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Record W3160359814 · doi:10.23977/aetp.2021.52018

Exploration of Extracurricular Practice Teaching Mode of Mechanical Innovative Design Based on OBE Concept

2021· article· en· W3160359814 on OpenAlexvenueno aff
Junbin Lou, Qing Ouyang

Bibliographic record

VenueAdvances in Educational Technology and Psychology · 2021
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsnot available
Fundersnot available
KeywordsModular designCurriculumMechanical designEngineering managementEngineeringProcess (computing)Training (meteorology)Scheme (mathematics)Quality (philosophy)Mathematics educationComputer scienceMechanical engineeringPedagogyPsychology

Abstract

fetched live from OpenAlex

In order to better arouse students' interest in mechanical innovative design, improve students' practical ability and innovative ability, and further enhance the competitiveness of mechanical major students, this paper explores the extracurricular practical teaching mode of mechanical innovation based on OBE concept. The training scheme is formulated from five aspects: training mechanism, training mode, curriculum system, scientific research and training system and quality assurance system. A modular curriculum system is constructed to improve students’ hard and soft abilities. Project cases of mechanical innovative design training, such as perforated water pipe puncher and carbon-free trolley design, are developed, which enables students to get the whole process ability training from design, manufacturing and evaluation. Finally, the paper established a diversified evaluation system, and inspected the effectiveness of the extracurricular practice teaching mode of mechanical innovation based on OBE concept from many aspects, which greatly improved students' comprehensive engineering literacy.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.018
GPT teacher head0.350
Teacher spread0.332 · 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
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
Published2021
Admission routes1
Has abstractyes

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