Supporting Multimodal Literacies in Early Learning Settings: A Case Study of Two Child Care Centres in Alberta
Bibliographic record
Abstract
In early learning settings, multiple modes of communication are used to help young children convey meaning. These modes, or multimodal literacies, include signs, images, gestures, sounds, speech, movements, and actions. In this doctoral research, I explored how early learning and childcare educators support multimodal literacies in young children. Using a multiple case study, I utilized video walk-throughs of eight different educator playrooms, interviews with early childhood educators, and pedagogical documentation collected from educators to further my understanding of how multimodal literacies are supported in early childhood settings. The findings of this study revealed that educators conceptualize multimodal literacies differently; however, they include agency, embodiments, intentionality, and play as key aspects of children’s multimodal literacies. Conceptualization and understanding of the multiliteracies pedagogy are also examined. The findings also showed that educators of young children use multiple strategies to support multimodal literacies including pedagogical documentation, responsive environments and a co-inquiry model of noticing, naming, and nurturing. Lastly, my findings reveal that educator participation and finding a balance between supporting play and ideas and following children’s lead in play is critical in supporting multimodal literacies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".