Digital Storytelling in Education: A Systematic Review of the Literature
Bibliographic record
Abstract
Digital Storytelling is one of the most recurrent art media used today to give a voice to invisible, silenced or marginalised groups. In this way, this methodology is framed within the cultural and educational sphere, and through a narrative approach it is capable of integrating multi-format resources that project information and stories of great richness from a hypermedia language. In fact, digital storytelling is occupying a priority role in identity studies in education. In this paper, we present a systematic review of the literature on digital storytelling in education. The methodological process was based on the PRISMA protocol. The results offer a qualitative and bibliographical synthesis of educational studies focused on digital storytelling. Firstly, there is a growing interest in the development of lines of research that focus on digital storytelling as a privileged vehicle for exploring digital stories. This is based on the premise of giving voice to the voiceless, a priority aspect in cultural, educational and social research. For this reason, throughout the systematic review we explored different research that decided to study these discourses from a democratic and participatory approach. Finally, we encourage future research to explore this new resource further.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.022 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".