rifflearning / zenhub

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Participant Score #240

Open adonahue opened 4 years ago

adonahue commented 4 years ago

As a user, I want some context and meaning for the individual metrics I get after a meeting, so I understand why it matters and what I should do.

The design goal for this story is:

Acceptance Criteria

brecriffs commented 4 years ago

Seems like more thought/discussion is needed before estimating.

adonahue commented 4 years ago

Possible Visual Displays (with some modifications) https://www.amcharts.com/demos/radial-histogram/
https://www.amcharts.com/demos/angular-gauge/ https://www.amcharts.com/demos/bullet-chart/

adonahue commented 4 years ago

@jaedoucette - can you weigh in on the description text (in story above) and make any suggestions you have about how to explain / describe the participant score? Thx!

jaedoucette commented 4 years ago

I suggest:

"Your participation score is a summary of how effectively you interacted with other meeting participants. This is a beta feature, so if you disagree with our system's assessment, please let us know!"

Ideally, we'd also want to allow the user to drag the bar to the setting they think is most accurate, and then record both the value we showed and the value they suggested in a database. We could then construct a model that would give ratings that more accurately reflected a typical user's self-assessment.

adonahue commented 4 years ago

Thanks @jaedoucette . Is there anything we can say to the user about what makes a good score and how they can improve it? E.g. turn-taking, influence and affirmations will all increase your score.

I think it would help people understand the meaning, and also take positive action.

jaedoucette commented 4 years ago

@adonahue I think that from a long term perspective, it's better not to ask people to change their behaviors, but instead to tell us what they think about the score. The individual components of the score are sound, but right now the score itself may not convey a lot of information about the best way to behave. If we get some data from users, then we can do a simple regression and find proper weights for the 4 components. With proper weights, we could then make more meaningful suggestions to the user about how they can improve.