Notes on open government, open data and open pedagogies

Talk about open government/data and open pedagogies including underlying philosophy, practices, benefits and challenges with specific reference to NRC

So let’s talk about open government and open data.

Geodata – MuniMall – where are our layers?

Open government, open data – tends toward confluence – centralized systems, Statistics Canada

The problem – David Eaves – open data is expensive to build, expensive to maintain.

Open Learning - The phases of openness – Taylor / Daniel – open resources, open teaching, open assessment – but… closed credentialing.

Open pedagogies – what does that even mean?

- learning design, history of – Koper, EML

- James Dalziel, LAMS

- Diana Laurillard – patterns

- Grainne Conole, open pedagogies

Process – flow – tends toward confluence

Things that are centralized… cost.

Pivot – if we want open data, we probably have to create it for ourselves. If we want open government, we have to create it for ourselves. If we want open pedagigy, we have to create it for ourselves.

OERs – models of sustainability – different approaches – but - two major models:
- publisher model
- prosumer model

Open data – RSS – but other initiatives have stalled – we need open people, open geo, etc. – the idea that what we do is open – open work, open teaching – the model of connectivism.

Open government – not some system of consultation, not some more effective way of delivering services (not ‘please tweet one idea)

But rather, a devolution, a distribution of authority, function – a way to build a ‘Moodle’ for yourself.

Note that this can be a disaster if done incorrectly – need distribution of power as well – Wikipedia is a fabulous idea is everybody is equal, a nightmare if there are ‘thought police’ editors running around.

But what would it look like? Instead of creating mechanisms to influence & hear back from some centralized government, rather, the development of mechanisms that enable us to govern ourselves.

Open pedagogies – process, flow vs. open environment-based approach. Need to resist the traditional – eg. Open textbooks, even open ‘courses’.

The great divide in AI – ‘Expert Systems’ vs parallel distributed processing. Things that embed previously discovered ‘knowledge’ vs things that think for themselves.

Instead of creating mechanisms to teach us, the development of mechanisms that enable us to learn for ourselves.

The specific skills (also = the basis for a new ‘pedagogy’ of distributed learning) (also = the basis for a new ‘democracy’ of self-governance):

- pattern recognition – seeing principles, rules, laws of nature, similarities, metaphors, etc

- ‘making meaning’ but also assigning sense, determining the basis for truth, identifying coherence, setting value or importance, assigning priorities

- action, and especially, action with tools – capacity, affordances – declaration, interrogation, warning, direction

- situating and localizing – interpreting, translating, comprehension, awareness of environment, awareness of context, recognition of alternatives, determination of value space, language

- inference and discovery – deduction and induction, explanation, definition, description

- change management – sensibility to different types of change (ie., not all change linear, not all change exponential, not all change is cyclic) – processes of change, assignment of values, setting of variables – ‘riding the wave’

These are pedagogies that ought to be thought of as a palette, affordances, a toolbox (pick your metaphor)

State-based learning design. Pedagogy as function of objects rather than systems design.