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A comparison of student performance between conditioned and naturally ventilated classrooms∗ A. K. Mishra†and M. Ramgopal Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, India – 721302

Abstract This study presents a comparison of classroom learning performance between courses taught in naturally ventilated (NV) rooms and air-conditioned (AC) rooms. This is done to examine effect of thermal comfort standard followed — PMV based or adaptive thermal comfort — on learning. The same group of students, attending different courses in the two classroom types over two years, was chosen to avoid inter-student difference of aptitude and ability. Performance was measured on basis of final grades scored in the particular courses. Data from a set of transverse thermal comfort surveys was used to find levels of satisfaction prevalent amongst students about their thermal environment in the two room types. Statistical tests were carried out to do pair wise comparisons of the performance of students. Comparison results did not show significant difference in performance for the courses considered. It is concluded that ability and avenues to adapt may help maintain long term average performance over a range of thermal environments. Keywords: adaptive thermal comfort; classrooms; natural ventilation; student performance

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Introduction

It is rather obvious that indoor environment would affect occupants’ physiology. Along the same lines, several studies and meta-analysis of studies confirm the important role indoor environmental quality (IEQ) plays in performance and productivity of occupants [Hancock and Vasmatzidis, 2003, Lee et al., 2012, Mendell and Heath, 2005, Wyon et al., 1979]. With introduction of the alternative, adaptive comfort approach into ASHRAE Standard 55 [ANSI/ASHRAE, 2004], a major turn of philosophy regarding thermal comfort in indoor environment began. The adaptive comfort standard was based upon ∗ †

Building and Environment. doi:10.1016/j.buildenv.2014.11.008 Corresponding author: [email protected]

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the results from several field studies on thermal comfort. Since introduction of these standards, many more fields studies have been conducted whose results support the conclusions presented by the standard i.e., occupants in NV buildings are comfortable and satisfied over a wider range of temperatures than occupants in AC buildings. Studies done across India have also shown this for Indian climatic conditions [Dhaka et al., 2013, Indraganti, 2010, Indraganti et al., 2013, Mishra and Ramgopal, 2014, Pellegrino et al., 2012, Rajasekar and Ramachandraiah, 2010]. However, how this alternate comfort standard affects performance of occupants has not yet been studied in detail. A study on this aspect, for office buildings, has been reported by Toftum et al. [2009]. They used a Bayesian Network approach to simulate and compare occupant performance between buildings using PMV model for thermal comfort and buildings using the adaptive model of thermal comfort. Their results showed that the indoor temperatures varied considerably between the two building configurations — especially for the building simulated in tropical climate — but the simulated performance did not differ much. Maximum decrement in estimated performance was found to be only 0.8%. One important factor contributing to these results could have been that their simulation used different thermal sensation distributions, as appropriate for indoors using PMV and adaptive comfort standard. Toftum et al. concluded that using the adaptive comfort model can result in considerable energy savings without having a significant effect on productivity. To the best of our knowledge, any other such comparison, of performance between buildings adhering to two thermal comfort standards, has not been reported and specifically, none has been reported for educational buildings. Hence, the current study was designed to look at performance of undergraduate students in courses taught in AC and NV classrooms. The study compares grades secured in such courses to find out if any statistically significant difference can be found between performance in the two classroom types.

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A brief review of previous works

While there are a few studies focusing on such aspects of IEQ as lighting, noise, odour, and wall colour, researchers have mainly focused on two aspects of indoor environment: ventilation and thermal environment. As the current study is about performance in buildings with different thermal comfort standards, we pay more attention to the studies dealing with thermal environment’s effect on performance while only summarily discussing ventilation.

2.1

Effect of ventilation

Ventilation is an important aspect of indoor environment and studies show that improving ventilation rates improves performance both in office [Sepp¨anen et al., 2006b, Wargocki, 2008, Wargocki et al., 2000] and classroom [Wargocki and Wyon, 2007] environments. Further, low ventilation rates in classrooms have also been associated with lowered attention, ability to concentrate [Bak´o-Bir´o et al., 2012], and

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increases absenteeism from class Shendell et al. [2004]. The work by Haverinen-Shaughnessy et al. [2011] is among the few that relate IEQ to long term performance — in terms of scores in national standardized tests —- and not with performance in simulated classroom tasks. Their work also shows a significant positive association between ventilation rates in classrooms and academic performance of students.

2.2

Effect of thermal environment

Fisk and Rosenfeld [1997] mention of an early study by the New York State Commission on Ventilation, during 1923, which found a significant relation between performance of manual tasks and air temperature but no such correlation for mental work. A later reanalysis of a portion of the Commission’s data refuted the earlier finding [Wyon, 1974]. Hancock et al. [2007] in their meta-analysis, conclude that thermal stress, caused by either of heat or cold, has a significant depreciative effect on performance. Their analysis showed that both types of thermal stressors have similar magnitude of impact on performance. Ngarmpornprasert and Koetsinchai [2010], in a study of productivity of office workers, concluded that optimal productivity can be achieved by air-conditioning set points at 26–28 ℃ during morning and at 24.5–26 ℃ for the afternoon and evening hours. There are some studies done for offices [Sepp¨anen et al., 2006a,b, Wyon, 1974] and one for classrooms [Wargocki and Wyon, 2007] in all of which, an optimal temperature for performance has been found around 21 ℃. One must however keep in mind that these studies were in conditioned buildings and occupant control over thermal conditions was all but absent. Mendell and Heath [2005] discuss of the work done by Wittersech and his group where performance of office workers did not change with increasing temperature but participants rated their own performance at lower levels. They also made mention of the work done by Pepler and Warner with school students wherein it was found that as temperature rose from 16.7 to 26.7 ℃, work speed decreased by 7% and errors also decreased by 17%. As the temperature further increased from 26.7 ℃ to 33.3 ℃, work speed and error rates both increased [Fisk and Rosenfeld, 1997]. Other studies investigating influence of thermal environment on work speed have shown a significant improvement in work speed when room temperatures were lowered, though error rate in simulated tasks was not affected [Lan et al., 2009, Wargocki and Wyon, 2007, 2013]. Along similar lines is the finding by Lan et al. [2014] that thermal discomfort had a greater impact on speed than on accuracy.

2.2.1

Role of task nature

In a typical office or classroom environment, occupants are engaged in a broad range of mental activities. It stands to reason that since different types of activities require different levels of exertion of mental faculties, performance of these activities should be affected to different levels by the indoor environment. Meta-analysis results of Hancock et al. [2007] showed that task nature as well as duration and intensity of the stressor have an impact in determining the optimal thermal environment. They put forth that if task types be categorized into perceptual, cognitive, and psychomotor classes, thermal stress has

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maximum impact on perception, followed by psychomotor response and finally cognition. A similar opinion — regarding nature of task being important in how and how much thermal environments affect performance — has been expressed in some other studies as well [Fisk and Rosenfeld, 1997, Lan et al., 2009, Wyon et al., 1979]. Fisk and Rosenfeld [1997] even state that for a few types of tasks that involve high complexity or creativity, optimal thermal comfort and optimal performance may coincide while for most other task types, a slight thermal discomfort may raise arousal level to improve performance of mental work. A couple of studies also found that time of day can affect optimal environment for productivity [Lan et al., 2009, Ngarmpornprasert and Koetsinchai, 2010]. Thus, apart from an expected variation due to individual preferences, task nature, duration, and time of execution can influence the optimal thermal environment for productivity. Therefore greater occupant control over their thermal environments has been suggested as an approach for enhancing task efficiency [Fisk and Rosenfeld, 1997].

2.2.2

Importance of subjective occupant satisfaction

Findings in certain studies stress upon the fact that more than the IEQ itself, subjective satisfaction (with temperature, air quality etc.) of the occupants impacts productivity [Bell et al., 2005, Hoque and Weil, 2014, Olesen, 2005, Wargocki, 2008, Wyon et al., 1979]. In the studies by Hoque and Weil [2014] and Bell et al. [2005], subjective comfort ratings are able to explain 34% and 48% of the variance in test scores, respectively. It is pertinent in this case to mention that the study by Hoque and Weil [2014] found a significant relation between discomfort votes and score but only a weak correlation between the actual air temperature and comfort votes.

2.3

Occupant adaptation and performance level

As of this date, numerous thermal comfort field studies provide evidence of the ability of occupants to adapt across a broad range of thermal conditions when they are provided with adaptive opportunities. It should be interesting to review here how behavioural or psychological adaptations may aid task performance. Any environmental stress, thermal or otherwise, would require occupants to cope by exerting additional mental faculties [Hancock et al., 2007]. Over certain ranges of thermal stressors (temperatures) and task durations, this kind of conscious additional effort can overcome the negative impacts of thermal stress on performance [Lan et al., 2009, Wyon et al., 1979]. As also observed by Wyon et al. [1979], among the different tasks given to subjects, such activities where the subjects had ample practice were not affected by moderate heat stress. As far as behavioural adaptations go, Mendell and Heath [2005], in their review of studies on thermal conditions and student performance, discuss of two studies where performance was not affected with varying temperature because either subjects had a ability to fine tune temperature to their own liking or they could adjust clothing to stay thermally neutral. A similar effect was observed by Wyon [1974] in the study of heat stress effect on typewriting. Subjects rated two temperatures, 4 K apart, as being

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equally comfortable even though they had no option to adjust clothing. Wyon suggests that subjects adapted to the increased temperature by working slower (as observed from performance data) and thus maintaining thermal comfort level. Hancock et al. [2007] present a perceptive analysis of such observations. Instead of the traditional ”inverted U” model for associating stress and performance, they advocate the use of an ”extended” U model. In the extended U model, there is no single point of optimal performance but an extended plateau over which performance keeps stable. Over this region, stress is tolerable because of compensatory measures — psychological or behavioural in nature. Beyond a certain point, ’adaptive’ measures are unable to recompense for the effects of stress and then performance levels would rapidly deteriorate.

2.4

Indoor environment, productivity, and cost benefits

As already discussed, IEQ has a substantial contribution towards the productivity of occupants. The analysis done by Fisk and Rosenfeld [1997] shows that improving IEQ has considerable benefits on health and productivity of office occupants which translates into savings that far exceed the costs incurred in such improvements. A similar analysis is not available for classrooms and the benefits from improving classroom environment are likely to be much less tangible than those from improving office environments. A better work environment would of course be beneficial to the workforce engaged in teaching activities. More importantly, training and educating its human resource is of primary concern to any society in terms of its future achievements and national productivity. That this training/education should happen in the most conducive environment should hence be an obvious concern. There is strong evidence that the indoors in classrooms across the world are less than adequate [Daisey et al., 2003, Van Dijken et al., 2006]. A further point of concern is that unlike office occupants, students are much less likely to voice their complaints about IEQ [Wargocki and Wyon, 2007]. These findings collectively stress on the multidimensional importance of classroom indoor environment. This short review shows that there is ample evidence showing an impact of indoor environment on work performance, in particular, thermal environment. For more detailed analysis, some of the review works we refer to may be consulted [Daisey et al., 2003, Fisk and Rosenfeld, 1997, Hancock et al., 2007]. While it is beyond doubt that thermal environment has a significant effect on performance, the detailed nature of this effect is not straightforward. Task nature, duration of exposure, occupant background, available means of adaptation, all have differing levels of contribution in determining the overall effect. Again, speed and accuracy of a typical task need not be affected to the same degree or even in the same manner. And, in a somewhat paradoxical effect, slight thermal discomfort can increase arousal and hence improve task performance in certain activities. One of the more important realisations to take from the review is about the extended U hypothesis presented by Hancock et al. [2007]. Like in many other aspects of life, human ability to adapt and adjust means that there is no one ideal temperature for optimal performance. Rather, over a certain wide zone, conscious effort by performers and small adaptive measures can help negate the debilitating effects of environmental stress.

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3

Methodology

A myriad of variables influence student learning and exam performance, including student ability and self-preparation, social and financial background, and a student’s personal interest. Beyond classroom type, at undergraduate level, the variable of self-study and good preparation can explain the comparison results of final scores across courses for a student. When trying to compare performance and find the effect of one particular variable, considerations need to be made to make the comparison possible and meaningful. The current work is concerned with performance difference between two building types that adhere to two different standards of thermal comfort. So, the major assumption made is that for classrooms in any one building type (AC or NV), student performance is equivalent in every manner. In interest of privacy, certain details like course names, specific course nature, year during which particular courses are taken etc. are not mentioned in this article.

3.1

Selection of students and courses

A class of 50 undergraduate students enrolled in an engineering course at the Indian Institute of Technology Kharagpur (IIT) were taken as the sample space for this study. Courses they took during the first or final year of their curriculum were not used for comparison since during first year, students have common courses but this group of 50 students would have been divided over several sections. Similarly, during final year, students have option to choose from several elective courses and again the 50 students get divided into smaller groups. Thus, courses taken by these students during their second and third year were identified for comparison and the corresponding classrooms were determined from records of time table data. In IIT, end of semester evaluation takes a weighted sum of marks scored in all the tests/assignments so that maximum possible marks is 100. A grade is then awarded based on the total marks. The performance data available for the different courses was in terms of grades. These grades were then converted into numerical equivalents by taking a cue from the grading system. Scores based on which these grades are awarded and the numerical equivalent used for each grade in the current analysis are given in Table 1.

Table 1: Grades and marks Grade Ex A B C D P F

Marks range 100–90 89–80 79–70 69–60 59–50 49–40 < 40

Numerical equivalent 95 85 75 65 55 45 35

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Apart from the said grades, students who were not able to complete a course — due to medical ground or lack of attendance or any other grounds — were given an ”incomplete” grade, characterized by an X. Since an incomplete grade may be given due to varied reasons, it was not deemed appropriate to compare the sample with incomplete grades in them. Students having incomplete grades or courses with a large number of incomplete grades were taken out of the comparison. After this, 44 students and 9 courses (5 in AC rooms and 4 in NV) were left in the final comparison sample. Some confusion may arise when comparing an ’F’ grade since any mark below 40 is given this grade but the numerical equivalent used is always 35. That is to say, two ’F’s need not be of similar performance level and they may not be equally lower than, say, a ’D’. However, there are only 10 ’F’s in the total 396 grades under consideration. Also, there is some strength to the logic that all unsuccessful grades (’F’) may be categorised as the same irrespective of how far from success they were. It is worth mentioning here that we took into consideration grades scored by students when they took the course for the first time, with all 50 students as a class unit. Unsuccessful students or students getting incomplete grades may later have cleared the course by retaking it or through supplementary examinations. Such grades were not taken for the comparison. For ease of reference, the nine course names were coded using the following scheme: the letters at beginning indicate if the course was taken in a NV classroom (letter code ’nac’) or in a AC classroom (letter code ’ac’), the first digit gives a serial number and the last digit gives the year of the undergraduate studies when the course was offered. Thus, ’nac43’ represents the fourth course in the list that was taken in NV classrooms during the third year of the undergraduate course while ’ac22’ represents the second course in the list that was taken in AC classrooms during the second year of the undergraduate course.

3.2

Comparing performance across courses

Considering a pairwise comparison between the performance of the same students eliminates the problem of having to deal with student difference in abilities and their backgrounds. This still leaves the problem of different toughness level of the courses, difference between instructors, difference of testing procedures etc. To overcome these factors, the approach elucidated by Manly [1988] is followed in the current work. It is reasonable to assume that for the same group of students, as opposed to different groups of students or students from different institutes, marks secured in different courses are similar quantities and can be subjected to comparisons. The courses were treated as two non interacting blocks — one for those taught in NV classrooms, second for those taught in AC classrooms. The method proposed by Manly is based on the assumption that for a particular student, the average of marks secured in different courses is a suitable standard for determining if individual course marks need to be scaled before making comparisons. If the marks in a course are comparable to those of other courses, for half the students, their mark should be greater than their average and in the other half, their mark should be lesser. If this is not the case, scores in the particular course would need to be scaled before proceeding with any kind of comparison. To avoid superfluous scaling, Manly proposes the measure CUMSUM for checking if the deviations

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encountered can be explained away by pure chance or if the deviations are too high to be due to chance P alone. CUMSUM is defined as CU M SU M = j (Sij − S¯j ), where, Sij is the mark in the ’i’th course, secured by the ’j’th student, S¯j is the across courses average for the ’j’th student, and the summation is for all students in the ’i’th course. It is unlikely that a student would have secured exactly the same grade/score in all the courses, even if the marks in these courses are comparable. So, CUMSUM almost certainly would not be 0. It follows that if CUMSUM, so found, is a large positive value, the marks in that course need to be scaled down and vice versa. As mentioned, it is possible that within certain limits, value of CUMSUM could be attributed to pure chance. To find out what these limits are, 1000 sets of marks for 44 students were randomly generated and CUMSUM is determined for each set. Using a 5% significance criteria, the limits were determined as the CUMSUM just above the bottom 2.5% and the one just below the top 2.5%.

3.2.1

Scaling of marks

For any courses with CUMSUM beyond these limits, scaling is required. In interest of thoroughness, three different types of scaling recommended in Manly’s work were utilised: additive scaling, “Broken stick” scaling and Wave scaling. If S is taken as the original score and S’ as the scaled score, the scaling models work as follows: • Additive scaling: S 0 = S + a • Broken Stick scaling: S0 =

  bS,

if S ≤ 50

 50b + (2 − b)(S − 50), if S > 50 • Wave scaling: S 0 = S + f (S), where f (S) is a cubic polynomial of the form f (S) = aS(S − 100) + bS 2 (S − 100) In the above relations, ’a’ and ’b’ are scaling parameters for the different scaling types and must be chosen in such a manner as to minimize the CUMSUM value. For the different scaling models, this is done in the following manner: • Additive: a =

1 n

P¯ S−

• Broken stick: b =

1 n

P

S

P PV , U

where,

  V = S¯

if S ≤ 50

and U = S

 V = S¯ − 2(S − 50)

and U = (100 − S)

if S > 50

¯ • Wave: a and b are determined by fitting least squares regression equation of S−S against S(S−100) and S 2 (S − 100) such that S¯ − S = aS(S − 100) + bS 2 (S − 100)

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It is useful to reiterate here that for calculating initial CUMSUM value and for any scaling procedure that follows, course codes of ’ac’ and ’nac’ were treated as separate, non-interacting blocks. Also, if scaling was required, the marks in ’ac’ or ’nac’ set were all scaled simultaneously. An alternative, and more complicated approach, could have been to scale a course’s mark and recalculate CUMSUM values before scaling another course.

3.3

Comparing ’ac’ and ’nac’ courses

The null hypothesis is: student performances in the two types of classrooms, as differentiated by the thermal comfort standard utilised, are not significantly different. All statistical analysis was done using the R statistical package [R Core Team, 2013]. A Shapiro-Wilk test of the scaled and unscaled marks revealed that the distributions were not normal. So, for comparing the marks, the Kruskal Wallis H test — a non-parametric equivalent of ANOVA — was chosen. Following the Kruskal Wallis test, a further pairwise comparison of scaled marks from the ’ac’ and ’nac’ sets was done using the Wilcoxon rank test. Between the five AC classroom courses and 4 NV classroom courses, a total of 20 comparisons were made for all three types of scaling. While performing a paired comparison using the Wilcoxon rank test, the starting test was a two-tailed one to check equivalence of the two marks. Only if this test showed non-equivalence at a 5% significance level, a one-tailed test was performed to find out which set of marks is greater.

3.4

Thermal comfort survey

During August to October 2013, a set of transverse comfort surveys had been conducted in classrooms spread across three buildings in the institute: CRB1, CRB2, and CRB3. While classrooms in CRB3 are AC type, CRB1 and CRB2 classrooms belong to NV type. Surveys were taken towards the end of one hour duration classes. The survey questionnaire involved several points dealing with IEQ. For the present work, answers to the seven point ASHRAE thermal sensation question and the thermal acceptability question were of interest (Figure 1). In each classroom where the survey was done, mean thermal sensation vote (MTSV) was calculated as an average of the individual thermal sensation votes. Acceptability percentage was evaluated based on the percentage of students who had voted to the left of ’Just Unacceptable’ on the thermal acceptability scale. Globe temperature was measured at the approximate centre of a classroom, during survey. Figure 2 shows interior views of three sample classrooms from the three buildings. The NV classrooms have operable windows and ample number of ceiling fans. The AC classroom lacks fans and operable windows. These images were taken during early morning, just before starting of classes. Images for the NV rooms were taken in natural lighting whereas for the AC room, fluorescent lamps were on since the film coated windows do not let in much natural light.

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Figure 1: Thermal sensation and acceptability questions

3.5

Study limitations

In the current work, there was no practical way to evenly distribute out ’hard’ and ’easy’ courses between AC and NV rooms. However, since the data taken is historical and spread over two years, it may be assumed that the courses in the final analysis have a reasonably random distribution across classroom types. Another limitation was that students for whom the performance data was taken were different from those for whom the thermal comfort survey was performed. But some of the classrooms (five out of the eight), both AC and NV, were the same for both performance data and comfort survey data. The thermal comfort survey results are being provided solely as a measure of the average satisfaction level amongst students in the two classroom types. One more limitation was that data was not available about where the exams/tests were conducted for the courses being analysed. These could have been in AC or NV rooms. However, the primary goal here is to compare and contrast the ability of AC and NV rooms to act as optimal learning environments, so this limitation should not greatly affect our conclusions. As stated in earlier sections, the current work focuses on the effect of thermal comfort standards on occupant performance. Hence influence of other IEQ parameters have not been analysed.

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Results and discussions

4.1

Results of thermal comfort survey

Summary results of interest from the transverse thermal comfort surveys are given in Table 2. Survey results showed that MTSV in the AC rooms were consistently on the negative side of the thermal sensation scale. It is interesting that even if the MTSV values in AC rooms remained below -1, satisfaction was above 80%. This was likely due to some combination of behavioural adaptation and meeting of expectations from an ’AC’ room. MTSV in the NV classes were mixed but they bore little resemblance to the indoor air temperatures recorded on the corresponding days. Nonetheless,

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Figure 2: Classroom interiors a)CRB2–2(NV) b)CRB1–2 (NV) c) CRB3–2 (AC) Table 2: Thermal comfort survey results Room CRB1–1 CRB1–2 CRB1–3 CRB2–1 CRB2–2 CRB3–1 CRB3–2 CRB3–3

(NV) (NV) (NV) (NV) (NV) (AC) (AC) (AC)

Indoor globe temperature (℃) 28 28 28 29 30 24 24 24.5

Number of students 34 43 41 43 39 99 99 50

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MTSV -0.59 -0.02 0.15 0.37 0.41 -1.29 -1.32 -1.46

Occupant satisfaction (%) 91 88 88 88 89 84 84 84

satisfaction levels remained high in both types of rooms. Between AC and NV rooms, even when recorded temperature differed by 4/5 ℃, satisfaction percentages did not differ by more than 5%. The occupant satisfaction percentages found in these surveys for the NV rooms were consistent with those reported for NV laboratory classes and NV classrooms in our previous works [Mishra and Ramgopal, 2014, April 2014]. These results are reasonable for acclimatized subjects who have the ability to adapt to their surroundings in terms of adjusting clothing, availability of ceiling fans, operable windows etc. The similar levels of satisfaction in both types of rooms is important in context of the discussions in Section 2.2.2 regarding subjective satisfaction’s role in performance. As an early indicator, comparable levels of satisfaction in the two room types do, at some level, imply similar levels of learning performance.

4.2

Scaling of marks and a qualitative comparison

From the random sets of marks generated, upper and lower limits of CUMSUM were determined as 243 and -220 respectively. Considering these as the limiting values, the CUMSUMs of four courses showed requirement of scaling: nac12, nac33, ac22, and ac42. For the sake of completeness, all further analysis carried out was done for two variants, one where marks in only the above four courses were scaled (OSS) and one where marks of all courses were scaled (AS). Comparisons were assumed to yield statistically significant differences if the ’p’ value obtained is less than 0.05. Figure 3 shows box plots for the case where marks of all courses have been scaled while Figure 4 gives box plots for the case where courses with significant CUMSUM deviations have been scaled. In each figure, for comparison, a box plot of the unscaled marks is also presented. From the plots, using the median lines of the boxes for comparison, it is apparent that scaling makes the marks more comparable, whether it be scaling of all the courses or just the four courses with significant deviation in CUMSUM. Also, the end effect of the three different scaling types is not very different. Box plots for the marks post scaling shows that marks ’ac’ and ’nac’ courses do not appear to be significantly different. For the plot presenting variant AS, in all three types of scaling, the median line for any of the courses does not cross below the lower quartile line of all other courses. In the variant OSS, this is not the case, in particular for one course: ’ac12’. For a better estimate, a quantitative approach is taken up next.

4.3 4.3.1

Statistical tests Kruskal-Wallis H test

Table 3 gives the results for Kruskal-Wallis H test. Kruskal-Wallis test was conducted on the ’ac’ and ’nac’ scores taken together to check if any of the columns (i.e. marks in a particular course) were significantly different from the other columns. This was repeated for all three types of scaling and the two variants of AS and OSS. As can be seen from Table 3, for none of the cases there was any statistically significant difference between the marks at 5% significance level. p-values found were lower in the case when only marks for courses with significant deviation were scaled. This is expected since scaling reduces

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Figure 3: Box plots of scaled marks and unscaled marks for AS variant variations among the ’ac’ and ’nac’ set of courses taken as a block each. This observation had also been made from analysing the box plots.

4.3.2

Wilcoxon signed rank test

To get a better resolution, than is possible by using solely Kruskal-Wallis H test, pair wise Wilcoxon signed rank tests were conducted between pairs of courses from the ’ac’ and ’nac’ groups. Results are shown in Tables 4 and 5. The tables need to be read as such: rows represnet courses from ’nac’ set while columns the ’ac’ set; at the intersection point of a row and a column, the comparison result (< or > or ≡) is given along with p-value of comparison in braces. Observing Table 4 for the AS variant, all the cross comparisons of scores for courses in NV and AC classes yield equivalence (except for a single case

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Figure 4: Box plots of scaled marks and unscaled marks for OSS variant Table 3: Results of Kruskal-Wallis H test Sample set

Kruskal-Wallis p-value χ− squared All marks scaled Additive 0.9899 0.9983 Broken stick 0.9315 0.9986 Wave 2.1831 0.9749 Only significant deviations scaled Additive 10.1087 0.2575 Broken stick 10.3282 0.2427 Wave 13.5367 0.09467 using Wave scaling). In the OSS variation, the majority of comparisons still yield equivalence. Only scores of ’ac12’ seem to be smaller than the scores for ’nac’ courses. This was also commented upon

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while analysing the box-plots of the OSS variation. Hence, based on the majority evidence, we fail to reject the null hypothesis we started with, i.e., student performances in the two types of classrooms, as differentiated by the thermal comfort standard utilised, are not significantly different.

Table 4: Wilcoxon rank test comparisons for the variation AS ac12 ac22 Additive scaling nac12 ≡ (0.51) ≡ (0.76) nac22 ≡ (0.81) ≡ (0.99) nac33 ≡ (0.42) ≡ (0.82) nac43 ≡ (0.60) ≡ (0.99) Broken stick scaling nac12 ≡ (0.52) ≡ (0.72) nac22 ≡ (0.91) ≡ (0.67) nac33 ≡ (0.61) ≡ (0.85) nac43 ≡ (0.79) ≡ (0.83) Wave scaling nac12 ≡ (0.43) ≡ (0.76) nac22 ≡ (0.93) ≡ (0.97) nac33 ≡ (0.56) ≡ (0.53) nac43 ≡ (0.79) ≡ (0.99)

ac32

ac42

ac53

≡ ≡ ≡ ≡

(0.90) (0.96) (0.69) (0.99)

≡ ≡ ≡ ≡

(0.14) (0.12) (0.16) (0.35)

≡ ≡ ≡ ≡

(0.87) (0.32) (0.26) (0.64)

≡ ≡ ≡ ≡

(0.80) (0.96) (0.69) (0.82)

≡ ≡ ≡ ≡

(0.95) (0.77) (0.48) (0.43)

≡ ≡ ≡ ≡

(0.68) (0.40) (0.26) (0.93)

≡ ≡ ≡ ≡

(0.90) (0.99) (0.64) (0.99)

≡ ≡ ≡ ≡

(0.71) (0.59) (0.45) (0.62)

≡ ≡ < ≡

(0.77) (0.72) (0.024) (0.96)

Table 5: Wilcoxon rank test comparisons for the variation OSS ac12 Additive scaling nac12 > (0.005) nac22 > ( (0.003) nac43 ≡ (0.17) Broken stick scaling nac12 > (0.01) nac22 > ( (0.003) nac43 ≡ (0.42) Wave scaling nac12 > (0.01) nac22 > ( (0.003) nac43 > (0.007)

ac22

ac32

ac42

ac53

≡ ≡ ≡ ≡

(0.76) (0.99) (0.82) (0.99)

≡ ≡ ≡ ≡

(0.90) (0.09) (0.50) (0.99)

≡ ≡ ≡ ≡

(0.14) (0.07) (0.60) (0.35)

≡ ≡ ≡ ≡

(0.90) (0.09) (0.50) (0.99)

≡ ≡ ≡ ≡

(0.72) (0.39) (0.70) (0.83)

≡ ≡ ≡ ≡

(0.80) (0.09) (0.50) (0.82)

≡ ≡ ≡ ≡

(0.95) (0.26) (0.87) (0.43)

< ≡ ≡ ≡

(0.01) (0.51) (0.29) (0.17)

≡ ≡ ≡ ≡

(0.76) (0.47) (0.79) (0.99)

≡ ≡ ≡ ≡

(0.77) (0.09) (0.50) (0.51)

≡ ≡ ≡ ≡

(0.71) (0.08) (0.70) (0.62)

< ≡ ≡ ≡

(0.01) (0.51) (0.29) (0.47)

Results from the statistical comparisons can be said to be concordant with those obtained by Toftum et al. [2009] from their Bayesian Network simulation of occupant performance. Toftum et al. found none to very low decrement of simulated performance for buildings maintained using the adaptive comfort model vs those using PMV model. Similarly, for majority of pair-wise comparisons we do here, performance of students in NV rooms was not significantly different from that in AC rooms. This could

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be explained by occupant satisfaction levels, occupant acclimatization, availability and use of adaptive opportunities. One aspect to keep in mind would be that we are comparing here what can best be termed as long term performance. From our results we cannot say if on certain days the AC rooms were more/less conducive than the NV rooms for learning. But what these results do show is that, over a semester, productivity of teaching-learning activity is similar for rooms that adhere to either the PMV model of thermal comfort or the adaptive model of thermal comfort. Students adapting to their thermal environments to keep up satisfaction and performance levels in NV rooms at par with their performance in AC rooms can be regarded as a corroboration of the extended U model of performance presented by Hancock et al. [2007]. As proposed in the extended U model, the ability to adapt and exert extra effort can help counteract the effects of stress over a broad enough region. It is likely that well designed NV rooms with ample adaptive opportunities keep within this region of thermal stressors, as do the AC rooms. High levels of occupant satisfaction, as found from thermal comfort surveys in both types of rooms, indicate that these classrooms are able to provide students with an expected level of comfort and within the zone of the relevant extended U model. A badly designed NV room is as likely to exceed these zones as a badly designed AC room or an AC room with malfunctioning equipment. The lack of any statistically significant difference in learning performance in the rooms studied in this work is very well explained by the extended U model of productivity vs stress.

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Conclusion

This study was an attempt towards evaluating the effect of two alternative thermal comfort standards on student performance in classrooms. Results from comfort surveys showed that in both classroom types, students had analogous levels of satisfaction. Similarly, the data on learning performance in the two room types were not significantly different. It would be fair to conclude that in well designed NV classrooms, where student satisfaction levels are not greatly affected, student performance should not be affected as well.

Acknowledgement We are thankful to the administration of Indian Institute of Technology Kharagpur for the permission to use anonymised student performance data. We are grateful to Prof. Michael Humphreys for his advice and insights on the topic which greatly helped our work. Our thanks also goes to Rahul and Geethanjali, two undergraduate students in our department, who performed the transverse comfort surveys in the classrooms.

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References ANSI/ASHRAE. Standard 55-2004. Thermal comfort conditions for human occupancy. ASHRAE, Atlanta, 2004. Z. Bak´ o-Bir´ o, D. J. Clements-Croome, N. Kochhar, H. B. Awbi, and M. J. Williams. Ventilation rates in schools and pupils’ performance. Build Environ, 48:215–223, 2012. R. Bell, A. V. Cardello, and H. G. Schutz. Relationship between perceived clothing comfort and exam performance. Fam Consum Sci Res J, 33(4):308–320, 2005. J. M. Daisey, W. J. Angell, and M. G. Apte. Indoor air quality, ventilation and health symptoms in schools: An analysis of existing information. Indoor Air, 13(1):53–64, 2003. S. Dhaka, J. Mathur, A. Wagnerb, G. D. Agrawal, and V. Garg. Evaluation of thermal environmental conditions and thermal perception at naturally ventilated hostels of undergraduate students in composite climate. Build and Environ, 66:42–53, 2013. W. J. Fisk and A. H. Rosenfeld. Estimates of improved productivity and health from better indoor environments. Indoor Air, 7(3):158–172, 1997. P. A. Hancock and I. Vasmatzidis. Effects of heat stress on cognitive performance: The current state of knowledge. Int J Hyperth, 19(3):355–372, 2003. P. A. Hancock, J. M. Ross, and J. L. Szalma. A meta-analysis of performance response under thermal stressors. Hum Factors, 49(5):851–877, 2007. U. Haverinen-Shaughnessy, D. J. Moschandreas, and R. J. Shaughnessy. Association between substandard classroom ventilation rates and students’ academic achievement. Indoor Air, 21(2):121–131, 2011. S. Hoque and B. Weil. Cold comfort: Thermal satisfaction in academia. In Proceedings of 8th Windsor conference, Counting the cost of comfort in a changing world., Windsor, UK, April 2014. NCEUB. M. Indraganti. Using the adaptive model of thermal comfort for obtaining indoor neutral temperature: Findings from a field study in Hyderabad, India. Build Environ, 45(3):519–536, 2010. M. Indraganti, R. Ooka, and H. B. Rijal. Field investigation of comfort temperature in Indian office buildings: A case of Chennai and Hyderabad. Build Environ, 65(0):195–214, 2013. L. Lan, Z. Lian, L. Pan, and Q. Ye. Neurobehavioral approach for evaluation of office workers’ productivity: The effects of room temperature. Build Environ, 44(8):1578–1588, 2009. L. Lan, P. Wargocki, and Z. Lian. Thermal effects on human performance in office environment measured by integrating task speed and accuracy. Appl Ergon, 45(3):490–495, 2014.

17

M. C. Lee, K. W. Mui, L. T. Wong, W. Y. Chan, E. W. M. Lee, and C. T. Cheung. Student learning performance and indoor environmental quality (IEQ) in air-conditioned university teaching rooms. Build Environ, 49:238–244, 2012. B. F. J. Manly. The comparison and scaling of student assessment marks in several subjects. J Roy Stat Soc C–App, pages 385–395, 1988. M. J. Mendell and G. A. Heath. Do indoor pollutants and thermal conditions in schools influence student performance? A critical review of the literature. Indoor Air, 15(1):27–52, 2005. A. K. Mishra and M. Ramgopal. Thermal comfort in undergraduate laboratories — A field study in Kharagpur, India. Build Environ, 71:223–232, 2014. A. K. Mishra and M. Ramgopal. Thermal comfort in classrooms in tropics: An analysis of student preference. In Proceedings of conference Efficient, High Performance Buildings For Developing Economies, 2014. ASHRAE, April 2014. S. Ngarmpornprasert and W. Koetsinchai. The effect of air-conditioning on worker productivity in office buildings: A case study in Thailand. In Building Simulation, volume 3, pages 165–177. Springer, 2010. B. W. Olesen. Indoor environment-health-comfort and productivity. Procedings of Clima, 2005. M. Pellegrino, M. Simonetti, and L. Fournier. A field survey in Calcutta. Architectural issues, thermal comfort and adaptive mechanisms in hot humid climates. In Proceedings of 7th Windsor Conference: The changing context of comfort in an unpredictable world, Windsor, UK, April 2012. NCEUB. R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2013. URL http://www.R-project.org/. E. Rajasekar and A. Ramachandraiah. Adaptive comfort and thermal expectations — a subjective evaluation in hot humid climate. In Proceedings of 6th Windsor conference Adapting to change: New thinking on comfort., Windsor, UK, 2010. NCEUB. O. Sepp¨ anen, W. J. Fisk, and Q. H. Lei. Effect of temperature on task performance in office environment. Lawrence Berkeley National Laboratory,, 2006a. URL http://escholarship.org/uc/item/ 45g4n3rv. Last accessed: 30 Oct, 2014. O Sepp¨ anen, W. J. Fisk, and Q. H. Lei. Room temperature and productivity in office work. Lawrence Berkeley National Laboratory,, 2006b. URL http://escholarship.org/uc/item/9bw3n707. accessed: 30 Oct, 2014.

18

Last

D. G. Shendell, R. Prill, W. J. Fisk, M. G. Apte, D. Blake, and D. Faulkner. Associations between classroom CO2 concentrations and student attendance in Washington and Idaho. Indoor Air, 14(5): 333–341, 2004. J. Toftum, R. V. Andersen, and K. L. Jensen. Occupant performance and building energy consumption with different philosophies of determining acceptable thermal conditions. Build Environ, 44(10), 2009. F. Van Dijken, J. E. M. H. Van Bronswijk, and J. Sundell. Indoor environment and pupils’ health in primary schools. Building Res Inf, 34(5):437–446, 2006. P. Wargocki. Improving indoor air quality improves the performance of office work and school work. In proceedings of International conference for enhanced building operations. Berlin, Germany, 2008. P. Wargocki and D. P. Wyon. The effects of moderately raised classroom temperatures and classroom ventilation rate on the performance of schoolwork by children (RP–1257). HVAC&R Res, 13(2): 193–220, 2007. P. Wargocki and D. P. Wyon. Providing better thermal and air quality conditions in school classrooms would be cost-effective. Build Environ, 59:581–589, 2013. P. Wargocki, D. P. Wyon, J. Sundell, G. Clausen, and P. 0. Fanger. The effects of outdoor air supply rate in an office on perceived air quality, sick building syndrome (SBS) symptoms and productivity. Indoor Air, 10(4):222–236, 2000. D. P. Wyon. The effects of moderate heat stress on typewriting performance. Ergonomics, 17(3):309–317, 1974. D. P. Wyon, I. B. Andersen, and G. R. Lundqvist. The effects of moderate heat stress on mental performance. Scand J Work Environ Health, 5:352–61, 1979.

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