“The Whole ME presented itself. KABOOM!”: Expressive Arts and Critical Reflection
Bibliographic record
Abstract
The importance of critical reflection in higher education highlights the importance of creating rich learning opportunities for students. Expressive arts (e.g., poetry, drama) ignites such opportunity drawing from more than students’ logical-cognitive understandings to include students’ creative, multi-modal and experiential capacities. This paper provides one university instructor’s reflective account of how an expressive arts final assignment (in a non-arts course) invited students to use the languages of the arts to enhance their critical reflection of course learning. Students’ expanded agency and complexity of understanding illustrated expressive arts as a valuable facet of academic work. L’importance de la réflexion critique en éducation postsecondaire rehausse l’intérêt de créer des occasions riches en apprentissage pour les étudiants. Les arts expressives (p. ex. la poésie, les arts de la scène) offrent de telles occasions en allant chercher au-delà des connaissances logiques et cognitives des étudiants pour puiser dans leurs capacités créatrices, multimodales et expérientielles. Cet article présente le compte rendu d’un professeur à l’université sur le rôle d’un travail final reposant sur les arts expressives (dans un cours ne relevant pas du domaine des arts) qui a encouragé les étudiants à utiliser le langage des arts pour mettre en valeur leur réflexion critique du cours. La marge de manœuvre élargie des étudiants et la complexité accrue de leurs connaissances reflètent le rôle important des arts expressives dans le travail académique.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".