Exploring Teaching Methods for Construction Contract Law Using Analytic Hierarchy Process
Bibliographic record
Abstract
Construction law holds significant importance due to its inherent rationale and pervasive presence at every stage of the project in the construction sector. The average value of construction disputes worldwide was USD 42.8 million in 2021. Complying with construction law and regulations plays a pivotal role in the seamless functioning of the sector. That is why construction law skills are vital for engineers. They ensure regulatory compliance, facilitate effective contract management, foster improved communication among stakeholders, and enhance risk mitigation. The substantial and unique nature of this subject makes it challenging to condense into a traditional course format. Despite being structured as a course, effectively conveying the practical aspects of construction contract law to engineers requires a distinct pedagogical approach. The present pilot study aims to rank student preferences for different teaching methods in construction contract law education in India using the analytic hierarchy process (AHP). This study contributes to the knowledge base by identifying preferred pedagogical approaches from the students’ perspective. Out of nine teaching methods, the lecture method is the highest ranked by Indian students. This research seeks to enhance subject understanding and effectiveness of construction law courses in the country. This can benefit the construction sector by producing engineers who are better equipped to navigate legal complexities.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".