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The relationship between comfort perceptions and academic performance in university classroom buildings


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This paper presents preliminary data on a series of building comfort experiments conducted in the field. We performed physical in-situ measurements and solicited responses from 409 (184 female; 225 male) university students in six different classrooms at the University of Massachusetts-Amherst during three seasons (fall, winter and spring). Our questions focused on student perception of comfort in varied environmental (temperature and humidity, and air speed) conditions. We collected records of student academic performance in the classes, correlating their comfort perceptions to their test scores. Statistical analysis of classroom environmental variables, thermal satisfaction, and student scores suggest that by enhancing thermal comfort, we can improve academic performance.
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108 Volume 11, Number 1
the relationship between comfort perceptions
and academic performance in universitY
classroom buildings
Simi Hoque1 and Ben Weil2
1. Dept. of Environmental Conservation, University of Massachusetts-Amherst,
2. Dept. of Environmental Conservation, University of Massachusetts-Amherst,
is paper presents preliminary data on a series of building comfort experiments
conducted in the eld. We performed physical in-situ measurements and solicited
responses from 409 (184 female; 225 male) university students in six dierent
classrooms at the University of Massachusetts-Amherst during three seasons (fall,
winter and spring). Our questions focused on student perception of comfort in
varied environmental (temperature and humidity, and air speed) conditions. We
collected records of student academic performance in the classes, correlating their
comfort perceptions to their test scores. Statistical analysis of classroom environ-
mental variables, thermal satisfaction, and student scores suggest that by enhancing
thermal comfort, we can improve academic performance.
thermal comfort, student performance, university classrooms, energy conservation
Indoor Environment Quality (IEQ) impacts occupant productivity [1]. Occupant perfor-
mance is correlated to healthy indoor air, as well as acoustic, thermal and visual comfort [2-4].
Despite this, building engineers and managers design and operate buildings with the perspec-
tive that IEQ is maintained through a constant and uniform environment. Static models set
xed parameters for temperature, humidity, and air ow regardless of outdoor climate, occu-
pant preference, and context. is approach leads to an increased reliance on mechanical con-
trols and potentially energy intensive systems for thermal conditioning. ese impacts have
an additional academic penalty when the building serves an educational purpose. ough
numerous studies suggest poor indoor air quality (IAQ) aects student performance [5-7], air
quality and ventilation rates are not the whole story [8, 9]. e hypothesis of this research was
that student performance suers when students feel discomfort and increasing thermal satis-
faction in university classrooms should translate into improved academic achievement among
Journal of Green Building 109
Research on human thermal comfort began in the 1960s [10] and has been the basis for
a number of comfort models and standards [11] that drive the design and operation of indoor
environmental systems. ese models are based on the idea that despite dierent climates,
living conditions, and cultures, the temperature range people nd comfortable under similar
conditions of clothing, activity, humidity, and air movement are equivocal [12-14]. Quantify-
ing the thermal comfort of a body in an environment involves measuring independent envi-
ronmental parameters like air temperature, mean radiant temperature, relative humidity, and
relative air velocity as well as independent personal variables such as metabolic activity and
clothing. ese parameters are modeled in aggregate to analyze the relationship between the
environmental and physiological factors. e model is then used to produce a thermal sensa-
tion scale known as the PMV (Predicted Mean Vote) [10].
An understanding of occupant needs is important for the building and operations pro-
cess--from designers, engineers, and developers to facility managers. Research has demon-
strated that IEQ has considerable impact on human health, stress, productivity and wellbeing
[15-18]. is has largely been driven by the awareness that IEQ issues impact office-based
workforces and sick building syndrome [1]. Much of the existing scholarship is focused on
quantifying the relationship between occupant comfort and thermal conditions (temperature
and relative humidity), acoustic quality, IAQ, and visual access, based on the adaptive comfort
model. e adaptive comfort model, in contrast to heat balance models, suggests comfort is
a variable condition inuenced by behavioral, physiological, and psychological processes [19,
20]. Adaptive models provide evidence on how people naturally adapt or make adjustments to
themselves and their surroundings to reduce discomfort.
ere are a range of direct and indirect mechanisms inuencing comfort, such as outdoor
conditions, gender, age, clothing, activity schedules or levels, as well as control over air move-
ment, ventilation, and local temperatures. However, little is known about whether and how
much perceptions of comfort aect academic performance [21]. As mentioned above, most
studies on the link between thermal comfort and performance focus on productivity, stress,
and well-being in commercial oce settings [1]. Lee, et al. [22] analyzed IEQ in air condi-
tioned university classrooms in Hong Kong against self-reported learning performance, where
respondents used a percentage value to best describe their own performance in four learning-
related activities: calculating, reading, understanding and typing. Bell, et al. [23] examined
the relationship between clothing comfort and cognitive performance; student test scores
were compared against comfort ratings in a single class. While these studies suggested a rela-
tionship between perceived comfort and academic achievement, there is little information
available about how environmental parameters inuence both sensations of thermal discom-
fort and student performance. e purpose of this research is to quantify the relationship
between thermal comfort parameters (temperature, humidity, and air speed), the psychologi-
cal comfort, and academic performance (test scores). is paper presents preliminary analyses
of measurements from 409 students in six dierent classrooms during three dierent seasons.
This study was conducted at University of Massachusetts-Amherst during a nine-month
period between January-May (spring term) and September-December (fall term) 2013. It
focused on classroom teaching activities typically scheduled between 9:00 a.m. and 6:00 p.m.
from Monday to Friday. We solicited faculty who would be willing to participate and collected
110 Volume 11, Number 1
data on class meeting time, location, and nominal room capacity. Preliminary class capacity
assessments were made to ensure our study population would contain a sucient number of
As per requirements for human subject research, we applied for and were approved by
the Universitys Institutional Review Board (IRB) for this study (IRB Protocol Number 2013-
1614). Following approval, we developed the questionnaire and obtained informed consent
from the subject population. All of the procedures used in this study were conducted in accor-
dance with principles and procedures for the protection of human subjects.
2.1 Study Area
is analysis focuses on four dierent buildings (B-type) in three 60-seat seminar rooms
(S-type) and three 80-seat lecture halls (L-type). Table 1 provides additional characteristics
of the six dierent rooms analyzed. Data collection and surveys were conducted on weekdays
during specic class periods Monday–Friday, 10:30 a.m. – 3:30 p.m. during the spring and
fall terms.
Air speed, temperature and humidity parameters were collected in each classroom using
a Kestrel Meter 4400 at six dierent points, at student head height, over the class period
(approximately 75 minutes) and averaged. Outdoor measurements for temperature and
humidity were also taken using a Kestrel 4400 weather meter. Instruments were calibrated
according to the manufacturer’s instructions prior to every measurement. ese instruments
were within 99% accuracy based on calibration tests. MRT, or Mean Radiant Temperature, is
TAB L E 1. Characteristics of the classrooms.
TAB L E 2 . Average values for environmental parameters in and around classrooms.
Journal of Green Building 111
a parameter used to describe the overall radiant temperature of the room and can sometimes
reect more accurately an occupant’s thermal sensation. MRT was calculated in each class-
room using the globe temperature reading from the Kestrel meter based on ISO 7726 [24].
Data are presented in Table 2.
2.2 Student Population
Cross-sectional data were collected from students (N=409) enrolled in six dierent under-
graduate courses at the University of Massachusetts. ese were second or third year science
or engineering courses. Data about student gender and age were collected. e mean age was
20.7 (SD = 4.2 years); 45% (n=184) were female.
We measured the environmental parameters on the day the students took an exam and
collected data on thermal comfort perceptions from the students. e nal three questions
on the exam were questions pertinent to this study. Students were asked for gender, age, and
to characterize their comfort level using the ASHRAE descriptive scale based on ISO 7730
[25] (Table 3). While the ASHRAE thermal sensation scale is a seven-point scale, we used a
collapsed ve-point Likert scale to assure statistical robustness. Using an ordinal scale with a
large number of choices increases the likelihood that for any given replicate, there might be
a small sample size selecting any given choice [26]. We did not collect data about clothing or
activity prior to class though these data may have had some inuence on comfort perceptions.
We assumed typical winter indoor clothing with a large portion of the students wearing heavy
overcoats over thermal layers (Clo = 1.1) because data collection took place largely during the
colder months with outdoor temperature ranging from -12 to 19C. is was conrmed by
observation, where a majority of students removed their overcoats but kept on thermal layers.
e metabolic rate was calculated as a time-weighted average of sitting for a test for 45 min
and walking to class for 15 min (Met = 1.2).
We asked questions that would require little time and eort, to minimize the data collec-
tion time, i.e., not interfere with the exam.
2.3 Academic Performance Measurements
We obtained the student exam scores, calculated from 0 to 100, and responses to the three
study questions. Statistical methods, including Pearson’s correlation, multiple hierarchical
linear regression, and ANOVA were used to analyze data to dene the association between
environmental parameters, comfort indicators, and test scores. SPSS was used for statistical
analysis [27]. e aim was to nd the combination of thermal variables (temperature, humid-
ity, and air speed) which the students considered ‘neutral’ or ‘comfortable’ and associate this
with their test scores.
TAB L E 3 . Indicators using a modified thermal comfort scale.
112 Volume 11, Number 1
3.1 Comfort perception and academic performance
Due to the inherently subjective nature of comfort, the rst question of interest is whether
there is a relationship between self-reported comfort and performance as measured by test
scores. We converted the comfort scale (-2, cold; 0, neutral; +2, hot) using absolute values to
a discomfort scale (0, comfortable; 2, high thermal discomfort) to evaluate this question. e
discomfort scale is useful because there is no consensus in the literature regarding the relative
impact of feeling too hot or too cold. Also, as discussed below, we found only a very weak
relationship between actual dry bulb temperature and individual self-reported perception of
feeling cold or hot. Prior to analysis, checks of the assumptions of normality, linearity, and
homoscedasticity were met. As evident in table 4, increased thermal discomfort is associated
with lower mean test scores. Indeed, there was a statistically signicant negative correlation
between thermal discomfort and test scores (p < 0.001). e negative correlation meant, in
general, students who felt thermal discomfort performed worse on tests than those with no
thermal discomfort. Following Cohen [28] a correlation (r) of +/-0.5 should be considered
large. In addition, the eect size (η2= .338) indicated approximately 34% of the variance
is accounted for by thermal discomfort. Given most of the variance in test scores may likely
be attributable to factors not measured in this study, such as the diculty of the material,
whether students studied well and the natural aptitude of the students, this eect size is large.
A one-way analysis of variance (ANOVA) was calculated on the test scores comparing
groups by their levels of thermal discomfort. e analysis was signicant, F(2,406) = 103.5,
p < 0.0001. Participants with no thermal discomfort had higher test scores than those with
more thermal discomfort, and those with moderate thermal discomfort had higher test scores
than those with high thermal discomfort (see Table 4 for means and standard deviations).
Cohens [28] eect size f was calculated for the ANOVA, f = 0.57. is is considered a large
eect. Comparisons indicated that all dierences between groups were signicant. e eect
size r was calculated for all t-tests. e “high thermal discomfort” condition was signicantly
dierent from the “moderate thermal discomfort” condition, t(278) = 9.95, p < 0.0001,r =
0.51. e “high thermal discomfort” condition was signicantly dierent from that for “no
thermal discomfort,” t(209) = 14.75, p < 0.0001,r = 0.71. e “moderate thermal discomfort”
condition was signicantly dierent from the “no thermal discomfort condition,” t(325) =
5.92, p < 0.0001,r = 0.31. While almost half of the dierence between “high discomfort” and
“no discomfort” groups may be related to their thermal discomfort, only 9% of the dier-
ence between moderate and no discomfort groups may be related to thermal discomfort. As
evident in the box plot in gure 1, however, there is a high degree of variability in test scores;
thermal discomfort alone may not be used to predict test score outcomes.
TAB L E 4 . Test score means, standard deviations, and sample size for three levels of thermal
Journal of Green Building 113
None of the other variables, including age, gender, class size, number of students, and
environmental variables (such as temperature and humidity) were correlated to, or showed
any signicant mean dierence in mean test scores.
3.2 Factors influencing comfort perception
e combination of six well known factors to dene thermal comfort should be more predic-
tive of reported comfort rating [11]. We used Fanger’s comfort calculation [29] to compare
the predicted mean vote (PMV) to the actual mean comfort rating reported by study par-
ticipants for each room. Of the six factors, air and globe temperature, air speed, and relative
humidity, were recorded directly. Metabolic rate and clothing level were applied as average
values. For convenience, we entered these values into the CBE ermal Comfort Tool [30]. e
PMV values and the mean comfort rating values used appear in table 5.
ese values are only weakly correlated, without statistical signicance (r= 0.556, p = 0.252).
To the degree these values are correlates, there might be an indication of some similarity in direc-
tion, but the values are almost unrelated because PMV calculated values are negative (i.e., “cool”)
while the reported values are mostly positive (i.e., “warm”). As shown in gure 2, only two of the
classrooms mean comfort rating and PMV lie near the 1:1 correspondence line.
fIgURE 1: Plot of Thermal Discomfort against Test Scores.
TAB L E 5 . PMV values and mean comfort rating values.
114 Volume 11, Number 1
fIgURE 2: Correspondence between mean comfort rating and calculated PMV.
In the above analysis, we used the absolute value of the participant’s comfort rating in
part because of the weak relationship between measured dry bulb temperature and reported
thermal perception. Comfort on a scale from “too cold” = -2 to “too hot” = +2 was weakly
correlated (r=0.122) to indoor measured temperature (p=0.014). Some other combination of
factors may help to better explain thermal comfort ratings. Factors we considered included
how crowded a room might feel, radiant asymmetry, gender, and age. e crowding variable
was calculated as a ratio of room population to room capacity. Radiant asymmetry was calcu-
lated based on the relative dierence between the measured average air temperature and MRT.
As shown in table 6, none of these variables were strongly correlated to comfort; however,
small correlations were signicant. We hypothesized a linear hierarchical regression model
could be constructed, such that these variables together explain variability in thermal comfort
better than any single variable separately.
TAB L E 6 . Mean, Standard Deviations, and Correlation Coefficients among variables
Journal of Green Building 115
TA B LE 7. Hierarchical Regression Predicting Thermal Comfort Rating.
None of the variables were correlated above 0.3, suggesting an absence of multi-collin-
earity. Prior to analysis checks of the theoretical assumptions underlying multiple regression
were undertaken, including normality, linearity, and homoscedasticity. e assumptions were
met, and a hierarchical regression analysis controlling for gender was undertaken. Each vari-
able was entered as a separate step to assess the impact of each variable on the strength of the
model (Table 7).
is combination of variables signicantly predicted comfort rating; three variables sig-
nicantly contributed to the prediction and gender contributed the most, as females were
slightly more likely than males to report feeling cold. Even so, the R2 value was only 0.1, sug-
gesting this model only explained 10% of the variance.
is study attempted to associate thermal satisfaction and student scores. Data suggested
by enhancing thermal comfort, we can improve academic performance. If high thermal dis-
comfort is a factor in decreased academic performance (as measured by test scores), then the
practical implication is to increase the emphasis in providing increased thermal comfort in
academic, oce and other buildings where occupant performance is highly valued. However,
because thermal perception is apparently dicult to predict using environmental variables,
this study oers little guidance for assuring thermal comfort. is creates a greater challenge
for facilities management sta who are responsible for maintaining building temperature and
116 Volume 11, Number 1
humidity set points, and complicates building HVAC systems design and sizing strategies
because we may know less about how to quantify comfort in high occupancy buildings. e
lack of association between environmental variables and thermal comfort perception – and
the small eect size of associated variables—suggested a large variety of factors may lead to
varying of dierent perceptions of comfort. ough not demonstrated by this study, it seems
likely each individual may experience a wide range of thermal comfort responses to the same
environmental stimuli at dierent times and in dierent emotional or social contexts.
One artifact of a collapsed Likert scale with ve instead of seven gradations, is that “-1”
and “+1” cannot be interpreted as “acceptable” as they are in the seven-point ASHRAE scale.
Because +2 and -2 are the most extreme choices available, these necessarily indicate the highest
level of thermal discomfort. Since respondents had only their own perceptions as reference
points, there is no way to standardize their responses (i.e., to be sure that one person’s “-2”
is not equivalent to another persons “-1”). However, within the acknowledged limitations
correlational studies such as this one, in the aggregate the response vector allows for a reason-
able assessment of eect size even if the dimensions of the ordinal values are of unknown (or
unknowable) [31]. It is worth noting the limitations of a comparison between our ve-point
scale and the seven-point scale of the PMV including the necessarily generalized assumptions
we used to calculate the PMV for each replicate. e purpose here is not to question the valid-
ity or robustness of the PMV method, but rather to contextualize the counterintuitive nd-
ings of this exploratory study.
e results of this study are exploratory and associational, and thus it is impossible to
determine causation. A future research design would involve controlled experimental condi-
tions. ese would include providing identical educational content to research subjects, con-
trolling the air temperature, humidity, air speed and surface temperatures (this study used
existing conditions not controlled by the researchers). is would allow not only control and
test groups (for between group eects), but also control of temperature extremes (the temper-
atures in this study were warmer than may be typical, and had no examples of atypically cool
temperatures). Other environmental variables potentially assessed in future studies include
ventilation rates, wall colors and light color temperatures, as these factors may also contribute
to perceptions of thermal comfort [32].
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... Several studies evaluated the effect of learning and the results show that the learning ratio decreases with the rise in temperature [7,[37][38][39][40][41]. The change in performance in office work was also tested. ...
... Performance/Productivity Assessment [7,14,40,[62][63][64][65][66][67][68][69][70][71][72] Subjective assessment. [38,43,[73][74][75] Physiological/subjective assessment. [76,77] Subjective/experimental assessment. ...
... Effects of critical factors of the built environment on the occupants of commercial buildings with green certification. [38] Temperature, humidity and air velocity. [40] Predicted mean vote, CO2, personal factor. ...
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The thermal environment is one of the main factors that influence thermal comfort and, consequently, the productivity of occupants inside buildings. Throughout the years, research has described the connection between thermal comfort and productivity. Mathematical models have been established in the attempt to predict changes in productivity according to thermal variations in the environment. Some of these models have failed for a number of reasons, including the understanding of the effect that several environment variables have had on performance. From this context, a systematic literature review was carried out with the aim of verifying the connection between thermal comfort and productivity and the combinations of different thermal and personal factors that can have an effect on productivity. A hundred and twenty-eight articles were found which show a connection between productivity and some thermal comfort variables. By means of specific inclusion and exclusion criteria, 60 articles were selected for a final analysis. The main conclusions found in this study were: (i) the vast majority of research uses subjective measures and/or a combination of methods to evaluate productivity; (ii) performance/productivity can be attained within an ampler temperature range; (iii) few studies present ways of calculating productivity.
... Other studies relate several environmental aspects with academic performance, such as the work done by Torrecilla, Javier, and Martinez-Garrido (2012) in 9 countries of Ibero-America, which demonstrated that there are no statistically significant relationships between performance in mathematics and language and the classroom environment, since all of the spaces have acceptable conditions. From the relationship between comfort conditions in the classroom and academic performance, thermal factors have been widely studied in recent decades (Holmberg and Wyon 1969;Hoque and Weil 2016;Schoer and Shaffran 1973) demonstrating that those who study in classrooms with inadequate ventilation and heating systems have a lower academic performance than those who study in classrooms with natural ventilation (Kalamees et al. 2015). Additionally, a lower room temperature increases the speed of the work, including some other aspects, like attention and deduction (Jiang et al. 2018). ...
... This discovery is consistent with studies that found an inverse relation between temperature and work speed, attention and deduction (Jiang et al. 2018), as well as other studies made by Wargocki, Wyon, Matysiak and Irgens, which found a direct relation with a decrease in attention errors of 10% (Wargocki et al. 2005). In this respect, cognitive performance is higher when there is thermal comfort, as indicated on previous work (Hoque and Weil 2016), although the results are not absolutely conclusive. Nevertheless, the findings of the current research contradict the hypothesis maintained by the adaptive model, which states that those students accustomed to a warm climate do not see an effect in their performance due to temperature variations, which is mainly due to factors like being accustomed to the temperature and adaptation capacity to those environments. ...
This paper examines the influence of the thermal and lighting performance in classrooms on the cognitive productivity of students attending public schools in the principal three cities of Colombia: Bogota, Medellin and Cali. The methodology used involves the application of cognitive performance tests and thermal and visual perception surveys, along with measurements of climatic parameters in 34 classrooms of 14 schools in 2017 and 2018. The results were analyzed using transversal correlational regressions. Among the conclusions, this study found that the operative temperature turned out to be the most conclusive variable explaining cognitive performance relationships.
... In addition to interior redesign planning, the improvement of academic public space environments is of great significance for the improvement of visual and thermal comfort [42]. By improving thermal comfort, we can improve our academic performance [43]. For visual environments, light can be divided into artificial light and natural daylight due to the presence of window media. ...
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The aim of this study is to determine the relationship between occupants’ emotional attitude, decision behavior, and environmental cognition toward window seats and learning efficiency and the mechanism of this relationship in public spaces (represented by academic libraries). Surveys were delivered to the academic library of Shanghai Jiao Tong University. A total of 280 valid face-to-face interview questionnaires was collected and analyzed for correlation and validation of theoretical models. The results show that learning experience, as a mediator of learning efficiency, has a significant impact on the model of occupants’ attitude toward window seat consumption. The impact mechanism was determined, and it indicated that in order to improve the learning efficiency of occupants, indoor re-planning should be carried out to improve the seat satisfaction and occupancy rate. This study introduces the concepts of service design and architectural consumption and constructs an occupant emotional consumption context with the window seat as the consumption product. In addition, it also has guiding value for seat reallocation in public buildings in the COVID-19 era. This theoretical framework provides a direction for the simulation of future construction consumption behavior.
... Although this may be a specific issue related to the case, the need considering environmental comfort is supported by empirical research in many situations (Bluyssen, Aries, & van Dommelen, 2011;Haverinen-Shaughnessy, Shaughnessy, Cole, Toyinbo, & Moschandreas, 2015;Hwang, Lin, & Kuo, 2006). Hoque and Weil (2016) found through a series of building comfort experiments that those who experience thermal discomfort performed worse academically. Additionally, experimental research also found that the combined effects of light sound and temperature impacts students mood and learning capabilities (Marchand, Nardi, Reynolds, & Pamoukov, 2014). ...
This paper reports on research undertaken to identify the specific learning space preferences of built environment students within a UK university. Through an instrumental case study design, utilizing a sequential exploratory mixed methods approach, this research explored learning space requirements for built environment students. Initial focus groups were conducted to identify elements of the learning spaces that are important in students learning spaces, which were then used to develop questions for the survey phase of the research. From this, we proceeded to develop a framework for learning space design for built environment students. Eight important learning space factors were identified; access to space, convenient workspaces, environment, layout, sense, integrated space, esthetics, and identity. Initial findings are presented regarding differences between disciplines in their rating of elements of the learning space. A framework is presented for practitioners to use in the design process for the development of built environment disciplines learning spaces. This research adds to current understanding regarding student-centered learning and workplace research, highlighting preferences for specific learning space factors. The current research is part of a bigger study but presents built environment disciplines outcomes which are applicable to a larger group.
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Several studies found that classrooms' indoor environmental quality (IEQ) can positively influence in‐class activities. Understanding and quantifying the combined effect of four indoor environmental parameters, namely indoor air quality and thermal, acoustic, and lighting conditions on people is essential to create an optimal IEQ. Accordingly, a systematic approach was developed to study the effect of multiple IEQ parameters simultaneously. Methods for measuring the IEQ and students' perceived IEQ, internal responses, and academic performance were derived from literature. Next, this systematic approach was tested in a pilot study during a regular academic course. The perceptions, internal responses, and short‐term academic performance of participating students (n = 163) were measured. During the pilot study, the IEQ of the classrooms varied slightly. Significant associations (p < 0.05) were observed between these natural variations and students' perceptions of the thermal environment and indoor air quality. These perceptions were significantly associated with their physiological and cognitive responses (p < 0.05). Furthermore, students' perceived cognitive responses were associated with their short‐term academic performance (p < 0.01). The observed associations confirm the construct validity of the systematic approach. However, its validity for investigating the influence of lighting remains to be determined.
Purpose Aligned with the United Nations 2030 agenda of leaving no one behind, a project called The Nest was initiated to create an in-house intentional learning space at two public housings in Klang Valley. In a small unit of public housing, most children in these houses sit on the floor in the living room to do their schoolwork or study with the TV switched on. Poor indoor environmental quality and lack of personal space are among the main reasons that lead to children not being able to study at home comfortably. Design/methodology/approach The research employed a quasi-experimental approach as the research field setting was not randomly assigned. Observation on the change in the quality of space and post-evaluation interviews with beneficiaries were conducted. Findings The findings show that besides the tangible space that the Nest project has created, it also has created in-tangible space. It has shown that even small spatial changes to existing spaces could improve children's active learning and the participation of parents in their children's learning. Research limitations/implications This study focuses on the home learning experience and parental involvement in their children's learning, so perhaps future research can be done to measure the impact of home learning space on academic achievement. Practical implications The research outcomes show that a good quality of learning space influences the children's learning experience at home and the parents' involvement. It will also contribute to the development of the building regulation for high-rise affordable housing in Malaysia. Particularly in improving the optimum requirement to achieve better comfort quality for the learning space at the residential unit. Social implications The Nest project will contribute to raising the awareness among all residents of public housing on the importance of in-house learning space and encourage them to build their own learning spaces through the Do-It-Yourself Guidelines. Originality/value The research outcomes show that both parental involvement and the quality of learning space influence the children's learning experience at home. It will contribute to the development of the building regulation for high-rise affordable housing in Malaysia. Particularly in improving the optimum requirement to achieve better comfort quality for the learning space at the residential unit.
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It has been challenging for designers to identify the appropriate design parameters that would reduce building energy consumption while achieving thermal comfort for building occupants. This study aims to determine the most important architectural building design parameters (ABDPs) that can increase thermal comfort and reduce energy use in educational buildings. The effect of 15 ABDPs in an Australian educational lecture theatre and their variabilities on energy consumption and students’ thermal comfort for each parameter were analysed using Monte Carlo (MC) techniques. Two thousand simulations for every input parameter were performed based on the selected distribution using the Latin hypercube sampling (LHS) technique. Sensitivity analyses (SA) and uncertainty analyses (UA) were used to assess the most important ABDPs in terms of thermal discomfort hours and energy consumption. The study found that the ABDPs, such as cooling set-point temperatures and roof construction, significantly reduce the operative temperature by up to 14.2% and 20.0%, respectively. Consequently, these reductions could significantly shorten the thermal discomfort hours, thereby reducing energy consumption by 43.7% and 41.0%, respectively. The findings of this study enable building designers to identify which ABDPs have a substantial impact on thermal comfort and energy consumption.
In school buildings, indoor thermal conditions are significant, considering the academic performance of the students. Thus, this study aims to evaluate and improve thermal comfort and productivity of occupants while analyzing its effect on energy consumption in a multipurpose school building. The building represents both the workplaces (offices), learning spaces (lecture halls, seminar rooms), and leisure areas (halls, canteens). Each thermal zone was evaluated separately based on the actual conditions. Interactions between environmental conditions, control strategies and annual heating/cooling loads have been analyzed through dynamic building modeling, using DesignBuilder and EnergyPlus software. The thermal comfort of the building has been evaluated concerning the actual conditions based on Fanger’s PMV index, and the results are compared with the site measurements done. The verified model was used to examine the effect of ambient temperature, supply airflow, HVAC and shading element operational schedule on thermal comfort and occupant productivity. As a result, discomfort hours were reduced by 17.6%, while it also led to an increase in annual energy consumption by 11.7%. When the change in productivity is analyzed, typing and thinking productivity were increased by 46%. The results showed that the building shell and design specifications of each zone should be considered when developing HVAC operational and design strategies to ensure better thermal comfort and productivity.
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This study reports the outcomes of a systematic literature review, which aims to determine the influence of four indoor environmental parameters -indoor air, thermal, acoustic, and lighting conditions - on the quality of teaching and learning and on students' academic achievement in schools for higher education, defined as education at a college or university. By applying the Cochrane Collaboration Method, relevant scientific evidence was identified by systematically searching in multiple databases. After the screening process, 21 publications of high relevance and quality, were included. The collected evidence showed that the indoor environmental quality (IEQ) can contribute positively to the quality of learning and short-term academic performance of students. However, the influence of all parameters on the quality of teaching and the long-term academic performance could not be determined yet. Students perform at their best in different IEQ-conditions, and these conditions are task-dependent, suggesting that classrooms which provide multiple IEQ classroom conditions facilitate different learning tasks optimally. In addition, the presented evidence illuminates how to examine the influence of the IEQ on users. Finally, this information supports decision-makers in facility management and building systems engineering to improve the IEQ, and by doing so, allow teachers and students to perform optimally.
While there is broad evidence of the impact of tangible factors (i.e. room temperature, indoor air quality) on work well-being and productivity, the objective measurement of intangible factors (i.e. ergonomics and privacy) is still an under-researched subject. A holistic approach to indoor environmental quality (IEQ) has been developed in this study by combining the research dimensions of IEQ factors (tangible vs. intangible), outcome (workplace satisfaction, health, productivity), method (subjective vs. objective assessment) and impact (direct vs. indirect effects), and it has been tested in a laboratory (n = 180). The main findings are that (i) workplace satisfaction, health and productivity are more strongly affected by intangible factors than by tangible ones, (ii) impaired privacy leads to sick building symptoms and less creativity, (iii) negative self-assessment impairs objective performance in the form of a self-fulfilling prophecy, while (iv) personality traits correlate differently with ergonomics and privacy. Hence, a holistic IEQ approach that also considers interrelations between the research dimensions is beneficial for creating supportive workplace designs.
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Research shows that poor indoor air quality (IAQ) in school buildings can cause a reduction in the students' performance assessed by short-term computer-based tests; whereas good air quality in classrooms can enhance children's concentration and also teachers' productivity. Investigation of air quality in classrooms helps us to characterise pollutant levels and implement corrective measures. Outdoor pollution, ventilation equipment, furnishings, and human activities affect IAQ. In school classrooms, the occupancy density is high (1.8-2.4 m 2 /person) compared to offices (10 m 2 /person). Ventilation systems expend energy and there is a trend to save energy by reducing ventilation rates. We need to establish the minimum acceptable level of fresh air required for the health of the occupants. This paper describes a project, which will aim to investigate the effect of IAQ and ventilation rates on pupils' performance and health using psychological tests. The aim is to recommend suitable ventilation rates for classrooms and examine the suitability of the air quality guidelines for classrooms. The air quality, ventilation rates and pupils' performance in classrooms will be evaluated in parallel measurements. In addition, Visual Analogue Scales will be used to assess subjective perception of the classroom environment and SBS symptoms. Pupil performance will be measured with Computerised Assessment Tests (CAT), and Pen and Paper Performance Tasks while physical parameters of the classroom environment will be recorded using an advanced data logging system. A total number of 20 primary schools in the Reading area are expected to participate in the present investigation, and the pupils participating in this study will be within the age group of 9-11 years. On completion of the project, based on the overall data recommendations for suitable ventilation rates for schools will be formulated. r A review of over 300 peer-reviewed articles of indoor air quality (IAQ), ventilation and building-related health problems in schools [1] has shown that ventilation is inadequate in many classrooms and was considered to be the main cause of health symptoms. Mendell and Heath [2] review evidence that certain conditions commonly found in US schools have adverse effects on the health and the academic performance of many of the more than 50 million US school children. They propose actions throughout the life of each existing and future school building to include adequate outdoor ventilation, control of moisture, and avoidance of indoor exposures to microbiologic and chemical substances considered likely to have adverse effects. A recent Dutch study [3] carried out in homes and in classrooms also showed that pupils' health appear to be associated with both the school and domestic exposure. Poor IAQ in schools was indicated; out of the 11 classrooms studied CO 2 levels were all above the recommended level of 1000 ppm with only one exception. Scandinavian research has shown that poor IAQ in school buildings can cause a reduction in the students' performance, whereas good air quality in classrooms can enhance children's concentration and also teachers' productivity [4,5]. An International Society for Indoor Air Quality (ISIAQ) Task Force report on Nordic schools [6], has identified the following areas for further research: ARTICLE IN PRESS 0360-1323/$-see front matter r
Conference Paper
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The relationship between classroom ventilation and pupils’ performance was investigated in primary schools in the United Kingdom. The concentration of carbon dioxide and other parameters were monitored for three weeks in two selected classrooms in each school. A direct air supply system through the windows was used to alter the ventilation rates in the classrooms. The system was set either to provide outdoor air or to re-circulate the classroom air while all other physical parameters were left unchanged. Computerised Assessment Tests (CAT) and Paper-based Tasks were used to evaluate pupils’ performance. The present paper shows preliminary results of the computerised tests from 6 schools. Due to the intervention the outdoor air exchange rate in the classrooms was altered from 0.8±0.5 h-1 (1.6±1.3 L/s per person) to 4.0±0.4 h-1 (6.8±1.4 L/s per person) which significantly improved pupils’ reaction time measures by 3%, Picture Recall Memory by 8% and Word Recognition by 15%.
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This paper explores the relation of self-assessed productivity to the objective environment and also to user satisfaction with air quality, acoustic, visual, and thermal conditions. The data are from the SCATs Project (Smart Controls and Thermal Comfort) and were obtained during monthly surveys in 26 office buildings in five European countries. More than 4500 desk visits were made, during which the office environment was measured and the subjective reports were obtained. Statistical analysis shows that the self-assessed productivity was coherently and significantly related to satisfaction with the various aspects of the office environment, while the relation with the measured conditions was indirect and weak. The self-assessed productivity was maximal when the comfort was greatest, over a wide range of environments. This result implies that to relate productivity directly to environmental variables, without first considering the overall comfort of the worker, is likely to be misleading. The effects of various methods of environmental control on the perceived productivity were compared. No one type of environmental control was necessarily superior.
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Despite a paucity of rigorous scientific evidence causally linking Indoor Environmental Quality (IEQ) issues to office occupants’ productivity, there is a widespread belief that such causality exists; excellent or poor IEQ translate into productivity gains or losses respectively. The aim of this study is to better understand relationship between perceived building performance on specific IEQ factors and occupants’ overall satisfaction with their workspace. Kano’s satisfaction model, developed originally in the context of marketing, is adapted and tested for its suitability in the context of building occupants’ satisfaction. Analyses were conducted on the occupant survey database from Center for the Built Environment (CBE) to estimate individual impacts of 15 IEQ factors on occupants’ overall satisfaction, depending on the building’s performance in relation to those IEQ factors. These empirical analyses identified nonlinearities between some IEQ factors and occupant satisfaction; some IEQ factors had a predominantly negative impact on occupants’ overall satisfaction when the building underperformed. These have been labelled Basic Factors in the Kano Model of satisfaction and include ‘temperature’, ‘noise level’, ‘amount of space’, ‘visual privacy’, ‘adjustability of furniture’, ‘colours & textures’ and ‘workspace cleanliness’. Other IEQ factors had a predominantly linear relationship with overall satisfaction – increments or decrements of equal magnitude in the building’s performance on these factors lead to a broadly similar magnitude of enhancement or diminution of occupants’ overall satisfaction. These were labelled Proportional Factors, and include ‘air quality’, ‘amount of light’, ‘visual comfort’, ‘sound privacy’, ‘ease of interaction’, ‘comfort of furnishing’, ‘building cleanliness’ and ‘building maintenance’.
This study evaluates the indoor environmental conditions and childrens comfort levels in 8 classrooms in three Italian primary schools. It is a development of a pilot study previously carried out by the authors in other educational buildings. Spot and long-term measurements were made to evaluate microclimatic conditions (i.e. air temperature, relative humidity, CO2 concentration, and illuminance). A questionnaire, distributed three times, investigated the students perception of thermal and lighting comfort, their satisfaction with building-related factors, and their interactions with the environment. Predicted mean vote and predicted percentage dissatisfied indexes were calculated and an adaptive approach was also applied, but their results did not correspond to the students subjective evaluation of thermal comfort. An innovative multivariate ranking method was developed as a possible tool to assess building stocks in order to establish priorities for repair, maintenance, and refurbishment. The problem which the students complained about the most was the thermal comfort in the hot season and solar penetration. Moreover, the school with the worst microclimatic conditions was also judged the worst for building-related and psychological factors.
Working or studying in a comfortable environment enhances not only well being, but also satisfaction and therefore productivity and learning. This research collects some pictures of indoor environmental conditions taken in seven primary schools near Venice (Italy, North-East). Spot measurements were recorded in 28 non air-conditioned classrooms, in springtime, while 614 children (age 9–11) completed a questionnaire about the evaluation of indoor environmental conditions and the related psychological impact, their behaviour towards discomfort and if their level of interaction with the environment (opening a window, switching off a light etc.). Nonparametric statistical tests were carried out to find significant differences between schools and between girls and boys in the same school and to see if gender might influence perception. Moreover, physical measurements were compared to the answers given to the questionnaire to find a relationship between them. Finally, children's reactions towards discomfort were evaluated to understand if pupils behave like “passive users” as frequently occurs with adults. Monitoring revealed very high CO2 concentration levels, which confirm insufficient air exchange by means of open windows, occasional insufficient lighting levels over the desks and, in general, nonuniform illuminance-distribution, probably due to improper solar shading use or even inappropriate shades. Pupils complained mostly about thermal conditions in warm seasons, poor indoor air quality and noise. Classroom conditions depended strongly on teachers' preferences; therefore a building management system would be advisable to provide good indoor environmental quality, which cannot be otherwise guaranteed.
This study investigates the relationship between Indoor Environmental Quality (IEQ) and learning performance in air-conditioned university teaching rooms via subjective assessment and objective measurement. Together with the data of air temperature, relative humidity, air speed, mean radiant temperature, CO2 concentration, equivalent sound pressure level, horizontal illumination level, occupant activity and clothing insulation level measured in four classrooms and four large lecture halls, self-reported learning performance (in calculating, reading, understanding and typing) and perceived IEQ are evaluated. The results show strong associations of the overall IEQ votes with the environmental parameters. While thermal comfort, indoor air quality and visual environment are of comparable importance, aural environment is the major determining factor. The study also reveals that all IEQ complaints have similar impact on learning performance and there is a good correlation between learning performance and the number of complaints. To aid design needs, empirical expressions that approximate the impact of unsatisfactory IEQ on learning performance loss are proposed.
S. S. Stevens (1946) stated that there is a relationship between psychological measurement scales and statistical procedures such that parametric techniques require the presence of at least interval scale data. This idea was incorporated into numerous statistics books, but has been attacked and shown to be fallacious. This problem is reviewed, and measurement scales and statistical aspects are considered. The misconception was previously and is presently based on a confusion between measurement theory and statistical theory. For statistical tests of null hypothesis, "the numbers do not know where they came from." (19 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)