Optimizing for Happiness and Productivity: Modeling Opportune Moments for Transitions and Breaks at Work
Optimizing for Happiness and Productivity: Modeling Opportune Moments for Transitions and Breaks at Work
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优化幸福感和生产力：为工作中的过渡和休息建模合适的时刻
DOI：
10.1145/3313831.3376817
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Shamsi T. Iqbal
中科院分区：
文献类型：
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作者：
Harmanpreet Kaur;Alex C. Williams;Daniel J. McDuff;M. Czerwinski;J. Teevan;Shamsi T. Iqbal
信息工作者从事的工作需要持续的多任务处理，从而导致环境切换、生产力下降、压力和不快乐。能够调解任务转换和休息的系统有可能让人们保持高效和快乐。我们探索了实现这一目标的关键第一步：找到合适的时机来建议过渡和休息，而不会在注意力集中的情况下打扰人们。使用来自三周实地研究 (N=25) 的情感、工作站活动和任务数据，我们构建模型来预测一个人是否应该继续他们的任务、过渡到新任务或休息一下。我们模型的 R 平方值高达 0.7，错误率仅为 15%。我们要求用户评估依赖这些模型的推荐器提供的推荐的时间。我们的研究表明，用户认为我们的过渡和休息建议非常及时，准确率分别为 86% 和 77%。最后，我们讨论了智能系统对引导任务转换和管理工作中断的影响。
Information workers perform jobs that demand constant multitasking, leading to context switches, productivity loss, stress, and unhappiness. Systems that can mediate task transitions and breaks have the potential to keep people both productive and happy. We explore a crucial initial step for this goal: finding opportune moments to recommend transitions and breaks without disrupting people during focused states. Using affect, workstation activity, and task data from a three-week field study (N=25), we build models to predict whether a person should continue their task, transition to a new task, or take a break. The R-squared values of our models are as high as 0.7, with only 15% error cases. We ask users to evaluate the timing of recommendations provided by a recommender that relies on these models. Our study shows that users find our transition and break recommendations to be well-timed, rating them as 86% and 77% accurate, respectively. We conclude with a discussion of the implications for intelligent systems that seek to guide task transitions and manage interruptions at work.