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首页> 外文期刊>International Journal of STEM Education >Development and application of a multi-modal task analysis to support intelligent tutoring of complex skills
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Development and application of a multi-modal task analysis to support intelligent tutoring of complex skills

机译：开发和应用多模式任务分析以支持复杂技能的智能辅导

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Background Contemporary work in the design and development of intelligent training systems employs task analysis (TA) methods for gathering knowledge that is subsequently encoded into task models. These task models form the basis of intelligent interpretation of student performance within education and training systems. Also referred to as expert models, they represent the optimal way(s) of performing a training task. Within Intelligent Tutoring Systems (ITSs), real-time comparison of trainee task performance against the task model drives automated assessment and interactive support (such as immediate feedback) functionality. However, previous task analysis (TA) methods, including various forms of cognitive task analysis (CTA), may not be sufficient to support identification of the detailed design specifications required for the development of an ITS for a complex training task incorporating multiple underlying skill components, as well as multi-modal information presentation, assessment, and feedback modalities. Our current work seeks to develop an ITS for training Robotic Assisted Laparoscopic Surgery (RALS), a complex task domain that requires a coordinated utilization of integrated cognitive, psychomotor, and perceptual skills. Results In this paper, we describe a methodological extension to CTA, referred to as multi-modal task analysis (MMTA) that elicits and captures the nuances of integrated and isolated cognitive, psychomotor, and perceptual skill modalities as they apply to training and performing complex operational tasks. In the current case, we illustrate the application of the MMTA method described here to RALS training tasks. The products of the analysis are quantitatively summarized, and observations from a preliminary qualitative validation are reported. Conclusions We find that iterative use of the described MMTA method leads to sufficiently complete and robust task models to support encoding of cognitive, psychomotor, and perceptual skills requisite to training and performance of complex skills within ITS task models.
机译：背景技术智能培训系统的设计和开发中的当代工作采用任务分析（TA）方法来收集知识，这些知识随后被编码为任务模型。这些任务模型构成了在教育和培训系统中智能解释学生表现的基础。也称为专家模型，它们代表执行训练任务的最佳方式。在智能辅导系统（ITS）中，受训者任务绩效与任务模型的实时比较可驱动自动评估和交互式支持（例如即时反馈）功能。但是，先前的任务分析（TA）方法（包括各种形式的认知任务分析（CTA））可能不足以支持识别为开发包含多个基础技能组件的复杂训练任务的ITS所需的详细设计规范。 ，以及多模式信息呈现，评估和反馈模式。我们当前的工作旨在开发一种用于训练机器人辅助腹腔镜手术（RALS）的ITS，这是一个复杂的任务领域，需要协调地利用综合的认知，心理运动和感知技能。结果在本文中，我们描述了对CTA的一种方法扩展，称为多模式任务分析（MMTA），该方法引起并捕获了整合的和孤立的认知，心理运动和知觉技能模式的细微差别，因为它们适用于训练和执行复杂操作任务。在当前情况下，我们说明了此处描述的MMTA方法在RALS训练任务中的应用。对分析的产品进行了定量总结，并报告了初步定性验证的观察结果。结论我们发现，所描述的MMTA方法的迭代使用导致足够完整和健壮的任务模型，以支持对ITS任务模型中的复杂技能的训练和表现所必需的认知，心理运动和知觉技能的编码。

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