Strategies for Improving Quantity Surveyors’ Education Training in Uganda
Bibliographic record
Abstract
Education and training of Quantity Surveyors (QSs) has been a topical subject amongst academics, the industry, and professional institutions, often leading to a discussion about education versus training, in which case, the industry sometimes argues that QSs are often ill-prepared for work. The current study investigated strategies for improving QSs’ education training in Uganda, with a focus on devising ways of engendering better graduates that are fit for the industry. A semi-structured online questionnaire was used to collect data. The research population included QSs practicing in the Ugandan Construction Industry. The majority of respondents agreed to a great extent that QSs, and thus their training, are still relevant in the current construction industry. Majority of respondents desired the teaching curriculum to include more practical aspects that expose students to real challenges in practice. It was suggested that early exposure of students to real field practice was paramount to students’ training. Engagements such as industrial training and internship placements in Quantity Surveying firms were highly encouraged. The curriculum also needs to be responsive to recent advances in industry practices, such as Building Information Modelling (BIM). Meanwhile, the University should intensify action research with the industry, and forge collaborations with all the key players in the construction sector to streamline the training. The findings of this study, if implemented, could potentially improve the quality of Quantity Surveying programmes at institutions of higher learning in similar developing countries. This would hopefully produce graduates who are industrially relevant and with a sound academic background.
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How this classification was reachedexpand
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".