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Record W4391053862 · doi:10.23977/aetp.2024.080101

Teaching Reform in the Context of Engineering Education Accreditation—Taking the Mechanical Design, Manufacturing and Automation Major as an Example

2024· article· en· W4391053862 on OpenAlexvenueno aff
Wang Fei, Xinhua Wang

Bibliographic record

VenueAdvances in Educational Technology and Psychology · 2024
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsnot available
FundersUniversity of Shanghai for Science and TechnologyNational Natural Science Foundation of China
KeywordsAccreditationStandardizationCurriculumEngineering ethicsEngineering educationContext (archaeology)Engineering managementEngineeringQuality (philosophy)Outcome-based educationMedical educationPedagogyComputer scienceSociologyMedicine

Abstract

fetched live from OpenAlex

Engineering education accreditation is primarily aimed at assessing the quality of engineering education, ensuring that higher education in engineering aligns with the demands of modern society, thereby cultivating more excellent engineering and technical talents. The concept of continuous teaching reform is embodied by strengthening the process-oriented assessment of engineering accreditation, deepening the standardization of curriculum teaching, and integrating the philosophy of Outcome-based Education (OBE). The reform organically integrates ideological and political education with teaching activities and simultaneously improves students' comprehensive capacity and practical ability through measures such as monitoring students' behaviors, providing personalized guidance, and implementing personalized assessment. Eventually, the teaching reform driven by engineering education accreditation helps achieve the improvement of the education system, the standardization of the teaching methodology, the optimization of the curriculum matrix, the deeper involvement of the teachers, and the more fair assessment of their teaching qualities.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.279
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.014
GPT teacher head0.317
Teacher spread0.303 · 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 designTheoretical or conceptual
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
Published2024
Admission routes1
Has abstractyes

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