Category: Visualisation

My Project

Back in June 2008, I started writing this WordPress blog (link). I had written on other blogs before and had first dipped my toes with Geocities in the late 1990s.

In 2008, I was emboldened by CCK08 (link) to explore thoughts openly about learning in a digital world. I had not considered that what I wrote would be of interest to any other reader. It was framed by the delight of thinking out loud.

This delight in thinking out loud led me to explore many ways to share openly through emerging cloud resources. Many of these accounts remain and include wikis, talks, slides, documents and data. I was even naive enough to start Facebook pages for some of my units.

Another preoccupation of mine has been the linking of ideas about learning, coaching and performing enriched by my formative experiences of social sciences, teacher education, human movement studies, performance analysis and analytics. This has led me to think deeply about how ideas are formed in social contexts. Many of my posts are about how performance analysts and their collaborators emerged at particular times and particular places and constructed knowledge.

My blog at Clyde Street continues to be my platform for this sharing. I hope to add many more posts to the 1800 produced already. My new guide is the R community that is providing exciting ways to share openly and my old guide, the ever inspiring, Stephen Downes (link).

It has been fascinating how this project has emerged and changed.

Photo Credit

Blue sky thinking (Keith Lyons, CC BY 4.0)

Writing a report

Earlier this week, Avinash Kaushik wrote about Responses to Negative Data (link). Shortly after his post was published, I found a link to a Turing Institute blog post, written by Franz Kiraly, What is a data scientific report? (link).

Both posts have helped me to think about the why, what and how of sharing observations, analyses and insights.

Franz, the author of the Turing blog post suggest that a stylised data report is characterised by:

  1. Topic. Addresses a domain question or domain challenge in an application domain specific to a data set.
  2. Aim. Data-driven answers to some domain question.
  3. Audience. Decision-makers or domain experts interested in ‘evidence’ to inform decision-making.

Franz suggest five principles that inform good reporting:

  1. Correctness and veracity
  2. Clarity in writing
  3. Reproducibility and transparency
  4. Method and process
  5. Application and context

Whilst there are some issues I take with Avinash’s and Franz’s posts, I do think they both raise some fundamental issues for us as we contemplate sharing our data-informed stories. I am particularly interested in how the curiosity and openness Avinash describes meets Franz’s five principles.

As I was concluding this post, up popped a link to Samuel Flender’s post How to be less wrong (link). This will be an excellent companion to the two posts discussed here. It also gives me an opportunity to extend my interest in Bayesian perspectives.

Photo Credit

Photo by Sandis Helvigs on Unsplash

Sharing news

Each week an O’Reilly newsletter arrives in my email inbox. I am not sure when I signed up but I am delighted I did.

This week the newsletter brought an article by Avinash Kaushik titled Responses to Negative Data (link). In it, Avinash discusses the reception of negative news and four data leadership archetypes:

  • Bubble
  • Attacker
  • Rationaliser
  • Curious

I found the Curious leadership description particularly interesting. Avinash suggests that Curious Ones have two critical attributes: they demonstrate open mindedness in the face of negative data; and they look forward.

I am particularly intrigued by the feedforward aspect of curiosity in changing times. Avinash contextualised this in his opening remark: “A decade ago, data people delivered a lot less bad news because so little could be measured with any degree of confidence”.

His next sentence encouraged me to think in pedagogical and practice terms how we might support those who are learning to analyse data carefully and thoughtfully: “In 2019, we can measure the crap out of so much. Even with the limitations of tools, government regulations, and the astonishing fragmentation of everything (attention, devices, consumption sources, identities and more)”.

I am starting to imagine all sort of learning scenarios where the ‘leader’ can receive news and respond stereotypically … and the conversations we might have to share news effectively.

Photo Credit

Photo by N. on Unsplash