Using Interactive Workspaces for Team Design Project Meetings
Bibliographic record
Abstract
An Interactive Collaboration Laboratory (ICL) has been established at the University of New Brunswick (UNB) to research the application of interactive information and communication environments for the architectural, engineering, and construction (AEC) industry. This paper provides a quick overview of the laboratory within the wider context of interactive collaborative workspaces. It identifies opportunities to enhance information communication, and group decision-making offered by the laboratory, and focuses on lessons learned to date from its use. The paper reports on a survey conducted among final year undergraduate students who used the environment over the course of three months for their senior design project meetings. A questionnaire was distributed to those students to investigate the impact of the environment upon the effectiveness of their meetings and decisions, the issues and processes where the environment was more (or less) useful, and the context within which the environment and tools were used. The questionnaire also investigated the impact of the environment and its tools upon their project, the quality of their work, and their overall satisfaction. Students found the laboratory to be specifically useful at the preliminary design stage when designing, viewing, and analyzing the site and building layouts of their projects, and determining the project’s sustainability requirements, and targets. The laboratory enabled student groups to view information from different perspectives, access remote information, and save captured information instantaneously. It also enabled them to collaborate more effectively, make more educated decisions, make better use of their time, produce higher quality work, and develop among them a relationship of trust, respect and mutual understanding. Investigating how best to use the lab’s technology to serve their needs, occasionally slowed down their progress and distracted them at times from focusing on their work.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".