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Record W4399030610 · doi:10.29007/rsh8

Flipped Classrooms and Peer-to-Peer Learning in a Construction Management Course

2024· article· en· W4399030610 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEPiC series in built environment · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsSAIT Polytechnic
Fundersnot available
KeywordsFlipped classroomClass (philosophy)Peer learningPeer feedbackMathematics educationComputer sciencePeer instructionLearning ManagementPsychologyArtificial intelligence

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.791
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.343
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it