Metacognitive Writing Strategies for Emerging Dancer-Scholars: Uncovering Supportive Links Between Academic Writing and Choreographic Processes
Bibliographic record
Abstract
Canadian graduate programs in Dance at the Masters level frequently accept students with long professional careers in dance but limited academic background in writing essays. Writing term papers, with perhaps only dim memories of high school writing instruction to draw from, can pose challenging experiences for such emerging dancer-scholars. While long standing metacognitive reading strategies are commonly available to assist those new to graduate studies with interpreting their academic readings, no comparable metacognitive writing strategies appear in the literature to support an academic writing process. \n\tHowever, metacognition theory regarding the role of affect in monitoring and controlling ones progress through the completion of a task offers potential applications to support academic writing. Furthermore, re-imagining academic writing as an experience deeply informed by affect resonates with recent research into articulating the affective or felt sense understanding of ones creative processes in composing a choreographic work. Investigating connections between how dancers process composition tasks in the two disciplines revealed metacognitive processing parallels. The findings implied several considerations for designing a writing pedagogy specific to the needs of emerging dancer-scholars. \n\tThis dissertation research with graduate dance students in Canada and the US incorporated ethnographic and educational action research approaches for identifying, addressing and documenting participants perceived essay writing problems. Initial group workshops prepared the participants for individual Case Study research sessions, which were characterized by practice-led research/research-led practice methods of generating, developing, performing and theorizing. The research investigated the howness of each participants writing process across a series of analytical writing assignments. Participants and I collaborated in uncovering the focus and potential structure for each paper using visual-spatial-dialoguing techniques. Participants expressed affective experiences during these video- or audio-taped sessions and in emailed reflections. Their gestural and verbal metaphors generated metacognitive knowledge about the source of writing frustrations versus the support provided by using familiar processing techniques from their choreographic practices. Their retrospective analyses demonstrated the participants metacognitive evolution from personal awareness to co- and self-regulated learning about the characteristic processing traits underlying their writing and choreographic practices. A comparative analysis of three Case Studies suggested metacognitive writing strategies for supporting emerging dancer-scholars.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".