FocusBoost
FocusBoost is a web application designed to enhance productivity among especially among students. In today's digital era, many students struggle to maintain focus due to distractions from social media and other online tools, which can lead to decreased academic performance. FocusBoost aims to address this issue by providing a comprehensive solution that tracks time spent on productive versus non-productive activities. By offering insights into a user habits, this application helps students identify their distractions and show how they can improve their productivity levels.
This software is primarily intended for students. The main objective is to empower students to enhance their productivity and effectively manage their time accordingly.
From an end-user's perspective, FocusBoost will offer the following features:
-
Personalized User Profiles: Each student will have their own profile to track and view their productivity data.
-
Productive and Non-Productive Timers: The application will feature two timers:
- Productive Timer: Activates when users are engaged in focused work sessions.
- Non-Productive Timer: Activates when users are on non-productive tabs.
-
Activity Reports: Users will receive detailed reports on their productivity patterns, including time spent on productive vs. non-productive activities. This will include top 5 or top 10 most viewed tabs and categorize those tabs.
-
Sleep Tracking: Users can input and track their sleep patterns. This sleep trackings will later be used to correlate with productivity levels.
-
Pomodoro Timer: A built-in Pomodoro timer will help users manage their work sessions and breaks effectively. This time will also be added to the productivity timer.
Additionally, the system may also track water intake and nutrition to provide data that could influence productivity levels. Furthermore, it may include a feature for creating flashcards to aid in studying and retention.
This project is feasible for a group of 4-6 programmers to complete within a single semester. The focus will be on developing the core features necessary for productivity tracking and user registration, as well as exploring how sleep data correlates with productivity data.