Modeling the Modeler: An Empirical Study on how Modelers Learn to Create Simulations
Modeling the Modeler: An Empirical Study on how Modelers Learn to Create Simulations
复制标题
为建模者建模：关于建模者如何学习创建模拟的实证研究
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Anthony Barraco
中科院分区：
文献类型：
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作者：
Hamdi Kavak;José J. Padilla;S. Diallo;Anthony Barraco
本文介绍了我们的新的努力自动捕获和分析用户数据的离散事件仿真环境。我们收集了操作数据，例如添加/删除块和运行模型，该模型可以创建计算数据字段并检查它们在专业知识组中的关系。我们发现，与中级用户相比，初级用户使用更多的块/边，并犯更多的构建错误。在检查具有更高专业知识的用户时，我们注意到与在工具中花费的时间有关的差异，这可能与用户参与度有关。初学者级别用户的模型运行失败可能表明构建模型的尝试和错误方法，而不是建立过程。我们的研究开启了一条关键的调查路线，重点关注用户参与，而不是流程建立，这是社区目前的焦点。除了这些发现之外，我们还报告了这些用户行为数据的其他潜在用途和经验教训。
This paper presents our novel efforts on automatically capturing and analyzing user data from a discrete-event simulation environment. We collected action data such as adding/removing blocks and running a model that enable creating calculated data fields and examining their relations across expertise groups. We found that beginner-level users use more blocks/edges and make more build errors compared to intermediate-level users. When examining the users with higher expertise, we note differences related to time spent in the tool, which could be linked to user engagement. The model running failure of beginner-level users may suggest a trial and error approach to building a model rather than an established process. Our study opens a critical line of inquiry focused on user engagement instead of process establishment, which is the current focus in the community. In addition to these findings, we report other potential uses of such user action data and lessons learned.