IS 213 -- User Interface Design & Development

ReadingTree: Problem Statement


 
 
problem statement
 
 
 
 

 

Problem Statement

Our project will evaluate the interface to Reading Tree, an online literary community for children, centered upon a book recommendation service. ReadingTree is designed for children in grades 1 - 5 who would like to get suggestions for books that match their interests and reading abilities. Although there are book recommender systems for adults and sites that are oriented towards child users, there are no sites that provide book recommendations for children. In order to address this need, Amity and Kirsten developed the prototype for this website during the fall semester Electronic Publishing seminar. Currently the interface is slow to download, difficult to navigate, and confusing to use. Our goal this semester is to redesign the interface, using iterative testing, to make the site more useful, usable, satisfying, and appealing to its audience.

Initial Suggested Improvements and Justification

  • Clarify and improve the navigational structure. The overall structure of the site is not clear from the menu buttons, and there are few cues to orient a user as to their location within the site.
  • Determine and implement the searching methods preferred by our target users.
  • Address page layout, reconsidering the tree metaphor as a unifying feature. The current design is somewhat cluttered and limits the amount of text that can be displayed on a screen.
  • Make display of book recommendations scalable, so that as the database of books grows, the users are not overwhelmed by the options presented
  • Add features to make the site both fun and useful for children. We will be guided by the work of professionals such as Allison Druin and Malcolm Gladwell who have researched and reported extensively on children's interaction with technology.
  • Add and improve graphics to make the site as a whole more engaging and appealing. Specifically focus on the graphics used to gather information from users about their interests, to help improve the recommendations.