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Record W2785005020 · doi:10.33524/cjar.v18i2.334

USING A CRITICAL REFLECTION FRAMEWORK AND COLLABORATIVE INQUIRY TO IMPROVE TEACHING PRACTICE: AN ACTION RESEARCH PROJECT

2018· article· en· W2785005020 on OpenAlexvenueno aff
Patricia Briscoe

Bibliographic record

VenueThe Canadian Journal of Action Research · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAction researchCritical reflectionReflection (computer programming)Action learningPsychologyProcess (computing)PedagogyAction (physics)Reflective practiceTeaching methodMathematics educationCooperative learningComputer science

Abstract

fetched live from OpenAlex

This action research reports on a three-year collaborative learning process among three teachers. We used current literature and a critical reflection framework to understand why our teaching approaches were not resulting in increased student learning. This allowed us to examine our previously unrecognized and uninterrupted—and often, problematic—beliefs and values. Our findings revealed key barriers related to unexamined judgments, beliefs, assumptions, and expectations that affected our ability to facilitate effective learning. This encouraged conceptual change and led to a transformation in our teaching practice: We became more socioculturally responsive teachers. Our findings led us to conclude that professional learning needs to move beyond the acquisition of skills and strategies, and include the critical reflection necessary to deconstruct problematic beliefs and thought-patterns that can impede student learning.

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)0.149
metaresearch head score (Gemma)0.110
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.149
Threshold uncertainty score0.789

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1490.110
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0170.028
Scholarly communication0.0170.012
Open science0.0080.017
Research integrity0.0070.009
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 head0.803
GPT teacher head0.709
Teacher spread0.094 · 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 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
Published2018
Admission routes1
Has abstractyes

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