Beyond museum walls: A transmedia approach to fostering multimodal literacy and STEM engagement in Science Discovery Children’s Museums
Bibliographic record
Abstract
As digital transformation reshapes early childhood education, Science Discovery Children’s Museums (SDCMs) emerge as uniquely positioned spaces to foster rich, multimodal learning environments that extend early literacy learning beyond the museum walls through family engagement. This paper presents a case study of an 8-week transmedia program co-designed with an Australian SDCM to foster young children’s multimodal literacy development through STEM-based family interactions across physical and digital contexts. The entry-level transmedia approach layered curated digital content and home-based activities onto an existing STEM exhibition, combining online activities, hands-on problem-solving, and museum visits. A total of 76 families, including 85 children aged five to nine, participated. Data sources included individual semi-structured interviews with 20 children and 16 adults from 15 families, along with 12 SDCM staff involved in program development and implementation, as well as digital platform analytics and social media interactions. Four narrative vignettes illustrate outcomes. Through analysis, themes were constructed to illustrate how children applied literacy practices across modalities such as gesture, image, text, speech, and material exploration, supported by parental scaffolding and collaborative reflection. The program fostered multimodal literacy development, intergenerational learning, and sustained engagement through STEM contexts. Framed within a multimodal view of literacy, computational thinking was conceptualised as a literacy practice involving the purposeful use of symbolic systems to make meaning, solve problems, communicate ideas, and construct knowledge. Children demonstrated computational thinking as a literacy practice through decomposition, pattern recognition, and algorithmic reasoning while developing scientific identities through playful inquiry. Parents became co-learners, creating reciprocal exchanges that strengthened family connections. Findings position SDCMs as transformative early childhood literacy learning environments and offer practical strategies for equitable, accessible digital engagement. This study contributes to understanding literacy as a socially situated, multimodal practice and provides a replicable, resource-efficient approach for cultural institutions seeking to extend multimodal literacy learning beyond physical boundaries.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".