A STUDY OF TEACHING UNDER THE WEATHER: THE INFLUENCE OF WEATHER ON
PARTICIPANT MOOD AND ENGAGEMENT DURING AN INFORMAL SUMMER CAMP
PROGRAM.
Joseph Carstensen
A Thesis
Submitted to the Graduate College of Bowling Green
State University in partial fulfillment of
the requirements for the degree of
Master of Education
May 2013
Committee:
Jodi Haney, Advisor
Tracy Huziak-Clark
Eric Worch
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ABSTRACT
Jodi Haney, Advisor
The research question in this thesis was to find out if and how weather influenced
students’ positive and negative mood, as well as cognitive, emotional, and behavioral
engagement. This study also inquired if these results were similar to what prior research has
found regarding how weather affects people’s mood and learning. The results from this study
provided information to help teachers make decisions on how to plan instruction for their
students based on the weather. Some suggestions include; an impromptu discussion on a sunny
day, or allowing students some more time to do a hands-on activity by themselves when it rains.
The research took place at the Toledo Zoo and included 26 participants. Participants were
surveyed at the end of each day for the five days that they were at the Toledo Zoo Junior Zoo
Keeper Summer Camp Program, creating a total of five surveys per participant. The data were
analyzed via a series of sequenced ANOVA tests, known as a MANOVA, which compared the
students across the 5 days, week-to-week and good-to-bad-weather days. There are five
MANOVAs in the series. One for each of the following: positive mood, negative mood,
behavioral engagement, cognitive engagement, and emotional engagement. The data collected
showed that the only factor found to be statistically significant and likely to be influenced by
weather was behavioral engagement. The results of this study showed information that would
allow the zoo access to constructive feedback and ideas on how to potentially improve programs.
Educational activities should be focused in different ways for different weather, which could lead
to increased learning of the students at the zoo and create a higher retention rate in student
knowledge as well.
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This document is dedicated to my loving wife,
who puts up with me.
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ACKNOWLEDGMENTS
Thank you for all your help in making this document happen.
Committee:
Jodi Haney, Tracy Huziak-Clark, and Eric Worch
Family and Friends who reviewed:
Notably Kristyn and Linda Carstensen
Toledo Zoo Staff:
Notably Josh Minor, Steve Oswanski, Lindsey Silverman, and Caitlyn Dailey
Staff at Human Subject Review Board and Office of Applied Statistics Research
Participants
Students who participated in this study
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TABLE OF CONTENTS
Page
CHAPTER I. INTRODUCTION ..........................................................................................
Rationale
1
............................................................................................................
1
Research Questions ....................................................................................................
3
Definition of Terms....................................................................................................
4
CHAPTER II. LITERATURE REVIEW .............................................................................
5
The Influence of Weather on Mood. ..........................................................................
5
How Learning was Influenced by Engagement .........................................................
8
Changes in Mood Manipulated how Humans Learn .................................................
10
Intriguing Implications about Weather and Learning ................................................
12
Literature Review Conclusions ..................................................................................
15
CHAPTER III. METHOD ....................................................................................................
16
Zoo Program and Participants....................................................................................
16
Weather Observations ................................................................................................
17
Student Affective Behaviors and Engaged Reactions Scale (SABERS) ...................
18
Variables in Study ......................................................................................................
20
Data Analysis: MANOVA .........................................................................................
20
Chapter Summary ......................................................................................................
21
CHAPTER IV. DATA ANALISYS .....................................................................................
22
If and How Weather Influences Students’ Mood and Engagement ...........................
25
Are The Findings From This Study Similar to What Prior Research Has Found......
28
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If Weather Does Influence Mood and Engagement, How Big is the Effect .............
28
Chapter Summary ......................................................................................................
28
CHAPTER V. CONCLUSIONS ..........................................................................................
30
Discussions
.............................................................................................................. 30
Implications .............................................................................................................. 32
Limitations
.............................................................................................................. 33
Future Studies ...........................................................................................................
REFERENCES:
34
............................................................................................................
36
APPENDIX A. WEATHER DEMOGRAPHICS ................................................................
39
APPENDIX B. COMPLETE WEATHER DATA TABLE ................................................
41
APPENDIX C. SABERS SURVEY .....................................................................................
45
APPENDIX D. CONSENT FORMS ....................................................................................
50
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LIST OF FIGURES/TABLES
Figure/Table
Page
1
Comparative Figure 1: “Good” Versus “Bad” Weather Days ...................................
23
2
Averaged Weather Data Summary Table ..................................................................
24
3
MANOVA Summary Table Positive Mood ..............................................................
25
4
MANOVA Summary Table Negative Mood .............................................................
25
5
MANOVA Summary Table Cognitive Engagement .................................................
26
6
MANOVA Summary Table Emotional Engagement ................................................
26
7
MANOVA Summary Table Behavioral Engagement ...............................................
27
8
Comparative Figure 2: The Influences of Differing Weather ....................................
27
1
CHAPTER I
INTRODUCTION
From the moment people wake in the morning until they go to sleep, weather is a
constant factor in their daily lives. One of the key observations that influenced this research was
that animals tend to abandon an area before a natural disaster like a hurricane or a tsunami.
Animals do this by instinct, which is an innate behavioral response. Instincts are not just
responses to impending doom, but they also can be behavioral responses to many different
stimuli, including weather. This can be seen in anything from a weather loach that changes the
color of its skin due to changes in barometric pressure; to a ground hog that bases its hibernation
on the amount of cloud cover. Since humans are considered to be part of the animal kingdom, we
should still have some kind of instinctual response to changes in the weather. This idea, that
humans still have a connection to the weather, can be observed in our mood and engagement
relative to the weather.
Rationale
Affective states, or moods, are emotional states of feeling at any particular time (Watson
& Clark, 1994). The surroundings and activities that a person interacts with change how a person
feels. Some authorities believe that since weather is something that is constantly surrounding
people, it has an observable impact on their moods (Howarth & Hoffman, 1984; Forgas,
Goldenberg, and Unkelbach, 2009; Keller, Fredrickson, Ybarra, Côt, Johnson, Mikels, & Wager,
2005). Moods, whether positive or negative, have been shown to affect the way people learn
(Bless, 1995). This has led some to postulate that weather can influence how people learn by first
shaping the mood they are in (Howarth & Hoffman, 1984; Forgas, Goldenberg, & Unkelbach,
2009; Keller et al., 2005).
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According to Merriam-Webster (n.d.), engagement is the act of occupying the attention
or efforts of an individual over a period of time, by using a person’s emotional involvement or
commitment. There are many types of engagement, such as behavioral, emotional, and cognitive.
Measures of engagement can be used to see how interested someone is in a particular aspect of
their environment, as well as how likely they are to continue their interaction (Partin, Haney,
Worch, Underwood, Nurnberger-Haag, Scheuermann, & Midden, 2011). Because engagement is
based on a personal emotional involvement, weather may influence how an individual is
occupied in either a behavioral, emotional, or cognitive activity.
There are still many unknowns about how people learn and what conditions inspire the
most retention of information and learning overall. Several authorities consider that mood is one
of the factors that keep people involved in learning (Howarth & Hoffman, 1984; Forgas,
Goldenberg, & Unkelbach, 2009; Keller et al., 2005). Different kinds of learning occur based on
a person’s mood and engagement (Forgas, Goldenberg, & Unkelbach, 2009). If weather is a
constant force in the shaping of mood and engagement, then researchers suggest that it may
affect how people learn (Forgas, Goldenberg, & Unkelbach, 2009; Keller et al., 2005).
Therefore, this study examined the relationship between students’ mood and engagement and
daily weather conditions. For the overall purpose of this thesis, the assumption was made that
weather does influence mood, which then leads to how weather could influence the learning that
took place.
This research was based at the Toledo Zoo for several reasons. The Toledo Zoo Junior
Zoo Keeper Summer Camp Program had the same educational material repeated weekly instead
of yearly during its summer classes. It was important to eliminate as many influences on mood
and engagement as possible; including the teacher, subject matter, time on task by student, and
3
observer bias. First, the teacher for daily lessons needed to be consistent to avoid an issue of
different styles of instruction, and this was the case during the Junior Keeper Zoo Program. Since
it was the same teacher each week, the teacher was a constant in this study. Second, consistent
subject matter that did not differ drastically from one week’s classes to the next was needed for
this study so that students could be compared. Otherwise it would have been similar to
comparing scores in math to those in language arts. Third, the amount of time students had to
complete various activities could have influenced mood and engagement. The zoo scheduled and
structured each week of the camp, which allowed for consistent activities and timing. Finally, the
concern of observer bias based on their own mood and engagement could have had influenced
the results. This was eliminated with the use of surveys to collect the participants’ ideas.
This study set out to develop a better understanding of how weather affects student mood
and engagement, which impact learning, so that educators might vary their teaching methods
during various forms of weather. Students are already a varied group of individuals who do not
all learn in the same ways. This study had the potential to modify the way teachers prepare
educational activities for our children and students, as well as potentially broaden the
understanding of mood, and engagement in regards to weather. If certain types of weather
elicited specific moods and learning outcomes, teachers may need or desire a battery of lesson
plans to accommodate weather-related moods and engagements.
Research Question
The research question in this study was to find out if and how weather influenced
students’ positive and negative mood and cognitive, emotional, and behavioral engagement.
4
Definition of Terms
Weather: the state of atmosphere with regards to humidity, temperature, moisture, and wind.
Mood: the general state of mind or quality of feeling at a particular time of a person or a group.
Mood is also known as affective state (Watson & Clark, 1994).
Positive Mood: Good conscious state of mind or predominant emotion, such as happy.
Negative Mood: Bad conscious state of mind or predominant emotion, such as sad.
Engagement: is the way to gain the attention and keep people focused on the material they are
presented with (Meece, 1988).
Cognitive Engagement: People focus on material or activity because it leads to a greater of
understanding of a topic (Rotgans & Schmidt, 2011).
Behavioral Engagement: How someone is expected to behave in order to participate in an
activity (Ponitz, Rimm-Kaufman, Grimm, & Curby, 2009).
Emotional Engagement: People participate in order to feel their emotional needs are being met
(Elffers, Oort, & Karsten, 2012).
Learning: knowledge or skill acquired by instruction or study
Retention: how much a person can remember, and is based on where the memory is stored and is
a part of the learning process. A person can generally hold about seven unrelated pieces
of information in their short-term memory, while long-term memory is exponential larger
(Santrock, 2008).
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CHAPTER II
REVIEW OF LITERATURE
In this chapter, studies of weather, mood, engagement and learning, and how these
variables are interrelated were reviewed. Specifically, the literature review is organized into four
main sections: the influence of weather on mood, how learning is influenced by engagement,
changes in mood manipulated how humans learn, and intriguing implications about weather and
learning. This review served as the framework for data collection in this study.
The Influence of Weather on Mood
Weather surrounds and affects people each day. Merriam-Webster (n.d.) defined weather
as the “state of the atmosphere at a place and time as regards heat, cloudiness, dryness, sunshine,
wind, rain, etc”. Humans have been shown to feel differently during various types of weather
(Howarth & Hoffman, 1984). One study determined that the major influences from weather
were the humidity and temperature (Howarth & Hoffman, 1984).
There are conflicting studies on whether weather really has an influence on mood
(Forgas, Goldenberg, & Unkelbach, 2009; Watson, 2000). The majority of the studies suggested
that if weather influenced any part of the human psyche it was mood (Howarth & Hoffman,
1984; Forgas, Goldenberg, & Unkelbach, 2009; Keller et al., 2005; Bardwell, Ensign, & Mills,
2005). The conflict seems to result from a lack of consistency in research being done on a
specific area of weather, and not taking into account the other aspects. It is very difficult to
isolate the one factor of weather (i.e., temperature, humidity, precipitation, and cloud cover to
name a few) for a study and exclude the rest. For instance, when looking at weather versus
productivity, one of the easiest ways to measure the weather factor is to only measure
temperature and ignore the other factors. For example, Howarth and Hoffman’s (1995) study
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looked at multiple factors of mood in 24 subjects over a period of 11 days. The intent of the
study was to determine if weather influenced mood enough to change behavior. The subjects’
mood factors where individually compared initially to isolated factors of weather such as wind
velocity. However, this has yielded widely varied results because it is a coupling of temperature
and humidity that influence productivity not just temperature (Howarth & Hoffman, 1995).
Sanders and Brizzolara (1982) reported a similar finding, in their study which analyzed how
weather influenced people’s behavior. They stated, “It’s not the temperature that gets you down,
but the humidity” (Sanders & Brizzolara, 1982, p. 156). They showed that humidity could cause
negative feelings, such as depression, regardless of the temperature.
Howarth and Hoffman’s (1995) multidimensional approach showed that different factors
of the weather influence different aspects of people’s moods. For example, they found that an
increase in humidity and a decrease in pressure led to a decrease in concentration and the subject
would lose interest in what was being presented. Similarly, Bardwell, Ensign, and Mills (2005)
found that not only does weather cause a change in mood, but also it can have lingering effects.
These researchers were trying to determine how strenuous training and bad weather would
influence mood and if it would produce any long term impact effects when compared to the
population norm. To determine this, they studied the moods of 60 soldiers that had been exposed
to very harsh climates for an extended period of time and then surveyed them again after 30 and
90 days. They found that being exposed to long periods of harsh weather and challenges resulted
in a lasting negative mood. This implied that given a long period of exposure to uncomfortable
weather changed how people would normally function.
These two studies suggested that weather did not interact with just one specific attribute
of a person’s mood, but influenced moods of both individuals and populations. If one kind of
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weather lasted for an extended period of time, it could create a lasting effect on mood (Bardwell,
Ensign, & Mills, 2005). This concept was initially found to be unusual, but became more
accepted when the perception of long-term weather was equated to the climate of a season.
Keller et al. (2005) researched how weather influenced mood based on time spent outside and the
shifts in temperature season to season. They found people’s mood changed with the seasons.
Keller et al. (2005) used summer as a baseline indicator of what optimal mood should be like and
how it shifts away from this to become more negative in the fall and winter, only to return in
spring. They studied 605 participants in two correlation projects that compared time spent
outside and good weather (days with higher temperature or barometric pressure). They found that
positive mood, better memory, and “broadened” cognitive style increased with the amount of
good weather.
In contrast, one study found that weather did not have an influence on human mood and
engagement (Watson, 2000). Watson’s study focused on a large number of data points gathered
from 478 students over an academic year, and found no positive correlation between weather and
either positive or negative mood. However, the main issue with this study is that he did not
recruit participants who grew up or had spent a large portion of their life in the same area,
therefore, their points of reference differed, which could be a limitation of this study. Because
Watson used a mixed group of students at a university, not everyone had the same experience for
what was considered good or bad weather. For example, someone from Florida may have
thought that Michigan summers were too cold, whereas the person from Michigan may have
thought they were too hot. On the other hand, perhaps Watson (2000) was correct in that there is
no relationship between weather, mood, and behavior.
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Learning Influenced by Engagement
Engagement by definition is the act of occupying the attention or efforts of an individual,
over a period of time by using a person’s emotional involvement or commitment. There are
different ways that a person can be engaged, all of which influence a person’s willingness to
commit to learning. No studies were found that focused on the effects of weather and student
engagement. However, if weather influenced a person’s mood, then it stands to reason that it
may also influence a person’s engagement. People who are engaged in what they learn often do
better at it than others who are not engaged. For instance, Linnenbrink-Garcia, Rogat, and
Koskey (2011) demonstrated that engagement could be a powerful motivator of student learning
in their study of small group learning environments. Through their study, the researchers
emphasized the importance of engagement by comparing the works of peers (n=16) and
suggested how the results should influence the future of education.
There are three types of engagement that were chosen to be examined for this study,
Cognitive, Emotional, and Behavioral, based on the work of Partin, Haney, Worch, Underwood,
Nurnberger-Haag, Scheuermann, and Midden (2011). Each of type of engagement acted as a
stimulus for a person to keep learning. Cognitive engagement was defined by Rotgans and
Schmidt (2011) as “a psychological state in which students put in a lot of effort to truly
understand a topic and in which students persist studying over a long period of time” (p 465).
Rotgans and Schmidt (2011) looked at how problem based learning caused an increase in
cognitive engagement with difficult topics with a sample size of 312. They found that students
who were interested in finding the solution to the problem had higher cognitive engagement
when they were working autonomously because they wanted to get the right answer. Meece
(1988) had similar findings in her study, which looked at how students (n=275) sought validation
9
for correct answers to satisfy their cognitive engagement. Meece (1988) had her students
complete a questionnaire that separated students by the level of achievement they were seeking
to attain. The conclusions showed that higher cognitive engagement came from students who
were more focused. Meece (1988) suggested that this was more likely to occur in classes where
there was a low student-to-teacher ratio or if students were oriented on task-mastery goals.
Emotional engagement is typically defined as how people feel their emotional needs are
being met (Elffers, Oort, & Karsten, 2012). For example, students who are sad because they are
worried that they will never have a friend are less likely to care about math. Elffers, Oort, and
Karsten (2012) did a study in which they looked at how low achieving students (n=909) gained a
sense of belonging within a school system and how this sense of belonging stopped them from
dropping out. Their study was conducted by using a multilevel regression analysis of students'
background characteristics, experiences, their interactions with fellow students, and the students’
overall emotional engagement with school. The results showed that low achieving students who
had friends in school often stayed in school. Elffers, Oort, and Karsten (2012) considered this
evidence that emotional engagement is essential for learning to be maintained. However, a study
done by Linnenbrink-Garcia, and Pekrun (2011), suggested that the current research on the topic
was unreliable because of the broad spectrum of material incorporated in emotion. Yet, these
researchers did agree that getting students emotionally involved with academics could lead to
higher learning rates.
Lan, Ponitz, Miller, Li, Cortina, Perry, and Fang (2009) completed a study of classroom
practices that increased behavioral engagement, which in turn, kept students’ attention. Their
study focused on Chinese (n=8) and American (n=7) classrooms. Lan et al. (2009) considered
behavioral engagement to be a participant’s willingness to stay focused on the task that a teacher
10
was trying to explain to his/her classes. This meant that people were doing what was expected of
them. It was suggested that students who had a structured classroom had increased behavioral
engagement (Lan et al., 2009). Ponitz, Rimm-Kaufman, Grimm, and Curby, (2009) found in
their study with low-income rural kindergarten classrooms, that when students where
behaviorally engaged either as a group or a class, they were able to learn at accelerated rates.
Regardless of the type, engagement was essential in helping people be active learners.
Changes in Mood Manipulated how Humans Learn
The theory that a person’s overall mood influenced how they learned has been researched
for several years. One of the earliest studies was by Mischel, Ebbesen, and Zeiss (1973), who
examined how different moods related to cognitive ability and attention, by testing 60
participants’ perception of their positive and negative personality. To do this they surveyed
participants about their intellectual ability. The group was divided in half, with 30 participants
expecting additional testing and the remaining 30 not expecting more testing. They found that
participants who considered the experiences to be complete after the first test had a higher
positive mood than participants who thought that the test would be repeated. Mischel, Ebbesen,
and Zeiss (1973) considered this to be evidence that mood plays a role in how people prepare to
learn.
In a more recent study, Robinson (2011) examined how extreme mood shifts could cause
dynamic shifts in the information that people were able to express. Robinson (2011) looked for
the impact of the same state of mood, specifically arousal and stress, on how people recall
information. The study enrolled 60 participants who were tested in both same mood situations
and differing mood situations on recall and visual recognition. Mood was found to cause a larger
effect in both visual recognition and recall of memorized material when mood was the same at
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the time of the encoding (Robinson, 2011). In other words, a person’s mood can have a direct
effect on their learning. However, all three studies reviewed, showed or implied that different
moods have an enormous effect on learning (Robinson, 2011; Edelman, 2005; Mischel, Ebbesen,
& Zeiss, 1973). This suggested that if mood is influenced by the weather, so is learning because
learning is influenced by a person’s mood. If the weather in the area was what people in the
region considered to be “bad weather” outside, then those people may be more likely to be in a
“bad” mood. That mood, in turn, might determine how well that person learned, or if they gained
access to their prior knowledge (Robinson, 2011). Robinson’s (2011) findings were similar to the
work of Edelman (2005), who researched how mood affected student retrieval during tests. As
Edelman (2005) points out, "Students taking exams, for example, may report retrieval problems,
such as that ‘their mind just went blank’ during stressful assessments" (p.58). These results were
not just for bad moods; good moods can have their own influence on learning, as well (Edelman,
2005).
Schwarz (1996), on the other hand, researched how moods influenced general knowledge
structures, such as heuristic processing. This study (n=82) performed experiments that had
participants focus on their past emotions and then asked participants to complete a task in what
was considered a positive mood setting, regardless of the participant’s mood. Schwarz’s (1996)
evidence indicated that people who were in a positive mood showed increased use of general
knowledge structures to learn, while people in negative moods exhibited a decrease. This implied
that it is not just what people can remember that is influenced by weather, but how they learn as
well.
Since mood influenced learning through encoding, retention, and recollection and
weather-influenced mood, weather likely had an influence on learning. It is unlikely that weather
12
was the sole factor in determining one's mood, but it did play a part. Moods were influenced by
many factors, such as what a person eats or the daily stress of work (Ioakimidis, Zandian, Ulbl,
Bergh, Leon, & Sodersten, 2011; Stewart, 1996). It is unclear how large the influence of weather
can on learning be compared to these other factors, but weather is constantly present in our daily
lives.
Intriguing Implications about Weather and Learning
As mentioned earlier, there is a link between mood and retention. This link has been
studied since Mischel, Ebbesen, and Zeiss (1973) began doing research on induced mood states
and how they affect cognitive processes. One study of particular interest done by Bless, Clore,
Schwarz, Golisano, Rabe, and Wölk (1996), demonstrated negative moods helped retrieve
memories better than positive moods. Storbeck and Clore (2005) had participants (n=100)
partake in two experiments. The first looked at the difference of positive and negative moods on
encoding, while the second looked at why negative moods caused an increase in accurate recall
then positive moods. Based off the results, Storbeck and Clore (2005) predicated that negative
moods cause for more accurate memories because participants become more focused on creating
detailed memories of the experience.
However, one of the biggest changes suggested by Bless et al. (1996) was that positive
moods induce a relational processing phase. This means there is interference in the retrieval of
related events and often there is crossover of relational and rational information, resulting in
forgetting. In addition, extreme moods, such as high levels of stress or arousal, also interfere
with retrieving data. For instance, Edelman (2005) described how students taking exams often
suffered from this phenomenon. Students claimed that their memory was insufficient when
asked to recall the information on tests. Robinson (2011) performed an experiment, which
13
examined the relationship between mood and retention. Their findings suggest, "Participants
who experienced the same mood state at learning and retrieval fared significantly better on both
the retention and recall tasks than those who experienced mismatched context" (Robinson, 2011,
p.66). This means that if the weather context is the same, students did better on recall tasks.
When the weather context changed, recall and retrieval decreased, meaning that retention or
recall of material changed simply because there was a change in the weather.
At the start of this literature review, there was an expectation to find little to no
information on the relation between weather and retention. However, three rigorous research
studies regarding the effects of weather on memory were found. The main idea seems to be that
"bad" weather can induce a worse mood that, in turn, influences a persons’ ability to memorize
detailed information.
First, Forgas, Goldenberg, and Unkelbach (2009), studied the impact of moods and
memory with regard to weather. They studied the memory of 73 random-selected participants
who entered the test site over a 14-day period. After each participant left the site they completed
a brief survey about what they saw in the site as well as how they felt. Neither, the workers at the
site nor the objects inside the site changed from day to day. The only changes from day to day
were the weather and the participants. What the researchers found was that on cloudy or rainy
days the people in their study had a worse mood, but were able to express a greater detailoriented memory. On sunny and bright days they found participants to be in a better mood, but
they had worse recognition in regard to detail-oriented memory. Participants on sunny warm
days also had a higher heuristic process. This suggested that they were more willing to accept
readily accessible, though loosely applicable, information to solve problems. This information
often came from other people. Forgas, Goldenberg, and Unkelbach (2009) suggested, based on
14
their results, that on warm sunny days people would be more willing to listen and work with
others, while on cold rainy days, people are more inclined to solve material by themselves.
Likewise, Bäuml and Kuhbandner (2007) found similar results by inducing negative,
positive, and neutral moods in their subjects (n=27) and then testing them for what they could
recall from lists of six emotionally-neutral words. Positive moods encouraged relational
processing, which was when memories were tied to other similar memories (Bäuml &
Kuhbandner, 2007). Negative moods, on the other hand, encouraged item-specific processing,
which was when a single memory was focused on in great detail (Bäuml & Kuhbandner, 2007).
Their findings suggested that different moods lent themselves to making people forgetful,
especially positive moods, because they often created a relational memory or scheme, instead of
what actually occurred. This could be explained because people were more likely to talk and
interact on warm weather days, giving them access to more relational memories to experience.
However, since people rationalize things differently, conversations could be stored in diverse
ways (Storbeck & Clore, 2005).
Finally, Keller et al. (2005) studied the correlation between weather and how it
influenced mood and retention of 605 participants. The researchers broke the research study
down into three topics. First, they compared the relationship between temperature and pressure
with regard to mood. Next, they compared people indoors and outdoors to see if they were
affected differently by weather, mood, and weather memory relationships. Finally, they
examined whether seasons could have a large effect on the influence of weather on mood and
retention. One of the most influential seasons seemed to be the spring because there was a trend
of days that were warming, with more hours of daylight, so people were in a better mood (Keller
et al., 2005). The researchers predicted, based on their analysis of the results, that higher moods
15
in spring, in turn, causes an increase in cognition because the feelings of depression that build up
over the course of fall and winter are alleviated by the arrival of “good” weather.
Literature Review Conclusion
It is clear from the literature that, to some extent, weather has an influence on a person’s
mood, and that different moods correlate to different styles of learning, whether it is through
relational or item specific encoding, recollection, and retention (Forgas, Goldenberg, &
Unkelbach, 2009; Bardwell, Ensign, & Mills, 2005; Robinson, 2011; Schwarz, 1996; Storebeck
& Clore, 2005; Bäuml & Kuhbandner, 2007; Keller et al., 2005). Although not specifically
examined in this study, it was assumed that there is a strong influence from weather on learning.
If the weather were constant prior to and during learning, then the learning level should be better
than if the weather differed (Bardwell, Ensign, & Mills, 2005; Keller et al., 2005). It would also
appear that weather would influence how a person would present material based on their mood
(Forgas, Goldenberg, & Unkelbach, 2009).
The literature showed that weather can inspire people to learn in different ways, whether
it be fact-based learning on bad weather days or heuristic-based learning on good weather days
(Forgas, Goldenberg, and Unkelbach, 2009). Another reason that weather influences people is
that their mood changes with the season and that their cognition is influenced along with it
(Keller et al., 2005). However, given what these aforementioned articles contain, fact based
learning lends itself more to an introverted, hands-on learning environment, while heuristic
learning should be open discussion and debate oriented (Forgas, Goldenberg, & Unkelbach,
2009).
16
CHAPTER III
METHOD
This research study focused on how the weather impacted students’ mood and
engagement in regards to learning. Several previous studies suggested that there are strong
influences on human mood from weather (Howarth & Hoffman, 1984; Forgas, Goldenberg, &
Unkelbach, 2009; Keller et al., 2005). However, at least one study suggested that the correlation
is minimal and that weather does not have a major influence (Watson, 2000). With this in mind,
this study used quantitative methods including participant surveys and observations of weather to
determine if there were significant differences between human mood and engagement on good
vs. bad weather days in attempt to answer the following research question; “If and how weather
influenced students’ mood and cognitive, emotional, and behavioral engagement?”
Zoo Program and Participants
The study took place at the Toledo Zoo during the Junior Zoo Keeper Summer Camp
program. The 26 total final participants in the camp were between the ages of 11 and 14 and
attended the camp for one week, Monday through Friday from 8:00 am to 2:30 pm. The Junior
Zoo Keeper Summer Camp was held for a total of six weeks, of which five were originally
included in this study. In total, there were 31 girls and 13 boys participating in this study. The
average age of the participant was 12 years. The target number of participants for this study was
44 students over the course a five-week period of time. In the end however, there were only 26
participants with usable data, 8 boys and 18 girls, each of whom answered the survey questions
five times per week. The other 18 participants were dropped from the study because the weeks
that they were in camp only had days that where considered “good”. In order to analyze the data
using MANOVA analysis, dependent samples were required. Therefore weeks consisting of both
17
good and bad days were needed to make the comparison of mood and engagement within the
same week by the same set of participants.
Each week, campers came to the Toledo Zoo Junior Zoo Keeper Summer Camp, learned
about zookeepers’ jobs, took tours of the exhibits, and assisted in the cleanup of the animals. In
addition, they learned topics that ranged from adaptations to wildlife conservation. Over the
course of the week, campers were tasked with creating a presentation about an animal Bio-fact,
which is an artifact either from an animal that has passed away naturally or something that an
animal has interacted. For example, one of the Bio-facts at the Toledo Zoo is a two-inch diameter
steel bolt that an elephant broke off a wall. At the end of each day Campers were asked to
respond to the survey about their engagement and reactions to the weather and the activities of
the day.
This study took place at the Toledo Zoo for two main reasons. First, at the zoo there was
a high rate of repetition with the same subject matter and will have the same teacher for each
week of the study. Second, the zoo had learning environments that were exposed to both weather
inside and outside. This allowed for access to indoor and outdoor activities for data collection
while controlling the variable for material covered. This helped isolate emotions that were
caused by weather and not the material being covered.
Weather Observations
The weather observations collected in this study reflect numeric information on weather
factors including temperature, precipitation, relative humidity, and cloud cover. These factors
were chosen because of the related research on mood and weather conducted by Howarth and
Hoffman (1984), Forgas, Goldenberg, and Unkelbach (2009), and Keller et al. (2005). The
Global Learning and Observations to Benefit the Environment (GLOBE) Program protocols
18
(2011) were used to guide the weather observation in order to determine the quality (good or
bad) of the weather for each day. Weather observations for each of these factors were taken at
three times throughout the day (8 AM, 11 AM, and 2:30 PM) since weather varies throughout the
day. Figure 1(Chapter 4) includes the weather observation record sheet and cut off indicators for
“good”, “neutral” and “bad” weather observations. In order for a weather observation to receive
a “good” rating for the entire day, all of the weather data for the day were averaged as follows.
Scores of “3” or “good” were given in each category for temperatures between 70 – 90 degrees
Fahrenheit, 30% or less cloud cover, relative humidity between 40 – 55%, and no precipitation.
Table 1 (Chapter 4) also shows the break down for “neutral” and “bad” weather categories as
described above. Any observation data determined to be “neutral” received scores of “2” and
observation data determined to be “bad” received scores of “1.” The entire day itself was then
categorized as a “good” day if the average weather score (consisting of the three observation
points for the temperature, cloud cover, humidity, and precipitation data) was 2.40 or higher. A
day was considered a “neutral” day if the weather score for the day fell between 2.39 and 1.6 and
a “bad” day consisted of a daily weather score of 1.59 or lower. Appendix B includes the raw
scores for all weather observations as well as the average daily score and summary weather score
rating (“good’, “neutral” or “bad). For example, day two of the second week had a final average
weather score of 2.90. Therefore, this day received a weather rating of “Good”.
Student Affective Behaviors and Engaged Reactions Scale (SABERS)
Prior to attending the program, parents of the participants completed the approved
consent form and the participants completed the student assent form (Appendix D). After the
program each day, students filled out a survey that assessed their mood and engagement. The
survey was designed to retrieve information about how the students’ affective behavior and
19
engagement varied during their time in the Toledo Zoo Junior Zoo Keeper Summer Camp
Program. The affective behavior or mood was measured using a modified form of the Positive
And Negative Affect Schedule-Expanded Form (PANAS-X), which was created by Watson and
Clark (1994). The PANAS-X surveyed students on how their mood changed over time periods
and had questions that related to what was considered good, bad, and other moods. In a previous
study, Watson and Clark (1994), the PANAS-X reported a reliability of ∝ = .83 for negative
affect and a reliability of ∝ = .79 for positive affect. For this study, the scale has been modified
to look at only the good and the bad mood scales. Another modification in this study was that the
survey was used to collect data only once each day for five days in a row, instead of multiple
times in one day as in the PANAS-X. The items on the PANAS-X were rated by participants
using a one to five Likert Scale with a “1” representing “never” and a “5” representing “always.”
Example mood items participants were asked to rate on this survey include “happy”, “tired”,
“cheerful”, “annoyed”, “angry”, “nervous”, and “bold.”
The second half of the survey was modified from a section of Partin, Haney, Worch,
Underwood, Nurnberger-Haag, Scheuermann, and Midden’s, (2011) student attitude, motivation,
and engagement survey. They had reliability scores of ∝ = .821 for behavioral engagement, an∝
= .921 for emotional engagement, and an ∝ = .725 for cognitive engagement for their survey.
Their survey separated engagement into one of the following categories, behavioral, emotional,
or cognitive and was modified for this study by removing the attitude and motivation portions
and keeping only the engagement questions. Another change to their survey was that the word
“class” was replaced throughout the survey with the word “camp.” Again, the items were rated
using a Likert Scale with a “1” representing “never” and a “5” representing “always.” Example
20
engagement items participants were asked to rate on this survey include: “I pay attention in
camp”, “I am excited by work in this camp”, and “my mind wanders off topic during this camp.”
The modified PANAS-X and student attitude, motivation, and engagement surveys were
combined in this study to form the Student Affective Behavior and Engaged Reactions Scale
(SABERS). The SABERS survey incorporates questions that are designed to gather information
on student mood, and engagement (See Appendix C). Negative items on the SABERS were
reverse coded prior to data analysis as indicated in Appendix C.
Variables of Study
While researching if and how weather influenced participants’ mood and engagement, the
following variables were considered. Weather was the independent variable because it was
theorized to affect participants’ mood and engagement. The dependent variables where divided
into five categories, two which related to mood (positive and negative) and three which related to
engagement (cognitive, emotional, and behavioral).
Data Analysis: MANOVA
The complete data analysis included the data from the SABERS along with the
observational data about the weather. The total scores from the SABERS were used to reference
how students' mood and engagement differed between good and bad weather days. In order to
complete this analysis, the data were compared using a series of analysis of variance tests
(ANOVAs), known as a MANOVA or a multivariate analysis of variance. A MANOVA, as
described by Scheiner (2001), is a multivariate test that looked for differences among groups,
while helping to reduce errors by making ranked comparisons. MANOVAs were the appropriate
test to run because they helped to eliminate doubt from multiple variables. This test examined the
difference between moods and engagements influenced by good and bad weather days. The
21
MANOVA was run first at the individual level with each student who took the survey to look for
variation from student day to day. Next, students were compared to each other to look for
averages week to week. Last, a total score from weeks with both good and bad weather where
compared to come up with a final statistic. A MANOVA was conducted for each of the variables
in question, positive mood, negative mood, emotional engagement, cognitive engagement, and
behavioral engagement. The probability score for this study was set at p = .05 prior to running
the MANOVAS since it was determined that the data analysis was best represented by a two
tailed test (moods and engagement could increase or decrease based on the weather).
Chapter Summary
In summary, the zoo was chosen as a study site because it provided an education facility
with a course repeated multiple times, by the same instructor, over a short period of time,
allowed for multiple subjects, and a variety of weather conditions. Data was collected to examine
participants’ mood and engagement. These data were compared to the type of daily weather
occurring, which provided an answer to the research question, “If and how weather influenced
students’ positive and negative mood and cognitive, emotional, and behavioral engagement?”
22
CHAPTER IV
RESULTS AND DISCUSSION
This research study focused on if and how weather influenced students’ positive and
negative mood and cognitive, emotional, and behavioral engagement. This was accomplished by
surveying students during the Toledo Zoo Junior Zoo Keeper Summer Camp program. The
overarching goal of this study was to find the connection, if any, of weather’s influences on
factors related to learning.
The analysis of the data collected provided potential insight into the main research
question as well as two sub questions. 1) If and how weather influences students’ mood and
engagement? 2) Is this similar to what prior research has found? 3) If the influence does exist
how big is the effect?
Figure 1 depicts the fifteen days taking place over the three-week observation period and
shows each day by total daily weather score. Moreover, this figure indicates how the cut off
scores for both “good” and “bad” weather days. That is, figure 1 indicates eight of the fifteen
days were “good” and seven were considered to be “bad.” Table 1 shows the average scores for
the days.
23
Figure 1. “Good” Versus “Bad” Weather Days, included in study.
Good Versus Bad Weather Days
0
0.6
1.2
1.8
2.4
D1W2
D2W2
D3W2
D4W2
D5W2
D1W3
D2W3
D3W3
D4W3
D5W3
D1W4
D2W4
D3W4
D4W4
D5W4
"Good"
"Bad"
3
24
Table 1. Average Weather Data: Good = 2.4 and above; Bad = 2.39 and below.
Temperature Cloud Cover Humidity Precipitation Averaged
Day
Averaged
Averaged
Averaged Averaged
Total
D1W1*
3
2
2
3
2.5
D2W1*
1.67
3
2.67
3
2.6
D3W1*
2.33
3
2.33
3
2.7
D4W1*
2.33
2.67
2.67
3
2.7
D5W1*
2.33
2.67
2.33
3
2.6
D1W2
2.67
2
1.67
3
2.3
D2W2
2.67
3
3
3
2.9
D3W2
3
3
2.33
3
2.8
D4W2
2.67
1.67
2.67
3
2.5
D5W2
2.33
3
2.33
3
2.7
D3W1
2.33
2.33
2
3
2.4
D2W3
1.67
1.33
1
2
1.5
D3W3
1.67
2
1.67
2
1.8
D4W3
3
1
1
2.33
1.8
D5W3
2.33
1
1
2.33
1.7
D1W4
2.33
2.33
2.67
2.33
2.4
D2W4
3
1
1.67
3
2.1
D3W4
3
2.33
3
3
2.8
D4W4
2.33
1
1.67
2.67
1.9
D5W4
3
3
2.33
3
2.8
D1W5*
3
1.67
3
3
2.7
D2W5*
2.67
2.33
3
3
2.8
D3W5*
3
3
2
2.33
2.5
*Excluded from study because of lack of “bad” weather during the week.
Day
Type
Good
Good
Good
Good
Good
Bad
Good
Good
Good
Good
Good
Bad
Bad
Bad
Bad
Good
Bad
Good
Bad
Good
Good
Good
Good
The reliability scores for the SABERS constructs reported alpha coefficients of ∝=.78 for
positive mood, ∝=.80 for negative mood, ∝=.73 for cognitive engagement, ∝=.83 for behavioral
engagement, and ∝=.92 for emotional engagement. Alpha coefficients above .70 are generally
considered to signify construct reliability of the scale (Fleiss, 1986).
25
If And How Weather Influences Students’ Mood and Engagement
In regards to the main research question, “If and how weather influences students’ mood
and engagement?” the following results were found in the survey. Tables 2 and 3 indicates that
there was no significance in the means between “good” and “bad” weather and positive or
negative mood. [Positive Mood: Wilk’s λ=0.94, F (1, 26) =1.65, p=0.271; Negative Mood:
Wilk’s λ= 0.10, F (1, 26) =0.09, p=0.763]. Positive mood had a mean of 3.68 (SD = 0.458) on
good days and had a mean of 3.804 (SD = 0.497) on bad days. Negative mood had a mean of
1.73 9(SD = 0.458) on good days and a mean of 1.71 (SD = 0.415) on bad days. In other words,
weather did not influence participant mood.
Table 2: MANOVA Summary Table: The Effect of Weather on Positive Mood
Source
DF Manova SS
Mean
F
Square Value
GOOD BAD
1 0.19487714 0.19487714
Error(GOOD
BAD)
25 2.95574786 0.11822991
1.65
Pr > F
0.2110
Table 3: MANOVA Summary Table: The Effect of Weather on Negative Mood
Source
DF Manova SS
Mean
F
Square Value
GOOD BAD
1 0.00964877 0.00964877
Error(GOOD
BAD)
25 2.60570617 0.10422825
0.09
Pr > F
0.7634
Nor was there a significant difference between “good” and “bad” weather for either
cognitive or emotional engagement, as seen in Tables 4 and 5. [Cognitive Engagement: Wilk’s
λ=0.99, F (1, 26) =0.09, p=0.768; Emotional Engagement: Wilk’s λ= 0.99, F (1, 26) =0.24,
26
p=0.3627]. Cognitive engagement had a mean of 2.58 (SD = 0.304) on good days and a mean of
2.62 (SD =0.337) on bad days. Emotional engagement had a mean of 4.75 (SD =0.251) on good
days and a mean of 4.73 (SD =0.351) on bad days. Again, this signifies that weather did
influence participant emotional or cognitive engagement.
Table 4: MANOVA Summary Table: The Effect of Weather on Cognitive Engagement
Source
D
F
Mean
F
Square Value
Manova SS
GOOD BAD
1
0.00771368 0.00771368
Error(GOOD
BAD)
25
2.15395299 0.08615812
0.09
Pr > F
0.7672
Table 5: MANOVA Summary Table: The Effect of Weather on Emotional Engagement
Source
DF
Manova SS
Mean
F
Square Value
GOOD BAD
1
0.01797009 0.01797009
Error(GOOD
BAD)
25
1.85258547 0.07410342
0.24
Pr > F
0.6267
There was statistical significance, however, in behavioral engagement, which can be seen
in Table 6. The results indicate that good weather days led to higher behavioral engagement than
bad weather days. [Behavioral Engagement: Wilk’s λ=0.85, F (1, 26) =4.30, p=0.049].
Behavioral engagement had a mean of 3.48 (SD =0.296) on good days and a mean of 3.36 (SD =
0.194) on bad days. The results can also be seen comparatively in Figure 2.
27
Table 6: MANOVA Summary Table: The Effect of Weather on Behavioral Engagement
Source
DF
Mean
F
Square Value
Manova SS
GOOD BAD
1
0.19081731 0.19081731
Error(GOOD
BAD)
25
1.10987714 0.04439509
4.30
Pr > F
0.0486
Figure 2. The Influence of Differing Weather on the Moods and Behaviors of an Individual
(n=26). Black bars on mean represent the standard deviation.
The Influence of Differing Weather on the
Moods and Behaviors of an Individual (n=26)
Cognitive Engagement Bad
Cognitive Engagement Good
Emotional Engagement Bad
Emotional Engagement Good
Behavioral Engagement Bad*
Behavioral Engagement Good*
Negative Mood Bad
Negative Mood Good
Positive Mood Bad
Positive Mood Good
0
* Signicant at p <.05
1
Maximum
2
Mean
Minimum
3
4
5
28
Are The Findings From This Study Similar To What Prior Research Has Found
The secondary research question of whether these results are similar to prior research can
also be answered from these data. Forgas, Goldenberg, and Unkelbach (2009) found that on bad
weather days the participants in their study reported a worse mood and on good weather days
they reported a better mood. This is not the result of this study, since there was not a difference
in mood when compared to weather. These results supported the ideals of Watson (2000), who
suggested that there is no impacted of weather on mood.
If Weather Does Influence Mood and Engagement, How Big Is The Effect?
When looking at the final research question of if the influence of weather on mood and
engagement does exist how big the effect (η2) is, there seems to only be one significant factor to
discuss. Green, Salkind, and Akey (2000) showed that η2 is between zero and one, with zero
meaning no relationship between the independent and dependent variable, and one being the
strongest relationship. According to Green, Salkind, and Akey (2000), it is unclear what values
of η2 are considered to be small, medium or large. The effect size for the behavioral engagement
MANOVA was found by taking one minus λ to the first power. The power was set to one
because that was the number of dependent variables in the MANOVA. This resulted in an effect
size of η2=0.15.
Chapter Summary
To conclude, weather was found to have a significant influence over behavioral
engagement. This influence is small, but enough to be observable. This is in contrast to the
majority of research articles found, which suggested that it was mood and not engagement that
29
was influenced by weather (Howarth and Hoffman, 1984; Forgas, Goldenberg, and Unkelbach,
2009; Keller et al., 2005; Bardwell, Ensign, & Mills, 2005).
30
CHAPTER V
CONCLUSIONS
Weather is a constant factor in people’s everyday life. Many sources, such as Forgas,
Goldenberg, and Unkelbach (2009) believed that weather has the potential to change how people
learn, because in part, of their mood. However, this study found that weather only statistically
influenced behavioral engagement, and did not impact mood or other types of engagement
(emotional or cognitive). The results from the data show that there was not a significant
difference in mood. Although contrary to the findings of Forgas, Goldenberg, & Unkelbach
(2009) and Keller, et al (2005), the results of this study support Watson’s (2000) conclusion that
weather has no impact on mood.
Discussion
If the findings from this study are correct, then the weather does not influence mood or,
ipso facto, learning via mood. There are two reasons that this could be the case. First, the
participants in this study could be undergoing a ceiling effect because they are so excited to be at
the zoo that the majority of their responses indicate a highly positive mood are high regardless of
the weather. This would mean that no matter how humid or cloudy the weather became, the
majority of the participants were going to mark a “5” down for “happy” because they considered
seeing animals fun. This can be seen, for emotional engagement and positive mood (see Figure
2). Second, only having the participants for a week may have been an insufficient time to collect
enough data to differentiate mood. As described by Bardwell, Ensign, & Mills (2005), the long
periods of good weather prior to the bad weather may have caused for a continuance of the good
mood despite the bad weather. Summer is considered an optimal time for students to learn
material (Keller et al., 2005).
31
Yet, the results also showed that there was a significant difference in the behavioral
engagement scores based on weather, with lower behavioral engagement taking place on “bad”
weather days and higher behavioral engagement on “good” weather days. Behavioral
engagement is a person’s willingness to stay on task and follow established rules and procedures.
This finding regarding behavioral engagement is related to the idea that people preferred to work
as individuals on bad weather days (Forgas, Goldenberg, & Unkelbach, 2009). Similarly, people
are more likely to want work in groups on good days because they are more willing to accept the
answers from others instead of seeking information themselves (Forgas, Goldenberg, &
Unkelbach, 2009). One research study concluded that there could be less learning during bad
weather days was because of lower behavioral engagement (Ponitz, Rimm-Kaufman, Grimm, &
Curby, 2009).
Interestingly, the other four factors, positive mood, negative mood, cognitive
engagement, and emotional engagement, all were shown in previous studies to support higher
learning rates. Robinson (2011) showed that moods, positive or negative, could influence the
way people learn. Meece (1988) suggested the cognitive engagement is a driving force in
learning as along as learners are interested in task mastery, which is not effected by weather.
Elffers, Oort, and Karsten (2012) showed that emotional engagement needs to be met in order for
learning to progress. An argument might be made that behavioral engagement (a variable
influenced by weather) could influence both cognitive and emotional engagement (variables
found to be independent of weather). If so, one might expect that behavioral engagement would
play a larger role in learning. Although learning is not a variable examined this research study,
perhaps a more critical related question is how favorable participant ratings for all five of the
variables included in this study might enhance learning?
32
Implications
With the findings from this research study in mind, education facilities like the Toledo
Zoo may want to take into account that learners (and maybe even educators) might be
undergoing low behavioral engagement when the weather is bad. On bad weather days it may be
wise to have an activity set aside that learners could delve into by themselves without much
guidance. It should have the potential to eliminate interaction between groups so they are
focused on cognitive or emotional engagement instead of behavioral engagement. This
individualized activity should nevertheless relate to material being covered during the unit.
According to Ponitz, Rimm-Kaufman, Grimm, and Curby (2009) this would be beneficial
because it would give students an activity that would give them a way to behave in accordance
with class rules. Second, education facilities like the Toledo Zoo that can create and maintain a
high level of excitement prior to learners even arriving could might going the opposite route.
Instead of having an individualized learning activity, which is hands on and they could try to
create a whole group interactive game that elicits behavioral engagement by tapping into the
excitement of all learners.
Teachers might not be able to change the weather, but they could restructure how they
teach during these periods in ways that elicit better behavioral engagement and perhaps even
enhanced learning. Keeping in mind that educators might experience the same depression in
behavioral engagement due to the weather, asking them to modify their lessons to account for
changes in weather may be a difficult challenge. Therefore, having educators construct good and
bad weather lesson plans during extended periods of good weather may be in order (Bardwell,
Ensign, & Mills, 2005).
33
Furthermore, the analysis of this data has also led to some assumptions about the
correlation between entire classroom’s mood and weather, or the individual’s mood. The
previous research suggested that there is a correlation (Howarth, & Hoffman, 1984). However,
finds from this study suggests differently. Out of the five weeks that of observations students,
four out of the five weeks had the majority of good weather days. Based purely on anecdotal
evidence (journal notes based on observations made by the sole researcher), four of the five
weeks had camps where participants were generally more attentive, less combative, and easier to
engage. The week with the majority of bad weather days on the other hand, appeared to have
more distracted and aggressive campers. One of the campers, for instance, wrote on the side of
the survey during a bad weather day in the worst weather week of the summer that she felt more
hostile because of one of her fellow campers. Her hostile feelings may relate to lower behavioral
engagement taking place during bad weather. That is not to say these campers were less
interested in the material that was being covered at the zoo during that week. They just were
more difficult to engage. The question that remains is, was the observable negative behavior
influenced by the weather or was it just a more rambunctious group of campers that happened to
be attending camp that week?
Limitations
There are several limitations to this study. First, there was likely a ceiling effect with
some of the data collected, with both positive mood and emotional engagement where very close
to “5” on “good” and “bad” weather days (See Figure 2). This ceiling effect may stem from the
elevated excitement associated with going to the zoo. Second, the total number of participants
included in the study (n=26) was lower than expected (n=44) do to the uncontrollable variable of
weather that forced two weeks of the study to be dropped because these weeks consisted of only
34
“good” weather (See Figure 1). Overall, the weather uncertainty led to a smaller sample size then
was initially planned. Third, the survey was unable to account for the prolonged exposure effect
described by Bardwell, Ensign, & Mills (2005). They suggested that there were lingering effects
form long periods of continuous weather, which could be another explanation for the ceiling
effect that occurred in this study. In general, the summer consisted of many strings of “good”
weather days. Participants who did experience bad weather in week two would have been
exposed to a long period of very good weather prior to the two bad weather days in the week
they were observed. Finally, the error of not assigning codes to each student at the beginning of
each week may account for lost in data significance. The data that was collected from the Toledo
Zoo was organized by week and day, but the data was not initially matched from day to day by
participant. This caused a problem because the data analysis technique (MANOVA) requires
matched data. This issue was resolved using participant drawings and response analysis. This
allowed for participants’ surveys to be matched up over the course of the week. It is unlikely that
every participants’ survey where matched up correctly, which could limit a study that has a small
sample size.
Future Studies
This study failed to determine whether weather influenced mood or not. Yet, it serves as
a stepping-stone that could be used to create a more in depth and meaningful study. It would be
beneficial to first include a section on the survey question that elicits what participants think is
for good or bad weather for the season. This would help researchers gain a better understanding
of what people from different areas consider good and bad weather and eliminate some bias.
Since there did not seem to be a large difference from week to week in mood, a school setting
might be an idyllic place to stage the future studies. Participants could again fill out the surveys
35
five times a week over the course of a school year. If this was done over the course of a year
which typically spans three separate seasons, reducing the potential error of sustained weather, or
climate, effects on mood and engagement. The data collected should be compared both over the
course of the year and by season. Another thing that future studies could do to further this study
would be to increase the sample size as long as the sample stays with the same geographic
region. Watson (2000) had an impressive sample size but they were from all different locations
so their cultural reference for what was considered good and bad weather was skewed.
Finally, a decision was made to drop pressure (a weather variable that was observed)
from the study because it was in the neutral category every day. By removing pressure from the
study, this study obtained more participants because the study gained a third week that had
mixed weather days. However, future studies may want to include pressure as weather factor,
especially if the studies take place over the course of a year, where pressure fluctuations would
be more prevalent.
36
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39
APPENDIX A
WEATHER DEMOGRAPHICS
40
Observation Demographics:
Date:
Day of week:
Time:
Weather:
Good
Temperature:
70-90°F
Cloud Cover:
Humidity:
Neutral
60-70°F
Bad
<60°F, or >90°F
30%
40-60%
70%
40-55%
56-69%
<40% or >70%
Precipitation:
None
Drizzle
Rain
Other factors:
Cloud Types:
Total Precipitation:
Audience: Total = _______; % male; _____ %female_____;
41
APPENDIX B
COMPLETE WEATHER DATA TABLE
42
Day
Time
Temperature Cloud Humidity Precipitation Temperature Cloud Humidity Precipitation Total
Day
On Day
Cover On Day On Day
scored
Cover Scored Scored
without Type
On
Pressure
scored
Day
D1W1
8:00
70
0
76
none
3
3
2
3
11:00
84
60
63
none
3
2
1
3
2:30
90
70
55
none
3
1
3
3
l Average
3
2
2
3
2.5 Good
D2W1
8:00
70
20
69
none
3
3
2
3
11:00
92
10
53
none
1
3
3
3
2:30
95
10
41
none
1
3
3
3
Average
1.67
3
2.67
3
2.6 Good
D3W1
8:00
75
0
69
none
3
3
2
3
11:00
90
5
45
none
3
3
3
3
2:30
95
10
59
none
1
3
2
3
Average
2.33
3
2.33
3
2.7 Good
D4W1
8:00
75
50
55
none
3
2
3
3
11:00
90
0
56
none
3
3
2
3
2:30
91
5
47
none
1
3
3
3
Average
2.33 2.67
2.67
3
2.7 Good
D5W1
8:00
71
30
75
none
3
3
1
3
11:00
89
40
55
none
3
2
3
3
2:30
93
30
47
none
1
3
3
3
Average
2.33 2.67
2.33
3
2.6 Good
D1W2
8:00
65
1
63
none
2
3
2
3
11:00
72
50
63
none
3
2
2
3
2:30
76
70
35
none
3
1
1
3
Average
2.67
2
1.67
3
2.3 Bad
D2W2
8:00
67
0
51
none
2
3
3
3
11:00
75
0
48
none
3
3
3
3
2:30
79
10
49
none
3
3
3
3
Average
2.67
3
3
3
2.9 Good
D3W2
8:00
72
10
57
none
3
3
3
3
11:00
70
0
31
none
3
3
1
3
2:30
86
0
40
none
3
3
3
3
Average
3
3
2.33
3
2.8 Good
D4W2
8:00
69
40
52
none
2
2
3
3
11:00
90
70
62
none
3
1
2
3
2:30
90
50
45
none
3
2
3
3
Average
2.67 1.67
2.67
3
2.5 Good
D5W2
8:00
71
10
35
none
3
3
1
3
11:00
83
10
45
none
3
3
3
3
2:30
92
20
50
none
1
3
3
3
Average
2.33
3
2.33
3
2.7 Good
43
D3W1
D2W3
D3W3
D4W3
D5W3
D1W4
D2W4
D3W4
D4W4
D5W4
D1W5
D2W5
8:00
11:00
2:30
Average
8:00
11:00
2:30
Average
8:00
11:00
2:30
Average
8:00
11:00
2:30
Average
8:00
11:00
2:30
Average
8:00
11:00
2:30
Average
8:00
11:00
2:30
Average
8:00
11:00
2:30
Average
8:00
11:00
2:30
Average
8:00
11:00
2:30
Average
8:00
11:00
2:30
Average
8:00
11:00
80
85
93
84
91
93
85
95
96
73
82
85
65
69
80
70
86
92
76
81
86
74
79
89
64
73
79
70
76
86
72
80
87
68
74
20
40
20
50
90
100
30
60
80
95
70
100
100
80
80
100
0
5
100
90
65
40
25
40
100
100
100
20
10
30
45
70
50
100
30
46
33
62
80
86
93
55
72
73
76
83
89
79
74
75
61
55
45
30
35
45
55
53
55
55
72
73
36
45
45
47
50
47
55
55
none
none
none
none
drizzle
rain
none
rain
drizzle
none
none
rain
rain
none
none
rain
none
none
none
none
none
none
none
none
none
none
drizzle
none
none
none
none
none
none
none
none
3
3
1
2.33
3
1
1
1.67
3
1
1
1.67
3
3
3
3
2
2
3
2.33
3
3
1
2.33
3
3
3
3
3
3
3
3
1
3
3
2.33
3
3
3
3
3
3
3
3
2
3
3
1
3
2.33
2
1
1
1.33
3
2
1
2
1
1
1
1
1
1
1
1
1
3
3
2.33
1
1
1
1
2
3
2
2.33
1
1
1
1
3
3
3
3
2
1
2
1.67
1
3
3
1
2
2
1
1
1
1
3
1
1
1.67
1
1
1
1
1
1
1
1
2
3
3
2.67
1
1
3
1.67
3
3
3
3
3
1
1
1.67
1
3
3
2.33
3
3
3
3
3
3
3
3
3
3
3
2
1
2
3
1
2
2
3
3
1
2.33
1
3
3
2.33
1
3
3
2.33
3
3
3
3
3
3
3
3
3
3
2
2.67
3
3
3
3
3
3
3
3
3
3
2.4 Good
1.5 Bad
1.8 Bad
1.8 Bad
1.7 Bad
2.4 Good
2.1 Bad
2.8 Good
1.9 Bad
2.8 Good
2.7 Good
44
2:30
Average
D3W5
8:00
11:00
2:30
Average
79
71
75
77
10
10
40
40
55
39
45
47
none
none
none
none
3
2.67
3
3
3
3
3
2.33
3
2
2
3
3
3
1
3
3
2
3
3
3
3
3
2.33
2.8 Good
2.5 Good
45
APPENDIX C
SABERS SURVEY
46
Student Affective Behavior and Engaged Reactions Scale (SABERS)
Affective Behavior: Adapted from Positive and Negative Affect Schedule-Extended Form
Never = 1; Rarely, Sometimes, Usually, Always= 5
• Positive Affective Scale Adapted From PANAS-X (Watson, 1994)
1. Active
2. Paying Attention
3. Determined
4. Happy
5. Excited
6. Delighted
7. Cheerful
8. Confident
9. Bold
10. Daring
Negative Affective Scale Adapted From PANAS-X (Watson, 1994)
1. Sad
2. Blue
3. Alone
4. Angry
5. Hostile
6. Annoyed
7. Sleepy
8. Tired
9. Drowsy
10. Nervous
11. Scared
12. Afraid
Engagement: Adapted from Students Attitude. Motivation and Engagement Scale (Partin et al.,
2011)
Never = 1; Rarely, Sometimes, Usually, Always= 5
• Behavioral Engagement
1. I pay attention in camp.
2. When I’m in camp, I just act as if I am working, but I am really off task. *
3. I follow the rules and policies of this camp.
4. I am disruptive in this camp. *
5. I do what is asked of me in this camp.
•
•
Emotional Engagement
6. I am happy in this camp.
7. I am excited by work in this camp.
8. I like being in this camp.
47
9. I am interested in the work in this camp.
10. This camp is a fun place to be.
•
Cognitive Engagement
11. I am eager to share my answers or ideas in this camp.
12. After I leave this camp, I think about and/or tell others about what I am learning.
13. On my own time, I read, search the web, or watch videos/TV to learn more about things
we are studying in this camp.
14. My mind wanders off topic during this camp. *
* These items where inversely coded. Meaning 1 was a 5, 2 was a 4, 4 was a 2, and 5 was a 1.
48
Student Survey
Please complete the following survey below by circle the number that represents the answer that
based reflects you opinion. It is your choice to complete the survey. Feel free to skip any that you
are not sure of.
What you are Never=1 Rarely=2 Sometimes=3 Usually=4 Always=5
feeling
today?
Happy
1
2
3
4
5
Tired
1
2
3
4
5
Cheerful
1
2
3
4
5
Confident
1
2
3
4
5
Annoyed
1
2
3
4
5
Paying
1
2
3
4
5
Attention
Alone
1
2
3
4
5
Afraid
1
2
3
4
5
Drowsy
1
2
3
4
5
Angry
1
2
3
4
5
Scared
1
2
3
4
5
Determined
1
2
3
4
5
Delighted
1
2
3
4
5
Blue
1
2
3
4
5
Active
1
2
3
4
5
Daring
1
2
3
4
5
Sleepy
1
2
3
4
5
Excited
1
2
3
4
5
Hostile
1
2
3
4
5
Nervous
1
2
3
4
5
Sad
1
2
3
4
5
Bold
1
2
3
4
5
What you think today?
I pay attention in camp.
When I’m in camp, I just act as if
I am working, but I am really off
task.
I follow the rules and policies of
this camp.
I am disruptive in this camp.
I do what is asked of me in this
camp.
I am happy in this camp.
Never=1 Rarely=2 Sometimes=3 Usually=4 Always=5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
1
1
2
2
3
3
4
4
5
5
1
2
3
4
5
49
I am excited by work in this
camp.
I like being in this camp.
I am interested in the work in
this camp.
This camp is a fun place to be.
I am eager to share my answers
or ideas in this camp.
After I leave this camp, I think
about and/or tell others about
what I am learning.
On my own time, I read, search
the web, or watch videos/TV to
learn more about things we are
studying in this camp.
My mind wanders off topic
during this camp.
1
2
3
4
5
1
1
2
2
3
3
4
4
5
5
1
1
2
2
3
3
4
4
5
5
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
50
APPENDIX D
CONSENT FORMS
51
52
53
54
DATE:
May 25, 2012
TO:
FROM:
Joseph Carstensen
Bowling Green State University Human Subjects Review Board
PROJECT TITLE:
SUBMISSION TYPE:
[317761-3] A Study of Teaching Under the Weather
Revision
ACTION:
APPROVAL DATE:
EXPIRATION DATE:
REVIEW TYPE:
APPROVED
May 25, 2012
May 7, 2013
Expedited Review
REVIEW CATEGORY:
Expedited review category # 7
Thank you for your submission of Revision materials for this project. The Bowling Green State University
Human Subjects Review Board has APPROVED your submission. This approval is based on an
appropriate risk/benefit ratio and a project design wherein the risks have been minimized. All research
must be conducted in accordance with this approved submission.
The final approved version of the consent document(s) is available as a published Board Document in
the Review Details page. You must use the approved version of the consent document when obtaining
consent from participants. Informed consent must continue throughout the project via a dialogue between
the researcher and research participant. Federal regulations require that each participant receives a copy
of the consent document.
Please note that you are responsible to conduct the study as approved by the HSRB. If you seek to
make any changes in your project activities or procedures, those modifications must be approved by this
committee prior to initiation. Please use the modification request form for this procedure.
You have been approved to enroll 50 participants. If you wish to enroll additional participants you must
seek approval from the HSRB.
All UNANTICIPATED PROBLEMS involving risks to subjects or others and SERIOUS and UNEXPECTED
adverse events must be reported promptly to this office. All NON-COMPLIANCE issues or COMPLAINTS
regarding this project must also be reported promptly to this office.
This approval expires on May 7, 2013. You will receive a continuing review notice before your project
expires. If you wish to continue your work after the expiration date, your documentation for continuing
review must be received with sufficient time for review and continued approval before the expiration date.
Good luck with your work. If you have any questions, please contact the Office of Research Compliance
at 419-372-7716 or [email protected]. Please include your project title and reference number in all
correspondence regarding this project.
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This letter has been electronically signed in accordance with all applicable regulations, and a copy is retained within Bowling Green
State University Human Subjects Review Board's records.
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