的控制条件进行了比较。我们确定了内适应条件的性能优势,特别是对于最初表现不佳的个人。对结果的仔细检查表明,平均难度是在自适应条件下提高性能的驱动因素。数据支持情景内适应计划有效管理认知负荷以促进学习收益。自适应条件之间根据之前的场景表现调整了后续场景的难度。我们将这两种条件与在整个实验过程中保持恒定难度的控制条件进行了比较。我们确定了内适应条件的性能优势,特别是对于最初表现不佳的个人。对结果的仔细检查表明,平均难度是在自适应条件下提高性能的驱动因素。数据支持情景内适应计划有效管理认知负荷以促进学习收益。我们确定了内适应条件的性能优势,特别是对于最初表现不佳的个人。对结果的仔细检查表明,平均难度是在自适应条件下提高性能的驱动因素。数据支持情景内适应计划有效管理认知负荷以促进学习收益。我们确定了内适应条件的性能优势,特别是对于最初表现不佳的个人。对结果的仔细检查表明,平均难度是在自适应条件下提高性能的驱动因素。数据支持情景内适应计划有效管理认知负荷以促进学习收益。

更新日期:2021-05-30
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