PROSELF - Semi-automated Self-Tracking Systems to Improve Personal Productivity
Debus Maike Elisabeth
PROSELF aims at investigating how emerging mobile and wearable technology can help provide an understanding of what makes people feel (and be) productive, and subsequently, to assist users with managing their productivity on a daily basis.The use of technology to monitor workplace activities is not new but often raises concerns regarding worker rights and privacy - invoking alarmist headlines and a dystopian vision of “big brother” surveillance. This is because such technology has typically been driven by employers to exercise control or evidence efficiency savings in the context of low-skill workplaces. In PROSELF, we instead advocate a focus on knowledge workers with the goal of empowering employees to self-reflect upon their own activities, well-being, and performance at work.Technologies that allow people to self-monitor their work activity already exist, e.g., to track and visualize their computer applications usage or to trigger an alarm if one spends too much time on social networks. A wealth of so-called “productivity apps”, e.g., for to-do-list management or collaborative work, allow individuals to organize their work efficiently. Yet while all these tools help people to better monitor and control work activities, they typically neglect the wider context in which these activities take place. In particular, existing systems miss the opportunity to consider environmental, behavioral, and psychological factors in one’s feeling of (and indeed, observed) performance at work, and thus fail to help users understand why one day felt more productive than another.In order to be truly effective and drive productivity improvement, self-monitoring technologies for the workplace must not only collect and visualize data, but also reason over it and use this cognizance to drive anticipatory actions to both improve the self-monitoring and promote productivity improvements. To this end, PROSELF will: (1) Provide novel methods to enable self-monitoring at work in a semi-automated manner; (2) Develop a sound modeling framework to identify proxies of specific performance indicators in users’ context data; (3) Devise an adequate architecture to support the definition and execution of anticipatory decisions; and (4) Critically evaluate these approaches in-situ in a range of field studies.If successful, PROSELF will advance the understanding of how computing technologies can help individuals observe, balance, and improve their performance and well-being at the workplace. It will contribute a clear definition of the design space of self-monitoring systems for knowledge workers, as well as a sound methodology for their design and development. The validation of the proposed methods through large-scale field studies will give the obtained results the strength necessary for other researchers to build upon them. Ultimately, our work seeks to lay the foundations for new systems to help improve the effectiveness of knowledge workers - with significant consequential benefits for society at large.