EMAssistant: A Learning Analytics System for Social and Web Data Filtering to Assist Trainees and Volunteers of Emergency Services
EMAssistant: A Learning Analytics System for Social and Web Data Filtering to Assist Trainees and Volunteers of Emergency Services
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发表时间：
2019
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通讯作者：
Rahul Pandey;Gaurav Bahl;Hemant Purohit
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作者：
Rahul Pandey;Gaurav Bahl;Hemant Purohit
越来越多的基于机器学习的系统被设计用于过滤和可视化来自社交媒体和网络流的相关信息，以进行灾害管理。考虑到灾害事件的动态变化， 相关信息不断发展，因此，主动学习技术通常被认为是不断更新用于相关信息过滤的预测模型。然而，由人类注释者提供的用于更新模型的主动相关反馈没有被验证。因此，它们可能会在人类的学习过程中引入无意识的偏见，并可能导致预测系统不准确或效率低下。因此，本文描述了一个基于开源技术的学习分析系统的设计与实现。 EMAssistant 对于应急志愿者或从业人员-被称为受训者，通过因果推理来增强他们的体验式学习周期，为机器学习模型提供相关反馈。这种原因（提供反馈）和结果（从更新的模型观察预测）之间的视觉形式的持续整合可能会提高学员的理解，以提供更准确的反馈。我们建议提出系统设计，以及为会议提供动手练习。
An increasing number of Machine Learning based systems are being designed to filter and visualize the relevant information from social media and web streams for disaster management. Given the dynamic disaster events, the notion of relevant information evolves, and thus, the active learning techniques are often considered to keep updating the predictive models for the relevant information filtering. However, the active relevant feedback provided by the human annotators to update the models are not validated. As a result, they can introduce unconscious biases in the learning process of humans and can result in an inaccurate or inefficient predictive system. Therefore, this paper describes the design and implementation of an open-source technology-based learning analytics system ‘ EMAssistant ’ for the emergency volunteers or practitioners - referred as the trainee, to enhance their experiential learning cycle with the cause-effect reasoning on providing relevant feedback to the machine learning model. This continuous integration between the cause (providing feedback) and the effect (observing predictions from the updated model) in a visual form will likely to improve the understanding of the trainees to provide more accurate feedback. We propose to present the system design as well as provide hands-on exercises for the conference session.