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Classroom thermal comfort has a direct effect on student health and educational outcomes. However, measuring thermal comfort (TC) is a non-trivial task. It is represented by several subjective metrics e.g., Thermal Sensation Vote, Thermal Comfort Vote, Thermal Preference Vote, etc. Since machine learning (ML) is being increasingly used to predict occupant comfort, multiple TC metrics for the same indoor space may yield contradictory results. This poses the challenge of selecting the most suitable single TC metric or the minimal TC metric combination for a given indoor space. Ideally, it will be a metric that can be used to predict all other TC metrics and occupant behavior with high accuracy. This work addresses this problem by using a primary student thermal comfort dataset gathered from 11 schools and over 500 unique students. A comprehensive evaluation is carried out through hundreds of TC prediction models using several ML algorithms. It evaluates the ability of TC metrics to predict (a) other TC metrics, and (b) the adaptive behavior of primary students. An algorithm is proposed to select the most suitable single TC metric or the minimal TC metric input combination. Results show that ML models can accurately predict all TC metrics and occupant-adaptive behavior using a small subset of TC metrics with an average accuracy as high as 79%. This work also found Thermal Sensation Vote to be the most significant single TC predictor, followed by Thermal Satisfaction Level. Interestingly, satisfaction with clothing was found to be as equally relevant as thermal preference. Furthermore, the impact of seasons and choice of ML algorithms on TC metric and occupant behavior prediction is shown.

Children differ from adults in their physiology and cognitive ability. Thus, they are extremely vulnerable to classroom thermal comfort. However, very few reviews on the thermal comfort of primary school students are available. Further, children-focused surveys have not reviewed the state-of-the-art in thermal comfort prediction using machine learning (AI/ML). Consequently, there is a need for discussion on children-specific challenges in AI/ML-based prediction. This article bridges these research gaps. It presents a comprehensive review of thermal comfort studies in primary school classrooms since 1962. It considers both conventional (non-ML) studies and the recent AI/ML studies performed for children, classrooms, and primary students. It also underscores the importance of AI/ML prediction by analyzing adaptive opportunities for children/students in classrooms. Thereafter, a review of AI/ML-based prediction studies is presented. Through an AI/ML case-study, it demonstrates that model performance for children and adults differs markedly. Performance of classification models trained on ASHRAE-II database and a recent primary students’ dataset shows a 29% difference in thermal sensation and 86% difference in thermal preference, between adults and children. It then highlights three major children-specific AI/ML challenges, viz., “illogical votes”, “multiple comfort metrics”, and “extreme class imbalance”. Finally, it offers several technical solutions and discusses open problems.

In Northeast China, most classrooms in primary and secondary schools still use natural ventilation during cold days in winter. This study investigated the thermal comfort and the perceived air quality of children in primary schools in severe cold regions in China. Field measurements were conducted in four typical primary classrooms in two naturally ventilated teaching buildings in the winter of 2016 in the provincial city of Shenyang. Six field surveys were distributed to 141 primary students aged 8 to 11, and 835 valid questionnaires were collected. The results showed that the indoor temperature and the daily mean CO2 concentrations of the primary school classrooms ranged from 17.06 to 24.29 °C and from 1701 to 3959 ppm, respectively. The thermal neutral temperature of the primary school students was 18.5 °C, and the 90% thermal comfort temperature ranged from 17.3 to 20.1 °C. Children were able to respond to changes in indoor air quality, but there was no significant correlation between the children’s perceptions of air quality and the carbon dioxide levels in the classroom. In general, children have a lower comfort temperature than adults. In addition, children are more sensitive to temperature changes during the heating season than adults. Due to differences in thermal sensation between children and adults, the current thermal comfort standard based on adult data is not applicable to primary school buildings and children. The air quality evaluation during heating season indicates that it is necessary to add indoor air environment monitoring instruments and purification equipment to the naturally ventilated classrooms. At present and in the future, more research based on children’s data is needed to solve the indoor air environment problems in primary school buildings.

This study aims to optimize indoor thermal environment assessment methods for primary school classrooms in regions with hot summers and cold winters, enhancing air-conditioning management efficiency and accuracy. Given the complexity of Predicted Mean Vote (PMV) calculations and its reduced accuracy under high temperature and humidity, this research explores the use of Thermal Sensation Vote (TSV) as a simpler alternative. Field measurements and subjective assessments were conducted to analyze the relationship between TSV and PMV, leading to a regression model linking predicted TSV (TSVp) with temperature and humidity. Results indicate that temperature and humidity significantly impact TSV, with regression coefficients of 0.499 and 0.055, respectively. Furthermore, when TSV is ≥1, the proportion of PMV of ≥0.5 remains stable, validating TSVp as a reliable indicator. Based on these findings, energy-efficient air-conditioning management strategies are proposed, recommending a temperature setting of 28 °C for thermal comfort. This study provides insights into climate control strategies in educational buildings, promoting sustainable development.

The physical environment of classrooms has a strong relationship with student learning performance and health. Since the outbreak of COVID-19 in 2019, almost all universities have begun implementing closed instructional management, which has forced students to spend a much longer amount of time inside the classroom. This has also led to an increasing problem of thermal comfort in classroom indoor environments. In this paper, classrooms evolved from three dominant teaching modes at Zhejiang Sci-Tech University (ZSTU), located in the Hot Summer and Cold Winter (HSCW) zone of China, were selected as experimental spaces. Meanwhile, 12 learning groups with 60 students (30 of each sex) were selected as the tested samples. The relationship between thermal comfort and learning efficiency of the tested students was established through thermal comfort questionnaires and learning efficiency tests under the typical natural conditions in transition seasons. Based on this, improvement strategies were proposed for the current state of the classroom environment, providing a database for optimizing the environmental conditions of university classrooms in HSCW zone on the basis of improving students’ learning efficiency.

One main challenge of air distribution in classrooms is to guarantee ventilation performance under different usage conditions. In this study, the indoor climate in summer and winter conditions with different occupancy densities in the classroom is presented. Thermal condition measurements of a half-size classroom were performed in a test room with four air suppliers: wall-grilles, ceiling diffusers, perforated duct diffusers, and displacement ventilation. Those measured data were used for CFD validation of the whole classroom. With CFD simulations, indoor climate parameters with different air diffusers are compared in summer and winter conditions. The results show that displacement ventilation gives the best performance in the occupied zone. The air change efficiency can be reached with displacement ventilation of 1.4 and of only 1 with the other three air diffusers. The air velocities were reasonably low (<0.3 m/s), and the indoor was quite uniform with ceiling diffusers, which is another well-performing solution for classrooms. Corridor wall-grilles give uniform thermal conditions but can have high velocities (0.4 m/s) on the perimeter side of the room space. The air distribution from the perforated duct diffuser is unstable, which causes high local draft (over 20%) in the occupied zone.

In hot and humid regions, many classrooms depend on air conditioning systems equipped with mixing ventilation to maintain acceptable indoor temperatures. However, this method often proves inadequate in delivering satisfactory thermal comfort due to elevated temperature and poor air distribution. This research explores the potential of zonal air supply strategies to enhance thermal comfort in a classroom situated in Jeddah, Saudi Arabia. During July 2024, field data—including measurements of airflow velocity, air temperature, relative humidity, and globe temperature—were collected to find key thermal comfort indices: Predicted Mean Vote (PMV) and Predicted Percentage of Dissatisfied (PPD). In addition, a survey was administered to assess students’ thermal perceptions. Computational Fluid Dynamics (CFD) simulations were utilized to predict air temperature, velocity, and humidity distribution, evaluating the impact of zonal air supply designs on thermal comfort within the classroom. Parametric analysis was used to identify the most effective zonal air supply configuration for reducing PMV and PPD values. The findings show that, under existing ventilation conditions, PMV and PPD indices at different heights exceed the recommended limits established by ASHRAE Standard-55, indicating thermal discomfort during peak temperature periods. Further investigation demonstrated that introducing a 4-zonal air supply system could reduce PMV by 16–32% and PPD by 32–36%, thereby significantly improving thermal comfort in the classroom. Article Highlights Thermal indices and surveys showed that the existing ventilation failed to ensure classroom summer comfort. Field measurements and CFD simulations show strong agreement with ± 5% deviation. The four-zonal air supply system with mixing ventilation improved the thermal comfort inside the classroom.

In northern Shaanxi, China, the air quality and thermal insulation properties of primary school classrooms should be given more attention due to the relatively low temperatures in the winter, which are significant to the learning processes of students in classrooms. Some sustainable building measures have been designed and constructed to improve the air quality and thermal comfort of classrooms in this region; however, is still unclear how these measures influence air quality and temperature. This study investigated the indoor air quality and thermal environment of a typical primary school classroom in Yulin city, Shaanxi Province, China. The classroom was characterized by sustainable structures, including double-sided corridors and an underground ventilation pipe, for better thermal insulation. By conducting on-site monitoring in the classroom and performing various numerical simulations based on finite element software, the variations in the indoor air quality (carbon dioxide, water vapor concentration) and temperature over time, and under different conditions, were investigated. Moreover, influences (i.e., of corridors, ventilation pipes, window areas, classroom areas, and the number of students) on the air quality and temperature were analyzed. It was proven that double-sided corridors, underground ventilation pipes, and windows with heights/widths equaling 1 could provide energy-efficient and livable building structures for primary school classrooms in the northern Shaanxi region of China.

There is a growing interest in using classroom response systems or clickers in science classrooms at both the university and K-12 levels. Typically, when instructors use this technology, students are asked to answer and discuss clicker questions with their peers. The existing literature on using clickers at the K-12 level has largely focused on the efficacy of clicker implementation, with few studies investigating collaboration and discourse among students. To expand on this work, we investigated the question: Does clicker use promote productive peer discussion among middle school science students? Specifically, we collected data from middle school students in a physical science course. Students were asked to answer a clicker question individually, discuss the question with their peers, answer the same question again, and then subsequently answer a new matched-pair question individually. We audio recorded the peer conversations to characterize the nature of the student discourse. To analyze these conversations, we used a grounded analysis approach and drew on literature about collaborative knowledge co-construction. The analysis of the conversations revealed that middle school students talked about science content and collaboratively discussed ideas. Furthermore, the majority of conversations, both ones that positively and negatively impacted student performance, contained evidence of collaborative knowledge co-construction.

Indoor particles and carbon dioxide concentration are major indices to evaluate indoor air quality. Based on the two-dimensional filler sieving model of the direct evaporative cooling segment, the porous media model was used for the simulation of the water filler section, the filtering efficiency of particle was simulated by adjusting the water drenching density and airflow velocity in different operating conditions. The three-dimensional classroom model used to change the exhaust outlet position and control the use of air conditioners simulated the indoor thermal environment and the changes in pollutant concentration. The Euler method and Lagrangian method were used to analyze the indoor flow field and particle sieving in the direct evaporation section, respectively. Conclusions show that in the application of evaporative cooling and stratum ventilation air conditioning system in classroom, the position of the exhaust port affects the concentration of carbon dioxide in the student’s breathing area. The water filler section can effectively reduce the concentration of particle and carbon dioxide supplied indoors. The filtration efficiency of particle in outdoor air passing through the direct evaporative cooling section based on diffusion, inertial collision, and interception is affected by the combined effect of particle size, onward wind speed, and water spray density. The filtration efficiency of particle increases as the density of the spray water increases. With the increase of head-on wind speed, the filtration efficiency of coarse particulate matter is higher than that of fine particulate matter. The research results help policy makers decide whether to install evaporative cooling air conditioning in schools and determine which exhaust outlet positions are most effective in improving indoor air quality.