Software Application Employed in Architectural Design Education: The Case of KNUST
Bibliographic record
Abstract
Computer software has come to replace the manual form of designing in both architectural education and practice. The use of drawing boards had been employed in architectural education and practice for a long time. Since the first half of the twentieth century, computer hardware and corresponding software have seen dramatic change and development manufactured and tailored to meet the demand of changing technological and human needs. Architecture has had its fair share since the advent of computers and has seen major milestone changes in its integration into the profession. In the last century, architectural education in Ghana has also witnessed this revolution. From the year 2000 and thereon since Computer Aided Architectural Design (CAAD) was introduced in the Department of Architecture (DOA) in the Kwame Nkrumah University of Science and Technology (KNUST) there has been tremendous improvement in the CAAD tools used in architectural design education. There is therefore the need to evaluate the CAAD software used by the students and faculty. This paper looked at the existence and the mode in which CAAD software is applied in the department, the predominant software used by students and the mode of acquisition of the software. The findings proved that CAAD is taught as part of the curriculum in the DOA and has helped improve architectural design education over the years. However, the full potential and benefit of CAAD use has not been realized as a result of challenges faced by students and faculty in teaching, learning and acquisition of software.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".