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首页> 外文期刊>Frontiers in Psychology >Lost in Learning: Hypertext Navigational Efficiency Measures Are Valid for Predicting Learning in Virtual Reality Educational Games
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Lost in Learning: Hypertext Navigational Efficiency Measures Are Valid for Predicting Learning in Virtual Reality Educational Games

机译：迷失在学习中：超文本导航效率措施对于预测虚拟现实教育游戏中的学习有效

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The lostness measure, an implicit and unobtrusive measure originally designed for assessing the usability of hypertext systems, could be useful in Virtual Reality (VR) games where players need to find information to complete a task. VR locomotion systems with node-based movement mimic actions for exploration and browsing found in hypertext systems. For that reason, hypertext usability measures, such as “lostness” can be used to identify how disoriented a player is when completing tasks in a serious educational game by examining steps made by the player. An evaluation of two different lostness measures, global and local lostness, based on two different types of tasks, is described in a VR educational game using 13 college students between 14 and 18 years old. Both lostness measures appeared to correlate with results on a post-game knowledge questionnaire but only one of these measures, local lostness, was significant. Therefore, we found that local lostness was able to predict how well participants would perform on a post-game knowledge test indicating how well they learned from the game. In-game experience aspects were also evaluated and, interestingly, it was also found that participants learned less when they felt more present in the game. We believe these two measures relate to cognitive overload, which is known to have an adverse effect on learning. Further research should investigate the lostness measure for use in an online adaptive game system and design the game system in such a way that cognitive load, and the risk of cognitive overload, is minimized when learning, resulting in higher retention of information.
机译：丧失衡量标准，最初用于评估超文本系统的可用性的隐式和不显眼的措施，可以在虚拟现实（VR）游戏中有用，其中玩家需要找到完成任务的信息。 VR Locomotion系统具有基于节点的运动模拟动作，用于超文本系统中的探索和浏览。因此，超文本可用性措施，例如“失去”，可以用来识别通过检查玩家所做的步骤在严肃的教育游戏中完成任务时令人幻想的彩色。根据两种不同类型的任务，在VR教育游戏中描述了两种不同类型的丧失措施，全球和局部丧失丧失的评估，使用13名14至18岁。丧失丧失措施似乎与游戏后知识调查问卷的结果相关，但只有其中一个措施，局部丧失才能是显着的。因此，我们发现当地失物能够预测参与者将如何在游戏后的知识测试中表现出他们从游戏中学到的程度。游戏内的经验方面也得到了评估，有趣的是，它也发现当他们觉得更多存在于游戏中时，参与者就会少了解。我们认为这两项措施与认知过载有关，已知对学习产生不利影响。进一步的研究应调查在线自适应游戏系统中使用的丧失措施，并以这样的方式设计游戏系统，即在学习时最小化认知负载和认知过载的风险，导致信息保留更高。

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