Experiential Student Learning through Collaborative Simulated Bidding Competition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This evidence-based paper will show the effectiveness and achievement potential of a collaborative experiential learning project to support student success in post-secondary education. Student projects involving simulation have proven to be beneficial for many stakeholders including students, faculty and industry. This paper reviews literature surrounding common features of experiential learning projects and uses an annual national bidding competition in Canada as a case study to highlight and support the findings. This simulated student bid competition is a yearly event to introduce students to the real life challenges associated with the construction estimating and bidding process. Students across Canada submit complete bids based on a set of construction documents. The bids are judged based on three criteria: closest to the target price, most accurate bid package, and most outstanding professional submission. Students worked collaboratively with industry mentors, including faculty from the area of construction management and accounting. The findings of this paper show the benefits and provide recommendations to effectively embed experiential projects into curriculum.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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