Creative in finding creativity in the curriculum: the CLIL second language classroom
Bibliographic record
Abstract
Abstract Modern education is often characterized by a tension between learning and creativity (Connery et al. in Vygotsky and creativity: A cultural-historical approach to play, meaning making, and the arts, 2010). “The Arts”—if attended to at all—is often positioned as a distinct element of the broader curriculum, and separate from teaching and learning within other curricular domains. Yet, despite being largely neglected within contemporary social constructivist literature, Vygotsky’s sociocultural theory of mind (Vygotsky in Mind in society, 1978; Vygotsky in The collected works of L. S. Vygotsky (Vol. 1: Problems of general psychology, 1987)) has as its core a fundamental concern for creativity, affect, and emotion as the basis for human development. This paper argues that Vygotsky’s understanding of catharsis—in particular, the transformative potential of emotion—gives cause to rethink the qualitative nature of pedagogy, and especially the importance of “mundane creativity” (Holzman in Vygotsky and creativity: A cultural-historical approach to play, meaning making, and the arts, 2010, p. 27) at the core of teaching and learning. This, in turn, opens up new possibilities for conceiving of how creativity might be understood and realized within and across different dimensions of the curriculum more broadly. For an empirical example to explore these constructs, the paper considers data from a “content and language integrated learning” (CLIL) context. Emerging in the mid-1990s as a European response to the success of the Canadian French immersion method for teaching languages (Johnson and Swain in Immersion education: International perspectives, 1997), CLIL sets out several guiding principles for integrating second language (L2) with content to develop both simultaneously. With a focus on how Japanese mediates a unit of work on Geography, the study highlights how the integrated language/content focus affords a space for creative pedagogical engagement in terms of learners making their own creative choices on what language to use, and how it could be used, to facilitate the learning of both language and content (Bachman and Palmer in Language assessment in practice: Developing language assessments and justifying their use in the real world, 2010; Mahn and John-Steiner in The gift of confidence: A Vygotskian view of emotions, 2002).
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.026 | 0.006 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".