MétaCan
Menu
Back to cohort
Record W2256179917 · doi:10.18260/1-2--5005

An Application Of Customer Satisfaction Standards In Engineering Management Courses

2020· article· en· W2256179917 on OpenAlexaff
Stanislav Karapetrović, John Doucette

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsQuality assuranceUsabilityComputer scienceCustomer satisfactionQuality (philosophy)Quality of analytical resultsEngineering managementQuality management systemEngineering educationQuality managementSoftware engineeringManagement systemEngineeringOperations management

Abstract

fetched live from OpenAlex

This paper discusses the use of two international standards for quality management in engineering education, more specifically in teaching four engineering management courses.However, neither one of these standards is the commonly-recognized ISO 9001.Rather, they are the still widely-unknown, but quickly-applicable, ISO 10001 and ISO 10002.The respective guidelines for codes of conduct and complaint handling were deployed to establish and follow three codes for student satisfaction and quality assurance, as well as to setup and use a simple system for handling unsolicited student feedback and improving course delivery based upon it.Illustrations of the use of the codes, together with sample results of code performance tracking and student surveys on the usability of these codes, are provided.Examples of the feedback received from the students and its processing through a standardized system are also displayed.

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)0.014
metaresearch head score (Gemma)0.033
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 score0.014
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.033
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.229
Teacher spread0.225 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

Explore more

Same topicEngineering Education and Curriculum DevelopmentFrench-language works237,207