Implementing Collaborative Learning Platforms in Construction Management Education
Bibliographic record
Abstract
Implementing Collaborative Learning Platforms in Construction Management Education Ralph Tayeh, Fopefoluwa Bademosi and Raja R.A. Issa Pages 1114-1120 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: Over the last few decades, the investments in more complicated construction projects, involving multiple disciplines and different teams, have increased the need for more complex communication means. The purpose of communication methods is to ensure higher levels of coordination between project participants (owners, architects, engineers, contractors, suppliers, etc.). Adequate communication brings many benefits to a project, such as improved team performance due to information exchange, increased knowledge of other participants skills or their availability. Building Information Modelling (BIM) has the ability to aggregate information on construction projects and facilitate the design, construction, and facility management processes. Therefore, including BIM classes in construction management education is of utmost importance for the success of students. Moreover, introducing cloud collaboration to these classes helps students better understand the collaborative aspect of the construction industry. The purpose of this paper is to study the benefits of Autodesk Next Gen BIM 360 brought to a graduate BIM class. Students of this class were divided into groups and asked to model the different disciplines of a project using Autodesk Revit© while collaborating the project on Next Gen BIM 360. At the end of the semester, students reported the benefits and drawbacks of Next Gen BIM 360. The benefits included the ease of use of the platform, better communication of ideas and concerns using Next Gen BIM 360 cloud services, real-time collaboration opportunities, and model coordination on the cloud. Keywords: BIM; Next Gen BIM 360; Collaboration; Construction; Education DOI: https://doi.org/10.22260/ISARC2019/0148 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".