利用用户做出的预测来帮助理解个人行为模式

Miriam Greis, Tilman Dingler, A. Schmidt, C. Schmandt
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引用次数: 3

摘要

人们使用越来越多的应用程序和设备来量化日常行为,如步数或电话使用量。单纯地展示收集到的数据并不一定能帮助用户理解他们的行为。在最近的研究中,提出了诸如反思学习之类的概念来促进基于个人数据的行为改变。在本文中,我们引入用户自制预测来帮助用户理解个人行为模式。因此,我们开发了一个Android应用程序,可以跟踪用户手机上的屏幕打开和解锁模式。该应用程序要求用户根据他们以前的使用数据预测他们的日常行为。在一项有12名参与者的用户研究中,我们展示了在量化自我方法中利用用户做出预测的可行性。通过尝试在研究过程中改进他们的预测,参与者自动发现了对个人行为模式的新见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Leveraging user-made predictions to help understand personal behavior patterns
People use more and more applications and devices that quantify daily behavior such as the step count or phone usage. Purely presenting the collected data does not necessarily support users in understanding their behavior. In recent research, concepts such as learning by reflection are proposed to foster behavior change based on personal data. In this paper, we introduce user-made predictions to help users understand personal behavior patterns. Therefore, we developed an Android application that tracks users' screen-on and unlock patterns on their phone. The application asks users to predict their daily behavior based on their former usage data. In a user study with 12 participants, we showed the feasibility of leveraging user-made predictions in a quantified self approach. By trying to improve their predictions over the course of the study, participants automatically discovered new insights into personal behavior patterns.
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