Advancing Architecture and Engineering Education for Project Value Delivery
Bibliographic record
Abstract
Delivering project value primarily depends on understanding project stakeholders' different needs and requirements and translating these needs into a well-constructed facility. This concept is usually insufficiently revealed using different terminologies during the educational journey of architecture, engineering, and construction (AEC) professionals. The goals of this research are to (1) investigate students' and practitioners' familiarity and knowledge about the concept of value, (2) explore underlying gaps in teaching value in AEC education, and (3) propose essential practices to overcome identified educational shortcomings. For this purpose, combined qualitative and quantitative approaches were used to evaluate responses by students and practitioners, including a structured survey, interviews, and statistical analysis. The paper introduced a framework for educational content that supports value delivery using lean principles, design thinking, sustainability, and digital collaborative technologies. The survey and interviews revealed a major deficiency in students' and practitioners' familiarity with the concept of delivering value and the tools needed to enhance it. Thus, a knowledge gap about delivering project value was identified in AEC curricula. Additionally, cross-disciplinary engagement and collaboration efforts were found to be insufficient. Students and practitioners revealed doubts about the relevance of academic projects. Nonetheless, participants confirmed the importance of providing a better understanding of the value concept and related practices. The proposed framework for better incorporating the value concept into AEC curricula has the potential to improve project outcomes and satisfaction in the AEC industry.
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.
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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".