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Article

A Perceptual Assessment of the Physical Environment in Teaching Buildings and Its Influence on Students’ Mental Well-Being

by
Qiang Wen
1,*,
Qiang Zhou
1,
Huiyao Ye
2,
Qinghai Guo
1,
Jingwen Shan
1 and
Zhonghui Huang
1
1
School of Civil Engineering and Architecture, Zhejiang Sci-Tech University, Hangzhou 310018, China
2
Department of Architecture, Zhejiang University, Hangzhou 310018, China
*
Author to whom correspondence should be addressed.
Buildings 2024, 14(6), 1790; https://doi.org/10.3390/buildings14061790
Submission received: 8 May 2024 / Revised: 5 June 2024 / Accepted: 11 June 2024 / Published: 13 June 2024
(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)
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Abstract

:
Numerous studies have examined the impact of the built environment on mental health, yet there remains an underexplored area concerning how microenvironments within educational buildings affect students’ mental well-being from a physical environment standpoint. This paper fills this gap by utilizing data from 440 valid questionnaires to develop regression models that assess students’ perceptions of physical environment factors in college teaching buildings and their impact on anxiety likelihood. This study examined the physical environment of the teaching building’s interior, courtyard, and semi-outdoor areas. Findings indicate that students’ perceptions of specific physical environment factors—such as classroom ventilation (p < 0.01, OR = 0.330), lighting (p < 0.01, OR = 0.444), noise conditions (p < 0.01, OR = 0.415), courtyard thermal comfort (p < 0.01, OR = 0.504), and the views from semi-outdoor areas (p < 0.01, OR = 2.779)—significantly influence the likelihood of experiencing anxiety. Optimal physical conditions are linked to reduced student anxiety. The suitability of the physical environment of teaching buildings is interrelated, and it is urgently necessary to address issues related to unsuitable lighting in window areas of classrooms, as well as problems with ventilation, lighting, and noise caused by the corridor layout within teaching buildings. These insights are crucial for the design and renovation of academic buildings to enhance students’ mental well-being.

1. Introduction

Contemporary college students face multiple pressures, such as role transition, academic tasks, interpersonal relationships, and employment [1,2,3,4]. These stressors often contribute to emotional distress, increasing their vulnerability to mental health disorders. This may affect students’ physical health and learning efficiency, potentially triggering social isolation and undesirable behaviors [5]. Anxiety, characterized by intrusive and discomforting feelings related to anticipated threats [6], manifests as excessive worry about both tangible matters and social interactions [7]. A significant percentage of students worldwide experience anxiety disorders. In China, the number of students enrolled in higher educational institutions exceeded 41 million in 2020 [8]. Nearly half of these students report anxiety-related issues, with 8–13% experiencing mild anxiety, 20% struggling with moderate symptoms, and 4–6% enduring severe anxiety [9].
The built environment significantly shapes individuals’ psychological well-being [10,11], particularly in relation to anxiety and sadness [12,13]. Recent research has extensively explored the relationship between physical surroundings and mental health [14], examining the effects of work environments [15], green spaces [16,17], and indoor air quality [18] on mental well-being. Studies have revealed that poorly identified environments are associated with increased anxiety [19], and adverse physical conditions such as overcrowding, noise, extreme temperatures, and inadequate lighting can contribute to anxiety [20,21], often stemming from external stressors perceived as beyond one’s control or uncertain [22,23]. Furthermore, social and built environment factors are important for physical and mental health in both urban and rural areas [2], with building layout and environment shaping an individual’s mental health by affecting their relationship with nature, their sense of personal control, and indoor air quality [20]. Studies suggest that within educational environments, students experience improved mental well-being when exposed to natural features like blue and green spaces [24]. Additionally, schools with supportive environments foster lower levels of student anxiety [19]. Adolescents prefer lower indoor temperatures but are more sensitive than adults to daily light levels, noise, and PM2.5 concentrations [25].
Ventilation plays a crucial role in classrooms, particularly during transitional seasons and summer, to manage thermal comfort and enhance air quality. Increasing indoor air velocity not only boosts students’ thermal comfort but also improves their perception of air quality and classroom humidity [26]. Teaching buildings have a higher risk of indoor air pollution than other buildings due to high population density [27]. Several studies have highlighted the correlation between insufficient ventilation, air pollution, and detrimental effects on mental well-being [20,28,29,30]. Specifically, an increase in fine particulate matter (PM2.5) has led to a decline in mental health [31], and poor ventilation has been linked to the development of anxiety symptoms [30,32], which are considered to be an important trigger for depression and psychiatric disorders [33]. Inadequate ventilation rates can elevate indoor carbon dioxide levels, posing serious health risks [34]. Indoor air pollutants not only endanger physical health, leading to respiratory issues [35] and cognitive decline [36], but may also indirectly impact mental well-being by discouraging outdoor activities [37].
Extensive research has been conducted on the impact of thermal environments on student health, with a particular focus on the relationship between temperature, thermal comfort, health, and academic performance [38,39,40]. Thermal comfort significantly contributes to students’ satisfaction with their classroom environment and is a crucial factor in both their physical and mental health. It also influences individual motivation, attention, and mood [41], highlighting its importance in educational settings. Studies have shown that ambient temperature and mental health outcomes are significantly correlated [42]. The relationship between temperature, air quality, and health is V-shaped [2]. Low temperatures are associated with decreased negative mental health, while high temperatures tend to increase negative mental health [43]. High temperatures have been correlated with various mental health issues, including depression, anxiety, mood disorders, and aggression [44]. They also diminish positive mood, amplify negative mood, and contribute to fatigue [45]. Additionally, high temperatures may exacerbate existing mental disorders, increase the risk of suicide and psychiatric hospitalization [46], and lead to a decrease in healthy activities, thereby affecting psychological well-being [47].
A wealth of research highlights the negative impact of noise on mental health [48,49]. There are direct links between noise-induced irritability and higher rates of anxiety and depression [48]. Noise from neighbors, as well as urban and traffic sounds, is significantly correlated with deteriorated mental health outcomes [50]. Excessive noise levels can impair students’ hearing and comprehension abilities [51]. Conversely, a conducive acoustic environment is essential for helping students clearly understand their teachers, which can improve their academic performance [52].
Research findings suggest that optimal lighting conditions can effectively alleviate stress, anxiety, and mood disturbances. Conversely, inadequate lighting increases the likelihood of depression by a significant 60% [21]. Both daylighting and artificial lighting are associated with reduced fatigue, relief from sadness, and a decrease in depressive symptoms, along with various other health benefits [53]. Optimal lighting environments are vital for visual comfort, impacting not only health and well-being but also satisfaction, learning, and visual performance [54]. Additionally, the non-visual impacts of lighting on students, such as visual efficacy and mood [55], play a significant role in shaping their learning status and outcomes [56]. These findings underscore the importance of proper lighting in educational settings.
Numerous studies have highlighted the positive effects of nature exposure on mental well-being [57,58]. Studies have shown that exposure to nature enhances physical and mental health [59] and cognitive abilities [60] and that green space accessibility has a significant positive effect on the mental health of older adults [61]. Conversely, limited exposure to nature can lead to adverse mental well-being outcomes [20]. Plants have been found to have a positive impact on psychological healing [62], and individuals living in areas with limited green spaces are more susceptible to mental well-being issues when unwell [63]. The presence of trees on college campuses is linked to reduced anxiety among students [64] and having natural views from windows is directly associated with improved mental well-being [65].
College students face a multitude of challenges, including academic pressures, social dynamics, and financial constraints [66]. These challenges can make them more vulnerable to adverse behaviors and negatively affect their physical and mental well-being [67]. Academic buildings, as primary spaces for learning and living, play a crucial role in shaping students’ mental health. The design and condition of these environments can either mitigate or exacerbate mental well-being issues. However, most of the research exploring the relationship between mental well-being and the built environment has centered around either extensive urban landscapes or compact residential settings [2]. There needs to be more investigation into how the physical environments impact the mental well-being of students within academic facilities on a smaller scale. Previous research on the impact of school building environments on students’ mental health [3,4] has primarily examined them at a larger campus scale, neither adequately examining the unique physical attributes of these environments nor integrating the findings with the specifics of the educational setting. Therefore, there is an urgent need for a thorough examination of how each physical environment element within school buildings affects students’ anxiety.
The physical environment of teaching buildings encompasses three main dimensions: indoor spaces, semi-outdoor areas, and courtyards. This article aims to evaluate students’ perceptual assessments of different aspects of the physical environment within the school building. Additionally, it seeks to analyze the impact of these perceptual evaluations on students’ “anxiety or not” (AON). It conducts cause profiling and proposes optimization strategies. This study hypothesizes that students with low perceptual ratings of various physical environment factors in the academic building are more likely to experience anxiety. To mitigate confounding factors, a multifactorial holistic model was developed. The study’s findings will guide decision-making in building design with a focus on promoting health. These findings will also serve as a reference for designing and renovating school buildings, aiming to alleviate students’ anxiety and enhance their mental well-being by improving the physical environment.
The paper is structured as follows: Section 2 outlines the study design, statistical methodology, and the physical environment factors under investigation. Section 3 presents the statistical findings. Section 4 offers an analysis and discussion of these results, along with a discussion of the study’s limitations. Lastly, Section 5 summarizes the main findings and conclusions.

2. Material and Methods

2.1. Physical Environment Factors

This study focuses on Building Two at Zhejiang Sci-Tech University (Figure 1). This building is a primary location for student classes and is highly utilized. The building has a typical U-shaped layout, enclosing a semi-open courtyard, a common spatial configuration in educational buildings, which offers significant research and application value. The building’s spaces are organized around an internal courtyard (Figure 1b), with the courtyard open on the east side and bordered by 4- and 5-story teaching buildings on the north, west, and south sides (Figure 1d). An 8 m wide semi-outdoor corridor runs through the center of the courtyard (Figure 1b,g), connecting the south and north buildings. Therefore, the physical spatial environment of the teaching building examined in this study includes three dimensions: the classroom area, the semi-outdoor area, and the courtyard area (as shown in Table 1). This study investigates the physical environment factors affecting students’ experiences within the teaching building based on the existing literature and preliminary field surveys. These factors primarily include landscape views and perceptions of physical performance such as ventilation, lighting, noise, and thermal comfort environments. These factors may impact students’ mental health.
The classroom area is the most frequently used indoor space by students (Figure 1e). The primary physical environment factors for the classroom area include ventilation, lighting, thermal comfort, and noise conditions. According to field surveys, students often close the curtains to avoid direct sunlight, thereby excluding landscape view as a factor. The physical environment’s subfactors are detailed based on the actual layout of the classrooms in Building 2. Due to the inner corridor layout of the teaching building (Figure 1c), the ventilation subfactors include ventilation for south-facing classrooms, north-facing classrooms, and the inner corridor. The lighting subfactors include lighting for south-facing classrooms, north-facing classrooms, and the inner corridor. Since thermal comfort is significantly affected by seasonal changes, the subfactors for classroom thermal comfort include thermal comfort for south-facing classrooms and north-facing classrooms, as well as summer, winter, spring, and autumn thermal comfort.
The semi-outdoor area refers to non-enclosed indoor spaces within the teaching building, primarily including covered terraces and corridor areas (Figure 1d,g). These spaces are convenient for students to reach after class to rest, allowing them to enjoy outdoor nature while being sheltered from wind and rain. The quantity, landscape view, and quality of the physical environment in semi-outdoor spaces have a direct impact on students’ spatial experience and mental relaxation. Based on preliminary field surveys, the cold weather and strong north wind in winter are major considerations, so the wind environment and winter thermal comfort are included in the research model. Therefore, the subfactors affecting students’ experience in semi-outdoor areas include quantity, landscape view, wind environment, and winter thermal comfort.
The courtyard area refers to the semi-enclosed courtyard surrounded by buildings on three sides of the teaching building (Figure 1b,d). This partially enclosed outdoor space usually features planted greenery and some recreational facilities, making it a convenient outdoor relaxation area for students after class. In addition to landscape features, the microclimate environment of the courtyard also affects students’ outdoor experience. Therefore, the main physical environment factors of the courtyard area include courtyard landscape, courtyard ventilation, and courtyard thermal comfort.

2.2. Research Design and Statistical Methods

This paper investigates students’ perceptual evaluations of various factors in an academic building’s physical environment and examines how these evaluations affect students’ AON. Initially, relevant factors are identified based on the existing literature. The study then proceeds by designing a questionnaire and gathering data aligned with the research objectives. A mathematical model is created to perform a statistical analysis of the survey data, followed by a discussion that integrates findings from current studies and concludes with a conclusion.
Statistical analysis was conducted using SPSS version 26.0. Initially, descriptive statistics were used to explore differences in AON among demographic groups and to summarize perceptual evaluation scores for each environmental factor. Subsequently, a correlation analysis matrix was constructed, and a covariance test was performed on all variables to identify any interdependencies. Finally, t-tests and chi-square tests were utilized to analyze the relationships between dependent and independent variables, beginning with the initial impact assessment of each independent variable on the dependent variable.
This study developed three multifactorial models to mitigate confounding effects and isolate the influence of individual factors. First, a multiple linear regression model was constructed, incorporating eight physical environment factors as independent variables, alongside gender and grade as demographic controls. The dependent variable was the “Overall Assessment of the Physical Environment” (OAPE) of the school building, aimed at assessing the impact of the evaluation of each physical environment factor on the OAPE. Next, a binary logistic regression model (model A) was formulated to analyze factors influencing college students’ AON based on the perceptual ratings of the total physical environment factors. This model included the AON as the dependent variable and the perceptual ratings of the eight total physical environment factors, gender, and grade level as independent variables, as detailed in Table 2. Lastly, Model B, another binary logistic regression, was designed to examine the impact of perceived ratings of 16 subfactors of the physical environment on students’ anxiety, using these subfactors as independent variables and the AON as the dependent variable, as outlined in Table 3.
In the binary logistic regression model, the dependent variable is AON. This variable is assigned a value of “0” to indicate the absence of anxiety and “1” to signify the presence of anxiety. Anxiety symptoms were assessed using the Generalized Anxiety Disorder Assessment (GAD-7), which has a scoring range from 0 to 21. Scores were classified as follows: normal (0–4), mild anxiety (5–9), moderate anxiety (10–14), and severe anxiety (15–21), with a threshold of 5 distinguishing non-anxious from anxious responses. Based on this, participants were grouped into non-anxious (GAD-7 scores below 5) and anxious (GAD-7 scores of 5 or higher) [68]. In the multiple linear regression model, the dependent variable was OAPE, collected via a survey.
The questionnaire was divided into three sections: the first section collected demographic information, the second section assessed the students’ OAPE, and the third section evaluated the students’ perceptions of each total factor or subfactor of the physical environment. The students’ OAPE and their perceptual evaluation of each total physical environment factor were rated on a 5-point Likert scale from 1 (“very poor”) to 5 (“very good”). Perceived ratings of each subfactor of the physical environment were measured on a dichotomous scale, with “0” indicating “good” and “1” indicating “poor”.

2.3. Questionnaire Collection and Reliability

Data collection was conducted using a hybrid approach: an in-person questionnaire survey and an online questionnaire hosted on the “Questionnaire Star” platform. The survey was carried out in April 2023 at the #2 teaching building of Zhejiang Sci-Tech University. To ensure data representativeness, 465 college students from various grades and genders were invited to participate, resulting in 440 valid responses after screening. A reliability test yielded a high Cronbach’s alpha value of 0.858, affirming the internal consistency of the scale questions. Furthermore, the validity test results, using factor analysis (KMO value = 0.888, approx. chi-square = 1419.600, p < 0.001), confirmed a significant correlation among the items, demonstrating the strong validity of the research data, as illustrated in Table 4.

3. Results

3.1. Summary Statistics for the Variables

The survey included 440 valid respondents, consisting of 227 males and 213 females. As detailed in Table 5, the distribution across different school years was as follows: 28.864% in the first year, 26.818% in the second year, 25.909% in the third year, and 18.409% in the fourth year. Out of the 440 students, 107 (24.318%) reported feeling anxious, while 333 (75.682%) did not report anxiety. The prevalence of anxiety was higher among females, with 64 (30.047%) of the females and 43 (18.943%) of the males experiencing anxiety. By year, second-year students showed the highest anxiety rate at 34.746%, followed by third-year students at 29.825%, first-year students at 15.748%, and fourth-year students at 14.815%.
Table 6 and Figure 2 present the students’ overall perception of the school’s physical environment, which received a moderate mean score of 3.22 (SD = 0.809). When broken down by area, the courtyard within the academic building had the highest rating at 3.54. Semi-outdoor areas followed with a score of 3.44, while classroom areas received the lowest score at 3.05. Within the classroom areas, perceptual ratings were generally neutral; lighting received the highest rating at 3.27 (SD = 0.880), and thermal comfort received the lowest at 2.66 (SD = 0.961). In the courtyard areas, ventilation received the highest rating at 3.69 (SD = 0.794), while landscaping was slightly lower at 3.46 (SD = 0.809). The semi-outdoor areas displayed consistent ratings, with an average perceptual rating of 3.44 (SD = 0.899).
Figure 3 illustrates the mean percentage of students’ perceptual evaluations for various subfactors of the physical environment. The data show an average favorable rating of 62.756% (SD = 20.677) and an unfavorable rating of 37.244% (SD = 20.677). The highest percentage of unfavorable ratings was observed for “thermal comfort in summer” at 81.591%. That was followed by 66.364% for “thermal comfort in north-facing classrooms” and 59.091% for “thermal comfort in south-facing classrooms”. In contrast, students’ perceptions of “classroom thermal comfort in fall” and “classroom thermal comfort in spring” received the best evaluations, with only 6% and 7% unfavorable ratings, respectively.

3.2. Interrelationship of Variables

3.2.1. Correlation Analysis and Covariance Test

Table 7 presents the Pearson correlation matrix for all total factor perception ratings of the physical environment, revealing that the correlation coefficients do not exceed 0.7, indicating the absence of strong correlations (≥0.8). The highest correlation observed was 0.629 between the perceived ratings of “courtyard landscape” and “courtyard ventilation”, slightly higher than the 0.620 correlation between “courtyard ventilation” and “courtyard thermal comfort”. The weakest correlation (r = 0.297) was found between the ratings of “classroom thermal comfort” and the “semi-outdoor area”, Additionally, correlations between the total factors of the physical environment and the OAPE ranged from 0.402 to 0.580, with the highest at 0.580 between the OAPE and “classroom ventilation” and the lowest at 0.402 between the OAPE and “classroom noise conditions”.
To address potential covariance issues among variables, which could impact the accuracy of the regression model, a covariance test is advised. This test involves calculating tolerance and Variance Inflation Factors (VIFs) by incorporating both dependent and independent variables from the binary logistic regression model into a linear regression model, ensuring the reliability of the model’s estimates without distortion from covariance. According to the data in Table 8 and Table 9, the VIF for each independent variable is below 5, with the highest recorded VIF being 2.110 for “courtyard thermal comfort”. This indicates negligible multicollinearity among the variables, allowing their inclusion in the regression model without complications.

3.2.2. Tests of Variability

In binary regression model A, the independent variables include perceived evaluations of various physical environment factors, measured using a Likert five-point scale, treated as continuous variables in the study. To preliminarily assess the relationship between continuous independent variables and the dependent variable (anxiety or not) in binary logistic regression model A, an independent samples t-test was used. This test examined whether the mean perceived evaluations of various physical environment factors significantly differed between the anxious and non-anxious student groups. The test results indicated (see Table 10) that there were significant differences in the perception scores of various physical environment factors among students with different anxiety levels (p < 0.01).
On the other hand, the independent variables in binary regression Model B include students’ perceived evaluations of various physical environment subfactors, treated as binary variables. The independent variables, such as gender and grade level in binary regression model A, are categorical variables. Therefore, a chi-square test was employed to preliminarily analyze the relationship between these categorical independent variables and the dependent variable (anxiety or not) to determine whether there are significant differences in perceived evaluations of various physical environment subfactors, as well as whether students’ anxiety levels significantly differ by gender and grade level. The statistical results are shown in Table 11. The results indicate that, except for “classroom thermal comfort in winter”, “thermal comfort in south-facing classrooms”, and “thermal comfort in north-facing classrooms”, where no significant differences were observed (p > 0.05), there were significant differences in students’ perceptions of other various physical environment subfactors based on different anxiety levels (p < 0.05). Among demographic variables, gender and grade level had a significant impact on students’ anxiety levels.

3.3. Multiple Linear Regression Analysis

A multivariate linear regression model was developed with the OAPE as the dependent variable, combined with demographic variables and eight physical environment total factors as independent variables. The model demonstrated statistical significance, evidenced by an F-value of 39.798 and a p-value of less than 0.001. The coefficient of determination (R2) was 0.694, indicating that the six significant independent variables accounted for 69.4% of the variance in the overall assessment of the physical environment. Additionally, the Durbin–Watson statistic of 1.988 suggests good independence of the observations.
As shown in Table 12, the multiple linear regression model revealed significant influences on the OAPE from the perceptual ratings of various physical environment factors. Notably, “classroom ventilation” had the strongest impact (t = 8.697, p < 0.001), followed by significant effects from “semi-outdoor area” (t = 3.645, p < 0.001), “classroom lighting” (t = 3.108, p < 0.01), “courtyard landscape” (t = 2.854, p < 0.01), “classroom noise conditions” (t = 2.705, p < 0.01), and “classroom thermal comfort” (t = 2.138, p < 0.05). Based on the magnitudes of the standardized coefficients, the effect hierarchy is as follows: “classroom ventilation” (Beta = 0.369) exerts the most substantial effect on OAPE, with “semi-outdoor area” (Beta = 0.155), “courtyard landscape” (Beta = 0.138), “classroom lighting” (Beta = 0.128), “classroom noise conditions” (Beta = 0.113), and “classroom thermal comfort” (Beta = 0.089) following in order of decreasing impact. These findings suggest that “classroom ventilation” and “semi-outdoor area” are the primary factors affecting students’ OAPE. The regression equation derived from the coefficients table, which can be used to predict OAPE, is given as follows:
OAPE = 0.403 + 0.316 × Q5 + 0.118 × Q6 + 0.075 × Q7 + 0.103 × Q8 + 0.126 × Q9 + 0.139 × Q12

3.4. Statistical Results of Binary Logistic Regression A

Binary logistic regression model A analyzed the impact of students’ perceptual ratings of eight key physical environment factors on their anxiety occurrence, with “anxiety or not” serving as the dependent variable. The results presented in Table 13 indicate that the Omnibus test confirmed the model’s statistical significance (χ2 = 184.237, p < 0.001), while the Hosmer–Lemeshow test verified a good model fit (p > 0.05). The −2 log-likelihood value (−2LL = 203.916) further quantifies the model’s goodness of fit. Overall, the model demonstrated strong predictive accuracy, correctly classifying 84.091% of the cases in the observed sample. It accurately identified 53.271% of cases as having anxiety and 93.994% as not having anxiety.
The findings from binary logistic regression model A, displayed in Table 14, reveal significant effects of students’ perceptual ratings on four total physical environment factors on their anxiety occurrence. Notably, classroom ventilation (p < 0.001, Exp(B) = 0.330), classroom lighting (p < 0.001, Exp(B) = 0.444), classroom noise conditions (p < 0.001, Exp(B) = 0.415), and courtyard thermal comfort (p < 0.01, Exp(B) = 0.504) all significantly influenced students’ anxiety. Conversely, the factors of classroom thermal comfort, courtyard landscape, and courtyard ventilation did not show a significant impact on students’ AON.
As depicted in Figure 4, perceptual ratings of four physical environment factors significantly influenced the likelihood of student anxiety. Classroom ventilation showed the most substantial negative impact, followed by classroom noise conditions, classroom lighting, and courtyard thermal comfort. Specifically, for each increment in the rating of classroom ventilation from very poor to very good, the probability of developing anxiety was 0.330 times that of not developing anxiety. Similarly, each rating improvement in classroom noise conditions reduced the probability of anxiety to 0.415 times that of not developing anxiety. For classroom lighting, this probability was 0.444 times, and for courtyard thermal comfort, it was 0.504 times. Additionally, the study highlighted a notable gender difference in anxiety development, with male students being only 0.529 times as likely as female students to develop anxiety, indicating a higher susceptibility to anxiety among female students.

3.5. Statistical Results of Binary Logistic Regression B

To further investigate the influence of physical environment subfactors on students’ AON, a binary logistic regression model B was employed. This model categorizes “anxiety or not” as the dependent variable and incorporates the perceptual evaluations of 20 physical environment subfactors as independent variables. According to Table 15, the Omnibus test confirms the model’s statistical significance (χ2 = 138.356, p < 0.001). The Hosmer–Lemeshow test also validated the model’s fit (p > 0.05), indicating that it appropriately matches the data. The −2 log-likelihood value (−2LL = 249.798) further quantifies the model’s fit. Overall, the model demonstrated high accuracy, correctly predicting 81.136% of the cases in the sample. Specifically, it accurately identified 45.794% of cases as having anxiety and 92.492% as not having anxiety.
Table 16 presents the statistical results from binary logistic regression model B, revealing that students’ perceptions of various physical environment subfactors significantly influence their anxiety levels. Key findings include the ventilation of inner corridors (p < 0.001, Exp(B) = 3.070), lighting in south-facing classrooms (p < 0.001, Exp(B) = 3.851), and lighting in the inner corridors (p < 0.001, Exp(B) = 2.955). Additionally, assessments of classroom thermal comfort during summer (p < 0.01, Exp(B) = 3.966), spring (p < 0.05, Exp(B) = 3.815), and fall (p < 0.05, Exp(B) = 3.530) all show significant impacts on anxiety. The number of semi-outdoor areas (p < 0.001, Exp(B) = 2.587) and the landscape views from these areas (p < 0.01, Exp(B) = 2.779) also significantly affect student anxiety. According to Figure 5, the most significant factor contributing to student anxiety is “classroom thermal comfort in summer”, closely followed by “lighting in south-facing classrooms” and “classroom thermal comfort in spring”.
The perception assessment of “classroom thermal comfort in summer” significantly influences students’ likelihood of experiencing anxiety. Students who perceived thermal comfort as poor during summer were 3.966 times more likely to be anxious compared to those who rated it as good. Similarly, the evaluation of “lighting in south-facing classrooms” shows a notable positive correlation with student anxiety; students dissatisfied with the lighting were 3.851 times more likely to feel anxious. The assessment of “classroom thermal comfort in spring” also has a marked positive effect on anxiety levels, with those rating it poorly being 3.815 times more likely to be anxious. The perception of “classroom thermal comfort in fall” further supports this trend, with a significant impact on anxiety, as students unhappy with the fall thermal conditions were 3.530 times more prone to anxiety. The evaluation of “ventilation in inner corridors” is another critical factor, where poor ratings increased the likelihood of anxiety by 3.070 times. Additionally, the perception of “lighting in inner corridors” strongly affects students’ anxiety, with poor evaluations correlating to a 2.955 times higher chance of anxiety. The assessment of “landscape views from semi-outdoor areas” also significantly affects students’ emotional well-being, with negative views increasing anxiety likelihood by 2.779 times. Lastly, the perception of the “number of semi-outdoor areas” shows a substantial impact, with students rating them as poorly experiencing anxiety 2.587 times more frequently than their counterparts.

4. Discussion

4.1. Physical Environment of Indoor Space in Teaching Building

The statistical analysis revealed that within the physical environment of academic building classrooms, students’ perceptions of ventilation, lighting, and noise conditions significantly influenced both their anxiety levels and the OAPE. Improvements in these factors were associated with higher OAPE scores and a decreased likelihood of experiencing anxiety. Among the various subfactors of the classroom’s physical environment, perceptual ratings of ventilation and lighting in corridors, lighting in south-facing classrooms, and thermal comfort during the summer, spring, and fall seasons significantly and positively affected student anxiety levels. However, perceived ratings of lighting in north-facing classrooms, along with ventilation and thermal comfort in both north- and south-facing classrooms and thermal comfort in winter classrooms, did not significantly impact the development of student anxiety.

4.1.1. Classroom Lighting

The classrooms surveyed were all designed with inner corridor layouts, as shown in Figure 1c. Students rated their perception of classroom lighting as moderate, with a mean score of 3.266 and a standard deviation of 0.880. Poor ratings of light in interior corridors and south-facing classrooms were associated with higher levels of student anxiety. The large windows providing one-sided lighting caused poor lighting conditions due to glare and uneven light distribution. Additionally, classrooms facing south experienced direct sunlight on the desks, adversely affecting the learning environment, particularly for students seated near the windows (Figure 1e). The design of the inner corridors contributed to inadequate lighting in both the central classroom area and the corridors themselves. To enhance lighting conditions at window seats conducive to learning, students frequently closed the curtains. Observational studies revealed that in south-facing classrooms, 85% of the curtains were drawn, compared to just 8% in north-facing classrooms, as illustrated in Figure 6. Students attempted to mitigate lighting issues by closing the curtains and using artificial light, but this did not improve their overall satisfaction with classroom lighting and was associated with higher anxiety levels. This phenomenon may be explained by the fact that, while areas without direct sunlight do not typically cause visual discomfort [54], human vision functions more effectively under natural daylight than artificial lighting [53]. Moreover, increased exposure to natural light is known to have antidepressant effects [69], suggesting that insufficient natural light in classrooms can increase the risk of anxiety. Additionally, the inner corridors were dimly lit due to the curtains being drawn over the outer windows and the corridor layout, coupled with the avoidance of artificial lighting during the day to conserve energy. This resulted in insufficient lighting (Figure 1f), which may significantly increase anxiety among students.

4.1.2. Classroom Noise

This study discovered that a lower student’s perceived rating of the noise environment in classrooms is associated with an increased likelihood of student anxiety. This finding aligns with previous research [3,70], which concluded that acoustic environments have a greater impact on anxiety relief than visual environments [70] and that noise is detrimental to mental health [48,49] and linked to both depression and anxiety [71]. The affected classrooms are typically situated near sources of noise, such as urban transport routes or internal school paths and sports areas. However, the classrooms in this study were located inside the school, away from traffic and playgrounds, with the primary noise sources being internal corridors and neighboring classrooms. Field studies indicated that 79% to 97% of the doors and windows in these corridors were kept closed (Figure 1f and Figure 6). While closing doors and windows in the corridors can mitigate noise and sound disturbances, this practice can also lead to inadequate ventilation and poor thermal comfort within these spaces. Conversely, achieving optimal ventilation and thermal comfort in classrooms with an inner corridor layout requires opening both the external and corridor doors and windows to enable effective convection ventilation. However, this often results in increased noise disturbance. Thus, balancing the various aspects of physical comfort in these environments remains a challenge, contributing to heightened student anxiety.

4.1.3. Classroom Ventilation

This study revealed that students’ perceived ratings of the ventilation in both classrooms and inner corridors significantly impacted their anxiety levels negatively, aligning with previous research that has shown poor ventilation contributes to air pollution, reducing indoor air quality and adversely affecting mental health [20], as well as being linked to negative mental states like depression and anxiety [28,29,30]. Students’ perceptions of classroom ventilation were rated as fair (M = 3.009, SD = 0.942), influenced by the building’s internal corridor layout and the ineffective ventilation in both the classrooms and corridors. Field observations indicated that the conditions at the window seats—affected by light and drafts—were unsuitable for learning, leading students to close the windows and the curtains. Consequently, over 90% of the classroom’s external windows and 85% of the curtains in south-facing classrooms were closed. Moreover, to minimize noise from the corridors and adjacent classrooms, most doors and windows in the inner corridors were also shut (Figure 6), hindering the development of convective natural ventilation and resulting in poor classroom ventilation. This lack of effective ventilation not only compromised air quality and thermal comfort but also created conditions where issues related to ventilation, lighting, noise, and thermal comfort could not be simultaneously resolved, thereby increasing the likelihood of student anxiety.

4.1.4. Classroom Thermal Comfort

This study revealed that poor ratings of classroom thermal comfort during the summer, spring, and fall significantly correlated with increased student anxiety. Hangzhou’s climate features hot summers and cold winters, with prolonged summer and winter periods but brief spring and fall. The internal corridor layout of the classrooms and the year-round closure of doors and windows contributed to suboptimal thermal comfort during these transitional seasons. However, thermal comfort improved during winter due to the enclosed environment and higher occupancy levels in classrooms. Students rated their perception of classroom thermal comfort poorly, with an average score of 2.657 (SD = 0.961). Specifically, 82% of students expressed dissatisfaction with summer thermal conditions, while only 7% and 6% reported poor comfort in spring and fall, respectively. The primary concern is that elevated temperatures can adversely affect mental health, potentially leading to depression and anxiety [44]. In contrast, 48% of students rated winter classroom thermal comfort as poor, primarily due to cold conditions; however, studies indicate that while higher temperatures may worsen mental health outcomes, cooler temperatures may mitigate them [43]. Thus, poor winter thermal ratings did not seem to influence anxiety levels among students. The study also found that classroom orientation, whether north or south, did not affect perceptions of thermal comfort, which were solely dependent on seasonal variations. Classrooms remained sealed throughout the year, with curtains and windows consistently closed, minimizing the impact of external weather conditions and leading to less temperature variation between classrooms regardless of orientation. Indoor ventilation plays a crucial role in determining thermal comfort [41], emphasizing the importance of proper natural ventilation in classrooms to enhance comfort levels [72,73]. Increasing air velocity can allow for a higher maximum comfortable temperature by approximately 2 °C [72]. Addressing the ongoing closure of classroom doors and windows, especially during transitional seasons, is essential for improving thermal comfort and, potentially, the mental health of students.

4.1.5. The Relevance and Improved Control of the Physical Environment of the Classroom

The perceived ratings of the four indoor physical environment factors were significantly correlated with each other, with the strongest correlation observed between perceived ratings of ventilation and thermal comfort (r = 0.444, p < 0.01). This was followed by correlations between classroom ventilation and lighting (r = 0.420, p < 0.01) and between thermal comfort and noise conditions (r = 0.359, p < 0.01). In school buildings with interior corridor layouts, these issues are interconnected. For instance, inadequate classroom ventilation and thermal comfort can often be traced back to the design featuring inner corridors and issues with the classroom lighting environment. To improve the light environment and heat comfort around window areas, students often close exterior windows and curtains. Similarly, closing doors and windows along the interior corridors mitigates noise from these areas and adjacent classrooms. However, these solutions introduce new challenges: classrooms become poorly ventilated, corridors poorly lit, thermal comfort is reduced during spring and summer, and there is an increased reliance on artificial lighting. Effectively managing these issues simultaneously proves challenging and sometimes unattainable, leading to situations that exacerbate student anxiety. This inability to control or adequately improve their physical environment is a fundamental cause of anxiety among students.
Mental well-being is influenced by an individual’s control over their body and environment [22,74,75,76]. Consequently, having control over one’s built environment can directly impact mental well-being [20]. Therefore, the increased likelihood of anxiety due to students’ poor perceptions of their physical environments largely depends on their ability to control or effectively mitigate these unsatisfactory conditions without compromising other aspects of the physical environment. For example, closing windows and curtains to enhance comfort in areas with windows often leads to inadequate ventilation and lighting in inner corridors. Additionally, this can cause discomfort due to poor thermal conditions in the warmer months of spring and summer. Not all physical environment enhancements can be implemented simultaneously, creating further challenges.
To enhance students’ mental well-being and reduce their anxiety, it is crucial to address the inadequate ventilation and lighting issues caused by the school building’s inner corridor layout and the suboptimal conditions near classroom windows. For side window lighting, improving classroom illumination can be achieved through the implementation of shading systems and innovative ceiling designs [77,78]. One solution is to install balconies outside classrooms and add shading on the southern side. This strategy not only shields from direct sunlight but also moderates the impact of the outdoor climate on the thermal comfort near the windows, while creating a semi-outdoor resting area. Such changes encourage students to regularly open windows and curtains, promoting a well-lit environment with effective natural ventilation. Additionally, the design of buildings with inner corridors often hampers ventilation when doors are closed and contributes to dimly lit corridors. Addressing these issues during the architectural design phase, such as incorporating light wells, can enhance natural ventilation and thermal comfort, especially during seasons when air conditioning is unnecessary. This approach also improves corridor lighting, alleviates student anxiety, and helps reduce energy use.

4.2. The Physical Environment of the Courtyard Area of the School Building

This study shows that students’ perceived evaluations of courtyard thermal comfort significantly impact their anxiety levels. The climate in Hangzhou is characterized by hot summers and cold winters, making outdoor spaces too hot in the summer and quite cold in the winter. Students’ average rating of courtyard thermal comfort is moderate, with an average score of 3.464 and a standard deviation of 0.809. Although the winter and summer breaks avoid the most extreme weather months, many students stay on campus to study during these periods. Observations indicate that in the first half of the year (spring and summer), March is relatively cold, April and May have moderate temperatures, and June to August is hot; in the second half of the year (autumn and winter), September is hot, October and November are moderate, and December to February is cold. As a result, students can only comfortably use the courtyard for less than four months a year. Additionally, the activity and rest spaces inside the U-shaped courtyard need more summer shading and winter windbreak facilities, leading to low outdoor thermal comfort for students.
The study indicates that the lower the courtyard thermal comfort rating, the higher the likelihood of student anxiety. That is partly because students using the courtyard in summer experience anxiety due to overheating, consistent with existing research showing that summer heat waves cause physical and emotional distress leading to anxiety [79] and that heat exposure negatively impacts mental health, especially in terms of depression and anxiety [44]. However, the outdoor courtyard space, intended as a place for students to relax, engage in activities, and connect with nature, is hindered by seasonal changes. For most of the year, the courtyard lacks comfortable microclimate conditions, preventing students from fully utilizing the space and increasing the risk of mental health issues. This is particularly true for students who stay on campus during winter and summer breaks, as they often face higher expectations and study pressure, leading to increased anxiety levels. Therefore, students dissatisfied with courtyard thermal comfort and giving it lower ratings are more likely to experience anxiety. Given the significant impact of courtyard thermal comfort on student anxiety, courtyard design must prioritize the creation of a favorable microclimate. This can be achieved by adjusting the courtyard’s height-to-width ratio and increasing the amount of greenery. Adequate shading is also a key factor in improving thermal comfort and should be considered in future design solutions [24].

4.3. The Physical Environment of Semi-Outdoor Areas of the School Building

This study found that perceptual ratings of the semi-outdoor areas of the academic building significantly and positively influenced the OAPE. Among the subfactors of these areas, poor ratings of the number of semi-outdoor spaces and the view of the landscape from these areas significantly increased the likelihood of student anxiety. This aligns with prior research [3], which indicates that viewing nature in semi-outdoor areas can reduce stress through psychological and physiological means, thus benefiting mental well-being [80]. These areas also offer multi-sensory experiences, such as hearing birds and smelling flowers [81], which enhance mental well-being through sensory engagement [82], as the combination of green scenes and natural sounds can significantly reduce anxiety [70]. For students in classrooms, semi-outdoor areas provide a natural contact point, protection from weather elements, and easy access, all of which are crucial for mitigating academic stress and anxiety. Adequate semi-outdoor areas help prevent overcrowding in indoor spaces, improving mood, as overcrowding is closely linked to depression and low spirits [83]. Additionally, semi-outdoor areas facilitate social interactions [84], which are essential for positive mental well-being; conversely, insufficient socialization can harm students’ mental well-being [85].
Through investigation and field experience, it was found that the thermal comfort of semi-outdoor corridor spaces is better than that of indoor spaces in spring, summer, and autumn but worse in winter. Since the corridor space in the middle of Building 2 is not enclosed to the north (Figure 1g), it is often subjected to cold north winds in winter, further reducing the winter thermal comfort of the semi-outdoor corridor space. Statistical analysis shows that 34% of students rated the winter thermal comfort of the semi-outdoor space poorly. The statistical results of binary regression equation B indicate that, at a significance level of 0.1, the winter thermal comfort of semi-outdoor spaces (p = 0.075) significantly affects the occurrence of student anxiety. Although, according to the environmental control theory [20], students can improve their dissatisfaction with winter thermal comfort by wearing more clothes or choosing not to use the semi-outdoor corridor space in winter, these semi-outdoor spaces are the closest areas to the classrooms and are convenient for students to relax during breaks. For students dissatisfied with winter thermal comfort, if they do not use the semi-outdoor corridor spaces, it is difficult for them to find better places to relax and connect with nature during short breaks, leading to unrelieved study pressure and an increased likelihood of anxiety.

4.4. Impact of Physical Environment Perception on Special Groups

Due to the central corridor layout, a lack of shading on the south side, and poor lighting in the window areas of the classrooms, the teaching buildings in this study have suboptimal ventilation, lighting, noise, and thermal environments. These factors significantly increase the probability of student anxiety and may have an even greater impact on students with disabilities, special needs, mental health issues, or learning disabilities. Poor ventilation might worsen asthma symptoms and anxiety and depression symptoms in students. Insufficient lighting can cause depression, and visually impaired pupils have trouble seeing the chalkboard and course materials. Noise interference makes it harder for hearing-impaired students to understand the teacher and stresses out anxious students. High temperatures might worsen anxiety in anxious students. Students with depression may feel oppressed by poor landscape views in semi-outdoor environments, worsening their mental health. Thus, enhancing classroom ventilation, lighting, noise, and thermal conditions, notably courtyard thermal comfort and semi-outdoor area landscape views, can greatly improve campus inclusivity and accessibility. These improvements include increasing natural ventilation in classrooms, improving lighting to avoid strong light and shadows, using noise-reducing materials to minimize noise interference, providing shading and cooling equipment to ensure comfortable classroom temperatures, using vegetation and water features to regulate courtyard temperature and humidity, and designing accessible facilities to make semi-outdoor spaces easy to use for all students. These enhancements improve learning and living for all students, but they are especially crucial for students with disabilities, special needs, and mental health difficulties, helping them adjust to campus life and improve their mental health and academic performance.

4.5. Limitations

This study presents certain limitations. It was conducted exclusively in the #2 teaching building at Zhejiang Sci-Tech University, focusing solely on how this specific building’s physical environment influences student anxiety. Future studies should broaden the investigation to include additional college buildings. Moreover, data collection relied on questionnaires, which may be susceptible to extraneous influences during completion, potentially causing discrepancies between the reported and actual psychological states of students. Due to the limited length of the article and the small amount of data collected, this study primarily focuses on the direct impact of independent variables on the occurrence of anxiety. Moreover, the relationship between physical environment factors studied in this article and anxiety may vary according to students’ gender, socioeconomic status, or other demographic characteristics. Therefore, in future work, the sample size will be increased to explore in-depth the potential interaction effects between various physical environment factors and their impact on student anxiety, as well as the differences in conclusions based on different student demographic characteristics. Additionally, other environmental factors related to student mental health also deserve further investigation, including landscape design, vegetation types, architectural aesthetics and colors, and environmental features. The accessibility of buildings can also be considered a potential physical environment factor that may influence student mental health. These elements could be considered as variable factors in future studies to enhance the understanding of their impacts on mental well-being, alongside an increase in the scale of questionnaire distribution. Moreover, it is beneficial to explore how students’ perceptual evaluations of the physical environment and its impact on mental health change over time and across different social contexts. Such research could extend the findings of this study and offer insights for health-oriented campus design in the future.

5. Conclusions

This research focuses on the influence of academic building environments on student anxiety. It utilizes survey data to study students’ perceptual evaluations of various aspects of the physical environment in university academic buildings and examines how these evaluations affect the likelihood of student anxiety. To investigate the independent effects of various physical environment factors, the research employs both multiple linear regression models and binary logistic regression models, analyzing their influence on the dependent variable. Our findings suggest that students’ perceptual ratings of four major physical environment factors significantly reduce the likelihood of student anxiety. These factors include classroom ventilation (p < 0.001, Exp(B) = 0.330), classroom lighting (p < 0.001, Exp(B) = 0.444), classroom noise conditions (p < 0.001, Exp(B) = 0.415), and courtyard thermal comfort (p < 0.01, Exp(B) = 0.504). Additionally, perceptual ratings of eight specific subfactors significantly increase the likelihood of student anxiety. These subfactors are ventilation of inner corridors (p < 0.001, Exp(B) = 3.070), lighting in south-facing classrooms (p < 0.001, Exp(B) = 3.851), lighting in inner corridors (p < 0.001, Exp(B) = 2.955), classroom thermal comfort in summer (p < 0.01, Exp(B) = 3.966), classroom thermal comfort in spring (p < 0.05, Exp(B) = 3.815), classroom thermal comfort in fall (p < 0.05, Exp(B) = 3.530), the number of semi-outdoor areas (p < 0.001, Exp(B) = 2.587), and landscape views from semi-outdoor areas (p < 0.01, Exp(B) = 2.779).
Moreover, this study found that students’ overall assessment of the physical environment is significantly influenced by their ratings of classroom ventilation, classroom lighting, classroom thermal comfort, courtyard ventilation, and semi-outdoor areas. The results indicate that enhancing classroom features such as ventilation, lighting, and thermal and noise conditions, along with improving courtyard thermal comfort, the number of semi-outdoor spaces, and the views from these areas, can lower the likelihood of student anxiety. Additionally, refining classroom ventilation, lighting, thermal comfort, views of the courtyard, and the layout of semi-outdoor areas is crucial for boosting student contentment with the physical aspects of academic buildings. Optimal physical environments are associated with lower levels of student anxiety. Addressing unsuitable lighting near classroom windows, along with ventilation, lighting, and noise issues arising from the building’s internal corridor design, is essential given the integral role of physical environment appropriateness in educational settings. The findings from this study offer valuable insights for designing and remodeling school facilities, emphasizing the link between students’ mental well-being and the physical environment. The objective is to enhance students’ contentment with their physical surroundings, bolster mental well-being, and reduce anxiety.

Author Contributions

Conceptualization, Q.W.; methodology, Q.W. and Q.Z.; software, Q.W. and H.Y.; validation, Q.W.; formal analysis, Q.W.; investigation, H.Y., J.S. and Z.H.; resources, Q.G. and Q.Z.; data curation, H.Y.; writing—original draft preparation, Q.W.; writing—review and editing, Q.W.; visualization, Q.W.; supervision, Q.W.; project administration, Q.W.; funding acquisition, Q.Z., Q.G. and J.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Social Science Fund of China (22BGL193), the National Natural Science Foundation of China (NSFC) [32371650] and the Zhejiang Provincial Philosophy and Social Sciences Planning Project (24NDQN174YBM).

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Zhejiang Sci-Tech University #2 teaching building: (a) campus master plan, (b) general plan of #2 school building, (c) third floor plan, (d) courtyard spaces, (e) interior spaces, (f) corridor spaces, and (g) semi-outdoor area.
Figure 1. Zhejiang Sci-Tech University #2 teaching building: (a) campus master plan, (b) general plan of #2 school building, (c) third floor plan, (d) courtyard spaces, (e) interior spaces, (f) corridor spaces, and (g) semi-outdoor area.
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Figure 2. Histogram of mean values of total physical environment factors.
Figure 2. Histogram of mean values of total physical environment factors.
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Figure 3. Stacked histogram of perceptual evaluations of physical environment subfactors.
Figure 3. Stacked histogram of perceptual evaluations of physical environment subfactors.
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Figure 4. Physical environment total factors that significantly influence “anxiety or not”.
Figure 4. Physical environment total factors that significantly influence “anxiety or not”.
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Figure 5. Physical environment subfactors that significantly affect anxiety tendencies.
Figure 5. Physical environment subfactors that significantly affect anxiety tendencies.
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Figure 6. Statistics on the opening and closing of classroom doors and windows.
Figure 6. Statistics on the opening and closing of classroom doors and windows.
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Table 1. The physical environment of the school building.
Table 1. The physical environment of the school building.
School BuildingTotal Factors of Physical EnvironmentSubfactors of Physical Environment
Classroom areaClassroom ventilationVentilation of south-facing classrooms
Ventilation of north-facing classrooms
Ventilation of inner corridors
Classroom lightingLighting in south-facing classrooms
Lighting in north-facing classrooms
Lighting in the inner corridors
Classroom thermal comfortClassroom thermal comfort in summer
Classroom thermal comfort in winter
Classroom thermal comfort in spring
Classroom thermal comfort in fall
Thermal comfort in south-facing classrooms
Thermal comfort in north-facing classrooms
Classroom noise conditionsClassroom noise conditions
Courtyard areaCourtyard landscapeCourtyard landscape
Courtyard ventilationCourtyard ventilation
Courtyard thermal comfortCourtyard thermal comfort
Semi-outdoor areaSemi-outdoor areaNumber of semi-outdoor areas
Wind environment in semi-outdoor area
Landscape views from semi-outdoor area
Winter thermal comfort in semi-outdoor area
Table 2. Variables and assignments for the binary logistic regression A and the multiple linear regression.
Table 2. Variables and assignments for the binary logistic regression A and the multiple linear regression.
CategoriesSerial No.Variable NamesAssignment of Variables
Dependent variablesBinary logistic regressionQ3Anxiety or not (AON)0 = No anxiety, 1 = Anxiety
Multiple linear regressionQ4Overall assessment of the physical environment (OAPE)1 = Very poor, 2 = Poor, 3 = Neutral, 4 = Good, 5 = Very good
Independent variablesDemographic background variables Q1Genders0 = Female, 1 = Male
Q2Grades1 = First grade, 2 = second grade, 3 = third grade, 4 = fourth grade
Total factors of physical environment perception assessmentClassroom areaQ5Classroom ventilation1 = Very poor, 2 = Poor, 3 = Neutral, 4 = Good, 5 = Very good
Q6Classroom lighting1 = Very poor, 2 = Poor, 3 = Neutral, 4 = Good, 5 = Very good
Q7Classroom thermal comfort1 = Very poor, 2 = Poor, 3 = Neutral, 4 = Good, 5 = Very good
Q8Classroom noise conditions1 = Very poor, 2 = Poor, 3 = Neutral, 4 = Good, 5 = Very good
Courtyard areaQ9Courtyard landscape1 = Very poor, 2 = Poor, 3 = Neutral, 4 = Good, 5 = Very good
Q10Courtyard ventilation1 = Very poor, 2 = Poor, 3 = Neutral, 4 = Good, 5 = Very good
Q11Courtyard thermal comfort1 = Very poor, 2 = Poor, 3 = Neutral, 4 = Good, 5 = Very good
Semi-outdoor areaQ12Semi-outdoor area1 = Very poor, 2 = Poor, 3 = Neutral, 4 = Good, 5 = Very good
Table 3. Variables and assignments for binary logistic regression B.
Table 3. Variables and assignments for binary logistic regression B.
CategoriesSerial No.Variable NamesAssignment of Variables
Dependent variablesBinary logistic regressionQ3Anxiety or not (AON)0 = No anxiety, 1 = Anxiety
Independent variablesSubfactors of physical environment perception assessmentClassroom ventilationQ5_1Ventilation of south-facing classrooms0 = Good, 1 = Poor
Q5_2Ventilation of north-facing classrooms0 = Good, 1 = Poor
Q5_3Ventilation of inner corridors0 = Good, 1 = Poor
Classroom lightingQ6_1Lighting in south-facing classrooms0 = Good, 1 = Poor
Q6_2Lighting in north-facing classrooms0 = Good, 1 = Poor
Q6_3Lighting in the inner corridors0 = Good, 1 = Poor
Classroom thermal comfortQ7_1Classroom thermal comfort in summer 0 = Good, 1 = Poor
Q7_2Classroom thermal comfort in winter 0 = Good, 1 = Poor
Q7_3Classroom thermal comfort in spring 0 = Good, 1 = Poor
Q7_4Classroom thermal comfort in fall0 = Good, 1 = Poor
Q7_5Thermal comfort in south-facing classrooms0 = Good, 1 = Poor
Q7_6Thermal comfort in north-facing classrooms0 = Good, 1 = Poor
Semi-outdoor areaQ12_1Number of semi-outdoor areas0 = Good, 1 = Poor
Q12_2Wind environment in semi-outdoor area0 = Good, 1 = Poor
Q12_3Landscape views from semi-outdoor area0 = Good, 1 = Poor
Q12_4Winter thermal comfort in semi-outdoor area0 = Good, 1 = Poor
Table 4. Reliability and validity test.
Table 4. Reliability and validity test.
Cronbach’s alpha0.858
KMO0.888
Bartlett’s Test of SphericityApprox. Chi-Square1419.600
Sig.0.000
Table 5. Statistics on demographic background variables.
Table 5. Statistics on demographic background variables.
Sample SizeNumber of Anxious People (%)
Totals440107 (24.318%)
GendersMale227 (51.591%)43 (18.943%)
Female213 (48.409%)64 (30.047%)
GradesFirst grade127 (28.864%)20 (15.748%)
Second grade118 (26.818%)41 (34.746%)
Third grade114 (25.909%)34 (29.825%)
Fourth grade81 (18.409%)12 (14.815%)
Table 6. Descriptive statistics of total physical environment factors.
Table 6. Descriptive statistics of total physical environment factors.
RegionAverage ValueTotal Factors of Physical Environment Perception
Assessment		Average ValueStandard
Deviation
General assessment3.22Overall assessment of the physical environment3.220.809
Classroom area3.05Classroom ventilation3.010.942
Classroom lighting3.270.880
Classroom thermal comfort2.660.961
Classroom noise conditions3.250.892
Courtyard area3.54Courtyard landscape3.480.885
Courtyard ventilation3.690.794
Courtyard thermal comfort3.460.809
Semi-outdoor area3.44Semi-outdoor area3.440.899
Table 7. Correlation matrix for total physical environment factors.
Table 7. Correlation matrix for total physical environment factors.
Factors of Physical Environment Perception AssessmentQ4Q5Q6Q7Q8Q9Q10Q11
Q4Overall assessment of the physical environment 1
Q5Classroom ventilation0.580 **
Q6Classroom lighting0.453 **0.420 **
Q7Classroom thermal comfort0.419 **0.444 **0.337 **
Q8Classroom noise conditions0.402 **0.274 **0.343 **0.359 **
Q9Courtyard landscape0.443 **0.317 **0.366 **0.316 **0.423 **
Q10Courtyard ventilation0.408 **0.381 **0.332 **0.298 **0.424 **0.629 **
Q11Courtyard thermal comfort0.413 **0.392 **0.374 **0.349 **0.432 **0.568 **0.620 **
Q12Semi-outdoor area0.441 **0.305 **0.344 **0.297 **0.390 **0.465 **0.462 **0.454 **
** Significant correlation at the 0.01 level (two-tailed).
Table 8. Collinearity diagnostics, binary logistic regression model A.
Table 8. Collinearity diagnostics, binary logistic regression model A.
Total Factors of Physical Environment Perception AssessmentCollinearity Statistics
ToleranceVIF
Q1Genders0.9781.022
Q2Grades0.9351.070
Q5Classroom ventilation0.5191.928
Q6Classroom lighting0.5721.748
Q7Classroom thermal comfort0.6961.436
Q8Classroom noise conditions0.6941.440
Q9Courtyard landscape0.6771.477
Q10Courtyard ventilation0.5051.982
Q11Courtyard thermal comfort0.4742.110
Q12Semi-outdoor area0.5031.986
Table 9. Collinearity diagnostics, binary logistic regression model B.
Table 9. Collinearity diagnostics, binary logistic regression model B.
Subfactors of Physical Environment Perception AssessmentCollinearity Statistics
ToleranceVIF
Q5_1Ventilation of south-facing classrooms0.7431.346
Q5_2Ventilation of north-facing classrooms0.7481.338
Q5_3Ventilation of inner corridors0.7841.275
Q6_1Lighting in south-facing classrooms0.7871.271
Q6_2Lighting in north-facing classrooms0.7841.276
Q6_3Lighting in the inner corridors0.7731.294
Q7_1Classroom thermal comfort in summer 0.6741.484
Q7_2Classroom thermal comfort in winter 0.891.123
Q7_3Classroom thermal comfort in spring 0.641.561
Q7_4Classroom thermal comfort in fall0.671.493
Q7_1Thermal comfort in south-facing classrooms0.7381.355
Q7_2Thermal comfort in north-facing classrooms0.7381.355
Q12_1Number of semi-outdoor areas0.9111.097
Q12_2Wind environment in semi-outdoor area0.7621.312
Q12_3Landscape views from semi-outdoor area0.9431.061
Q12_4Winter thermal comfort in semi-outdoor area0.7831.278
Table 10. Independent samples t-test with the AON.
Table 10. Independent samples t-test with the AON.
Total Factors of Physical Environment Perception AssessmenttSig. (2-Tailed)
Q5Classroom ventilation9.8970.000
Q6Classroom lighting9.1490.000
Q7Classroom thermal comfort6.8410.006
Q8Classroom noise conditions8.3380.008
Q9Courtyard landscape7.4670.000
Q10Courtyard ventilation7.8280.000
Q11Courtyard thermal comfort9.0600.000
Q12Semi-outdoor area6.7750.000
Table 11. Chi-square tests with the AON.
Table 11. Chi-square tests with the AON.
VariablesPearson Chi-SquareAsymptotic Significance (2-Sided)
Background variables Q1Genders7.362 0.007
Q2Grades17.893 0.000
Subfactors of physical environment perception assessmentQ5_1Ventilation of south-facing classrooms4.1200.042
Q5_2Ventilation of north-facing classrooms5.915 0.015
Q5_3Ventilation of inner corridors43.0650.000
Q6_1Lighting in south-facing classrooms15.7750.000
Q6_2Lighting in north-facing classrooms12.625 0.000
Q6_3Lighting in the inner corridors46.077 0.000
Q7_1Classroom thermal comfort in summer 7.7320.005
Q7_2Classroom thermal comfort in winter 3.3270.068
Q7_3Classroom thermal comfort in spring 14.727 0.000
Q7_4Classroom thermal comfort in fall8.077 0.004
Q7_5Thermal comfort in south-facing classrooms3.0860.079
Q7_6Thermal comfort in north-facing classrooms3.532 0.060
Q12_1Number of semi-outdoor areas24.0290.000
Q12_2Wind environment in semi-outdoor area16.445 0.000
Q12_3Landscape views from semi-outdoor area27.616 0.000
Q12_4Winter thermal comfort in semi-outdoor area16.875 0.000
Table 12. Multiple linear regression coefficients table.
Table 12. Multiple linear regression coefficients table.
Total Factors of Physical Environment
Perception Assessment		Unstandardized CoefficientsStandardized
Coefficients		tSig.Collinearity Statistics
BStd. ErrorBetaToleranceVIF
(Constant)0.4030.179 2.2430.025
Q1Genders0.0560.0570.0350.9890.3230.9811.020
Q2Grades0.0180.0270.0250.6830.4950.9361.069
Q5Classroom ventilation0.3160.0360.3698.6970.0000.6731.486
Q6Classroom lighting0.1180.0380.1283.1080.0020.7121.404
Q7Classroom thermal comfort0.0750.0350.0892.1380.0330.7021.425
Q8Classroom noise conditions0.1030.0380.1132.7050.0070.6891.452
Q9Courtyard landscape0.1260.0440.1382.8540.0050.5141.945
Q10Courtyard ventilation−0.0010.051−0.001−0.0210.9830.4742.110
Q11Courtyard thermal comfort−0.0120.049−0.012−0.2360.8140.5031.986
Q12Semi-outdoor area0.1390.0380.1553.6450.0000.6691.495
Table 13. Model tests (binary logistic regression model A).
Table 13. Model tests (binary logistic regression model A).
−2 Log Likelihood203.916
Omnibus testsChi-square184.237
Sig.0.000
Hosmer and Lemeshow testChi-square5.877
Sig.0.661
PredictedNo anxiety93.994
Anxiety 53.271
Overall percentage84.091
Table 14. Table of coefficients for binary logistic regression A.
Table 14. Table of coefficients for binary logistic regression A.
Total Factors of Physical Environment Perception AssessmentBS.E.WaldSig.Exp(B)95% C.I. for Exp(B)
LowerUpper
Q1Genders−0.6370.2924.7560.0290.5290.2980.937
Q2Grades−0.1060.1420.5590.4550.9000.6821.187
Q5Classroom ventilation−1.1080.22125.1270.0000.3300.2140.509
Q6Classroom lighting−0.8130.20316.0010.0000.4440.2980.661
Q7Classroom thermal comfort−0.1660.1790.8560.3550.8470.5961.204
Q8Classroom noise conditions−0.8790.21317.1110.0000.4150.2740.630
Q9Courtyard landscape−0.1180.2180.2920.5890.8890.5801.362
Q10Courtyard ventilation−0.1220.2400.2580.6110.8850.5531.417
Q11Courtyard thermal comfort−0.6840.2477.6940.0060.5040.3110.818
Q12Semi-outdoor area−0.2540.1951.6900.1940.7760.5291.137
Constant6.3401.20527.6760.000566.981
Table 15. Model Tests (binary logistic regression model B).
Table 15. Model Tests (binary logistic regression model B).
−2 Log likelihood249.798
Omnibus testsChi-square138.356
Sig.0.000
Hosmer and Lemeshow testChi-square5.999
Sig.0.647
PredictedNo anxiety92.492
Anxiety 45.794
Overall percentage81.136
Table 16. Table of coefficients for binary logistic regression B.
Table 16. Table of coefficients for binary logistic regression B.
Subfactors of Physical Environment Perception AssessmentBS.E.WaldSig.Exp(B)95% C.I. for EXP(B)
LowerUpper
Q5_1Ventilation of south-facing classrooms−0.0900.3200.0790.7790.9140.4891.710
Q5_2Ventilation of north-facing classrooms0.2540.3130.6610.4161.2900.6982.381
Q5_3Ventilation of inner corridors1.1220.31013.0800.0003.0701.6725.639
Q6_1Lighting in south-facing classrooms1.3480.34914.9120.0003.8511.9437.635
Q6_2Lighting in north-facing classrooms0.4950.3042.6570.1031.6400.9052.973
Q6_3Lighting in the inner corridors1.0830.29913.1540.0002.9551.6455.307
Q7_1Classroom thermal comfort in summer 1.3780.5077.3710.0073.9661.46710.724
Q7_2Classroom thermal comfort in winter 0.1140.2800.1670.6821.1210.6481.940
Q7_3Classroom thermal comfort in spring 1.3390.6004.9840.0263.8151.17812.359
Q7_4Classroom thermal comfort in fall1.2610.6124.2460.0393.5301.06411.719
Q7_5Thermal comfort in south-facing classrooms−0.3960.3071.6640.1970.6730.3691.228
Q7_6Thermal comfort in north-facing classrooms−0.4140.3271.5980.2060.6610.3481.256
Q12_1Number of semi-outdoor areas0.9500.29110.6310.0012.5871.4614.579
Q12_2Wind environment in semi-outdoor area0.2170.3250.4470.5041.2430.6572.350
Q12_3Landscape views from semi-outdoor area1.0220.29412.0940.0012.7791.5624.945
Q12_4Winter thermal comfort in semi-outdoor area0.5410.3043.1620.0751.7170.9463.116
Constant−4.4210.57559.0230.0000.012
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Wen, Q.; Zhou, Q.; Ye, H.; Guo, Q.; Shan, J.; Huang, Z. A Perceptual Assessment of the Physical Environment in Teaching Buildings and Its Influence on Students’ Mental Well-Being. Buildings 2024, 14, 1790. https://doi.org/10.3390/buildings14061790

AMA Style

Wen Q, Zhou Q, Ye H, Guo Q, Shan J, Huang Z. A Perceptual Assessment of the Physical Environment in Teaching Buildings and Its Influence on Students’ Mental Well-Being. Buildings. 2024; 14(6):1790. https://doi.org/10.3390/buildings14061790

Chicago/Turabian Style

Wen, Qiang, Qiang Zhou, Huiyao Ye, Qinghai Guo, Jingwen Shan, and Zhonghui Huang. 2024. "A Perceptual Assessment of the Physical Environment in Teaching Buildings and Its Influence on Students’ Mental Well-Being" Buildings 14, no. 6: 1790. https://doi.org/10.3390/buildings14061790

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