DMCA
Investigating the Feasibility of Extracting Tool Demonstrations from In-Situ Video Content
| Citations: | 2 - 2 self |
BibTeX
@MISC{Lafreniere_investigatingthe,
author = {Ben Lafreniere and Tovi Grossman and Justin Matejka and George Fitzmaurice},
title = {Investigating the Feasibility of Extracting Tool Demonstrations from In-Situ Video Content},
year = {}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Short video demonstrations are effective resources for help-ing users to learn tools in feature-rich software. However manually creating demonstrations for the hundreds (or thousands) of individual features in these programs would be impractical. In this paper, we investigate the potential for identifying good tool demonstrations from within screen recordings of users performing real-world tasks. Using an instrumented image-editing application, we collected work-flow video content and log data from actual end users. We then developed a heuristic for identifying demonstration clips, and had the quality of a sample set of clips evaluated by both domain experts and end users. This multi-step ap-proach allowed us to characterize the quality of “naturally occurring ” tool demonstrations, and to derive a list of good and bad features of these videos. Finally, we conducted an initial investigation into using machine learning techniques to distinguish between good and bad demonstrations. Author Keywords Help; learning; feature-rich software; video tooltips;
Keyphrases
extracting tool demonstration in-situ video content feature-rich software bad feature multi-step ap-proach individual feature effective resource short video demonstration screen recording help-ing user initial investigation tool demonstration log data bad demonstration real-world task author keywords help demonstration clip good tool demonstration domain expert end user actual end user work-flow video content sample set instrumented image-editing application
