Navigating creativity, technology, and human-centred learning: An open, collaborative education community reflection
Bibliographic record
Abstract
We live in a postdigital world - a messy and paradoxical condition of art and media after a series of digital technology revolutions (Anderson et al., 2014, cited in Jandrić et al., 2018). ‘Postdigital’ does not mean that we have moved beyond the influence of technology, but rather we exist in a digitally saturated landscape where it no longer makes sense to distinguish, say, between education and so-called Technology Enhanced Education. Technology is a fact of our educational lives. This paper examines the postdigital classroom as a dynamic space where technology is not merely adopted for its own sake but thoughtfully integrated to foster equitable, student-centred learning. Through provocative vignettes, the authors critically explore the interplay between digital tools and hands-on, embodied practices such as making, drawing, and play. They advocate for a reimagined postdigital classroom - one that is flexible, inclusive, and co-created by educators, technologists - and students. By striking a balance between technological innovation and human creativity, this vision moves beyond passive digital transformation toward a future where education is imaginative, adaptive, and deeply humane.
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 | 0.030 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.021 | 0.070 |
| Scholarly communication | 0.028 | 0.025 |
| Open science | 0.005 | 0.029 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".