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Record W7155085464 · doi:10.59236/ijea13n7

Learning Posts

2012· article· W7155085464 on OpenAlexaff
June Countryman

Bibliographic record

VenueInternational journal of education and the arts · 2012
Typearticle
Language
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsFraming (construction)Reflection (computer programming)Reflective practiceReflective writingJournal writingExperiential learningTeaching methodHigher educationIntersection (aeronautics)

Abstract

fetched live from OpenAlex

This article describes the effects of a year-long reflective writing assignment - weekly Learning Posts - designed for students in an undergraduate music education course. I created this assignment to cause students to regularly interrogate the teaching and learning they experience in their own daily lives. This study's research question emerged from critical reflection at the intersection of practice and theory. Does a weekly requirement to describe and interpret a personal learning experience encourage, over time, instances of significant learning (Fink, 2003). My data sources consist of regular entries in my teaching journal monitoring the assignment and post-course interviews conducted with four students. I identify the positive potential of Learning Posts, improvements to enhance their effectiveness and broader issues concerning the reflective writing we assign music education students. The concept of self-authorship confirms the importance of reflective writing for young adults and the notion of threshold concepts contributes a potential framing device.

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)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.233
Threshold uncertainty score0.781

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.2330.114

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 head0.027
GPT teacher head0.436
Teacher spread0.409 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2012
Admission routes1
Has abstractyes

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