Flipped Classrooms and Peer-to-Peer Learning in a Construction Management Course
Bibliographic record
Abstract
This paper presents a case study of the implementation of a combined peer-to-peer learning and flipped classroom approach in a course within a baccalaureate degree program in Construction Project Management. In a flipped classroom, students review the learning materials before class, complete lower-level cognitive tasks outside of the classroom, and engage in higher-level cognitive tasks during class time. While there are numerous advantages to flipping the classroom compared to traditional teaching, the strategy becomes ineffective if students cannot complete the assigned tasks before class and lack motivation to participate in higher-level learning during the class. To ensure motivation and continuous engagement, a combination of strategies was employed, including peer-to-peer learning and teaching, granting credits for classroom participation and peer evaluation, and establishing milestones for continuous evaluation of outputs. Over the years, the percentage of students who agreed that the course's delivery method provided opportunities for active participation in discussions and collaborative activities increased from 80% to 100%. Similarly, the percentage of students who completed the readings and homework on time increased from 85% to 100%.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 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".