The Impact of Social Networking and a Multiliteracies Pedagogy on English Language Learners’ Writer Identities
Bibliographic record
Abstract
This study examined the impact of using a multiliteracies pedagogy and the social networking site (SNS), Ning, to help 6th grade English language learners (ELLs) develop their writer identities, with the purpose of increasing the students’ confidence, sense of self, and language and literacy skills. To this end, we were interested in whether and how the development of a writer identity and an increase in social presence on the Ning would translate into face-to-face connections in the physical classroom and an induction into the academic learning community – a space in which the students may have previously felt intimidated. In doing this, we employed a qualitative case study analysis to investigate the experiences of two ELLs at an elementary school in Toronto, Canada. Our study found that incorporating multimodal tools and an SNS allowed the students to more freely express themselves; to share their work and their personalities with peers, which made the writing assignments more meaningful and engaging; and provided a platform for students to negotiate their values and beliefs. Ultimately, the increased interactions with peers online and the development of this new English-language literate identity translated into the development of students’ individual voices, a sense of ownership of English, and an increased social presence in the classroom.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".