Hands-On Exercise for Enhancing Students’ Construction Management Skills
Bibliographic record
Abstract
This paper presents a hands-on exercise on construction project management to augment traditional teaching methods and help students improve their construction experience and project management skills. The exercise involved 100 students participating in managing and constructing a 3.8-m (12.5-ft) model of Canada’s CN Tower within a 30-min deadline. Before construction, the groups prepared method statements, cost estimates, and schedules for constructing the model. During construction, the groups had to manage limited resources, procurement challenges, and forced interruptions to keep the project on track. To experience new project-tracking methods, students were introduced to a voice-based prototype for collecting progress information and automatically updating the schedule. The results indicate that students gained better understanding of the real construction environment and how to apply their acquired construction management skills to handle the involved complexities and interdependencies. The paper can be of interest mainly to researchers and educators. It presents the design, planning, and implementation of this educational exercise and discusses its practicality and ability to enhance the project management skills of students.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".