IVIS 2017 Students

As a first step I cleaned the data and reduced some of the dimensions. I found the information about what one is hoping to get out of the class irrelevant to group formation, therefore I completely removed it. Initially there are 9 dimensions of the quantitative data. This can be reduced to three main dimensions: mathematical/data skills (IVIS, Stats, Math), computer/programming skills (User, Prog, Graphics), artistic/user facing skills (HCI, UX, Art).

The x-axis represents the total average score for each student while the y-axis their score in their strongest domain. Colours indicate which domain students belong to in terms of their strength.

As data is based on self-reporting I thought exposing uncertainty in the data is important [2]. For students who have extremely high or low overall average this might be an effect of difference is self-confidence and therefore their highest score has to be looked at with an eye on their overall average.

This representation allows the performance of most low-level components of analytic activity (retrieve value, filter, find extremum, sort, determine range, distribution) as defined by Amar et al. [3].

The solution follows the Visual Information Seeking Mantra: overview first, zoom and filter, then details-on demand (zoom is unfortunately missing - lack of time) [1]. One can filter which domain they would like to see on the visualisation by clicking on the respective button - this way allowing better comparison within the domain.

Details on demand is implemented in a tooltip style: when hovering over a node one can find extra information: this case the Alias and the person’s interest. To allow better access to the details the node also changes color as we hover over it.

[1]Shneiderman, Ben. "The eyes have it: A task by data type taxonomy for information visualizations." Visual Languages, 1996. Proceedings., IEEE Symposium on. IEEE, 1996.

[2] Amar, Robert, and John Stasko. "BEST PAPER: A knowledge task-based framework for design and evaluation of information visualizations." Information Visualization, 2004. INFOVIS 2004. IEEE Symposium on. IEEE, 2004.

[3] Amar, Robert, James Eagan, and John Stasko. "Low-level components of analytic activity in information visualization." Information Visualization, 2005. INFOVIS 2005. IEEE Symposium on. IEEE, 2005.