2017 in review: curating an open educational resource for sport informatics and analytics

In the last year, I have been able to spend time on most days curating the OERu course Sport Informatics and Analytics.  The ease of editing Wikieducator makes this curation a delight rather than an obligation.

One of the features of the OERu guidelines for course sharing is the inclusion of an outline for the course that contains links to all pages and sections. This outline has grown significantly in 2017 as I have added topics to the course. I am particularly interested how these links (120 at the moment) can be used as microcontent and create an opportunity for open badges in 2018.

The main sections of the course are:

Course description


Pattern Recognition

Performance Monitoring

Audiences and Messages

Ethical Issues

The Quantified Self

Using R

Visualising Data


Communities of Practice

Knowledge Discovery


Microcontent: Georgy, diagrams and sport

A picture of Geory VoronoiEach day, I try to update some aspect of the #OERu course Sport Informatics and Analytics.

This week, much of my time has been spent developing some microcontent for theme 4 of the course, Audiences and Messages.

I have been researching Voronoi diagrams and their application in sport. The journey took me back to a French paper written by Georgy Voronoi in 1908 and on to the present day.

I have produced this resource to share my discoveries and create microcontent to support the visualisation component of the Audiences and Messages theme.

This is an ongoing project. My task is to ensure I have a comprehensive list of exemplars of the diagrams in sport contexts. I would welcome any advice you may have to offer about the content.

Photo credit

Georgy Voronoy (Public domain image, 1908)

Connecting Courses as Pathways


We are trying to find a range of learning pathways in performance analysis and analytics at the University of Canberra.

I have written about our open courses and shared news of Jocelyn Mara’s Graduate Certificate in Sports Analytics.

Roland Goecke is working on a Masters in Data Science. It is two-year full-time course. At present the draft framework is:

Semester 1

  • Introduction to Statistics
  • Introduction to R
  • Pattern Recognition
  • Basic Data Visualisation

(If students wish they can exit here with a Graduate Certificate in Data Science.)

Semester 2

  • Data wrangling
  • Data Recording
  • Python
  • Advanced Statistics or Software Computing or Advanced Data Visualisation

(If students wish they can exit here with a Graduate Diploma in Data Science.)

Semester 3

  • Research Methods
  • Research Project Planning
  • Domain Specialisation (Health, Finance, Sport)

Semester 4

  • Capstone
  • Research Project

Successful completion of the four semesters leads to an award of Masters in Data Science (with a domain specialisation such as Sport).

I am hopeful that all these pathways can offer microlearning opportunities too.

For example, the OERu course in Sport Informatics and Analytics has a topic on R:

  • The whole page is here.
  • There is fourteen-page list of R resources here to support the page.

OERu design protocols enable pages to be broken down into smaller components (sub-pages) suitable for microlearning opportunities. So for R this looks like:

I see these microlearning opportunities as discrete as well as cumulative. They can fit into no certification, Open Badge, Certificate, Diploma and Masters pathways.


I am looking forward to discussing with Roland and Jocelyn how our varying pathways might converge and diverge to give students on campus and on line the optimum opportunity to engage in self-directed analysis and analytics.

I am hopeful that my friends around the world might see opportunities to connect their pathways as open learning and fee-for-service possibilities. We could a most attractive map to offer … and negotiate.

Photo Credits

Crossroads (Eric Fischer, CC BY 2.0)

ACTION Leyland National – dashboard (ArchivesACT, CC BY-NC 2.0)