The Architecture Studio of Universiti Kebangsaan Malaysia (UKM): Has the Indoor Environmental Quality Standard Been Achieved?
Bibliographic record
Abstract
Studio-based learning is a shared learning environment in which ambiguous problems are addressed. This paper primarily focused on the lightings at the Universiti Kebangsaan Malaysia (UKM)’s architecture studio and to find out whether it had achieved the Indoor Environmental Quality (IEQ). A good design, involving a space in a building, requires sufficient daylight in order to perform a task. This can be achieved by providing enough means to let in diffused light from the sky, yet keeping out direct light from the sun to prevent heat gain and glare. The purpose of this research was to identify the importance of the IEQ in creating conducive studio-based learning environment. The IEQ is crucial for a learning institution since indoor environment factors can actually affect human comfort, health and productivity. Lighting is most important to students as high-quality lighting will improve students’ moods, behavior, concentration, and consequently, their learning. However, the effectiveness of learning in a studio cannot be fully achieved if the IEQ is being overlooked. Presently, artificial lights are being used most of the time in the UKM architecture studios in order to optimize students’ vision and comfort. Using an equipment, named LM-8100, and supported by a questionnaire survey to gauge the lighting comfort level from the students’ perspective, a lighting reading was taken for a duration of 10-hours for three days in the UKM third year architecture studio. The finding showed that the lighting setting is not within the range of the Malaysian Code of Practice on Indoor Air Quality (IAQ). However, the students have perceived it as normal and thus, the situation does not hinder them to stay long inside their studio. This situation will affect the students’ ability to perceive visual stimuli in the short-term and health, in terms of students' vision, in the long run.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".