能源行业的学习分析:衡量应急程序中的能力

David Boulanger, Jeremie Seanosky, Michael Baddeley, Vivekanandan S. Kumar, Kinshuk
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引用次数: 5

摘要

石油和天然气行业的几起重大事故的根源都是培训不足,导致严重伤害甚至人员伤亡,同时还会对设备造成极其昂贵的损害,并降低生产率。本文提出了一个程序评估/电子培训工具,称为PeT,用于跟踪受训者对应急操作程序的知识和信心。PeT在加拿大的一家石油和天然气公司进行了两次紧急程序测试。对每个程序进行基于文本的知识测试。每次测试都包括多项选择题。答案被分为完全正确、不完全但正确、部分正确、大部分不正确和完全不正确。本文还描述了PeT置信度计算的六因素置信度模型:知识、反应时间、停留、访问(修订)次数、选择次数和切换答案次数。每个信心因素衡量紧急情况下目标行为的一个具体方面。2014年在一家石油和天然气公司进行的两次实验的结果也展示了PeT能够实现的分析类型。简要介绍了将PeT转移到交互式培训环境的计划,以跟踪操作员在其工作环境中的行动,并将他们的互动转化为更高水平的能力。
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Learning Analytics in the Energy Industry: Measuring Competences in Emergency Procedures
Several major accidents in the oil and gas industry traced their source to deficient training resulting in serious injuries and even casualties along with extremely expensive damage to equipment and decrease in productivity. This paper presents a procedure evaluation/e-training tool called PeT to track the knowledge and confidence of trainees in emergency operating procedures. PeT was tested with two emergency procedures in an oil and gas company in Canada. A text-based knowledge test was implemented for each procedure. Each test consisted of multiple-choice questions. Answers were classified as perfectly correct, incomplete but correct, partially correct, mostly incorrect, and totally incorrect. The paper also describes the six-factor confidence model underlying the confidence computations in PeT: knowledge, reaction time, lingering, number of visits (revision), number of selections, and number of switching answers. Each confidence factor measures a specific aspect of the targeted behaviour in an emergency. The results of two experiments conducted in 2014 in an oil and gas company are also presented to show the types of analysis that PeT enables. A plan to move PeT into an interactive training environment to track the actions of operators in their work environment and translate their interaction into higher level competences is also briefly introduced.
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