Assessment for learning in the teaching of traditional Newfoundland craft
Bibliographic record
Abstract
Educational institutions are striving to adapt to critical economic, environmental, and social challenges (CMEC, 2013; National Research Council, 2012; NL Department of Education, 2012). Yet, in this very dynamic context, classroom assessment practices remain entrenched in a traditional Eurocentric feedback loop contingent on standards, measurement, and reporting of fixed criteria (Reeves, 2011; Marzano, 2010). This work emerges from these tensions and continues an ongoing investigation into assessment practices through an exploration of alternative approaches to teaching and learning relationships; specifically, the role of assessment and feedback while learning traditional activities in holistic and authentic settings. As part of a larger study investigating feedback during teaching in learning in traditional contexts, this work specifically regards feedback during the teaching of a traditional skill, rug hooking, to primary aged children. Video recordings of authentic learning moments will be analyzed using socio-semiotics (analysis of verbal and non-verbal communication) paying particular attention to the principles outlined in the Nunavut Inuit Ilitaunnikuliriniq Assessment Framework (Government of Nunavut, 2008) and a conventional formative assessment model (Stiggins, 2017).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".