Influence of environmental variables on students’ cognitive performance in indoor higher education environments

Abstract

BACKGROUND:

Educational environments can have environmental conditions that are incompatible with the needs of students, compromising their well-being and affecting their performance.

OBJECTIVE:

To identify the environmental variables that influence the performance of university students and measure this influence through an experiment in indoor environments.

METHODS:

The study applied an experimental methodology for three consecutive days in seven educational environments located in different regions of Brazil, measuring the environ-mental conditions, the students’ perception of the environment, and their cognitive performance. The impact of environmental variables and environmental perception on student performance was analyzed using Generalized Linear Models and a Structural Equation Model.

RESULTS:

Students who took the test at air temperatures between 22.4°C and 24.7°C had a 74.20% chance of performing better than those outside this range. Air temperatures between 26.2°C and 29°C were associated with an 86% chance of taking less time to complete the test. High illuminance levels increased the chance of taking longer to answer the test by 41.7%.

CONCLUSIONS:

Three environmental variables (relative humidity, lighting and air temperature) and two perceptual dimensions (light and thermal perception) directly influence student performance.

Keywords

Students classroom indoor environments environmental variables perception performance

1 Introduction

People are estimated to spend an average of 90% of their time inside buildings, including homes, workplaces, shops, and means of transport, among others [1 –3]. These places are characterized by being enclosed environments where air conditioning is usually performed by mechanical devices.

Thermal quality of indoor environments can significantly influence human health and comfort [4, 5]. Therefore, the scientific community has recognized that environmental quality is an important factor in the health, comfort, and performance of populations [6 –8].

As a result, studies were conducted to highlight appropriate comfort ranges in various environments, such as university kitchens and railway carriages, emphasizing the importance of investigating environmental variables [9 –11].

In the context of education, students spend most of their time in formal learning environments, such as classrooms and computer labs [12]. Such environments are enclosed spaces where opportunities for adaptation to physical and environmental conditions are limited during classes [13].

The existing environmental conditions and the length of stay in these places result in a combination of variables that can influence environmental adaptations and have negative repercussions for students [13 –15]. These situations are a warning and demonstrate the importance of investigating this topic within these environments.

Environmental adaptations can be described by a set of variables that together make up indoor environmental quality (IEQ). The main variables are thermal, acoustic, lighting, and air quality, which have known effects on health, comfort, acceptability, and performance, as well as considerable impacts on the teaching-learning process [15 –19].

Given the above, the central issue of this research is the influence of environmental variables on the performance of university students indoors. The general objective is to identify the environmental variables that influence the performance of university students and measure this influence based on an experiment in indoor environments in Brazil.

This research is relevant because studies with children have dominated this field over the years [15 , 20–24]. Therefore, there is a need to carry out other studies aimed at adults over 18 [13 , 25]. In addition, there is a tendency in the literature toward studies carried out in naturally ventilated environments [13 , 26–33].

This research was limited to an experimental analysis carried out in seven teaching environments in Brazil. The only variable manipulated in the experiment was the air temperature, however noise, lighting and air quality were also measured. The performance evaluation was limited to response time and the number of correct answers on the test.

2 Materials and methods

The methodological structure of this research was subdivided into four stages. Initially, the environments and characteristics of the sample were defined. Subsequently, the experimental conditions were defined. Next, the variables measured in the experiment were described. Finally, the method of data analysis was presented.

2.1 Stage 1: Environments and sample

The study was conducted in classrooms of higher education institutions located in the North, Northeast, Central-West, Southeast and South regions of Brazil. All regions of Brazil were chosen to provide a comprehensive and accurate analysis.

Seven computer labs were analyzed three times each, totaling 21 analyses between in two years. All environments had a mechanical ventilation system and two split-type air conditioners.

To standardize the samples, criteria were established since the analyses were carried out in different environments. Students were between 18 and 30 years old, with a body mass index (BMI) < 30, were studying the exact sciences, had no previous health problems, and accepted their participation in the research by signing the informed consent form.

This study was approved by the Research Ethics under the number 57844916.9.0000.5188.

2.2 Stage 2: Experimental procedure

Each environment was analyzed over three days; consequently, each student was al-so evaluated during that period. The methodological procedures applied were strictly identical in each environment.

The experiment was carried out over three days so that it would be possible to evalu-ate three different environmental conditions. Based on the literature, the air temperature was adjusted each day to 20°C, 24°C, and 28°C [13 , 35]. The other variables were not manipulated, so they were evaluated in their usual conditions. These experimental conditions were based on other research with university students [5 , 15].

On the first day, the research objectives, methodology, and data collection procedures were explained to the participants in an area that was not the data collection environment. Subsequently, the questionnaire on environmental perception and the cognitive performance test were explained in detail.

The data collection was not carried out in the same environment as the research ex-planation. The movement of people and the process of measuring weight and height gen-erated thermal loads that could influence the proposed experimental conditions.

Therefore, a specific environment was prepared exclusively for data collection, strict-ly respecting the previously established environmental conditions. This environment was the same as that used on the other two days of analysis.

On all three days, the students arrived 30 minutes before the data collection started. This procedure was important for body temperatures to stabilize and to acclimate to the proposed environmental conditions. In addition, on each day, they used computers to ac-cess the link that directed them to the perceptual questionnaire and the cognitive test.

While the students answered the questionnaires and carried out the battery of cogni-tive tests, the environmental variables were being measured at the same time. Through this experimental methodology, 21 environmental analyses, 750 analyses related to envi-ronmental perception, and 750 analyses related to cognitive performance were obtained.

2.3 Stage 3: Variables investigated

Regarding environmental aspects, the study evaluated variables in the following categories: thermal, acoustic, lighting, and air quality. The thermal variables collected were air temperature (°C) and relative humidity (%). For this was used an Instrutherm TGD 400 (accuracy±0.5°C) and a BABUC microclimate station.

For the acoustic variables, the focus was on noise level (dB), measured using a manual sound analyzer Bruel&Kjaer 2250 L-200 (manufactured by Bruel&Kjaer, with a meter range from 16.4 to 140 dB and precision of 0.2 dB). To assess lighting variables, the illuminance level (Lux) was analyzed using a Phywe lux meter (manufactured by Phywe, with a measurement range of 0 to 300,000 lux and 3% measurement precision). For air quality, Co2 levels and the number of particles were measured using a Fluke 983 (manufactured by Fluke, accuracy±5%, with measurement range above 0.3 mm). All instruments were positioned at 1ft (0.3 m) near the workstation, and 43in. (1.1 m), above the workstation floor based on the ASHRAE 55 Standard [36 –38].

Regarding environmental perception, a questionnaire was developed based on the scientific literature [15, 36]. This questionnaire was explained to the students before the cognitive performance test and contained objective questions related to the environmental aspects addressed in the study (Appendix A).

Finally, students’ cognitive performance was assessed using an adaptation of the Reasoning Test Battery [5, 13], which assesses abstract reasoning, verbal reasoning, spatial reasoning, and numerical and mechanical reasoning skills [5 , 39]. Performance tests were given to each student for three consecutive days, with questions designed so that they were not repeated on subsequent days. The total number of correct answers and response times for each student were recorded.

2.4 Stage 4: Data analysis

The data obtained during the experimental procedure were organized and tabulated in spreadsheets using Microsoft Excel. To analyze the relationship between environmental variables, environmental perception, and students’ cognitive performance, we used the R software.

2.4.1 Relationship of environmental variables to cognitive performance

The study utilized generalized linear models (GLMs) to investigate the relationship between environmental variables and cognitive performance. Firstly, a binomial logistic regression model was used to analyze the impact of environmental variables (independent variables) on the total number of correct answers (BPR5T, dependent variable). Secondly, an ordinal logistic regression model was employed to examine the influence of environmental variables (independent variables) on the response time (BPR5, dependent variable).

The final models that demonstrated significant relationships between environmental variables and performance underwent a series of diagnoses, including analysis of the link and variance function, assessment of the response variable distribution, and analysis of residuals. These diagnoses ensured the accuracy and consistency of the mathematical models developed.

The odds ratio (OR) was extracted as a measure of the effect size of the independent variables’ influence on the dependent variable after diagnosing the final models.

2.4.2 Relationship between environmental perception and cognitive performance

The investigation of the relationship between environmental perception and cognitive performance of students was carried out using structural equation modeling, which is a multivariate technique of data analysis that combines multiple regression and factor analysis to estimate a series of dependency relationships. In this type of modeling, the environmental perception questions were classified as independent variables, and the total number of correct answers (BPR5T) and response time (BPR5) were classified as dependent variables.

Initially, the independent variables were grouped by similarities among them, which are called environmental perception factors. To ensure consistency and applicability, the following indices were verified: p-value, CFI, TLI, RMSEA, and the statistical test of the minimum function.

The structural equation model enabled the association with dependent variables to be made using a multiple regression model. Therefore, the level of significance (p-value) was determined to diagnose a possible relationship between environmental perception and cognitive performance factors, i.e., the total number of correct answers and the response time in the test.

3 Results

The results were divided into two parts: (i) the relationship between environmental variables and cognitive performance, and (ii) the relationship between environmental perception and cognitive performance.

3.1 Environmental variables and cognitive performance

The presented models only reflect the environmental variables that exhibited a significant relationship with cognitive performance. Model 1 indicated that humidity, lighting, and a specific range of air temperatures had a significant association with the total number of correct answers on the test (refer to Table 1).

Table 1
Model 1 statistical data

Variable code Variable Estimate Standard error (SE) p-value OR

UMI Humidity 0.035 0.009 4.77e-05 1.036

ILU Lighting 0.003 0.001 0.01099 1.003

TA1 22.4° ≤Air temperature≤24.7° 0.555 0.178 0.00175 1.742

Variable code	Variable	Estimate	Standard error (SE)	p-value	OR
UMI	Humidity	0.035	0.009	4.77e-05	1.036
ILU	Lighting	0.003	0.001	0.01099	1.003
TA1	22.4° ≤Air temperature≤24.7°	0.555	0.178	0.00175	1.742

The parameters obtained for these variables were consistent at a 5% level of significance (p-value < 0.05), and were verified by comparing the maximum estimated probability of each parameter with its estimated standard error. Moreover, the odds ratios (ORs) values were also calculated from model 1. The UMI variable had an OR of 1.036, the ILU variable had an OR of 1.003, and the TA1 variable had an OR of 1.742.

The data from model 2 demonstrate that a specific range of illumination and three ranges of air temperature had a significant relationship with the response time (refer to Table 2). The parameters for these variables were consistent at a significance level of 5% (p-value < 0.05), and their estimates were verified by comparing the maximum estimated probability with the estimated standard error. The odds ratios (ORs) extracted from model 2 were as follows: ILU1 variable, OR = 1.417; TA2 variable, OR = 1.490; TA3 variable, OR = 0.14203; and TA4 variable, OR = 0.120.

Table 2

Model 2 statistical data

Variable code	Variable	Coefficient	Standard error (SE)	p-value	OR
ILU1	Lighting≥187 lx	0.388	0.175	0.0270 (*)	1.417
TA2	21° ≤Air temperature < 26.2°	0.712	0.231	0.0021 (**)	1.490
TA3	26.2° ≤Air temperature < 29°	–1.951	0.299	0.0001 (***)	0.14203
TA4	29° ≤Air temperature≤33°	–1.139	0.305	0.0002 (***)	0.120

3.1.1 Interpretation of the model results

Model 1 showed that there was a significant relationship between the variables relative humidity (UMI), lighting (ILU), and air temperature within a specific range (TA1) with the total number of correct answers on the cognitive test. An increase of 1% in the relative humidity of the air was associated with a 3.6% chance of student performance improving, while an increase in lighting by one lux was associated with a 0.3% chance of student performance improving.

However, the variable that exhibited the most consistent influence was air temperature. Students who took the test within the range of 22.4°C≤air temperature≤24.7°C had an approximately 74.20% chance of performing better than students outside this temperature range.

The results from Model 2 revealed that air temperature and lighting were variables that influenced response time. However, air temperature had varying effects on response time depending on the temperature range. For instance, when the air temperature was within the range of 21°C to 26.1°C, there was a 49% chance of taking longer to complete the test. On the other hand, when the air temperature was between 26.2°C and 29°C, there was an 86% chance of taking less time to complete the test. Furthermore, when the air temperature was between 29°C and 33°C, there was an 88% chance of taking less time to complete the test.

Regarding lighting, the results showed a direct relationship with response time. Specifically, when students were subjected to high illuminance levels, the chance of taking longer to answer the test increased by 41.7%.

3.2 Environmental perception and cognitive performance

Factor analysis was used initially to identify environmental perceptual factors, also known as perceptual dimensions. The perceptual environmental factors that were identified are presented in Table 3.

From the dimensions found, it was possible to statistically determine the relationships between environmental perception and the total number of correct answers (BPR5T) as well as the response time on the cognitive test (BPR5).

Table 3
Perceptual environmental factors

Dimension code Perceptual environmental factor (dimension) Perceptual question code Estimate z-value p-value

ML2 Lighting aspects Q8 1.00 – –

Q1 0.907 22.537 0.000

Q9 0.983 19.992 0.000

Q14 0.906 21.959 0.000

ML3 General and air quality conditions Q3 1.00 – –

Q17 0.842 22.493 0.000

Q2 0.812 21.489 0.000

Q10 0.821 19.531 0.000

ML1 Thermal preferences SENS 1.00 – –

DES –0.613 –9.324 0.000

ML4 Noise conditions Q7 1.00 – –

Q5 1.168 13.340 0.000

Dimension code	Perceptual environmental factor (dimension)	Perceptual question code	Estimate	z-value	p-value
ML2	Lighting aspects	Q8	1.00	–	–
		Q1	0.907	22.537	0.000
		Q9	0.983	19.992	0.000
		Q14	0.906	21.959	0.000
ML3	General and air quality conditions	Q3	1.00	–	–
		Q17	0.842	22.493	0.000
		Q2	0.812	21.489	0.000
		Q10	0.821	19.531	0.000
ML1	Thermal preferences	SENS	1.00	–	–
		DES	–0.613	–9.324	0.000
ML4	Noise conditions	Q7	1.00	–	–
		Q5	1.168	13.340	0.000

As presented in Table 4 only two dimensions (ML1 and ML2) exhibited a significant association with performance. The parameters identified for these associations were consistent at a significance level of 5% (p-value < 0.05).

Table 4

Significant relationships

Relationship	Estimate	z-value	p-value
ML2 ∼∼ BPR5T	0.458	3.898	0.00017
ML1 ∼∼ BPR5	–30.604	–5.284	0.00001

Interpreting these results, the structural equation models (SEMs) indicated that two of the four perceptual dimensions were significantly related to student performance. Specifically, a direct relationship was found between the dimension of Luminance Aspects (ML2) and the total number of correct answers, indicating that greater satisfaction with the lighting was associated with better performance.

Additionally, an inverse relationship was found between the Thermal Preference dimension (ML1) and the response time on the test. This suggests that students who perceived the environment as warmer had shorter response times, meaning they were able to complete the test more quickly.

4 Discussion

4.1 Environmental variables and performance

The most significant findings of the study can be summarized in two aspects: (i) students completed the test faster at higher temperatures, and (ii) students who took the test within the temperature range of 22.4°C to 24.7°C had a 74.20% chance of performing better than those outside this range. Therefore, air temperature was found to be the variable that had the most influence on the results, which is consistent with the findings of other researchers, who argued that only thermal conditions affected student performance [5 , 24].

The first aspect (i) can be explained by the students’ physiological responses to thermally uncomfortable situations. The human body responds to such situations by sending electrical pulses to the brain, which then seeks behaviors that minimize unhealthy effects [5 , 41]. As a result, response time becomes faster due to the need to get out of the state of discomfort, but these temperatures can also result in stress and negatively impact students’ concentration and attention, ultimately affecting their performance [14 , 43].

Regarding the second aspect (ii), the literature indicates that some researchers have diagnosed the relationship between air temperature and student performance. For instance, Wargocki et al. [43] observed that a reduction in the air temperature from 30°C to 20°C resulted in a 20% increase in the performance of student activities. Souza [5], a decrease in cognitive performance from 15 to 10 points when air temperature increased from 20°C to 33°C.

Jaber et al. [44] found that performance was higher at temperatures between 23° and 24°, while Wang et al. [45] suggested that the temperature conducive to the activity’s performance was slightly higher than expected, staying at approximately 27°C. Lau et al. [46] also found that the ideal temperature for acclimatized environments was approximately 26.7°C, with some variations below that.

Overall, the analysis of the scientific literature suggests that many studies have estimated favorable temperatures for student performance, but few have quantitatively indicated how favorable temperatures in this range can be. This study addresses this topic by demonstrating that students who take the test within a specific temperature range are more likely to perform better (74.20%) than those outside of this range.

4.2 Environmental perception, environmental variables and cognitive performance

In the scientific literature, it is widely established that thermal perception has a greater impact on the general perception of comfort than other environmental variables [15 , 48]. This indicates that an inverse relationship between thermal perception and response time is understandable.

When individuals are subjected to uncomfortable temperatures, their body structures emit electrical signals to the brain, prompting behaviors aimed at minimizing the impact of the situation [41]. This phenomenon explains the inverse relationship discovered in this study, which has also been observed by other researchers [5 , 39].

The direct relationship between light perception and the total number of correct answers can be explained in two ways. First, thermal perceptions may have an impact on light perceptions. This scenario is plausible and can be justified with other studies. For instance, Haldi and Robinson [49] demonstrated that individuals exposed to temperatures conducive to thermal comfort also indicated visual comfort, even though they were exposed to considerable variations in lighting levels. Yang and Moon [50] also found that visual comfort was influenced by thermal comfort. This scenario was also found in other studies [14, 15].

The other possibility is that lighting conditions can influence cognitive performance regardless of other variables. This situation has also been suggested by other studies [12, 51], but it seems less likely than the first possibility.

There are two reasons for this assessment. First, lighting was not manipulated in this study and did not show significant variation among different environments. Second, the objective and subjective aspects related to performance were associated with both thermal and lighting aspects, providing more evidence for the first possibility: thermal perception may have influenced light perception.

4.3 Limitations and further research directions

A principal limitation of this research was the lack of budget to conduct the experiment in all states of Brazil. Additionally, we focused on the variation of air temperature, intentionally not altering other variables. Finally, we did not use objective instruments to measure brain activity during the cognitive tests.

For future research, it is suggested to conduct experiments controlling other environmental parameters that were not investigated in this study, such as mean radiant temperature, air velocity, noise, and CO2 levels.

5 Conclusions

The general objective of this research was to identify the environmental variables that influence the performance of university students and measure this influence based on an experiment in indoor environments in Brazil. The key findings are summarized as follows:

An increase of 1% in relative humidity was associated with a 3.6% improvement in student performance.

An increase of one lux in lighting was associated with a 0.3% improvement in student performance.

Students who took the test at air temperatures between 22.4°C and 24.7°C had a 74.20% chance of performing better than those outside this range.

Air temperatures between 21°C and 26.1°C were associated with a 49% chance of taking longer to complete the test.

Air temperatures between 26.2°C and 29°C were associated with an 86% chance of taking less time to complete the test.

Air temperatures between 29°C and 33°C were associated with an 88% chance of taking less time to complete the test.

High illuminance levels increased the chance of taking longer to answer the test by 41.7%.

Light perception was directly related to students’ cognitive performance, while thermal perception was inversely related to response time on the test.

The findings suggest that thermal perception may influence light perception.

It was concluded that three environmental variables and two perceptual dimensions had a direct impact on student performance. Additionally, the results suggested a perceptual priority for thermal conditions over light conditions.

Ethical approval

This research was approved by the Ethics Committee of the Federal University of Paraíba (Case number 57844916.9.0000.5188).

Informed consent

Not applicable.

Conflict of interest

The authors declare that they have no Conflict of interest.

Footnotes

Acknowledgments

The authors thank the students of all universities and the support of the Coordination for the Improvement of Higher Education Personnel (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES).

Funding

This study was funded by the Coordination for the Improvement of Higher Education Personnel (CAPES) Brazil –Financing Code 001.

Appendix A

CODE	QUESTION	RESPONSE
Q1	Amount of light on the work table	1 = Very unsatisfactory;
Q2	Air quality (in general) in the work area	2 = Unsatisfactory;
Q3	Air temperature in your work area	3 = Slightly unsatisfactory;
Q5	Noise level from other people	4 = Neutral;
Q7	Background noise level (not from conversations) that you hear from your desktop	5 = Slightly satisfactory;
Q8	Amount of light for computer work	6 = Satisfactory;
Q9	Amount of reflected or glare light on the computer screen	7 = Very unsatisfactory
Q10	Air movement in your work area
Q14	Quality of lighting in your work area
Q17	Considering all the environmental conditions in your work area, what is your level of satisfaction with the internal environment of your work area, as a whole?
SENS	In terms of temperature, how are you feeling right now?	1 = Very hot;
		2 = Hot;
		3 = Warm;
		4 = Fine, neither hot nor cold;
		5 = Slightly cold;
		6 = Cold;
		7 = Very cold
DES	How would you rather be feeling now?	1 = Much warmer;
		2 = Warmer;
		3 = Slightly warmer;
		4 = No different;
		5 = Slightly cooler;
		6 = Cooler;
		7 = Much cooler

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Influence of environmental variables on students’ cognitive performance in indoor higher education environments

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSIONS:

Keywords

1 Introduction

2 Materials and methods

2.1 Stage 1: Environments and sample

2.2 Stage 2: Experimental procedure

2.3 Stage 3: Variables investigated

2.4 Stage 4: Data analysis

2.4.1 Relationship of environmental variables to cognitive performance

2.4.2 Relationship between environmental perception and cognitive performance

3 Results

3.1 Environmental variables and cognitive performance

Table 1 Model 1 statistical data Variable code Variable Estimate Standard error (SE) p-value OR UMI Humidity 0.035 0.009 4.77e-05 1.036 ILU Lighting 0.003 0.001 0.01099 1.003 TA1 22.4° ≤Air temperature≤24.7° 0.555 0.178 0.00175 1.742

3.2 Environmental perception and cognitive performance

4.1 Environmental variables and performance

4.2 Environmental perception, environmental variables and cognitive performance

4.3 Limitations and further research directions

5 Conclusions

Ethical approval

Informed consent

Conflict of interest

Footnotes

Acknowledgments

Funding

Appendix A

References

Table 1
Model 1 statistical data

Variable code Variable Estimate Standard error (SE) p-value OR

UMI Humidity 0.035 0.009 4.77e-05 1.036

ILU Lighting 0.003 0.001 0.01099 1.003

TA1 22.4° ≤Air temperature≤24.7° 0.555 0.178 0.00175 1.742