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Record W2803433223 · doi:10.5430/ijhe.v7n3p90

Use of Data Quality Index in Student Feedback for Quality Assurance of Engineering Programmes at the Military Technological College, Muscat, Oman

2018· article· en· W2803433223 on OpenAlexvenueno aff
Wasi Uz Zaman Khan, Abdullah Ahmed Ali AlAjmi, Sarim Al-Zubaidy

Bibliographic record

VenueInternational Journal of Higher Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Environments and Student Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsAccreditationQuality assuranceContext (archaeology)Quality (philosophy)EngineeringHigher educationEngineering educationEngineering managementMedical educationOperations managementMedicinePolitical scienceGeography

Abstract

fetched live from OpenAlex

This case study was undertaken to assess the effectiveness of the modifications into the engineering programmes adopted by the Military Technological College (MTC) to satisfy the needs of Omani armed forces. It discusses the role of Quality Assurance (QA) in engineering education and accreditation process in the context of four engineering programmes offered by the MTC. The study outlines the steps undertaken by the QA department at the MTC which are practiced by western institutes and universities. This study also included the student feedback as the most important parameter in measuring the effectiveness of modified engineering programmes. Due to low participation rate, the data quality index (DQI) approach was used for assessing the quality of the programme in a military learning environment. The MTC applied its anonymous over sighting the engineering programmes offered by the four engineering departments (Aeronautical, Civil, Marine, and Systems). The Student Evaluation Questionnaire (SEQ) used in the academic years 2014-15 and 2015-16 was modified and the improved version was used in academic year 2016-17. In year 2016-17 a total of 561 students participated online in the SEQ survey. The student’s satisfaction about the module and lecturer with low participation rate was above 50% in most modules which could be misleading. However, after transformation of the data to DQI the student feedback became more representative. On a scale of 0 – 100, a lower DQI value indicated higher student satisfaction. The DQI can be used as an institutional approach for maximum information and assessment of module performance. Out of 43 modules, the students were more satisfied in module MTCA5030 owned and managed by Aeronautical Engineering Dept.; in module MTCC3009 (section 2) owned and managed by Civil Engineering Dept.; in module MTCM5004 owned and managed by Marine Engineering Dept.; and in module MTCS5011 owned and managed by Systems Engineering Dept.

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)0.030
metaresearch head score (Gemma)0.070
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.160

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.070
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.003
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.103
GPT teacher head0.462
Teacher spread0.359 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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