MétaCan
Menu
Back to cohort
Record W2560297762 · doi:10.36510/learnland.v8i2.709

Opening Up to Student Voice: Supporting Teacher Learning Through Collaborative Action Research

2015· article· en· W2560297762 on OpenAlexvenueno aff
Emily Nelson

Bibliographic record

VenueLEARNing Landscapes · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCollaborative Teaching and Inclusion
Canadian institutionsnot available
Fundersnot available
KeywordsNoticeAction researchPedagogyAction (physics)PsychologyWork (physics)Mathematics educationPolitical scienceEngineering

Abstract

fetched live from OpenAlex

Student voice is promoted increasingly as a vehicle to enhance student learning and improve schools. Whilst the need for amplifying neglected student perspectives in learning and improvement processes is well established, supporting teachers to learn from students has received less attention. The author argues that collaborative action research supports teachers to engage with their students as decision-making partners in the classroom and to learn from them about effective pedagogy at the same time. The approach provides reflective spaces for teachers to notice and challenge takenfor-granted roles and practices, and to address expectations on their work sometimes contradictory to their student voice goals.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.036
metaresearch head score (Gemma)0.046
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.036
Threshold uncertainty score0.192

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.046
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0060.021
Scholarly communication0.0200.015
Open science0.0040.021
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.116
GPT teacher head0.481
Teacher spread0.365 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations28
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueLEARNing LandscapesSame topicCollaborative Teaching and InclusionFrench-language works237,207