MétaCan
Menu
Back to cohort
Record W2961011544 · doi:10.29173/iasl7216

The Role of Supporting for Learning of Subject in School by Jigsaw Method and Utilizing School Library-Collaboration of School Library and School Music Education(music appreciation)

2016· article· en· W2961011544 on OpenAlexvenueno aff
Makiko Asano

Bibliographic record

VenueIASL Annual Conference Proceedings · 2016
Typearticle
Languageen
FieldComputer Science
TopicEducational Research and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsJigsawSchool libraryMathematics educationClass (philosophy)Subject (documents)Point (geometry)PedagogyPsychologyComputer scienceLibrary scienceMathematics

Abstract

fetched live from OpenAlex

In this study, a music class at a junior high school introduces coordinated activity by the collaboration between a teacher and school library (the Jigsaw Method) in music appreciation. The research investigates what kind of changes the students show through such a type of lesson that they experience for the first time. From the point of teacher’s view, the study reveals the evaluation of this lesson support, and what sort of issues it contains. As a result, the lesson support of the school librarian that introduces the book materials and the Jigsaw Method, the teachers obtain a sense of accomplishment more than they expected in the area of learning attitudes and learning contents of the students. Therefore, this research concludes that it is obvious that they have effectiveness in the learning support program which introduces the active learning into the learning support by school library.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.023
GPT teacher head0.318
Teacher spread0.294 · 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 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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueIASL Annual Conference ProceedingsSame topicEducational Research and PedagogyFrench-language works237,207