Translanguaging as a mode of operation : About possibilities and challenges when using translanguging in Elementary School
Bibliographic record
Abstract
The aim of this research compilation is to seek further understanding of how translanguaging can be used as a method to assist students learning in Swedish elementary schools. In order to gain understanding I have read and thematically analyzed ten different research studies, eight of them set in a Swedish context, and two international studies from Canada and the Netherlands. Together these studies show a number of possibilities that can be seen and then implemented when using translanguaging and describe some of the challenges both teachers and pupils face. They also identify an array of translanguaging classroom practices that are used. The results show that translanguaging as a practice can work in both organized lesson plans and with a more spontaneous approach. The use of translanguaging seems to be beneficial for both language learning and knowledge development, as it draws on and connects to the pupils´ antecedent knowledge and experiences. At the same time some teachers are hesitant to adhere to a translanguaging approach, finding it a challenge to dare using languages unknown to themselves, or to organize activities in a classroom wherein many languages are spoken. Even some students find it hard to get used to a multiple language approach, though most, teachers, students and parents, are positive to translanguaging. This research compilation also reveals some areas where additional studies would contribute to an even deeper understanding of how translanguaging as a practice can be further developed and used in order to assist the learning and language development of elementary school students.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".