Welcome!
To use the personalized features of this site, please log in or register.
If you have forgotten your username or password, we can help.
My Menu
Saved Items

Designing Agent99 Trainer: A Learner-Centered, Web-Based Training System for Deception Detection

Jinwei CaoContact Information, Janna M. CrewsContact Information, Ming LinContact Information, Judee BurgoonContact Information and Jay F. NunamakerContact Information

(4)  Center for the Management of Information, University of Arizona, Tucson, AZ  85721, USA
Abstract
Research has long recognized that humans have many biases and shortcomings that severely limit our ability to accurately detect deception. How can we improve our deception detection ability? One possible method is to train individuals to recognize cues of deception. To do this, we need to create effective training curricula and educational tools. This paper describes how we used existing research to guide the design and development of a Web-based, multimedia training system called Agent99 Trainer to provide effective deception detection training. The Agent99 Trainer system integrates explicit instruction on the cues of deception, detection experience through practice, and immediate feedback with anytime, anywhere Web access. Our initial experiments show that our training improves human deception detection accuracy and the Agent99 Trainer system provides training as effective as instructor-led lecturebased training.

Contact Information Jinwei Cao
Email: jcao@cmi.arizona.edu

Contact Information Janna M. Crews
Email: jcrews@cmi.arizona.edu

Contact Information Ming Lin
Email: mlin@cmi.arizona.edu

Contact Information Judee Burgoon
Email: jburgoon@cmi.arizona.edu

Contact Information Jay F. Nunamaker
Email: nunamaker@cmi.arizona.edu
Fulltext Preview (Small, Large)
Image of the first page of the fulltext

References secured to subscribers.



Export this chapter
Export this chapter as RIS | Text
 
Remote Address: 38.107.191.105 • Server: mpweb22
HTTP User Agent: CCBot/1.0 (+http://www.commoncrawl.org/bot.html)