Week #5 #

📝 Feedback #

❓ Sessions #

Our team conducted 4 interviews with our first users. Now you can see summary of all interviews below

• 💡 Project Idea

  • User 1
    • “Cool idea, I think that personalosed roadmap will make learning much simpler.”
  • User 2
    • “It is cool, but I think that we have enough sites with courses. But Automatic roadmap generation is good idea.”
  • User 3
    • “I agree with User 2, furthermore, have you think about copyright”
  • User 4
    • “I really love your name.”

• 🎨 Site Design

  • User 1
    • “Good design, I really love capybara on main page”
  • User 2
    • “Good design overal, but I prefer dark theme”
  • User 3
    • “I think that yellow color is not the best, additionally where is dark theme?”
  • User 4
    • “You have animations, thats enough to satisfy me”

• 📝 Onboarding

  • User 1
    • “In case if I want to increase my python level, why I cant take it as a goal skill?”
  • User 2
    • “Maybe in education section you should add passed courses, to get better user background”
  • User 3
    • “I could not find all my skills, plus there are too many level selection pages”
  • User 4
    • “It was fast, too fast. I like it”

• 🗺️ Roadmap

  • User 1
    • “Good looking roadmap plus it was created from scratch, I like it”
  • User 2
    • “Some courses have less information that other ones”
  • User 3
    • “Good, but it could be even better with more interaction features”
  • User 4
    • “Lol, Your roadmap updates 100 times a second, you should fix your rerender”

• ♾️ General Impressions

  • User 1
    • “I think that this project have a good future. I think you will get an A”
  • User 2
    • “Good project, if you add more interactive features it would be perfect”
  • User 3
    • “Well, If you will work on it and add feature for automatic CV generation you totaly will succeed”
  • User 4
    • “I like it. Wish good luck on final presentation”

In case if you want to try our project visit kizak.ru

🔎 Analyze #

• Server lack of resources (not enough RAM)
• Black theme required
• More user statistics required
• Dataset with larger size required

💅🏻 Iteration & Refinement #

🚀 Implemented features based on feedback #

• Feedback feature (In progress)

  • User can send a feedback to generated roadmap
    • Exclude author
    • Exclude course

• Frontend optimisation

• Automatic course scrapping

All planned features could be found in our backlog

🏎️ Performance & Stability #

• Maximum load

  • ❓ To test this we ask our friends to DDoS our site
  • 📝 Our site can handle up to 10 active users
  • 🔮 In future we plan to increase the performance of project and upgrade our server

• Site performance

  • ❓ To test this we used utilities provided by React
  • 📝 Some parts of site were constantly updating (rerendering)
  • ✅ Our frontend developers optimised component rerender and increase site performance

• Backend and ML performance

  • ❓ To test this we will stress test our site
  • 🔮 In case of low performance, we plan to add load balancers

📄 Documentation #

• Small documantation in README
  • Contains requirements and instruction for project setup
• Built in API documentation by SwaggerAPI (works only while deployed)
  • Comes by default with FastAPI
  • Provides validation and test capabilities
• Simple markdown documentation
  • In case if SwaggerAPI not avaliable, user can see our docs

🤖 ML Model Refinement #

• Improved course ranking (see Weekly commitments)
• Added template for user feedback (see Weekly commitments)

📝 Weekly commitments #

📊 Individual contribution of each participant #

• Marsel Berheev: m.berheev@innopolis.university

  • Report
  • ML and Backend connection (see pull request)

• Maksim Malov: m.malov@innopolis.university

  • OAurh fix (see pull request)
  • JWT token (see pull request)
  • Code refactor

• Makar Egorov: m.egorov@innopolis.university

  • Automatic course scrapper (see pull request)

• Timur Farizunov: t.farizunov@innopolis.university

  • Roadmap design and API connection (see pull request)

• Sarmat Lutfullin: s.lutfullin@innopolis.university

  • Adaptive and authorisation (see pull request)

• Ulyana Chaikovskaya: u.chaikouskaya@innopolis.university

  • Course normalisation (see pull request)

• Kseniia Khudiakova: k.khudiakova@innopolis.university

  • Metrics update and course feadback feature template (see pull request)

🎯 Plan for Next Week #

• Increase dataset size
• Update deployment instructions
• Add load balancers
• Refine UI

Confirmation of the code’s operability #

We confirm that the code in the main branch:

• In working condition.
• Run via docker-compose (or another alternative described in the README.md).