.png){kind=icon}
.png){kind=icon}
Return
CHAIN: Developing model-driven contextual help for adaptive user interfaces
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jss.2017.10.017.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Adaptive user interfaces (UIs) change their presentation at runtime to remain usable in different contexts of-use. Such changes can cause discrepancies between the UI and static help materials, e.g., videos and screenshots, thereby negatively impacting the latter's usefulness (usability and utility). This paper presents Contextual Help for Adaptive INterfaces (CHAIN), which is an approach for developing model driven contextual help that maintains its usefulness across UI adaptations. This trait is achieved by interpreting the help models at runtime and overlaying instructions on the running adapted UI. A language called Contextual Help for Adaptive INterfaces eXtensible Markup Language (CHAINXML) and a visual notation were developed for expressing and depicting help models. A technique was also devised for presenting CHAIN help models over legacy applications, whether or not their source-code is available. A supporting tool was developed as an extension to Cedar Studio. This work was empirically evaluated in two studies. The first study performed a preliminary evaluation of CHAIN's visual notation. The second study evaluated CHAIN's strengths and shortcomings after using it to develop help for real-life adaptive Uls. The results gave a positive indication about CHAIN's technical qualities and provided insights that could inform future work. (C) 2017 Elsevier Inc. All rights reserved.
Keywords:
Contextual help
Adaptive user interfaces
Model-driven help
Software support
Design tools and techniques
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.1
Papers:
5.4K
Citations:
8.4K