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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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ReseaRch
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, simih@eco.umass.edu.
2. Dept. of Environmental Conservation, University of Massachusetts-Amherst, bweil@eco.umass.edu.
ABSTRACT
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.
KEYWORDS
thermal comfort, student performance, university classrooms, energy conservation
1. INTRODUCTION AND BACKgROUND
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
students.
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.
2. METhOD
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
respondents.
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. RESULTS
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
discomfort.
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.
4. DISCUSSION
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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... 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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... 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). ...
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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.
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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)