Translanguaging and Student Funds of Knowledge as a Teacher Resource
Bibliographic record
Abstract
Alberta classrooms are changing. Students present as more culturally and linguistically diverse, creating new social realities in schools today. Consequently, educators grapple with the question of how to teach students who speak languages with which they are not familiar with? Such questions illuminate the need for a reconceptualization of the role of language as a response to the progressively heterogeneous nature of the student body and the increased use of home languages in classrooms. Using a translanguaging framework (Garcia & Wei, 2014), this research involved the study of a monolingual teacher’s present-day teaching practices as he implemented the use of his students’ home language and their funds of knowledge (Moll, Amanti, Neff & Gonzalez, 1992) as a teacher resource. Its’ intent is to further current understandings of the pedagogical implications when a monolingual teacher changes his practice to utilize emergent bilingual students’ multiple languages and rich experiences. A Participatory Action Research methodology was utilized to investigate teacher change of perception and practice that included epistemological translanguaging frameworks within existing monolingual structures and contexts. The reflexive-dialogic cycles highlighted the authenticity and responsiveness of PAR to demonstrate further understandings of how a practice is produced and transformed from a monolingual teacher’s perspective. Results from the study indicates the criticality of a teacher’s willingness to explore the pedagogical possibilities of leveraging home language and “funds of knowledge” use in learning settings. It also suggests the validity of a student’s linguistic repertoires and “funds of knowledge” as a legitimate teacher resource. I argue there is space for the use of a translanguaging pedagogy with that of the teacher’s need to meet existing monolingual expectations and policies. Challenges such as the discrepancies in cultural and linguistic knowledge between teacher and student and teacher education are also discussed. If the communicative norm for students is to include personal linguistic knowledge as a path towards understanding the world, then it can be argued that it is no longer possible for teachers to isolate languages and use only one language as the medium of instruction.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| 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".