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Experimentation of Proactive Computing in Context Aware Systems: Case Study of Human-Computer Interactions in e-Learning Environment

机译:语境感知系统中主动计算的实验 - 电子学习环境中人机交互的个案研究

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摘要

In this paper we show the implementation of the concept of Proactivity applied as the core mechanism in our Proactive Context Aware System (PCAS), which is capable to detect and extract the related events of interest from the user's contextual situation and to provide the appropriate goal-oriented actions to this event with the objective to help or assist the user or the group of users. We have chosen the academic environment as the ongoing contextual setting for our system. From this perspective, we designed Proactive Scenarios for an automatic and enhanced management of the online learning and teaching activities on Moodle™ for both student and teacher users. Due to the diversity of the potential contexts and situations arising from the user's activity, we developed two kinds of Proactive Scenarios. The first type or Meta Scenarios are responsible for capturing the changes in the outward context. The second type or Target Scenarios are triggered off by Meta Scenarios and aim to undertake the appropriate actions in response to the conditions of the user's contextual situation. In order to test and validate the capability of our software as well as to analyse its context related outcomes we have performed empirical studies. The experiments consisted in creating two groups of students, on the one hand the study group, which used the Moodle™ platform enhanced by PCAS, and on the other hand the control group, which used the standard version of Moodle™. The subsequent data analysis showed significant differences in specific related results such as notable advantage of the study group outcomes in the category of passing the final exam, where the study group has performed by 11 percentage points better than the control group.
机译:在本文中,我们展示了在Proactive Context Aware System(PCAS)中作为核心机制应用的Proactivity概念的实现,该系统能够从用户的上下文情况中检测和提取感兴趣的相关事件并提供适当的目标针对此事件的针对性操作,目的是帮助或协助用户或用户组。我们选择了学术环境作为我们系统的持续上下文设置。从这个角度出发,我们设计了Proactive Scenarios,以针对学生和教师用户对Moodle™的在线学习和教学活动进行自动和增强的管理。由于用户活动引起的潜在上下文和情况的多样性,我们开发了两种主动方案。第一类或元方案负责捕获外部上下文中的更改。第二类或目标方案是由元方案触发的,旨在响应用户上下文情况的条件采取适当的措施。为了测试和验证我们软件的功能以及分析与环境相关的结果,我们进行了实证研究。实验包括创建两组学生,一方面是学习小组,他们使用了PCAS增强的Moodle™平台,另一方面是对照组,他们使用了标准版本的Moodle™。随后的数据分析显示,在特定的相关结果上存在显着差异,例如在通过期末考试的类别中,研究组的结果具有明显优势,其中研究组的表现比对照组高11个百分点。

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