Grazing on the periphery

It has been a great week for grazing … much of it enabled by Mara Averick’s open sharing.

It started with news of Alison Hill’s speakerdeck presentation.

Alison discusses courage, enchantment, permission, persistence and trust as elements of creative learning. She concludes with this slide:

What fascinated me about Alison’s presentation was her synthesis of profound ideas about sharing and learning with each other in an aesthetic that grabbed and held my attention for 94 slides.

She is part of a remarkable R community that shares openly.

Three other members of this community enabled even more grazing this week. Each offered me possibilities to extend my knowledge of visualisation using R.

Matt Dancho has shared the Anomalize package that enables a “tidy” workflow for detecting anomalies in time series data. There is a vignette for the package to share the process of identifying these events. I think this will be very helpful in my performance research as I investigate seasonal and trend behaviours.

Ulrike Groemping shared the prepplot package in which “a figure region is prepared, creating a plot region with suitable background color, grid lines or shadings, and providing axes and labeling if not suppressed. Subsequently, information carrying graphics elements can be added”.  There is a detailed vignette to support the package.

Guangchuang Yu shared the ggplotify package that converts “plot function call (using expression or formula) to ‘grob’ or ‘ggplot’ object that compatible to the ‘grid’ and ‘ggplot2’ ecosystem”.  Guangchang shares a detailed vignette that illustrates the potential of the package.

Mara, Alison, Matt, Ulrike and Guangchuand epitomise for me the delights in open sharing. A post in The Scholarly Kitchen, written by Alice Meadows, added to my grazing on the margins of openly sharing.

In the post Alice shares a wide range of resources. She makes a particular mention of the Metadata 2020 project that is “a collaboration that advocates richer, connected, and reusable, open metadata for all research outputs, which will advance scholarly pursuits for the benefit of society.”

The opportunities for such collaboration are increasing as we find new ways to share synchronously and asynchronously. These become easier as we make a bold decision to think out loud and share our thoughts with others.

Alison’s presentation includes this slide as a stimulus for that sharing:

This sharing permits grazing for me in the sense of the word used in Leonard Cohen’s Preface to the Chinese translation of his collection of Beautiful Losers poems includes this passage:

When I was young, my friends and I read and admired the old Chinese poets. Our ideas of love and friendship, of wine and distance, of poetry itself, were much affected by those ancient songs. … So you can understand, Dear Reader, how privileged I feel to be able to graze, even for a moment, and with such meager credentials, on the outskirts of your tradition.

Photo Credits

Slide grabs from Alison Hill’s speakerdeck.

Pictures from Twitter and Beuth Hochschule.

Collaboration image from Alice Meadow’s post.

Learning journeys and small stops

My email alerts this morning brought me links to discussions of learning journey plans and microlearning.

By coincidence, my physical journey to the University of Canberra campus today took me past the light rail building work on Northbourne Avenue, the first signs of a station stop under construction, and this poster:

The posts that took me on my metaphorical learning journey were:

Helen discusses tinkering, sense-making and creating in her post in the context of Harold Jarche’s seek, sense, share framework. She shares her personal learning plan template. Helen’s categories in this plan are:

  • Exploration
  • Reading
  • Contribution
  • Creation
  • Reflection
  • Action

Her template is available at this link.

Sharon’s post includes this visualisation of a learning journey:

Sharon will have a template to share that will require learners to plan for four phases (prepare, acquire knowledge and skill, build memory and transfer, use over time) and six steps of their journey:

  • Notice
  • Commit
  • Learn and practice
  • Repeat and elaborate
  • Reflect and explore
  • Sustain over the long term.

Shannon’s discussion of microlearning helped me think about how I might support the learning journey with small stops on the way. Shannon sought to counter seven myths about microlearning:

  • Microlearning is time-dependent
  • Microlearning is all about video
  • Microlearning is just chunking
  • Microlearning requires technology
  • Microlearning is one-size fits all
  • Microlearning is easy-peasy to create
  • Microlearning is a fad

Canberra’s light rail will have thirteen stops on the journey in phase 1 of the project. The Canberra Metro website shares this aspiration:

Light Rail in Canberra offers an exciting opportunity to transform Canberra and deliver a truly integrated transport system, which will provide more options in how Canberrans move around, which in turn will enrich lifestyles and enhance growth.

I wonder whether some of these ideas fit learning journeys too. I particularly like the idea of transformational journeys … with very flexible timetables.

Photo Credits

Frame 11 (N J Cull, CC BY-NC-ND 2.0)

Light rail coming poster 2018 (Spelio, CC BY-NC-ND 2.0)

The Analytic Turn

The volume and quality of sport analytics writing fills me with awe.

Each day I try to aggregate, curate and share examples from this analytic turn. My activity is partial and selective. I am mindful of a whole community of practice doing similar work. I see lots of comparisons in this activity with walking along striding edges.

My discoveries encourage me to continue with my learning journey and give me that Leonard Cohen feeling expressed in his Preface to the Chinese edition of Beautiful Losers:

So you can understand, Dear Reader, how privileged I feel to be able to graze, even for a moment, and with such meager credentials, on the outskirts of your tradition.

Two papers today reinforced my feeling of grazing on the outskirts.

Mark Taylor wrote about the intelligent use of numbers in football analysis. In his introduction, Mark notes “The use of data in analysing football allows us to take a more nuanced view of both the events that occur during a match and also to evaluate a side’s ongoing performance to make predictions about their future results”.

Mark’s nuanced view included:

  • Awareness of over-fitting non-sustainable events and a flawed projection of a team’s underlying quality.
  • How we might value goal-scoring and develop dynamic probabilities of win outcomes.
  • The use of Poisson calculations.

What struck me about Mark’s observations was the layers of expertise he used to share his story with an imagined audience. He prompted me to return to some of the early discussions about Poisson distributions (Moroney, 1951; Reep, Pollard and Benjamin, 1971; Maher, 1982).

My second paper comes from a friend who asked me to comment on a draft paper he is writing. The paper uses secondary data to explore winning performances in rugby union. What struck me about this paper was the learning journey my friend has made from full-time athlete to service provider as an analyst.

My friend’s paper included:

  • A classification model developed with the R package ‘randomForest‘.
  • The use of the R package ‘rfPermute‘ to estimate the significance of importance metrics for a Random Forest model by permuting the response variable.
  • Visualisation of partial dependency plots with the R package ‘pdp‘.
  • Z scores for McNemar’s test.

I spent a good part of the day exploring the data he shared. I was fascinated by his ease of use of R packages. He took me a long way from the comfort of my qualitative, ethnographic approach to performance observation and analysis.

His paper prompted me to ask him about the reproducibility of his work. I thought he might like another R opportunity … Przemysław Biecek and Marcin Kosiński’s (2017) archivist package designed to improve the management of results of data analysis.

The package enables:

  • management of local and remote repositories which contain R objects and their meta-data
  • archiving R objects to repositories
  • sharing and retrieving objects (and their pedigree) by their unique hooks
  • searching for objects with specific properties or relations to other objects
  • verification of object’s identity and context of its creation.

My friend intends to produce a number of papers about winning performances. Given his expertise in R, the archivist package would seem to be a great way to share his work openly.

Grazing at the margins of Mark and my friend’s work gave me great delight. Both reminded me the kind of learning required to be a polymath at this point in the analytic turn … and the navigation of paths along striding edges.

Photo Credits

Striding edge (The Yes Man, CC BY 2.0)

Mo Fa’asavalu (Charlie, CC BY 2.0)