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Record W2998719502 · doi:10.5430/wje.v9n6p57

The Introduction of a Thin-bending Wood Horn Speaker as Multipurpose Teaching Material in Japanese Junior High School Technology Classes

2019· article· en· W2998719502 on OpenAlexvenueno aff
Kiho Jung, Yuki Otaka

Bibliographic record

VenueWorld Journal of Education · 2019
Typearticle
Languageen
FieldComputer Science
TopicEducational Robotics and Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsFrench hornLiteracyMathematics educationTeaching methodComputer sciencePsychologyPedagogy

Abstract

fetched live from OpenAlex

As a case study of the introduction of efficient and effective teaching materials and methods that enhance students' technological literacy in different areas despite the relative lack of classroom hours devoted to technology education in Japanese junior high schools, a front-loaded horn speaker system was proposed as a component of multipurpose teaching materials and methods integrating several areas. Following the development of teaching materials and lesson plans, their effectiveness was evaluated through implementation. Acoustic measurements showed that the horn speaker provided high efficiency in the midrange. Moreover, the results of a post-lesson questionnaire administered to junior high school indicated that the speaker system was highly effective as teaching material. It was therefore concluded that the new design enhanced not only student interest but also their technological literacy, as they demonstrated an understanding of the acoustic mechanism and high satisfaction with their final products.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.005
GPT teacher head0.244
Teacher spread0.239 · 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 designBench or experimental
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

Citations2
Published2019
Admission routes1
Has abstractyes

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