Listening to the Multiple Voices in an Intercultural Telecollaborative Multilingual Digital Storytelling Project: A Bakhtinian perspective
Bibliographic record
Abstract
Although a growing number of studies have recently been focusing on the affordances of digital storytelling as a multimodal tool, relatively little attention has been given to the collaborative process during digital story construction and how that may affect what the participants gain from the experience. This paper focuses on an intercultural telecollaborative multilingual digital storytelling project between pre-service French as-a-second-language teachers in Canada and university-level EFL students in Taiwan. The researchers lean on Bakhtin's concept of dialogism and Fairclough's concepts of assumption/intertextuality to look into how the international partners negotiated to accomplish digital storytelling assignments, how their own voices were expressed during the telecollaborative writing process, and how this affected their completed digital stories. The findings of this study unveil both interpersonal and sociocultural dimensions of negotiation of meaning in technology-mediated collaboration. Based on the findings, the paper discusses pedagogical challenges and prospects of using multilingual digital storytelling as a transformational tool for intercultural learning, creativity, and language development, as well as a space for voicing selves through creative literary articulation.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.022 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".