A pedagogy of multiliteracies: Young children and multimodal learning with tablets
Bibliographic record
Abstract
Abstract The data reported in this paper are part of a larger case study with children from 2 to 12 years of age, that took place over 4 years. The data reported here pertains to children in the age range 4–8 years of age in Australia. The children were from low socio‐economic schools in one Australian state. The study was concerned with providing empirical evidence about learning ecologies in which teachers designed multimodal experiences to support young children to become literate in the 21st century. Using a participant observation methodology, both the pedagogical strategies and learning experiences of young children were documented in the form of narratives of early childhood practices. The aim was to consider the potential for new learning [Kalantzis, M., & Cope, W. W. (2012). New learning: elements of a science of education (2nd ed.). Melbourne: Cambridge University Press] with new technologies, and to support teachers to use tablets to transform their pedagogies and practices in the early years. The approach taken was to use the theoretical constructs of new learning and a pedagogy of multiliteracies [New London Group (1996). A pedagogy of multiliteracies. Harvard Educational Review, 60, 66–92] as the focus for designing the new learning ecologies. In this way, we moved away from an emphasis on digital childhoods to create contexts for multimodal learning in the 21st‐century childhoods. In doing this, the documented learning stories have multimodality as a uniting element, and digital technologies are viewed as being complimentary to other resources, rather than alternatives, or in competition with, traditional modalities. The paper illustrates the ways in which these multimodal learning ecologies can work to support emergent literacy which is viewed as a foundational skill needed by all children in order to thrive in their 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".