Living in the iWorld: Two Literacy Researchers Reflect on the Changing Texts and Literacy Practices of Childhood.
Bibliographic record
Abstract
In this article we document observations of our own young children's usage of technology in their out-of-school worlds. How might these technologies and practices be changing the understandings and usage of texts and literacies of the children who enter into classroom spaces? What transformative possibilities might these home technology practices announce for teaching and learning within classroom environments? In both Canadian and Australian curriculum documents, as well as in OECD reports, the need to develop innovative approaches to educational practices and the inclusion of digital technologies is acknowledged as necessary in facing 21 st century challenges. We provide examples linking to media news stories in both countries, addressing the use of touch-screen technologies in schooling and examine how these presentations are very different from the practices we have observed in our homes, where the children have relative openness and freedoms with their device usage. Within the article we demonstrate, using media links and images, the ways in which our own children have begun to navigate digital devices and texts and to create new sorts of narratives that open possibilities for literacies in multiple ways, as creators, designers, and experts. We argue that, once translated into classroom practice, technological tools tend to be domesticated by practices that resist the transformative affordances of these tools, and may even provide barriers to student engagement and practice. Finally, we conclude the article by making some practical suggestions for creating opportunities for transformative technology use in education.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.020 | 0.019 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.003 | 0.009 |
| 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".