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Record W2912860257 · doi:10.1080/09500693.2019.1571649

Discourse forms in a classroom transitioning to student-centred scientific inquiry through co-teaching

2019· article· en· W2912860257 on OpenAlexafffund
Carol Rees, Wolff‐Michael Roth

Bibliographic record

VenueInternational Journal of Science Education · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCollaborative Teaching and Inclusion
Canadian institutionsUniversity of VictoriaThompson Rivers University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsDialogicPedagogyDiscourse analysisMathematics educationSociologyTeaching methodStudent teacherPsychologyTeacher educationLinguistics

Abstract

fetched live from OpenAlex

Teacher–student discourse continues to be teacher-centred even though researchers and reform documents have recommended changes toward increased levels of student-centred discourse. In science education this situation is paralleled by effort to make scientific inquiry more student-centred. The purpose of this study was to investigate how discourse forms changed over time in a classroom where the regular teacher and his students were scaffolded in the transitioning to student-centred scientific inquiry. Video-recordings were collected at intervals over one academic year. Three prominent forms of discourse were identified: two teacher-authoritative forms and one more interactive, dialogic form. As the lessons increasingly turned into student-centred scientific inquiry, a shift to the dialogic discourse form was found. Co-teaching provided for (a) guidance towards an organisation of events in the classroom that included regular teacher–student dialogue (b) modelling of the more dialogic form of discourse.

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)0.007
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0050.009
Scholarly communication0.0070.004
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.444
Teacher spread0.408 · 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 designObservational
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

Citations17
Published2019
Admission routes2
Has abstractyes

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