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Record W2980721255

Pedagogical behaviour in pre-service teachers drops with increasing content knowledge

2019· article· en· W2980721255 on OpenAlexaff
Christine Lindstrøm, Megan C. Engel, Vinesh Rajpaul

Bibliographic record

VenueProceedings of The Australian Conference on Science and Mathematics Education · 2019
Typearticle
Languageen
FieldPsychology
TopicEducational Strategies and Epistemologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBachelorNorwegianMathematics educationPsychologyPedagogyService (business)Political science
DOInot available

Abstract

fetched live from OpenAlex

We present the results of a novel study investigating the relationship between pre-service science teachers’ content knowledge and pedagogical behaviour and how these evolve over time. Forty-one pre-service science teachers at the largest teacher education institution in Norway (Oslo Metropolitan University) were tested before and after a 12-hour module on astronomy at the end of the second and final physics course in the Bachelor of Teaching degree. Three free-response questions in the established Norwegian Introductory Astronomy Questionnaire (NIAQ) elicited astronomy knowledge and gave respondents an opportunity to engage in pedagogy. Student responses were analysed along two separate dimensionscontent knowledge and pedagogical behaviour (student-centred vs. teacher-centred)and interpreted in the framework of Pedagogical Content Knowledge (PCK). Overall, we find that the pre-service teachers become more knowledgeable after instruction (responses marked as ‘knowledgeable’ increased from 39% to 61%), even though a significant fraction remain disconcertingly ignorant. More notably, however, the pre-service teachers also displayed a strong trend of becoming less student-centred (from 36% to 11% of responses) as their content knowledge increased, merely stating the correct - or presumed correct - response without showing any concern for the hypothetical students in the question.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.485
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.190
GPT teacher head0.404
Teacher spread0.215 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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