On the periphery but listening

Wikipedia notes that legitimate peripheral participation “describes how newcomers become experienced members” of a community of practice” (link).

Zygmunt Bauman (2001) (link) has explored the characteristics of a community. He suggests:

Words have meanings: some words, however, also have a ‘feel’. The word ‘community’ is one of them. It feels good: whatever the word ‘community’ may mean, it is good ‘to have a community’, ‘to be in a community’

I do like this view of community and its has one that has struck me forcefully over the last decade as I have engaged with communities of practice particularly in open courses, open educational resources and the emergence of the hashtag in Twitter (link).

I feel there is nothing wrong with this mix of community, peripherality and centrality. I do think each of us adapts to a role that moves us close to being a driver of a course or series of engagements … or moves us away to a comfortable space where we are monitoring and checking.

I do think this develops as we become more experienced in openness and we start to be selective about what and how we share. And as we relax.

In the last decade I have had two amounts of optimal engagement as direct participant (CCK08) and driver (Sport Informatics and Analytics). More recently, following but not contributing to conversations as a joyful peripheral participant in the #RLadies exchanges on Twitter (link).

I see this latter community as the exemplar of how we might share and where we might go with a continuum of engagement.

I am mindful that divers do have different needs to participants and I understand the desire to change participants’ perceptions of digital presence as a course or resource leader.

However, I do think that in the last decade we have been been provided with a social media that does extend Edward Ayres’s (2013) (link) conversations about digital scholarship, Martin Weller’s consideration of the digital scholar (link) and the changes in academic practice that result from the use of a new technology.

Photo Credit

Samuel Sianipar on Unsplash


My son, Sam, has just written a post about systems and networks (link). I found the post really interesting in a paternal sense and an epistemological sense.

The paternal part of me is delighted to read a blog post by Sam and to learn about his observations and reflections as a member of the #INF537 (link) Masters of Education (Knowledge Networks and Digital Innovation) online at Charles Sturt University.

The epistemological delight is in my commitment to self organising networks hinted at in Sam’s post. I have written a lot about networks (link) and have been thinking about these issues a great deal since the distributed, open course CCK08 (link), and becoming an accidental connectivist (link).

I am keen to persuade Sam privately and publicly to explore self organising networks (link) and to read more about Stephen Downes’ (link) and Alan Levine’s (link) work. I appreciate Sam’s particular working environment constraints (systemic) but am determined to explore the action possibilities he can address as a community driver and facilitate network flourishing within those constraints (link).

I sense that with energy anything is possible even in constrained contexts.

Photo Credit

The Maze (Keith Lyons, CC BY 4.0)

Decision Intelligence and Data

Cassie Kozyrkov (link) has written about “decision intelligence is a new academic discipline concerned with all aspects of selecting between options”.

This decision intelligence “brings together the best of applied data science, social science, and managerial science into a unified field that helps people use data to improve their lives, their businesses, and the world around them”. Cassie views it as a “vital science for the AI era” and it turns “information into better actions at any scale”.

Cassie lists some basic terminology of the discipline:

Decisions: It’s through our decisions — our actions — that we affect the world around us. (It is “any selection between options by any entity’.)

Decision Maker: The person who is responsible for decision architecture and context framing, a creator of meticulously-phrased objectives.

Decision Making: taking responsibility for an action when there were alternative options.

Taxonomy: decision sciences (qualitative side). Cassie suggests “Think of the decision science side as dealing with decision setup and information processing in its fuzzier storage form (the human brain) rather than the kind that’s neatly written down in semi-permanent storage (on paper or electronically) which we call data“.

She adds: “Strategies based on pure mathematical rationality without a qualitative understanding of decision-making and human behavior are relatively naïve and tend to underperform relative to those based on joint mastery of the quantitative and qualitative sides”.

A large part of Cassie’s article looks at decision making and facts. She observes “with training in the decision sciences, you learn to reduce the effort that it takes to make rigorous fact-based decisions, which means that the same amount of work now gets you higher-quality decision-making across the board”. But, alas, “we live in the real world and often we must work for our information”.

She adds “Data science gets interesting when you’re forced to make leaps beyond the data…”. This involves the bringing together of diverse perspectives in decision intelligence.

It requires us, as Splunk suggests in a Priceonomics post (link) to make some important clarifications about dark data (“most of the data collected by businesses simply goes unused”) and our role in making decisions about the data we have collected and see as important.

Photo Credit

Frederick Tubiermont on Unsplash

Sylwia Bartyzel on Unsplash