In these days I am pretty busy preparing a controlled experiment for my thesis. The setting will involve two participants jointly solving a task which involves a certain degree of reasoning and coordination. I performed a couple of pre-tests using a low-tech prototype and I found that participants were discouraged after a couple of trials because they could not tell whether their proposed solution was correct.

This made me think that in complex situations we need to offer a feedback over the problem-solving process. This seems to be beneficial both for the meta-cognition process and for the engagement that the participants might have with the task. My colleague Patrick Jermann developed his thesis on the subject of mirroring and guidance systems for computer supported collaborative learning.

Subsequently, I developed my own feedback tool for the participant trying to offer some references for the execution of their task. The figure below shows two instances of the tool. The main concept is to show the history of the scores achieved with each proposed solution. The scores are displayed in a graph which evolves over time. In the task I have been designing, there are four main constraints that needs to be optimized by the participants and which contributes to the final score. Each of the four partial scores associated with each constraint are displayed with a different color to offer an additional information to the participants.

Finally the application has some networking abilities to synchronize the actions of the users across the network. The remaining time is also offered in the bottom-lower corner.

Some links:

[1] Tutorial on Threads Programming with Python

[2] Threading with Python

[3] Basic Threading with Python

Tags: controlled experiments, human communication, interaction design, interaction regulations