Integrating Visual Arts and Music to Help Adult Students Learn English in Canada
Bibliographic record
Abstract
Canada has recently seen an influx of newcomers who do not speak either of Canada’s two official languages—English and French—many of whom are adult English additional language learners (EAL). Though there are numerous studies on how to support young EAL learners, there is a dearth of literature on adult EAL learners, which is critical as the two populations have drastically different cognitive processes with respect to language learning. Thus, by analysing a critical literature review, the current study considers how multimodal practices and multiliteracies approaches can support this population. Anti-oppressive practices are likewise applied to identify the barriers that students may encounter in the classroom. The study concludes that incorporating the arts—specifically auditory practices that include music and visual learning strategies that include painting and image-rich content—can support adult language learners. Specific strategies that have proven to be effective include the use of song with strong rhythm and rhyme, dance, painting exercises, videos, video journals, highlighting and colour coding words, and technology-based engagement. When using these, it is proposed that teachers should focus on developing learners’ metalinguistic vocabulary to allow them to effectively cognize about the language process and give students the tools to understand elements of word structures, such as suffixes, to allow them to identify the meanings of words based on their context and structure. Teachers should also engage in critical self-reflection and solicit student input when designing lesson content. To ascertain the effectiveness of such approaches, future research should focus on experimental studies that compare traditional and multiliteracies classrooms, and qualitative and longitudinal studies to identify the nuanced ways that students engage with multiliteracy practices and their long-term impact on adult EAL learners.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".