The role of storytelling on language learning: A literature review
Bibliographic record
Abstract
Storytelling is one of the oldest forms of human communication, and much has been said in the literature about its effectiveness as a pedagogical tool in the development of language skills in first (L1) language, and also in a foreign or second language (L2), regardless of learners’ age or background (e.g. Isbell et al., 2004; Cameron, 2001). Furthermore, storytelling is even claimed to be more effective in language teaching than traditional teaching materials, such as textbooks. Indeed, studies generally believe that effectiveness of storytelling relies on the fact that it is fun, engaging and highly memorable, raising learners’ interest in listening to stories, as well as in speaking, writing and reading about them (e.g. Atta-Alla, 2011, Kim, 2010; Wajnryb, 2003). However, the studies in the literature generally lack specifics such as how exactly the effects of storytelling were measured or what specific skills are benefited from the use of storytelling, for example. Furthermore, the vast majority of the studies do not investigate any potential negative impact of the use of storytelling on language learning, giving us the impression that it is a pedagogical instrument that only brings a positive contribution to L2 learning. This literature review aims to provide an overview about what empirical studies say about the effects of storytelling on the development of language skills in L2, how storytelling compares to other teaching methods in its effectiveness, and 3) identify gaps in the current literature that should be addressed by future research. Addressing these questions will provide researchers and teachers with a clearer understanding about the role of storytelling in the language classroom, and, consequently, help them improve their teaching skills.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".