Use of Data Quality Index in Student Feedback for Quality Assurance of Engineering Programmes at the Military Technological College, Muscat, Oman
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Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it