Monthly Archives: September 2016

Open ended Experimentation in Node.js & Angular2

Too often 2 peoples look at the same data, image or video but never have the same opinion or judgment on it. A way to correct that and align the criteria of everybody at the same level is to have a golden sample with specific criteria of what the optimal answer would be and then train on it.

The Project:

This project was to build a training app for new personnel that get hire to let them align their criteria with our specification. It was also built as a white label so we can add as many images or criteria as needed. Allowing not all criteria to be display or filled depending on what is displayed. Some criteria can be black & white (buttons) and other are more opinion based (slider).

Technical Specs:

Exemples:

When someone start the training, it will select a picture from the Database at “random” according to an algorithm to avoid displaying the same picture many time. The user will fill some or all the criteria depending on the setup.

 

Training_pict

After adding all needed criteria, the user can add details as comment on the decision if needed. There is also external resources that can be added as link to help the user make the right decision. When all is complete the user must accept or reject the image quality in general.

testing_after_accepting

click_Accept_the_img

 

 

Once the user click Accept/Reject, the system will validate and tell the user visually if he was right or not and give some example on what was wrong. Error is human so if the user want to contest the golden sample he can and add a description on why he think it should be accepted or rejected.

The list of criteria and structure (button or slider) can be easily be changed or added in the database as part of the setup of the system. New golden picture can be added via the UI if you have the manager permission or directly via the node API.

Tracking:

Many items are tracked like Success/Failure but also how many time the image was rejected and the user click accepted. What kind of criteria fail the most, what image was the most contested, what image fail the most, histogram of the number of minutes pass per image per user vs all others, number of image trained on per day/weeks/months.

trainapp_analytics Having complex analytics can help the user better understand his weakness and also help the manager understand what is the most difficult to understand for people in general.

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