Harnessing educational digital storytelling: a systematic review of cognitive and affective learning outcomes in the recent decade(2014–2024)
Bibliographic record
Abstract
Purpose Educational digital storytelling (EDS) integrates multimodal digital components (text, images, audio, music, videos) with storytelling techniques to engage learners and promote learning. Despite its increasing adoption, there is a lack of systematic discussion on effective EDS design across disciplines and learning outcomes. This study aims to synthesize existing evidence on the cognitive and affective impacts of EDS and to identify its key benefits and challenges. Design/methodology/approach A systematic review was conducted combining meta-analysis and meta-synthesis. The meta-analysis included 17 independent interventions (683 participants) from quantitative studies examining EDS effects on cognitive and affective learning outcomes. The meta-synthesis incorporated 48 qualitative studies (2,162 participants) to explore learner and teacher perspectives of EDS practices. Findings The meta-analysis revealed a small-to-medium, statistically significant positive effect of EDS on both cognitive learning outcomes (Effect size = 0.40, 95% CI [0.007–0.794], p = 0.046) and affective learning outcomes (Effect size = 0.479, 95% CI [0.314–0.644], p < .001). The meta-synthesis identified six benefits of EDS: (1) providing a joyful learning experience, (2) developing technological skills and media literacy, (3) promoting collaboration through co-creation, (4) facilitating creativity, (5) fostering self-reflection and (6) stimulating emotional responses. Two challenges were also highlighted: (1) technical difficulties in creating digital stories and (2) the time-consuming nature of media production for EDS. Originality/value This review integrates meta-analysis and meta-synthesis to assess the impact of EDS on cognitive and affective learning outcomes, focusing on recent studies to reflect current technological and educational contexts. It systematically examines multiple aspects of EDS design, such as media, story type and creator, and links these features to learning outcomes, while also synthesizing student-reported benefits and challenges to offer practical, subject-specific recommendations and future research directions.
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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 | 0.012 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".