toggle visibility Search & Display Options

Select All    Deselect All
 |   | 
Details
   print
  Record Links
Author (up) Rahul Pandey; Gaurav Bahl; Hemant Purohit pdf  isbn
openurl 
  Title EMAssistant: A Learning Analytics System for Social and Web Data Filtering to Assist Trainees and Volunteers of Emergency Services Type Conference Article
  Year 2019 Publication Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management Abbreviated Journal Iscram 2019  
  Volume Issue Pages  
  Keywords Training System, Disaster Management, Active Learning, Humanitarian Technology, Social Media Mining  
  Abstract 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.
 
  Address George Mason University, United States of America  
  Corporate Author Thesis  
  Publisher Iscram Place of Publication Valencia, Spain Editor Franco, Z.; González, J.J.; Canós, J.H.  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2411-3387 ISBN 978-84-09-10498-7 Medium  
  Track T12- Tool Talks Expedition Conference 16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019)  
  Notes Approved no  
  Call Number Serial 1900  
Share this record to Facebook
Select All    Deselect All
 |   | 
Details
   print

Save Citations:
Export Records: