ABSTRACT The purpose of this study is to propose a methodology for establishing an augmented reality (AR)-based model for efficient OJT through object detection and causality mining, a novel text analysis method. Articles on hotel management published in the last decade and useful for OJT were collected, information on the causal relationships between them extracted, and related rules saved to a rule base. Using the same data, we detected various objects through SSD (Single Shot Multibox Detector), a real-time object detection system. Then we matched sets of causalities and displayed them to trainees wearing AR devices. This methodology reduces development and maintenance costs required to operate OJT programmes. Trainees are immersed in the training environment, which improves the effectiveness of the training. To show the feasibility of the proposed method, we developed a prototype AR-OJT system for hotel management training, automatically extracting knowledge on hotel management from articles according to the proposed method. The results demonstrate that the AR-OJT group shows better performance in terms of learning motivation and self-regulated learning than the control OJT group. No significant difference in learning performance was found between the two groups, which implies that traditional OJT can be substituted with AR-OJT.

中文翻译：

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结合目标检测和因果关系挖掘，有效开发基于增强现实的酒店管理在职培训系统

摘要本研究的目的是提出一种方法，通过对象检测和因果关系挖掘，一种新颖的文本分析方法，建立基于增强现实 (AR) 的高效 OJT 模型。收集了过去十年发表的对 OJT 有用的关于酒店管理的文章，提取了它们之间因果关系的信息，并将相关规则保存到规则库中。使用相同的数据，我们通过实时物体检测系统 SSD（Single Shot Multibox Detector）检测各种物体。然后我们匹配了一组因果关系并将它们展示给佩戴 AR 设备的受训者。这种方法减少了运行 OJT 计划所需的开发和维护成本。学员沉浸在培训环境中，提高了培训的效果。为了证明所提出方法的可行性，我们开发了一个用于酒店管理培训的原型 AR-OJT 系统，根据所提出的方法从文章中自动提取酒店管理知识。结果表明，AR-OJT 组在学习动机和自我调节学习方面表现出比控制 OJT 组更好的表现。两组之间的学习表现没有显着差异，这意味着传统的 OJT 可以被 AR-OJT 替代。结果表明，AR-OJT 组在学习动机和自我调节学习方面表现出比控制 OJT 组更好的表现。两组之间的学习表现没有显着差异，这意味着传统的 OJT 可以被 AR-OJT 替代。结果表明，AR-OJT 组在学习动机和自我调节学习方面表现出比控制 OJT 组更好的表现。两组之间的学习表现没有显着差异，这意味着传统的 OJT 可以被 AR-OJT 替代。