Putting Multiliteracies Into Practice: Digital Storytelling for Multilingual Adolescents in a Summer Program
Bibliographic record
Abstract
In this article we demonstrate how we created a context in which digital story- telling was designed and implemented to teach multilingual middle school stu- dents in the summer program sponsored by a local nonprofit organization, the Latin American Association, in a city in the southeastern United States. While implementing the notion of multiliteracies (New London Group, 1996) in the Digital Storytelling classroom, we designed tasks and activities that were aligned with the four components of a multiliteracies pedagogy (i.e., situated practice, overt instruction, critical framing, and transformative practice) in order to engage the students in exploring their multiple literacies and identities by using multiple semiotic modes and resources (e.g., texts, images, and sounds). Our digital sto- rytelling lessons show that multiliteracies practices can be a powerful venue for second-language learners and teachers. We further discuss how multiliteracies practices like digital storytelling can be adapted to other educational contexts.Dans cet article, nous expliquons la conception et la mise en œuvre d’une nar- ration numérique pour enseigner à des élèves plurilingues à l’élémentaire dans le cadre d’un programme d’été parrainé par un organisme local à but non-lu- cratif, la Latin American Association, dans une ville du sud-est des États-Unis. Pendant la mise en œuvre de la notion de littératies multiples (New London Group, 1996), nous avons conçu des tâches et des activités conformes aux quatre composantes d’une pédagogie axée sur les littératies multiples (c.-à-d., une pra- tique localisée, une pédagogie ouverte, un encadrement critique et une pratique transformative) de sorte à engager les élèves dans l’exploration de leurs littératies et leurs identités multiples par l’emploi d’une diversité de modes sémiotiques et de ressources (par ex. textes, images, sons). Nos leçons basées sur la narration numérique démontrent que les pratiques axées sur les littératies multiples peu- vent constituer de puissants outils pour les apprenants et les enseignants en langue seconde. Nous terminons par une discussion des possibilités d’adapter les pratiques axées sur les littératies multiples, comme la narration numérique, à d’autres contextes pédagogiques.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".