@Article{信息:doi 10.2196 / / jmir。3575，作者=“Morrison, Cecily和Doherty, Gavin”，标题=“通过可视化日志数据分析基于web的干预平台的敬业度”，期刊=“J Med Internet Res”，年=“2014”，月=“11”，日=“13”，卷=“16”，数=“11”，页=“e252”，关键词=“敬业度;日志数据分析;数据可视化;背景:在基于网络的干预措施的开发中，参与已经成为一个重要的跨领域关注。有人呼吁建立一种更严格的方法来设计基于网络的干预措施，以增加参与的数量和质量。一种方法是使用日志数据来更好地理解参与过程和使用模式。然而，一个重要的挑战在于组织日志数据进行生产分析。目的:我们的目的是对日志数据可视化的使用进行初步探索，以增强对基于web的干预的理解。方法:我们应用探索性顺序数据分析来突出日志数据的顺序方面，如时间或模块数量，以提供对参与度的洞察。 After applying a number of processing steps, a range of visualizations were generated from the log-data. We then examined the usefulness of these visualizations for understanding the engagement of individual users and the engagement of cohorts of users. The visualizations created are illustrated with two datasets drawn from studies using the SilverCloud Platform: (1) a small, detailed dataset with interviews (n=19) and (2) a large dataset (n=326) with 44,838 logged events. Results: We present four exploratory visualizations of user engagement with a Web-based intervention, including Navigation Graph, Stripe Graph, Start--Finish Graph, and Next Action Heat Map. The first represents individual usage and the last three, specific aspects of cohort usage. We provide examples of each with a discussion of salient features. Conclusions: Log-data analysis through data visualization is an alternative way of exploring user engagement with Web-based interventions, which can yield different insights than more commonly used summative measures. We describe how understanding the process of engagement through visualizations can support the development and evaluation of Web-based interventions. Specifically, we show how visualizations can (1) allow inspection of content or feature usage in a temporal relationship to the overall program at different levels of granularity, (2) detect different patterns of use to consider personalization in the design process, (3) detect usability issues, (4) enable exploratory analysis to support the design of statistical queries to summarize the data, (5) provide new opportunities for real-time evaluation, and (6) examine assumptions about interactivity that underlie many summative measures in this field. ", issn="1438-8871", doi="10.2196/jmir.3575", url="//www.mybigtv.com/2014/11/e252/", url="https://doi.org/10.2196/jmir.3575", url="http://www.ncbi.nlm.nih.gov/pubmed/25406097" }