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Record W3208367907 · doi:10.20360/langandlit29517

"I'm Not the Only Writer in The Room": A Framework for Co-Creating Confident Writing Classrooms

2021· article· en· W3208367907 on OpenAlexaffvenueabout
Jen McConnel, Pamela Beach

Bibliographic record

VenueLanguage and Literacy · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicCollaborative Teaching and Inclusion
Canadian institutionsQueen's University
Fundersnot available
KeywordsSelf-efficacyConversationContext (archaeology)PsychologyPedagogySocial cognitive theorySubject (documents)Identity (music)Mathematics educationSocial psychologyComputer science

Abstract

fetched live from OpenAlex

This study is rooted in social cognitive theory, specifically Bandura's work on self-and collective efficacy. The authors explore self reported confidence levels with writing instruction from secondary teachers across subjects in Canada and the United States by pairing a self-efficacy scale developed by Locke and Johnston (2016) with semi-structured interviews conducted via Skype. 60 teachers participated in the survey, with 25 from Canada and 35 from the United States. Although teachers report relatively strong levels of self efficacy in writing instruction, the responses of participants regarding collective efficacy are more mixed. Based on these results, coupled with six interviews (split evenly between teachers in Canada and the United States), the authors propose a framework to help teachers of all subject areas increase their confidence in writing instruction while also helping students develop their own confidence as writers. This three-pronged framework of identity, context, and authority, relies on co-creating community with students. The potential of this framework is creative, offering teachers (and students) multiple ways into a conversation about writing that will not only enhance confidence, but will create a classroom culture in which diverse writing strategies and perspectives are valued.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.255
Threshold uncertainty score0.748

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.371
Teacher spread0.354 · 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 teacher head, 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

Citations1
Published2021
Admission routes3
Has abstractyes

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