Visualizing Academics Without Borders’ Impact

Welcome to the official opening of dataconfab, where I plan to post lots of random and interesting things about data… or at least interesting to me! And for this first post, I’ve chosen to talk about my submission to one of the #vizforsocialgood projects supporting Academics Without Borders.

About vizforsocialgood and Academics Without Borders

Vizforsocialgood helps organizations to promote social good and understand their own data through data visualization. I joined #vizforsocialgood, as a volunteer 3 years ago, with my very first submission for the State of the World’s Children Report, which was also my first tweet ever and my first viz published on Tableau Public Gallery. I recently started a local Chapter in Luxembourg.

The last project was dedicated to support Academics Without Borders (AWB) in their mission to help people in the developing world through higher education. Founded in 2007, AWB helps universities in the least developed countries following a unique model, in which they do not bring people to Canada to learn, nor do they drop experts in during times of crisis, but instead, they are involved in strategic initiatives that enhance the ability to develop nation universities to succeed on their own.

Visiting Instructor vs. Teach-the-teacher approach

This unique approach is what I thought it would be more impactful as a data viz, so I decided to focus on comparing the impact of the traditional “Visiting Instructor Approach” to the “Teach-the Teacher” model by AWB.

Visiting Instructor Social Impact multiplier

1) 1 expat nursing faculty member teaches one class of students on a single extended visit (perhaps one semester)
2) 25 students in the class
3) Each of the 25 graduates (now nurse social workers) works with an average of 50 clients per year
4) 1250 clients (= 1 class X 25 graduates/class X 50) are served in the first year and subsequent years as long as the graduates continue to work in their field
5) Further trained graduates would only be possible with the return of the ex-pat faculty member

AWB Teach the Teacher Social Impact Multiplier

1) 1 AWB volunteer faculty member works with 5 local faculty members to develop new program curriculum and and practicums in a mixture of online and onsite work
2) 1 AWB volunteer faculty member co-teaches program with local faculty on a single extended visit (perhaps one semester), thus training them on how to teach the course
3) Each of the 5 faculty members then teach classes of 25 students in the program for a total 125 students per year
4) Each graduate of the new specialization program (now nurse social workers) then works with an average of 50 clients per year
5) 6250 clients (= 5 classes X 25 graduates/class X 50) are served in the first year as a result of the project, but…
6) Program is offered in each subsequent year by AWB trained local faculty with the same impact resulting in 125 graduates per year who each serve 50 clients
As a result the impact of the AWB project grows annually by the graduation of additional nurse social workers. The important impact is that trained local faculty both offer the program themselves and pass on teaching capacity to other faculty members and the program is sustained

Based on the descriptions above, the first step was to calculate the number of stakeholders involved in each model for years 1 and 2 (this could be extended to as many years as patience and time we have to do the calculations and create the data sets!)

Sorry about that…I’ve done the calculations on paper! For some things I’m old school. And to make quick calculations, I think better on paper. I will later use the totals to create the data set and to add the highlighter at the bottom of the circles.

Generating the data

The idea was to show two big circles, one next to the other, comparing the two methods for years 1 and 2. And I wanted to show in each circle one dot per person involved. There was no underlying data to create this viz, so I had to generate it myself to support this visualization.

In order to have one dot per person on the charts, I have to generate a dataset with one line per person. Additionally I need to add the year (I have done the calculations for 2 years), the method Visiting Instructor or Teach-the-teacher, and one id: Person id. Additionally, I use a stakeholder id, to facilitate the creation of the file, since the calculations above give the total numbers by group of stakeholders.

Data visualization in Tableau

First things first, the inspiration and use of calculated fields for the creation of this viz come from this fantastic viz by @p_padham.

In order to create the randomly distribute dots around the circles we use four calculated fields:

1) JitterIndex: INDEX() Calculated along Person ID

2) JitterRandom: Random() to randomize position of the dots.

To get a circle:

3) JitterX: SIN(RADIANS([JitterIndex]))*[JitterRandom]

4) JitterY: COS(RADIANS([JitterIndex]))*[JitterRandom]

First, we add to filters: year and method. We will duplicate the steps below, in order to create two circles, one per method; and then we will add the year filter to navigate through the impact of both methods in Year 1 and Year 2.

Next, we add Person ID and Stakeholder to Details, Stakeholder to Size and Color, JitterX to columns and JitterY to Rows.

Finally, to format the circles, I decided to use the same colors they use on the AWB website, to make the less frequent values bigger, and to bring them to the front.

And we repeat the same process for the other approach:

Next, we will create two navigation lines, one per method, that we will use on the final dashboard as highlighters.

We drag Stakeholder to Text and change the Mark to Line, then drag again stakeholder to colors and the number of people to Label.

And that’s it! We are ready to build the final dashboard:

In order to use the total lines as highlighters, we need set a Dashboard action for each of them:

A last detail, I activated the animations in Tableau to show the transitions between years more effectively. Format > Animations > On.

You can explore and download the final viz here.

I also had the opportunity to present my viz at the #vizforsocialgood online event. If you are interested on watching other fantastic visualizations on the subject, you can wath the recording of the presentations here.

Published by Aida Horaniet

Data analyst. In love with stories, mostly the ones told by the data.

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