Supporting Young Learners Through a Multimodal Digital Storytelling Activity
Bibliographic record
Abstract
This paper presents the results of a small-scale qualitative case study that explored a tutor’s role in supporting young learners through a digital storytelling (DS) activity through Microsoft PowerPoint. The two children who participated in this study were in grade one and attended private schools in Canada. Participatory observations, field notes, interviews, the children’s narratives, and observational narratives were the primary sources of data. The children carried out a DS activity during three separate sessions for each child that involved planning the story, enacting the story, creating and editing a storyboard with cameras and computers, and lastly, celebrating the stories they produced with their family members. We found that the tutor played an important role in making the activity purposeful, authentic, and passion-led (Anderson, 2016). We also found that the tutor helped the children represent and understand meaning through an integration of modes, supported their use of technology, engaged their interest throughout the activity, and encouraged self-reflection on their narrative writing skills. Our findings point to the need for future research on how digital storytelling activities can be carried out in mainstream classroom settings, where teachers can schedule one-on-one conference sessions to support children as they become multimodal composers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".