The Achievements of Engineering Master’s Coursework Students from Diverse Backgrounds
Bibliographic record
Abstract
The objective of this study is to investigate the performance of postgraduate students from various backgrounds on the basis of the following criteria: country, achievements during undergraduate study, English language requirement and previous work experience. The Civil Engineering Master’s Programme was taken as a case study. A data set sourced from students’ application forms and academic record for three consecutive session intakes, 2008/2009, 2009/2010 and 2010/2011, was used. As a measure of the students’ performance, Graduate Cumulative Grade Point Average (GCGPA) was considered as the key performance index. Students from Malaysia (28%), Iran (53%) and Iraq (19%) were chosen because their communities together represent the largest number of students in Malaysia. The mean achievements of the students from Malaysia, Iran and Iraq were comparable (mean GCGPA 3.52–3.60). The largest number of candidates (43.5%) who entered the programme had an Undergraduate Cumulative Grade Point Average (UCGPA) higher than 2.70 but lower than 3.00. The decision to continue the master’s study was most popular with students 1–4 years after graduation. Regarding the extent to which UCGPA contributed towards GCGPA, Malaysian students exhibited a weak relationship but a stronger correlation than the other student groups (r=0.331, p<0.05). For Iranian students, work experience was very significant (r=0.416, p<0.05). The results also indicate that English proficiency affected the performance of the students. The correlation between work experience and GCGPA differed for students with and without TOEFL/IELTS scores. It is hoped that the results of this work can contribute toward a more detailed study for determining the entry requirements of students seeking admission to master’s course programmes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".