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Record W2381128608

Good Teaching: One Size Fits All

2009· article· en· W2381128608 on OpenAlexaff
Shibao Guo

Bibliographic record

VenueJournal of Hebei University · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsConstructivism (international relations)Constructivist teaching methodsSocial constructivismApprenticeshipMathematics educationTeaching methodSociologyPedagogyComputer sciencePsychologyPolitical sciencePhilosophy
DOInot available

Abstract

fetched live from OpenAlex

In North America and even the whole world,there is a move within education to adopt a constructivist view of learning and teaching.While we do not argue with the basic tenets of constructivism,we do resist the rush to adopt any single,dominant view of learning or teaching.Through a wide range of disciplines,contexts,and cultures,our colleagues and we found a plurality of good teaching,not all of which rest on constructivist principles of learning.This article will introduce five different perspectives on teaching,each having the potential to be good teaching: Transmission,Developmental,Apprenticeship,Nurturing,and Social Reform.Hopefully,this will convince you to resist any ‘one size fits all’ approach to the improvement or evaluation of teaching.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.897
Threshold uncertainty score0.298

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 head0.030
GPT teacher head0.305
Teacher spread0.275 · 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 designNot applicable
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
Published2009
Admission routes1
Has abstractyes

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