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Record W2069890660 · doi:10.1680/mpal.13.00002

Virtual collaborative learning for building design

2014· article· en· W2069890660 on OpenAlexaffabout
Robby Soetanto, Mark Childs, Paul S. H. Poh, Stephen Austin, Jane Hao

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Management Procurement and Law · 2014
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsTeamworkEthosProcess (computing)PsychologyTeam buildingKnowledge managementMedical educationEngineeringMathematics educationComputer scienceEngineering managementPolitical scienceMedicine

Abstract

fetched live from OpenAlex

A building design project that requires civil engineering students in the UK and architectural students in Canada to collaborate virtually has been implemented at universities in the two countries. The aims were to obtain a greater understanding of the process, strategies and expected outcomes for a more effective implementation of problem-based learning to hone communication and teamwork skills. Data were obtained from a series of interviews with 23 students from seven groups, assessment results of 249 participating and non-participating students, and student evaluation. The findings suggest that the professional ethos of the groups and the consequent building of trust is the greatest factor in supporting successful collaborations. This has been found to be able to overcome many barriers related to technology and differences of culture, language, time zone and tasks. However, the activity did not seem to have any impact on student performance, but has improved the project management skills of participating students. The activity has also contributed positively to increasing student satisfaction. Several lessons for future implementation are presented, before limitations and further research are described.

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 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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.908
Threshold uncertainty score0.480

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.030
GPT teacher head0.278
Teacher spread0.248 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2014
Admission routes2
Has abstractyes

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