Skip to content
This repository has been archived by the owner on Jun 22, 2023. It is now read-only.

My masterthesis code I programmed. The goal of FocusSession is to support knowledge workers focus on a specific task by reducing distractions and context switches. https://youtu.be/RsMGO8-sHPs

License

Notifications You must be signed in to change notification settings

Phhofm/PersonalAnalytics-FocusSession

 
 

Repository files navigation

PersonalAnalytics - FocusSession

FocusSession is an approach to support knowledge-workers in being more productive. Knowledge-workers today are being bombarded with an avalanche of messages, while working or programming on projects. Assumedly, the sender of such a message expects the knowledge-worker to see and react to the message in a timely manner. This behavior leads to the knowledge-worker devoting valuable time to incoming messages, even though it might be a distraction from the task currrently in process.

In this regard, FocusSession now supports the knowledge-workers productivity with the concept of focus sessions. A focus session is a commitment of the knowledge-worker, to focus on a specific task at hand for a defined period of time.

There are two types of focus sessions: open ones, which are manually stopped by the user, and closed ones, which time out after a predefined time duration. During such a session, the tool helps the user stay focussed by suppressing disturbing notifications and flagging potentially distracting communication applications. At the same time, email-providers (currently outlook mail) and messaging applications (currently slack) can be connected to the tool. The tool will then, after a session, inform the user if there had been any missed messages on these channels, and how many. This way, the user does not need to fear of missing any important messages. At the same time, the tool is capable of external expectations management, by automatically replying to received emails (as to inform the sender of the recipient currently being in an active focus session), or responsing in the same manner on user mention in a public channel of the connected slack workspace through the focussession slack bot.

The tool also provides a short statistical view, listing how many sessions were run in total, how much time had been focussed in total for this day, week or month by using such sessions, how many messages were missed or automatically responded to in total, and how many times the window flagger had been active.

Since this tool is built upon the Personal Analytics project, it provides all its features additionally. It supplies the user with statistics on how much time has been spent in what programs, and other similiarly useful information, with the aim to help the user being more productive by extending the users perspection concerning on-screen time-usage behavior.

The Personal Analytics project was originally initiated by Prof. Dr. Thomas Fritz and André N. Meyer from the SEAL Lab at the University of Zurich (UZH). Our goal is to build a self-monitoring tool that knowledge workers (e.g. developers, designers, administrators) install on their computer and that allows them to get insights into their work and productivity, and come up with positive behavior changes. The basis are a number of computer interaction trackers (e.g. application usage, emails/meetings, user input) and biometric trackers (e.g. Fitbit, Polar, Garmin, Muse, Tobii) that non-intrusively track data, store them locally on the users machine (to avoid privacy issues!) and then visualize them in a daily/weekly summary, the retrospection.

You can learn more about the Personal Analytics Project on their website https://pluto.ifi.uzh.ch/PersonalAnalytics/ or on their github repo https://github.com/sealuzh/PersonalAnalytics

Contact

Philip Hofmann (philip.hofmann@uzh.ch)

About

My masterthesis code I programmed. The goal of FocusSession is to support knowledge workers focus on a specific task by reducing distractions and context switches. https://youtu.be/RsMGO8-sHPs

Resources

License

Stars

Watchers

Forks

Languages

  • C# 96.5%
  • HTML 3.4%
  • Other 0.1%