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Record W4412842337 · doi:10.15294/jvce.v10i2.31526

Nozzle Tester Teaching Tool to Enhance Learning Outcomes in Conventional Diesel Engine System for Light Vehicles

2025· article· en· W4412842337 on OpenAlexaff
Choirul Fatah Hidayatulloh, Muhammad Khumaedi, Samsudin Anis

Bibliographic record

VenueJournal of Vocational and Career Education · 2025
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsAutomotive engineeringNozzleDiesel engineDiesel fuelEngineeringComputer scienceAeronauticsMechanical engineering

Abstract

fetched live from OpenAlex

The nozzle tester, which is commonly used in the automotive engineering program at SMK N 1 Kedungwuni, is in a leaking condition, limiting its effectiveness in supporting learning and consequently affecting student learning outcomes. This issue is the main reason for conducting research on the development of a nozzle tester using a recycled jack, aimed at enhancing the learning of conventional diesel engine systems. This study employs a Research and Development (R&D) approach with the ADDIE model for development. The research method used is a pretest-posttest group design. The findings of the study include: (1) the developed teaching tool was considered feasible by media and subject matter experts, with content validity ratio (CVR), content validity index (CVI), and percentage of agreement (PA) analyses; (2) product trials showed a high level of effectiveness, demonstrated by the Independent Sample t-Test, where the N Gain Score resulted in a significant (2-tailed) value; and (3) the reproducibility coefficient (Kr) and scalability coefficient (Ks) both indicate that the application of the nozzle tester in the conventional diesel engine system learning process is very practical. The conclusion of this study is that the nozzle tester can significantly improve the learning outcomes of conventional diesel engine systems.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.451
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.008
GPT teacher head0.292
Teacher spread0.284 · 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 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
Published2025
Admission routes1
Has abstractyes

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