Systematic Review for the Assessment of Indoor Environment Quality Factors and Sub-Indicators of Classrooms
Why this work is in the frame
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Bibliographic record
Abstract
This systematic review aims to assess the literature on Indoor Environment Quality (IEQ) factors and sub-indicators in classrooms over the past decade (2014)(2015)(2016)(2017)(2018)(2019)(2020)(2021)(2022)(2023)(2024), focusing on models, assessment methods, and tools specific to these factors.The significance of this research lies not only in reviewing IEQ assessment holistically and panoramically (where 44 studies within the last 10 years were reviewed across major scientific databases) to highlight and reaffirm the most frequently measured IEQ sub-indicators, but it also aims to eliminate confusion that might occur for researchers by categorizing the assessment of IEQ.This helps readers clearly identify the specific type of assessment they are exploring, as past research on IEQ often features similar titles but differs in the type of actual assessment, whether it is occupant-based, holistic, multiple factors, or single factor.The main findings of the review highlight the most measured IEQ sub-indicators in the literature for each of the IEQ factors, and they are as follows; 1).For thermal comfort, air temperature and relative humidity are the primary measures.2) For indoor air quality, the key indicators are CO2 concentration, Volatile Organic Compounds (VOCs), and particulate matter (PM10 and PM2.5).4) Visual comfort is primarily assessed through illuminance and daylight factor, 3) acoustic comfort focuses on background noise level and reverberation time.Despite numerous studies on specific IEQ factors, there is a lack of comprehensive models integrating multiple components for holistic assessments.Our findings underscore the necessity for ongoing monitoring and enhancement of IEQ in classrooms to improve students' health, well-being, and academic performance.We recommend future research focus on developing a standardized, holistic tool designed specifically for classroom environments.Such a tool should allow for initial and rapid assessments, making it accessible for professionals and non-specialists in IEQ.
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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 | 0.005 | 0.001 |
| 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 it