Multimodal CLIL approaches in teaching Canadian literature: enhancing language and literary competence
Bibliographic record
Abstract
This article investigates the integration of multimodal strategies into a Content and Language Integrated Learning (CLIL) framework for teaching Canadian Literature in Ukrainian higher educational institutions. The study addresses a gap in current research by exploring how multimodal approaches – combining visual, auditory, textual, and digital elements – can support both language acquisition and literary engagement among university students. Canadian literature, with its focus on themes of identity, cultural hybridity, and migration, offers rich potential for such interdisciplinary and language-sensitive instruction. The aim of the study is to analyze theoretical and methodological foundations for implementing multimodal CLIL pedagogy in literature classrooms. To achieve this, the research employs methods of content analysis, generalization, and systematization, focusing on contemporary scholarship in linguistics, semiotics, and education with the particular attention to the concept of scaffolding as a core principle of CLIL methodology. Findings suggest that multimodal strategies serve as effective scaffolding tools in CLIL environments, enabling students to access and interpret complex literary texts more effectively. These strategies enhance language competence, support comprehension, and foster critical cultural awareness. The study proposes a model for integrating multimodal resources – such as visual modes (infographics, videos and interactive digital tools) – into the teaching of Canadian Literature in Ukrainain higher educational institutions. Observation shows that using infographics and videos supplements students’ understanding of sociocultural environment of the events as well as enhances language competence and the ability to analyze literary output. Interactive digital tools like creating a book trailer facilitates students’ understanding of the problems raised by the author, develops their critical thinking and the ability to pass judgement, which aims at developing literary competence. The article concludes by outlining practical implications for curriculum design and offering directions for future research on multimodal CLIL practices in literature education.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".