Digital Storytelling to Unlock Reflective Practice in the Classroom
Bibliographic record
Abstract
It is a common practice that teachers tell stories in the classroom when teaching literature. They are enthusiastic in their teaching profession but students nowadays are diverse in their learning styles and they need different approaches to be taught. Therefore, this study advocates teachers to reflect on their teaching practice to use technology specifically digital storytelling as a teaching method in the classroom. The single case study involved a pre-service teacher who underwent a micro-teaching session in teaching literature. The instruments used were peer observation checklist, the artefacts of digital storytelling, video recording, and reflective journal. The findings indicated that the participant was able to be more aware of her strengths and weaknesses in the crafts of storytelling, personalise her own learning and improve her teaching practice. The implications are for the educators to encourage pre-service teachers to use digital storytelling in the classroom, provide coaching and support to improve their crafts of storytelling in the teaching of children’s literature as well as use digital storytelling as a tool for reflective practice in teacher 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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".