Search History Visualization for Collaborative Web Searching

摘要

As a trend of industrial development, autonomous driving has been renewed as a hot research topic with the rapid increase of new technologies. Every year, many researchers devote themselves to the learning and research on autonomous driving technologies. However, due to the high barriers to entry in this interdisciplinary area, beginners often feel struggling even frustrating in their early learning process. Searching on the Web has become the most important way for people to gain knowledge; studies of user habits reveal that researchers engage in many online academic searching tasks involving asynchronous collaboration with others (e.g. collect relevant literature) to advance their researches. However, current web search engines are generally designed for a single user, searching alone, which are not friendly for researchers to collaborate with each other. To address this issue, we propose LogCanvas, a graph-based user history interface for search engines, to support researchers to conduct asynchronous collaborative web search (i.e., users are in a distinct remote location, with their own computer, carry out different search processes and save efforts by consuming previous users' search results). We take researchers in autonomous driving as an example to describe the development and usage of LogCanvas. In order to investigate the efficacy of LogCanvas, we extend the user scope of LogCanvas to general users and conducted an online crowd-powered experiment inviting 387 participants to use this platform. We studied users' behaviors and collected their feedback about user experience. The results indicate that LogCanvas could benefit users' asynchronous collaborative web search and their learning. (C) 2020 Elsevier Inc. All rights reserved.