#EL30 Graphing

Week 3 of Stephen Downes’ E-Learning 3.0 course is looking at Graphs.

Stephen recommended some resources for this topic. These included:

Vaidehi Joshi’s (2017) gentle introduction to graph theory. In her discussion of graphs, Vaidehi observes “in mathematics, graphs are a way to formally represent a network, which is basically just a collection of objects that are all interconnected”.  She distinguishes between directed graphs and undirected graphs and explains the ways edges connect nodes in these kind of graphs. An example of the former is Twitter (each edge created represents a one-way relationship), and of the latter Facebook (its edges are unordered pairs).

Vaidehi suggests a number of resources to provide details about graphs, one of them is Jonathan Cohen’s slide show Graph Traversal. He defines a graph as a “general structure for representing positions with an arbitrary connectivity structure” that has a collection of vertices (nodes) and edges (arcs). An edge connects two vertices and makes them adjacent.

A second resource shared by Stephen is Fjodor Van Veen’s (2016) Neural Network Zoo. In his post Fjodor shares a “mostly complete chart of Neural Networks’ and includes a detailed list of references to support his visualisation of the networks.

A third resource continues the visualisation theme. Vishakha Jha (2017) uses this diagram to inform the discussion of machine learning:

A fourth resource recommendation is Graph Data Structure and Algorithms (2017). This article aggregates a large number of links to graph topics. It includes this explanation:

One of the E-Learning 3.0 course members, Aras Bozkurt, exemplified this theme in this tweet and in doing so underscored the skills available within self-organising networks :

It was a great way to end and start conversations about graphs.

Photo Credits

Title image is from Gonçalves B, Coutinho D, Santos S, Lago-Penas C, Jiménez S, Sampaio J (2017) Exploring Team Passing Networks and Player Movement Dynamics in Youth Association Football. PLoS ONE 12(1): e0171156. https://doi.org/10.1371/journal.pone.0171156

Other images are frame grabs for the resources cited in this post.

A Coaching Mind

Lewis Lapham starts his post about the mind’s ability to reinterpret the past with a quote from Jeffrey Eugenides: “Biology gives you a brain, life turns it into a mind”.

Lewis’s post and Michio Kaku’s (2014) Future of the Mind prompted my thoughts about a coaching mind and consciousness.

I sense that these philosophical issues will become more important as coaches deal with performance data flows in their daily working environments. For example, Lewis’s observes:

The scientific-industrial complex focuses its efforts on the creation of artificial intelligence—computer software equipped with functions of human cognition giving birth to machines capable of visual perception, speech and pattern recognition, decision making and data management.

He adds:

Mind is consciousness, and although a fundamental fact of human existence, consciousness is subjective experience as opposed to objective reality and therefore outdistances not only the light of the sun and the moon but also the reach of the scientific method.

Michio notes “human consciousness … creates a model of the world and then simulates it in time, by evaluating the past to simulate the future”.

In my concept of a coaching mind, I have a sense of coaches’ experiencing different kinds of consciousness. Alain Morin (2006) identifies four kinds of consciousness that help me reflect on coaching as an emerging experience and learning journey:

  • Unconscious: being non-responsive to self and environment
  • Conscious: focusing attention on the environment; processing incoming external stimuli
  • Self-awareness: focusing attention on self; processing private and public self-information
  • Meta-self-awareness: being aware that one is self-aware

Alain concludes his paper with a recognition that there are other conversations to be had:

A great deal of effort still needs to be deployed in order to examine and compare additional consciousness-related concepts such as “meta-cognition”, “higher-order thought,” “autonoetic,” “visceral,” “first-order consciousness,” and “immediate self-awareness.”

Lewis, Michio, and Alain have helped me reflect on how coaches might flourish in an occupational culture that will extend the reach and application of artificial intelligence.

I am hopeful that the move towards and within meta-self-awareness might help us discuss how we, as mindful people, create our own intelligence augmentation and achieve a symbiosis with the tools that we integrate mindfully in our praxis.

Photo Credit

In the mind’s eye (Robert Couse-Baker, CC BY 2.0)

Feedback Loop (Robert Couse-Baker, CC BY 2.0)

Documenting and Sharing

Signal Noise, The Economist and Siemens have worked together to visualise the fan energy in FC Bayern Munich’s Allianz Arena.

The visualisation includes: game timelines; fan energy; highlights; players; and social ripple. The visualisation provides the user with a rich array of options.

I think this is a great example of the analytic turn in sport and highlights the data expertise available to sport.

Earlier this year, Signal Noise hosted a Data Obscura exhibition that explored the relationship between data and truth. The exhibition was launched with a panel discussion that considered whether transparency and truth should be the ultimate aim online, and asked “how much is ‘true enough’?”.

This interplay between practice, epistemology and ontology is fundamental to anyone contemplating a career in sport analytics at a time when:

Multiple filters are applied to the information that we see: algorithms distill a world of opinions to give us a distinct view of events, and authenticity is becoming an increasingly scarce commodity. (My emphasis) (Data Obscura, 2018)

This contemplation could lead to a consideration of epistemic cultures and the machineries of knowledge construction. Karin Cetina (1999) writes:

Everyone knows what science is about: it is about knowledge, the ‘objective’ and perhaps ‘true’ representation of the world as it really is. The problem is that no one is quite sure how scientists and other experts arrive at this knowledge. The notion of epistemic culture is designed to capture these interiorised processes of knowledge creation. It refers to those sets of practices, arrangements and mechanisms bound together by necessity, affinity and historical coincidence which, in a given area of professional expertise, make up how we know what we know. Epistemic cultures are cultures of creating and warranting knowledge.

This process involves what Maurizio Ferraris (2006) defines as ‘documentality’. For Maurizio, documents are social objects (such that they involve at least two persons) “characterised by the fact of being written: on paper, in a computer file, or simply in people’s heads”.

His theory develops in three different directions:

  • an ontology (“What is a document?”)
  • a technology (an explaination of how documents are distributed)
  • a pragmatics (an understanding of the efficient distribution of documents)

Sharing the Signal Noise, The Economist and Siemens venture into the Allianz Stadium here has led me to reflect on learning journeys.

The volume and quality of data analysis opportunities positions this generation of data analysts in sport in a very important ontological and pragmatic space.

There are more ways to share primary data and analysis than ever before. Each of us can make an informed and transparent decision about the machineries we choose to construct information sharing and stimulate conversations about knowledge and understanding.

In my case, I use the WordPress blog platform to connect ideas that strike me as important. I discovered news of the Signal Noise project on Twitter. The tweet came as I was re-reading Maurizio Ferraris and editing the Ethical Issues page of the wikiEducator course Sport Informatics and Analytics. In sharing this process openly, I am hopeful that readers can make informed decisions about authenticity and contemplate these issues as worthy of consideration.

Photo Credits

Frame grab Reimagine the Game

FC Bayern (Twitter)