Designing for Networked Learning in The Third Space
Bibliographic record
Abstract
The focus of the argument in this paper is first situated in an allegory based on Van Gogh’s Expressionist masterpiece, The Yellow House, in that, our argument shares Van Gogh’s theme of looking for a home for a diverse community, engaged in a shared social movement, imagined/acted upon to evoke change. Our argument is fraught with commitments, investments, hopes, debates, rifts, and conflicts involved in the tentative, emergent nature associated with social movements. Within this diverse and contested context, networked learning praxis is set apart from mainstream e-learning and educational technology theories and practices. The problem of designing learning, in general, and designing for networked learning, in particular, is critically examined through a comparison of the projects, histories, and tenets of instructional design (ID) and learning design (LD). Associated notions of teacher-centred, learner-centred, and community/context-centred approaches to design are compared. Contrasts are drawn and commonalities are identified. The shared LD/ID claims that their projects are pedagogically neutral is interrogated. We then introduce Third Space theory as a way to open a dialogue between ID/LD proponents/researcher-practitioners. Third Space theory begins with abandoning aspirations for emergence of consensus from difference, arguably a practical stance to take when dealing with wide-ranging diversities across multicultural, interdisciplinary, international contexts. Having abandoned consensus, Third Space theory is directed toward ‘multilogues’ that promote boundary crossings and hybridisations, which can result in the emergence new “presences”: newly co-constructed ways to identify and accomplish shared goals. If we conceptualise The Third Space as, (Dare we suggest, an Expressionist social movement?), then based on historical examples of earlier social movements, it is relatively safe to suggest that this space too will likely be marked by misunderstandings and incommensurabilities. Third space ‘multilogues’ will involve participants sometimes talking ‘past each other’ rather than ‘with each other.’ We can expect substantive disagreements and retreats to previously held positions prior to arriving at places of mutual recognition, and perhaps even one or more forms of reconciliation. The paper concludes with an invitation for LDs and IDs to enter The Third Space with a view to finding varied, but sustainable, hybridised conceptualisations of design theories and practices that can contribute to designing future opportunities for networked learning across multicultural, multilinguistic, international, interdisciplinary context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.042 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".