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Evaluating Feedback Strategies for Virtual Human Trainers
arXiv - CS - Human-Computer Interaction Pub Date : 2020-11-23 , DOI: arxiv-2011.11704
Xiumin Shang, Ahmed Sabbir Arif, Marcelo Kallmann

In this paper we address feedback strategies for an autonomous virtual trainer. First, a pilot study was conducted to identify and specify feedback strategies for assisting participants in performing a given task. The task involved sorting virtual cubes according to areas of countries displayed on them. Two feedback strategies were specified. The first provides correctness feedback by fully correcting user responses at each stage of the task, and the second provides suggestive feedback by only notifying if and how a response can be corrected. Both strategies were implemented in a virtual training system and empirically evaluated. The correctness feedback strategy was preferred by the participants, was more effective time-wise, and was more effective in improving task performance skills. The overall system was also rated comparable to hypothetically performing the same task with real interactions.

中文翻译:

评估虚拟人类教练员的反馈策略

在本文中,我们解决了自主虚拟培训师的反馈策略。首先,进行了一项试点研究,以识别和指定反馈策略,以帮助参与者执行给定任务。该任务涉及根据虚拟多维数据集上显示的国家区域对虚拟多维数据集进行排序。指定了两种反馈策略。第一个通过在任务的每个阶段完全纠正用户的响应来提供正确性反馈,第二个通过仅通知是否和如何可以纠正响应来提供建议性反馈。两种策略均在虚拟培训系统中实施并进行了经验评估。参与者更喜欢正确性反馈策略,它在时间上更有效,并且在提高任务执行技能方面更有效。
更新日期:2020-11-25
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