Team Name: 

What was our goal?

Consensus was born out of a passion for data as a learning mechanism for the world around us.  We feel people will benefit more from data if there is a way to engage with it, so we chose to create a quiz that uses data from multiple levels of government to make learning about the world around us a more enjoyable and entertaining experience.  We feel we have come up with a service that will help Australians get a better understanding of not only how much their own perceptions are in line with various statistics, but also how accurate the perceptions of other participants are .  

There were a few clear goals we aimed to meet with the way we presented the answers in the Minimum Viable Product (MVP):

1) We wanted to show the users guess in relation to the correct answer.  The hope was that the correct answer may be different enough to their expectations that it may pique there interest to either remember the correct answer, or learn more about the topic through further research.  Most of all we wanted a user to be confronted with what they thought as opposed to what was the reality in a way they could quantify easily.  

2) Every quiz will give you a question and answer.  What's special about is that it gives the user a means to compare their response against the rest of the quiz participants.  The use of Social Comparison Theory in our design aims to give the user greater incentive to learn more on a subject, and also share the quiz and their results with other people, virally growing the use of the product and therefore social awareness.  By focussing on more than just a "right or wrong" result we have a chance to expand the value of engagement beyond just "how much do I know about a subject" to "how well does society at large understand this subject".  This can also be incredibly useful to the government in assessing how well their communication on a wide variety of subjects is getting through to the collective public.

What did we do?

For this MVP we have taken questions from a number of local (Brisbane), State (QLD) and National/International datasets.  This demonstrates the potential of the product to show contextual information no matter where the user is located.    

Using the d3.js library we created a front end for the quiz which fetches questions from a Google Fusion table.  The user drags a slider to the answer they think is correct, and the answer is then recorded and sent back to the database. We manually populated the Google Fusion table and all questions and answers were selected from real data made available for GovHack.  When each user answered a question we compared their answer to the correct one and gave them an indicator of how far off they were, and we also showed a graph that lists how often every other available answer had been selected.  

What would we do with more time?

We see a lot of potential for expansion on the MVP we have submitted for the competition.

  1. Future development would source the questions programmatically from Government datasets and insert them into written question templates, allowing automated generation of new questions.
  2. Social media sharing and invitations would be added to allow for easier social sharing of results and to facilitate invites and viral growth of quiz participants
  3. Relevant links would be added when each answer is displayed in order to direct the user to learn more about the subject matter.  
  4. We would ask the user for relevant demographic information and tailor the questions to their locale, or even pre-selected interests for that user. 
  5. Results will be fed back to various Government departments to help communicate public understanding of their focus area.
  6. User will be able see how they rank in categories of data and be able to be served a category which is just data from their immediate region (as many of the data sets contain location)

  7. Allow players to create accounts to track their progress in different categories over time.

  8. Add a multiplayer option where you can directly challenge friends in a given category.










Datasets Used: 
ATO tax data - QLD Community Preparedness Survey - ABS population and economic data - GRIM - Health Expenditure in Australia - Australian Cancer Incidence and Mortality - Australian hospital statistics 2012-13 - Youth detention population in Australia 2013 - Intercountry Adoptions in Australia - Brisbane City Council - ​Brisbane City Council - ​Brisbane City Council - ​Brisbane City Council - Queensland Government - Queensland Government - Queensland Government - Queensland Government - ABS population and economic data - Australian Charities and Not for Profit Commission: Queensland Government Traffic Infringements:

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