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Record W4308417208 · doi:10.3389/feduc.2022.1042693

One teacher educator’s strategies for encouraging reflective practice

2022· article· en· W4308417208 on OpenAlexaff
Tom Russell

Bibliographic record

VenueFrontiers in Education · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsQueen's University
Fundersnot available
KeywordsReflective practiceActive listeningArgument (complex analysis)Reflection (computer programming)PedagogyPsychologyMathematics educationGrounded theoryMetacognitionReflective thinkingClass (philosophy)Process (computing)Computer scienceQualitative researchSociologyCognitionMedicine

Abstract

fetched live from OpenAlex

Teaching reflective practice to beginning teachers requires significant changes in a teacher educator’s implicit assumptions about how individuals learn to teach. Teaching reflective practice also requires significant changes in a teacher educator’s teaching practices. The familiar teach-them-theory-and-then-let-them-practice approach assumes that learning is complete before practice begins. In contrast, reflective practice in professions involves learning from firsthand experience and recognizes that the process of learning theory and research is incomplete before personal practice begins. Teaching reflective practice also requires recognizing that terms such as reflect and reflection are everyday words with multiple meanings and uses and little direct connection to reflective practice . The following argument, grounded in self-study methodology, describes indirect strategies for encouraging reflective practice. These strategies include an extended writing assignment focused on professional learning, teaching how to learn from personal experience, the unrecognized power of listening, and tickets out of class as listening and fostering metacognition. The argument closes with a summary of suggested strategies for encouraging reflective practice by those learning how to teach.

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)0.003
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.786
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
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 head0.028
GPT teacher head0.409
Teacher spread0.381 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

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