Tipping the canoe: What can be learned from a postdigital analysis of augmented and virtual reality in networked learning?
Bibliographic record
Abstract
In this paper, the authors use a postdigital lens to examine augmented (AR) and virtual reality (VR) as potentially effective tools for networked learning. The postdigital perspective suggests that the ‘digital’ is so pervasive that it is no longer considered novel or noteworthy; rather, it is so embedded in our day-to-day lives that it now evades notice. This examination draws upon the concepts of analogue and digital to explore ontological and epistemological characteristics of AR and VR as well as how media and materials may shift on a continuum or manifest both characteristics concurrently. Two vignettes are used to create a shared context and atmosphere from which to consider the pedagogical use of these technologies. One vignette describes a VR app that invites the learners into a canoe where they are immersed into a lesson about Indigenous constellations; the second describes an AR app in which the learners direct their smartphones up at the sky also to learn about constellations. While the learning goals are similar, the experiences are differently nuanced. The paper offers a discussion of considerations that may be useful in designing learning experiences with these technologies. The authors discuss the analogue and digital characteristics as well as the freedoms and constraints relative to sites of learning, activities, learner configurations, and representations of learning. A postdigital analysis benefits from ‘shifting work’; that is, much can be learned from shifting between analogue and digital. Such shifting may surface failures, depletion of resources, and the emergence of new entities. The value of examining the digital, analogue, ontology, and epistemology of AR and VR is that it helps to make the human-technology relationship more perceptible. In becoming more aware of the taken-for-granted aspects of learning technologies, it is possible to more effectively design for learning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.029 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".