Amanda Bourgeois
Annemaree Carroll
Julie Bower- School of Education, The University of Queensland, Brisbane, QLD, Australia
In the present research, three phases were conducted to develop a real-time emotional measure (S2* emotion application) to examine the emotional experiences and causes for those emotions in disadvantaged Australian adolescents. In the first phase, data were collected from 412 Year 10 students (aged 14–15 years) to understand their emotional experiences in the classroom. Second, the S2* emotion application was developed and trialed based on the initial findings from the first phase and relevant literature. In the third phase, the S2* was utilized with participants (N = 81) from Year 10 Math and English classes over three time points during an academic term. Results revealed that the most frequent reports of emotions and cause for that emotion were: boredom caused by schoolwork; happiness caused by peers; happiness caused by self; and boredom caused by teacher. When emotions and causes were examined separately, the most frequent reported emotion was boredom and the most frequent reported cause for emotion was peers. This tool may be used in future studies to further investigate these real-time emotional experiences allowing researchers to build on theoretical frameworks and provide skills and resources to best support educators.
Introduction
Students experience a vast range of emotions during their time at school. These emotional experiences are unique to each individual and can be caused by their interactions with peers and teachers, schoolwork, perceptions of their environment, as well as external causes beyond the scope of the school (e.g., home life, family). Classroom emotions have been a growing topic of research interest over the past 20 years (Pekrun et al., 2018; Goetz et al., 2020), with this expansion of research being in part due to the interconnected relationship between emotions, learning and achievement. Importantly, neuroscience research has revealed that emotion and cognition are sustained by interdependent neural processes, indicating that rather than interfering with learning, emotions are critical for learning (Immordino-Yang, 2016; Prokofieva et al., 2019; Li et al., 2020).
Disadvantaged young people in particular face heightened risk of poor achievement, school dropout (Chen and Vazsonyi, 2013; Platell et al., 2019) and mental health problems (Bower et al., 2015; Platell et al., 2019), yet there is little, if any, research exploring their emotional experiences in school. Research indicates that young people in lower socioeconomic communities are presented with a multitude of daily stressors, beyond those of most adolescents (e.g., violence, substance abuse, familial financial difficulties, and problematic behavior), which puts them at greater risk for intense emotional reactivity compared with their peers in more advantaged communities (Uink et al., 2018). It is essential to learn more about the emotional experiences of disadvantaged young people so we can inform educators how to best support their social and emotional learning and wellbeing.
The aim of the present research was to acquire a better understanding of the emotional experiences of Year 10 students from a low socioeconomic background in Math and English classes as well as the causes for these experiences. Furthermore, we sought to develop a computer-based application to capture these multifaceted experiences in real-time, without causing disruption to the classroom. Subsequently, the findings enrich the existing literature by providing greater insight to classroom emotional experiences of disadvantaged adolescents as well as the development of a dynamic new methodological approach that has the ability to capture emotional experiences and antecedents, with the capacity to do so in classrooms world-wide. While this research was conducted prior to the COVID-19 pandemic, the implications of this new form of measurement can assist in the need to prioritize student emotional wellbeing.
The context of classroom emotions
We recognize that emotions are physiological arousals which are caused by cognitive appraisal of a stimulus involving both the body and mind (Immordino-Yang and Sylvan, 2016). For the purposes of the present research, the term emotion is used to cover a broad range of emotional experiences and moods, comprised of psychological, cognitive, affective, expressive and motivational components (see Frenzel and Stephens, 2013 for a comprehensive definition). Control-value theory accepts the notion that individual and situational appraisals (i.e., individual reactions to specific events) are critical aspects of the establishment of an emotion (Pekrun, 2006). Appraisals play a valuable role from an educational viewpoint, as “they can be assumed to mediate the impact of situational factors and can be targeted by educational interventions intended to foster positive emotional development” (Pekrun, 2006, 317).
Ecological systems theory postulates that human development can only be understood by recognizing the entire ecological system surrounding the individual (Bronfenbrenner, 1979, 1994). As such, this theory is pertinent to understanding the multiple contextual factors influencing student emotions in the classroom. We adopted a holistic approach in our endeavor to understand the emotional experiences of students, recognizing that we as humans are influenced by the various environments which surround us. We acknowledge that young people cannot just turn off the contextual factors that affect them when they enter a classroom. Although antecedents to emotional experiences in the classroom have been investigated, the majority of research has limited the students’ ability to select from a range of experienced emotions (Moeller et al., 2020) and antecedents. The present research intends to provide students with the opportunity to select from a variety of emotional experiences and causes for those experiences in real-time classrooms. It is essential to capture a holistic picture of the emotional experiences of students, particularly those from more disadvantaged communities at greater risk, if we want to positively influence student motivation, interest and achievement and create more enriching classrooms.
Disadvantages of students in low socioeconomic status communities
Uink et al. (2018) argued that research examining daily reports of emotions amongst adolescents are primarily concentrated on young people from middle-income communities, which is concerning as it is disadvantaged youth who encounter greater daily stressors than their wealthier peers. One recent study examined a range of school-based feelings experienced by high school students in the United States (Moeller et al., 2020). They further investigated correlations of these feelings with demographic characteristics and found that while, as a whole, all groups reported mostly negative feelings, there was a small effect size implying that higher SES was correlated with greater reports of positive feelings and lower reports of negative feelings (Moeller et al., 2020).
Lamb et al. (2020) argued that within Australia “our education and training systems are dogged by inequality” (p. 130). In the latest report on educational opportunity in Australia, the percentage of senior year students of high SES meeting or exceeding the math, science and reading international benchmark standard was 86.2 compared to only 48.6 of their low SES counterparts, with a similar gap in the middle year students (Lamb et al., 2020). This is interesting given that Australia’s education system is relatively high performing in comparison to other countries (McConney and Perry, 2010). SES is also predictive of student engagement in Australia. Cognitive and emotional engagement specifically related to school is nearly double in students from high SES families compared to those in the lowest SES families (Lamb et al., 2015). Furthermore, disadvantaged students are more likely to become disadvantaged adults (State of Victoria Department of Education and Early Childhood Development [DEECD], 2010; Lamb and Huo, 2017; Perry, 2018), therefore, it is crucial to understand more about the emotional experiences of these students to help educators support their needs and find ways to engage them.
Measurements of classroom emotions
Emotion measurement is not a simple task. Given that emotional experiences are subjective and can change in an instant, the task of measuring emotions is challenging, yet essential to better understand the impact emotions have on student learning, wellbeing and academic success. In the education context, some approaches to measure emotions include asking students about their emotions through verbal accounts, visual stimuli or a combination of both (Frenzel and Stephens, 2013). Instruments designed to measure emotions in the classroom beyond test anxiety include both qualitative and quantitative self-report measures, as well as observations, however, the options are limited (Pekrun and Bühner, 2014).
Most educational research on emotions has been conducted using self-report measures (Pekrun, 2016) retrospectively. Researchers have argued that retrospective questionnaires measuring emotions or moods can often be altered by recall biases (Stone et al., 1998; Reid et al., 2009; Pekrun and Linnenbrink-Garcia, 2014a; Pekrun, 2016; Moeller et al., 2020). When individuals try to recall emotional experiences, their responses can often be skewed by current mood and abstract reflection (Stone et al., 1998; Reid et al., 2009). We aim to develop a tool to capture these emotional experiences in real-time, diminishing recall biases, and allowing participants to report these experiences throughout an entire class session, rather than afterward, or during controlled intervals.
While research on emotions in the classroom has been steadily growing over the past two decades, the capacity to collect these data using real-time measurements is just beginning to emerge. To our knowledge there have been only a few studies recently published that have attempted to collect real-time student emotions and they have all used the experience sampling method (ESM) (see Hektner et al., 2007). Moeller et al. (2020) conducted a study that sought to collect in-the-moment experiences of feelings using ESM. This involved using an App to have students complete short surveys on their phones at specific time points, toward the end of class. Participants were alerted with a beep and had an hour to complete the survey. They also completed online surveys retrospectively (Moeller et al., 2020). Goetz et al. (2020) also used ESM to investigate the academic emotions (enjoyment, anxiety, and boredom) of high school students in relation to their real-time perceptions of teaching characteristics. Similarly, participants had an electronic device that prompted students at one time-point during a 40-min lesson to report their experience (Goetz et al., 2020).
ESM limits impromptu collection of emotional experiences as participants are prompted at a specific timepoint to respond to the device, rather than reporting at free will. Interrupting participants during class time to report emotions can potentially interrupt classroom flow and learning (Feyzi Behnagh, 2019). Additionally, as the collection of data is prompted, this may lead to missing out on valuable data (Goetz et al., 2016). We believe this is the first study to develop a computer-based application based on the reported experiences of the participants, as well as to capture these real-time experiences from secondary students from a disadvantaged community. Furthermore, it is also the first study to collect both the emotional experience as well as the cause throughout entire class sessions, as prompted by the student rather than a device.
The present study
Although research on emotions in the classroom setting has been an evolving topic, there have been scarce advancements in instruments designed to capture these experiences through real-time measurement, from the students’ perspectives. Researchers defend the need for new tools to measure classroom emotions (Pekrun and Bühner, 2014) and to examine a range of student emotional experiences simultaneously (Moeller et al., 2020). Additionally, Reid et al. (2009) suggest that using an electronic device to capture data from adolescents may increase response rates, as the familiarization and use of these forms of technology are second nature to young people. The need for methodologically sound instrumentation for measuring emotions in education contexts is vital in the development of theoretical frameworks aimed at creating evidence-based approaches that will be useful for educators (Pekrun and Schutz, 2007; Pekrun and Linnenbrink-Garcia, 2014a).
Capturing real-time measurements of student emotional experiences and their causes would be of great value to educationalists and educational psychologists in better understanding and supporting the learning journey of their students/clients, particularly those from disadvantaged backgrounds. Therefore, the purposes of the present study were first to investigate the emotions and causes for emotion in disadvantaged Year 10 Australian students; second, develop a tool (the S2* emotion application) to measure these real-time emotions and causes for these emotions in a classroom setting; and third, confirm this tool, the S2* as a reliable and valuable measure of real-time emotions for use with disadvantaged Year 10 Australian adolescents in classroom settings. Four research questions were posed and are addressed over three phases:
(1) What emotions do disadvantaged Australian adolescents experience in the classroom, and what are the causes for these emotions? (Phase 1)
(2) How can classroom emotions and causes for these emotions be measured using real-time technology? (Phase 1)
(3) How do students respond to using the S2* emotion application in a classroom setting? (Phase 2)
(4) What are the real-time emotions and causes for these emotions of disadvantaged Year 10 Math and English students? (Phase 3)
Phase 1 item selection and S2* emotion application development
Methods
First, an exploratory study was conducted to better understand the context of emotions in the classroom for disadvantaged Australian adolescents. This was critical as there is a dearth of research on the emotional experiences for high school students from disadvantaged communities, which is essential given school is where they spend a significant amount of time developing socially and emotionally. Current researchers investigating emotions in educational settings have argued the need for new multi-method approaches, contending that it is essential in the advancement of the research field to provide quality and valuable data (Pekrun, 2006; Pekrun and Schutz, 2007; Pekrun and Linnenbrink-Garcia, 2014a; Feyzi Behnagh, 2019). Therefore, next, a prototype of the S2* was created. The aim was for a simplistic design, using just one screen to quickly capture the participant’s emotional state and cause, ensuring minimal classroom disruption. A web developer was contracted to assist with the construction of the application in a format that could be delivered on a tablet exclusive of the internet.
The prototype was delivered on a tablet, allowing 20 emotion words and five causes to be displayed on the screen. The development of the S2* was a two-step process: (i) to determine the 20 emotion words that would appear on the application, answering the question ‘How are you feeling right now?’; and (ii) to determine the five causes that would appear on the application, answering the question ‘What was the cause?’.
Participants
A total of 412 Year 10 students, aged 14–16 years (mean age 14.64 years) from three secondary schools agreed to participate. Of these participants, 44% identified as male, and 55% identified as female (180 males, 230 females, and 2 gender non-specified). The overall response rate was 90%. Between 8 and 15% of students identified as Indigenous and 8 to 41% of students had a language background other than English. According to the Index of Community Socio Educational Advantage (ICSEA), all three schools are located in a lower SES community in comparison to the Australian national averages (Australian Curriculum Assessment and Reporting Authority, 2017). ICSEA was developed by the Australian Curriculum Assessment and Reporting Authority (ACARA), and allows meaningful comparisons across schools nationally, by measuring key factors relating to student achievement, such as family background and geographical location (Australian Curriculum Assessment and Reporting Authority, 2015). The median ICSEA score is set at 1000, with a standard deviation of 100 (500 represents extremely disadvantaged, while 1300 represents very highly advantaged); all three targeted schools were ranked between 900 and 945, with a mean score of 926 (Australian Curriculum Assessment and Reporting Authority, 2016).
Instrument
Students completed an open-ended survey specifically designed for this study to collect information about Emotions and Triggers (see Supplementary Appendix A). The survey contained two columns asking students to list the emotions they have experienced in class (column one), as well as the triggers that caused these emotions (column two). Students also provided their gender, age, year level and class session.
Procedure
Approval for the ethical conduct of research was obtained from the administering university and the Queensland Department of Education. The principals at each of the three schools were invited to be involved in the project and were provided with an overview of the study, copies of information letters, consent forms and the abovementioned surveys. All principals agreed for students at their schools to take part and subsequently, information letters were distributed to the parents/guardians of all Year 10 students at each school. An electronic hyperlink to the research study was included in each schools’ monthly newsletter, which was available on the schools’ website. Year 10 students attended an information session whereby the researcher provided them with an overview of the study and students were invited to take part in the study. It was made clear that students were free to withdraw from the study at any time.
A researcher administered the survey to participants in a group setting over one class period (approximately 40 minutes). Participants were given the opportunity to ask questions before, during and after the survey was administered. Participants were asked to recall the emotions they experience at school. The only guidance they were given was to think about how they felt at school and the causes for those emotions. Where students sought clarification, an example was given (e.g., I’m happy when I’m with my friends, or I’m nervous when I have a test).
Results and discussion
Data from the surveys were reviewed and analyzed for frequency of experienced emotions, as well as frequency of triggers. Next, in keeping with Creswell’s (2014) guidelines, all responses were reviewed allowing the researcher to categorize the responses based on similar themes, as well as valence for emotions, and 20 emotion words and 5 triggers were identified for utilization in the application.
Emotion words
A frequency analysis of the emotion words was completed using Microsoft Excel. The emotion words were tracked by frequency of emotion, age and gender of participant. Variations of the same word (e.g., ‘happy’ and ‘happiness’; and ‘stressed’ and ‘stressed out’) were grouped together. Table 1 displays a portion of the frequency analysis, presenting the 10 most frequently reported emotions. A total of 200 different words were used to capture the students’ perception of emotion.
Table 1. Emotion words.
Emotion triggers
First, all legible participant responses were transcribed onto a Microsoft Excel workbook. Each row contained the trigger statement; the emotion/s associated with that trigger (as well as the frequency of each emotion); the frequency of the trigger, age, and gender (see Table 2 for an example). There were 2,821 responses, which were reduced by 45% to 1,270 different statements through removing replication of triggers, given that many students wrote the same or similar trigger (e.g., test/exams, good grades/marks, students/classmates).
Table 2. Emotion triggers.
Sequentially, the researcher followed the guidelines of data analysis for qualitative research as suggested by Creswell (2014). This involved thoroughly reading through and becoming familiar with the data, organizing the data into similar categories, coding the data into different themes, and interpretating the findings. Therefore, the researcher became familiar with the data, writing down ideas that were generated from reading through several worksheets. The triggers were then coded into multiple categories, based on reoccurring descriptive words.
The eight themes derived from the coding process were: Teacher, Peers, Schoolwork, Self, Health, Home/Family, School in General, and Other (including answers not related to school). To ensure consistency, an intercoder agreement (Creswell, 2014) was conducted, which involved four researchers cross-checking the consistency of a randomized 10% of data. The agreement for this process was 91%.
The eight themes resulting from the triggers for students’ emotions are in alignment with the literature on antecedents of classroom emotion, making them advantageous for the application. In this study, the teacher as a precursor to student emotions encompasses the characteristics of the teacher (Goetz et al., 2013; Pekrun and Linnenbrink-Garcia, 2014b; Pekrun and Perry, 2014; Li et al., 2020), as well as teaching styles (Goetz et al., 2013, 2019, 2020; Pekrun and Linnenbrink-Garcia, 2014b; Pekrun and Perry, 2014; Meyer, 2014). Classmates’ emotions as antecedents to student emotions (Goetz et al., 2013; Meyer, 2014; Pekrun and Linnenbrink-Garcia, 2014b; Pekrun and Perry, 2014; Li et al., 2020) encapsulates peers. The classroom climate (Klem and Connell, 2004; Goetz et al., 2013, 2019; Meyer, 2014; Pekrun and Perry, 2014; Li et al., 2020) and social interactions (Pekrun and Linnenbrink-Garcia, 2014b; Pekrun and Perry, 2014; Li et al., 2020) are acknowledged within teacher, peer, as well as school in general themes. Schoolwork and self are linked with achievement, epistemic and topic emotions. Furthermore, self, health, peers, school in general and other are triggers that may correspond to incidental emotions as external events. Next, the final 20 emotions words and five causes for the S2* were determined utilizing a two-step process as described below.
Step 1 – Selection of 20 emotion words
Using the survey data on emotions and causes, the 200 emotions were examined to determine the 20 most appropriate emotions for the S2*. Twenty words were chosen as it allowed for a relatively equal range of positive, negative, and neutral emotions. It also provided a suitable variety of options for students to choose from without getting distracted from the class lesson and the design on the application itself was easy to navigate.
The frequency analysis from Phase 1 provided the top 20 most frequent expressions of emotions (see Table 3), however, these word choices were not sufficient for inclusion in the application for four reasons. First, several words had very similar meanings (e.g., annoyed and irritated; and worried and nervous), and so were combined. Second, there was uncertainty as to whether some of the expressions should be categorized as emotional states (e.g., tired and hungry). Third, there were emotions that were often discussed in the literature as relevant to classroom contexts (e.g., jealous, surprised, and proud) (Scherer, 2005; Graham and Taylor, 2014; Shuman and Scherer, 2014) that were not as commonly recalled by participants in Phase 1, but still considered worthy of inclusion. Finally, it was important to ensure a balance of both positive and negative choices to ensure entries were not positively or negatively disposed.
Table 3. Top 20 frequent emotion responses.
A four-column table was constructed outlining (a) the top 20 most frequent words, highlighting words with similar meaning in similar colors; (b) whether or not the word listed was captured in the seven core literature reviews relevant to emotions and emotions in the classroom (Ekman, 1992; Weiner, 2007; Goetz et al., 2013; Graham and Taylor, 2014; Mazer et al., 2014; Pekrun and Linnenbrink-Garcia, 2014b; Shuman and Scherer, 2014); (c) whether or not the emotion word was captured in the top 20 emotions across all categories measured (i.e., all three schools combined, each individual school, gender, and age); and (d) other words written by students with similar meaning (e.g., “mad” and “pissed off” were both associated with angry). The remaining words were grouped into two categories: (a) additional emotional states for consideration; and (b) words not classified as emotional states – this included words that could not be fully deciphered.
Next a team of four researchers in the field of adolescent development performed an inter-judgment reliability (Gay et al., 2012) of the findings to ensure validity of the words chosen for the S2*. Each researcher was given the list of all 200 words generated by the students, a list of emotions discussed in the seven different reviews of the literature, related to the classroom, and the abovementioned spread sheet. Although the words tired and hungry were frequent responses from the students, collectively, the researchers decided not to include them given that they were the only two experiences that naturally result from a bodily response. Instead, the students would be guided to use them as a cause rather than an emotion (e.g., I am hungry, however I cannot eat in class therefore I am angry or frustrated). After reviewing the material, all researchers agreed on 18 out of 20 of their top 20 words for the computer-based application. The final two words were decided following a comprehensive discussion. The researchers agreed that it was important to utilize words that were recognizable, as well as a combination of words with both positive, negative, and neutral connotations. The final 20 emotion words were: happy, angry, sad, frustrated, nervous, bored, confused, excited, scared, calm, proud, lonely, interested, stressed, confident, jealous, embarrassed, surprised, relieved, and hopeful.
An important feature of the S2* is the ability to select more than one emotion and cause at a time. This option allowed for more flexibility in student responses and greater coverage of emotional experiences (e.g., irritated can be encompassed by selecting angry and frustrated). While enjoyment of learning is commonly cited in the literature (see Pekrun et al., 2002), only two students wrote the word enjoyment, and it was decided not to use it for the application as the concept of enjoyment could be comprised through a combination of happy and interested.
Step 2 – Selection of five triggers
The team of researchers from Step 1 completed an intercoder agreement (Creswell, 2014) on the eight themes derived from the coding process. An intercoder agreement entails employing other researchers to cross check a sample of the data to ensure qualitative reliability (Creswell, 2014). This provided us with a frequency response rate. The researchers were provided with 10% of randomized data. They were to read through the list of participant responses of triggers (i.e., causes for classroom emotions), along with the associated emotion, and select which of the eight themes they believed best described the trigger (i.e., teacher, peer, schoolwork, self, family/home, school in general, other). Coders were instructed to select one of the eight options; however, they could choose more than one only if they felt it absolutely necessary (e.g., “when I don’t understand the schoolwork,” while all coders felt this was triggered by schoolwork, several also categorized it as triggered by teacher or self). The following explanations were given to coders to define each trigger:
Teacher – anything related to the teacher (e.g., behavior, interaction, personality);
Peers – anything related to classmates, friends, bullying;
Schoolwork – anything related to assignments, classwork, specific classes;
Self – anything that is a direct response to oneself, or internalized;
Family/home – anything related to the home environment (e.g., parents, siblings, family members, events that specifically occurred at home);
Health – anything related to personal health, including tiredness and hunger (as noted in the trigger, not the emotion);
School in general – non-class/teacher/peer related causes within the school (e.g., policy, rules, lunchtime, etc.);
Other – anything that does not fit in another category
As displayed in Table 4, the most frequent triggers include: teacher, peer, schoolwork, and self. It was decided that these four triggers would appear on the application, along with other, which when selected would present a text box, allowing the participant to provide more detailed information, if they chose. Although school in general was more frequently reported than other, the majority of these triggers involved situations outside of the classroom environment (e.g., lunchtime, detention, assembly). Given that the application has been designed for use within a classroom setting, other was determined to be a more suitable option.
Table 4. Frequencies of emotion trigger.
The S2* (see Figure 1), is a tool that quickly captures the real-time dynamic of emotional states and causes of these states. This is a beneficial instrument to use in a classroom setting as it allows for instantaneous measurement, reducing recall bias. The use of this type of technology is very familiar to adolescents (Reid et al., 2009) and the emotion words listed on the application were derived from the language of the participants, which Creswell (2014) contends is advantageous.
Figure 1. Interface of the S2* emotion application.
Phase 2 trial of the S2* emotion application
In the second phase, the S2* was trialed in classroom settings to address the third research question and to test the usability of the application, to ensure all functions worked properly and to collect student feedback. This process allowed us to verify the functionality and accuracy of the S2* in collecting data. It also ensured that participants were familiar with the application before utilizing it in Phase 3 to examine emotions and causes for these experiences in low SES Year 10 classrooms.
The S2* emotion application was trialed in six Math and six English classes from one of the participating secondary schools from Phase 1. Only one of the three schools was selected due to substantial complexity of the study and to allow for rigorous confirmation of the instrument. Three criteria were used to select the school for the trial: (1) There had to be enough participating students in the same Math and English classes; (2) class schedules had to allow for alternating days of data collection and time to upload the data, recharge and sync the tablets; (3) Teachers had to consent to the research being collected in their classroom. While all three schools expressed interest, only one school met all three criteria.
Method
Participants
There were 89 Year 10 student participants (47% male, and 53% female) from one of the low SES schools who participated in Phase 1 as outlined above. Each student had the opportunity to participate twice, once in Math and once in English. The class sizes ranged from 11 to 22 students, and a total of 68% of all Year 10 students participated.
Instruments
The S2* emotion application
The S2* is a computer-based application, which was accessed on Acer tablets, independent of an internet connection. Participants logged in with an identification code and their date of birth. Once logged in, students selected the S2* function and were then taken to a screen (see Figure 1) which asked the question “How are you feeling right now?” presenting 20 different options (e.g., happy, angry, frustrated, bored). The question “What was the cause?” was asked at beneath the list of emotions with five entry options: Teacher, Peers, Schoolwork, Self, and Other, with Other allowing the option to provide more information in a text box. At the bottom of the screen there was a submit button. Once an entry was submitted the screen refreshed. Users were allowed to select the emotion or emotions they were currently feeling along with the cause or causes of that experience. This process is done quickly with an unlimited number of entries.
The S2* emotion application feedback form
The S2* emotion application Feedback Form was a short questionnaire obtaining the students’ gender, age and year level. The students answered the following three questions with yes or no responses, and the ability to explain in greater detail if answering yes: Did you have any problems with the S2* emotion application?; Were there any words you did not understand?; and Do you have any concerns about using this app in the future? Additionally, students were given the opportunity to provide extra feedback.
Procedure
Phase 2 preserved the same ethical approval as Phase 1 of this study and the S2* was trialed with students in their Math and English class (12 classes total, 6 Math and 6 English). At the start of the first class the researcher explained to the students how the S2* worked. Tablets and feedback forms were distributed to students. The students were given confidential identification codes with which to log in. The students had a few minutes to explore the S2* and ask any clarifying questions. Once the students were settled, class proceeded as normal and the students had the opportunity to use the S2* to capture their emotions, and causes for these emotions, throughout the remainder of class. There were no fixed time points, emotion entries were made at the discretion of the student. At the start of the second class, students entered the room, collected a tablet and logged in from their seats, requesting help if necessary. Students completed feedback forms at the end of their first class session, with the option to fill out an additional form after their second trial.
Results and discussion
Seventy-three of participating students (82%) submitted a feedback form, 38% male, 51% female, 11% unidentified, with the average age being 14.8 years. Ninety percent of participants returned their feedback forms without any concerns. Ten percent of students reported having a problem with the application, which consisted of the other button not working properly, the application crashing when trying to submit, and one of the students thought the application was boring. Only one student reported not understanding one word, however when followed up, this student did not have any concerns. Additional feedback was provided by 17.8% of students, primarily with positive responses including statements such as “the app is really easy to use,” “it was absolutely amazing to have this beside me as it helped let go of all my emotions.” The only critical responses were requests to add more emotions. All technical issues with the S2* were amended by the web developer prior to using the application in Phase 3.
This trial provided the opportunity for students to familiarize themselves with the technology and report any errors with the application. The trial of the S2* successfully demonstrated this tool as a beneficial instrument to capture emotions and causes for emotional experiences in a Year 10 classroom.
Phase 3 measuring real-time emotions and their causes
In the third phase, we used the S2* to answer the fourth research question: What are the real-time emotions and causes for these emotions of disadvantaged Year 10 Math and English students? To ensure that the participants were familiar and comfortable with using the S2* we conducted Phase 3 with the same students from Phase 2.
Method
Participants
The second study involved 81 of the 89 Year 10 participants from Phase 2. Participants included 43% males, 57% females, ranging from 14 to 16 years old (M = 15.09 years). This comprised 57% of the 143 actively enrolled Year 10 students, all who were invited to participate at the start of the semester. All Year 10 students were divided by the school into six cohorts, with each cohort attending the same Math and English classes together. Only one of these cohorts was streamed for mathematic ability.
Instruments
The S2* emotion application
The S2* as described above was used to capture the real-time emotions and causes for emotions.
Procedure
Phase 3 of the study took place over the fourth term of the academic calendar which consists of 8 weeks. The researcher collected data on real-time emotions and causes for those experiences from a total of 36 class sessions. This was achieved by collecting data at three different time-points from each of the 12 (six cohorts of both Math and English) Year 10 classes.
Prior to use, tablets were synchronized to the same server to ensure they all displayed the same accurate time. Just before the start of class, the researcher turned on all tablets and opened the S2*, so the login screen was displayed. As students entered class, they collected their tablets and logged in to the S2* using a unique identification number. During class time, students were (at any time) able to report their emotions and causes for these emotions on their S2*, while class proceeded as normal. The researcher was discreetly present during all class sessions, providing assistance if needed. Data were collected during regular Math and English lessons. The majority of the lessons were conducted in the same room at each time point; however, some lessons were moved to familiar computer laboratories when necessary for the classroom lesson activity. There was no active engagement from the researcher with the students during class sessions; lessons were authentically conducted during class time. The confidentiality of participants was maintained at all times.
Results and discussion
Of the 81 participants, 72 were included in this analysis. Nine students were excluded from the analysis because they did not report emotions in both their Math and English classes. Only students who reported emotions in both their Math and English classes were included in analysis to ensure equal representation. While the number of entries varied, all students reported at least one emotion in all 36 Math and English classes (i.e., six different groups of students over three Math lessons and three English lessons). The analysis focused on the sum of the reported 20 emotions, based on the five possible causes (teacher, peers, self, schoolwork, and other). The percentage of entries per cause (i.e., submitted emotion and cause of emotion, as reported by each student) were calculated by dividing the total number of entries per cause (i.e., teacher, peers, self, schoolwork, and other), by the sum of all entries (n = 2,024). Table 5 presents a summary of the number of entries for each cause, as well as the calculated percentage. Peers, schoolwork and self were the most frequently reported causes for emotions, followed by teacher and other.
Table 5. Total entries per cause.
Student emotions caused by peer accounted for 27.77% of all reported emotions and was the most frequently reported cause. As seen in Figure 2A, the most commonly reported peer induced emotions include happy at 26%, followed by calm at 13%, and bored at 12%. Emotions caused by schoolwork accounted for 24.06% of all reported emotions. As seen in Figure 2B, the most frequently reported schoolwork-provoked emotions included bored at 33%, followed by happy, stressed and calm all at 9% and frustrated at 8%. Self-caused emotions accounted for 24.01% of all reported emotions. As seen in Figure 2C, the most frequently reported self-caused emotions include happy at 24%, followed by calm at 20% and bored at 14%. Emotions caused by teacher accounted for 14.33% of all reported emotions. As seen in Figure 2D, the most frequent reported teacher induced emotions include bored at 32%, followed by frustrated at 14% and angry at 8%. Finally, Other induced emotions accounted for 9.83% of all reported emotions. As seen in Figure 2E, the most frequently reported other induced emotions include sad and frustrated at 13%, followed by bored at 12%, and happy and stressed at 9%.
Figure 2. (A) Sum of emotion caused by peers. (B) Sum of emotion caused by schoolwork. (C) Sum of emotion caused by self. (D) Sum of emotion caused by teacher. (E) Sum of emotion caused by other. (F) Frequency of all reported emotions.
Table 6 presents the top 10 most frequently reported emotions and causes for those emotions, out of all 100 possible emotion and cause combinations (i.e., five possible causes × twenty possible emotions = 100 possible combinations).
Table 6. Top 10 most frequent emotions and cause for emotion.
While the cause of the emotion provides context and insight to the students’ classroom experience, the frequency of each emotion also provides fruitful insight. These data are displayed in Figure 2F, revealing that the most commonly reported emotions include bored, happy, and calm.
General discussion
The emotional experiences and causes for these experiences of disadvantaged adolescents have been explored and measured in the present study and a new tool has been developed. Phase 1 provided greater clarity on the emotional experiences of young people in three schools in low socioeconomic communities allowing for the development and trial of the S2* emotion application in Phase 2. Phase 3 utilized the S2* to examine real-time emotions and causes for emotions of Year 10 students across Math and English classes at multiple time points over an entire academic term, contributing to the existing literature.
The findings reveal that boredom was the most commonly reported emotion amongst this sample of participants, accounting for 20% of all entries. Schoolwork and teacher expectedly were the top two causes for boredom. Noteworthy results indicate that boredom was one of the three most commonly reported emotions provoked by all possible causes (i.e., teacher, schoolwork, peers, self, and other). These findings are in line with other research, which reports boredom as a commonly reported emotion in the classroom setting (Pekrun et al., 2002, 2014; Goetz et al., 2007, 2019, 2020; Goetz and Hall, 2014; Moeller et al., 2020). Goetz et al. (2007) found ninth grade students reported boredom nearly half of class time on average. Middle school students have reported the experience of boredom 32% of class time (Larson and Richards, 1991). Likewise, a study conducted with 11th graders in math classes reported some level of boredom 58% of the time (Nett et al., 2011). More recently, Moeller et al. (2020) found that high school students report feelings of boredom at school 69.51% of the time. The results from this present study provide further insight to the antecedents of boredom in the classroom (i.e., teacher, peers, schoolwork, self, and other), which is lacking in educational research (Nett et al., 2011; Goetz and Hall, 2014).
Although intensity of emotion was not measured in this study, the high reports of boredom imply that these students may be considerably lacking a connection to the classroom. These findings have fundamental implications on student success. Students need to be emotionally invested, and engaged, to ensure their learning is meaningful and understood (Immordino-Yang and Damasio, 2016). The implications of these findings suggest a strong lack of motivation amongst participants, which is critical to their academic success. These results suggest a need for teachers to develop stimulating lesson plans, and work with students to find ways to motivate and engage them in the learning process. It may be beneficial for teachers to collect feedback and work with their students to minimize classroom boredom.
While the second most frequent reported emotion is happy, it is essential to focus on the context. Nearly as many students reported happiness, provoked by their peers (7.16%), as boredom provoked by schoolwork (7.86%). Appraisal theory (Lazarus, 1991), along with the theoretical models related to boredom as discussed above, imply that emotions in the classroom transpire from students’ cognitive evaluations of the current situation. Furthermore, Goetz and Hall (2014) contend that boredom presumably causes students to shift their attention toward a more stimulating interest. Therefore, based on the findings of the present study, it may be inferred that students find more interest in their classroom peers than the teacher and schoolwork. Supportive reasoning for this proposition is reinforced by limited entries of interest induced by the teacher and schoolwork, combined making up just 1.04% of all entries, which is less than students’ reported interest induced by peers and self, combined (1.6%).
This suggestion is further supported by the results demonstrating that the greatest cause for all reported emotions in this study was peers. The majority of students find happiness and a sense of calmness with their peers in the classroom environment. However, participants also reported a notable response of boredom, frustration, anger, and stress instigated from their peers. These findings are of great concern, given the large impact that social emotions can have on student engagement (Pekrun and Linnenbrink-Garcia, 2014b) and cognition (Li et al., 2020). Student engagement, as well as student motivation, have been attributed to the social environment of the classroom (Patrick et al., 2007). Therefore, it is essential that teachers conduct lessons that engage students emotionally and minimize negative peer interactions whenever possible.
Teachers in disadvantaged schools have reported that it is difficult to create a caring classroom atmosphere due to challenging family backgrounds, and a lack of social and emotional skills amongst the students (Bower et al., 2015). In one study on making a difference in high poverty schools and the role of leadership, an Australian principal stated, “In the first couple of years I had to forget about curriculum. I mean literally I had to get kids sitting in classrooms. And that was an enormous task.” (Ylimaki et al., 2007, p. 373). This study supports the findings of the present study and suggests that teachers in disadvantaged communities are unlikely to be successful in teaching academic curriculum without first gaining the interest of student peer groups. Furthermore, evidence suggests an association between problematic behavior and schools located in disadvantaged communities (Gottfredson, 2011). Given the responses from participants in this study, along with validation within the literature regarding disadvantaged youths, it may be assumed that the Year 10 students in the present study were disengaged and unmotivated by their teachers and schoolwork, therefore seeking out amusement from their peers.
Interestingly, the present study revealed that the top two emotions reported as caused by the students’ self were happy and calm. DeCuir-Gunby and Williams-Johnson (2014) contend that “the key issue that impacts the nature of the development of the self is the relationship between the self and others. It is within these relationships that emotions are experienced and expressed” (p. 541). Therefore, given the context of this study, it would make sense that self-induced reports of happy and calm are likely to be a result of the self in relation to the classroom environment. Keeping context in mind, while a student might have selected happy because they answered a question correctly, it is also possible that the feeling of happiness was caused because the student made one of their friends laugh.
This study provides insights to classroom emotions caused by a variety of antecedents. Emotions triggered by things other than the teacher, schoolwork, peers and self made up 9.83% of student entries. The highest reported emotions resulting from other causes include sad, frustrated and bored. Some of the frequent responses, provided with the option to enter ‘more information’ include family/home, as well as being tired, hungry, and not feeling well. While literature is minimal, incidental emotions and moods in the classroom have been associated with student motivation and engagement (Pekrun and Linnenbrink-Garcia, 2014b). It may be postulated that incidental emotions and moods have a more significant role in disadvantaged classrooms, due to the greater quantity of challenges at home and in the community, yet more research is needed to foster the potential academic and life-long success of these young people.
The implications of this research are two-fold. Theoretically, a new tool has been developed to measure real-time emotions and causes for these emotions. This tool has the potential to be used in similar school or related contexts. In addition, these findings add to the call for further research on classroom emotions that present collective evidence toward a meta-analysis, which will inform educators of valid strategies on how to best channel student emotions (Pekrun and Schutz, 2007; Pekrun and Linnenbrink-Garcia, 2014a). Feyzi Behnagh (2019) argues that “understanding when certain emotions are expressed by students, who shows those emotions, and what triggers them, could help the teacher intervene in time and deploy effective tactics to address negative emotions that could lead to disengagement and hinder learning” (p. 313).
While the breadth of knowledge on achievement emotions has grown substantially over the past 20 years, the findings of this study provide an extension to the literature around classroom emotional experiences beyond that of achievement emotions. This includes emotions directly linked to peers, the teacher, self and further recognition that there are a variety of emotions triggered by “other” unknown factors that still have an influence on the student. Furthermore, it provides greater insights to emotions in the secondary classroom, as well as an initial knowledge around these experiences for disadvantaged adolescents. This study implies a greater need to address boredom in the classroom, due to the frequent reports of boredom caused by schoolwork. This suggests a strong need for emotional engagement in disadvantaged classrooms. An expansion of theoretical frameworks, inclusive of all types of emotions, and causes for emotions is necessary to demonstrate to educators the core link between emotions and learning, as we know that the quality of learning is influenced by emotions (Goetz et al., 2006; Immordino-Yang and Damasio, 2016).
Moreover, the S2* provides a new form of measurement that can be used by researchers to gain a greater understanding of classroom emotions of different antecedents. This tool can be used in future studies to build on these theoretical frameworks. Through the use of the S2*, researchers are presented with a new tool to utilize in investigations of real-time emotional experiences and causes for these experiences in the academic setting. Future studies may consider using this tool to examine correlations between emotions and student–teacher relationships, achievement, and self-efficacy in domain specific classes.
Finally, worldwide, we are still learning how to best navigate educating our students during the COVID-19 pandemic. There have been shifts to remote or hybrid learning, adjustments to classrooms or school-wide policies including social distancing and mask wearing. While the evidence is rapidly growing, recent research on social and emotional wellbeing suggests a decline in wellbeing and an increase in mental health issues due to restrictions initiated from the pandemic (Ellis et al., 2020; Wang et al., 2020; Yang et al., 2020; Berger et al., 2021; Breaux et al., 2021; De France et al., 2021; Hu and Qian, 2021; Hough et al., 2021; Magson et al., 2021). Knudson and Cantor (2020) argue that as school systems continue to adapt to the challenges inflicted from COVID-19, they “can position students for academic success by prioritizing physical and emotional wellbeing; social, emotional, and cognitive development; and academic supports and strategies, all combined in a web of consistent and caring relationships” (p.11). Utilizing the S2* now, either in person or during remote learning could be a powerful tool in further supporting our students during these unprecedented times.
Limitations and directions for future research
While this study has provided greater insights to the real-time emotional experiences of disadvantaged adolescents it is not without limitations. Firstly, while students were briefed on how to use the S2* they were not forced at specific time to report their experiences. For some, participants may have made a number of entries, while others may have rarely entered anything, or only made entries if their emotional experiences were quite intense. Therefore, it was assumed that the emotions reported by students provided a general overview of students’ experiences in class regardless of how many times they submitted an entry.
Secondly, students were allowed to enter as many emotions and causes for emotions as they deemed appropriate. This meant interpretation relied on the discretion of the researcher. While excessive entries (five or more emotions reported at a time) were removed from the data, assumptions were still made. For example, if a student reported they were Angry and Frustrated, because of the Teacher and Schoolwork, this entry would account for each emotion to be triggered by each cause. Future research using a similar computer-based application may want to consider only allowing one emotion and/or one cause per submission to minimize postulation. Researchers may also want to control for the time-points that students enter emotions. While this option allows for more straight forward approaches to analysis, this may also force emotional responses, rather than allowing for genuine reports. Finally, Phase 3 was also conducted in the final term of the school year, therefore, it is plausible that the timing influenced the students’ experiences (i.e., anticipation of summer holidays, end of year events, exams, etc.). Future studies would benefit from investigating student emotional experiences throughout the school year for comparison.
Conclusion
The S2* provides a quick and easy method of capturing both emotional experiences and the causes for these experiences in a classroom context. Given the continual advancements in technology and use of technology in the classroom, the S2* provides an exciting opportunity for students to express their emotions in a safe and confidential space through the use of a familiar device. This instrument has the potential to make a progressive contribution to the field by adding to the limited tools currently available to effectively measure emotions in an education context. The findings from the present study, in collaboration with future research using the S2* are instrumental to building theoretical frameworks and informing successful teaching practices.
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors upon reasonable request.
Ethics statement
The studies involving human participants were reviewed and approved by Behavioural and Social Sciences Ethical Review Committee (BSSERC) at The University of Queensland and Queensland Department of Education and Training (QDET). Written and informed consent from the schools’ principals and participants were obtained. Participants’ legal guardian/next of kin were notified of the study and able to withdraw at any time.
Author contributions
This work was part of AB’s doctoral thesis, which was supervised by AC (primary) and JB (secondary). AB was responsible for the conceptualization, project administration, and data collection. AC and JB assisted AB with project design, methodology, and analysis. The original draft preparation was prepared by AB with reviewing and editing from all three authors for the final manuscript. All authors intellectually contributed to this work for conceptualization and development of the S2* and for submission of publication.
Funding
This research was supported by an Australian Government Research Training Program Scholarship. This work was also supported by an Australian Research Council (ARC) Special Research Initiative (SRI) Grant: ARC-SRI Science of Learning Research Centre (project number SR120300015).
Acknowledgments
The authors would like to acknowledge all the students and school staff that took part in, and supported this study. AB would also like to acknowledge the support of their supervisory team at The University of Queensland and Felicia Goh for assisting with data collection. The authors are further grateful for the web developers who assisted with the development of the S2*.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feduc.2022.959673/full#supplementary-material
Abbreviations
SES, socioeconomic status; ESM, experience sampling method; ACARA, Australian Curriculum Assessment and Reporting Authority; ICSEA, Index of Community Socio Educational Advantage.
References
Australian Curriculum Assessment and Reporting Authority (2015). My School: Guide to understanding ICSEA (Index of Community Socioeducational Advantage) values, from 2013 onwards. Available online at: https://www.myschool.edu.au/media/1067/guide_to_understanding_icsea_values.pdf (accessed August 14, 2018).
Australian Curriculum Assessment and Reporting Authority (2016). My School. https://www.myschool.edu.au/ (accessed August 14, 2018).
Australian Curriculum Assessment and Reporting Authority (2017). My School. Available online at: https://www.myschool.edu.au/ (accessed July 02, 2018).
Berger, E., Jamshidi, N., Reupert, A., Jobson, L., and Miko, A. (2021). Review: the mental health implications for children and adolescents impacted by infectious outbreaks – a systematic review. Child Adolesc. Ment. Health 26, 157–166. doi: 10.1111/camh.12453
Bower, J., van Kraayenoord, C., and Carroll, A. (2015). Building social connectedness in schools: Australian teachers’ perspectives. Int. J. Educ. Res. 70, 101–109. doi: 10.1016/j.ijer.2015.02.004
Breaux, R., Dvorsky, M. R., Marsh, N. P., Green, C. D., Cash, A. R., Shroff, D. M., et al. (2021). Prospective impact of COVID-19 on mental health functioning in adolescents with and without ADHD: protective role of emotion regulation abilities. J. Child Psychol. Psychiatry 62, 1132–1139. doi: 10.1111/jcpp.13382
Bronfenbrenner, U. (1979). Contexts of child rearing: problems and prospects. Am. Psychol. 34, 844–850. doi: 10.1037/0003-066X.34.10.844
Bronfenbrenner, U. (1994). Ecological models of human development. Int. Encyclopedia Educ. 3, 37–43.
Chen, P., and Vazsonyi, A. T. (2013). Future orientation, school contexts, and problem behaviors: a multilevel study. J. Youth Adolesc. 42, 67–81. doi: 10.1007/s10964-012-9785-4
Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches, 4th Edn. Thousand Oaks, CA: Sage Publications, Inc.
De France, K., Hancock, G. R., Stack, D. M., Serbin, L. A., and Hollenstein, T. (2021). The mental health implications of COVID-19 for adolescents: follow-up of a four-wave longitudinal study during the pandemic. Am. Psychol. 77, 85–99. doi: 10.1037/amp0000838
DeCuir-Gunby, J. T., and Williams-Johnson, M. R. (2014). “The influence of culture on emotions,” in International Handbook of Emotions in Education, eds R. Pekrun and L. Linnenbrink-Garcia (New York, NY: Routledge), 539–557.
Ekman, P. (1992). An argument for basic emotions. Cogn. Emot. 6, 169–200. doi: 10.1080/02699939208411068
Ellis, W. E., Dumas, T. M., and Forbes, L. M. (2020). Physically isolated but socially connected: psychological adjustment and stress among adolescents during the initial COVID-19 crisis. Can. J. Behav. Sci. 52, 177–187. doi: 10.1037/cbs0000215
Feyzi Behnagh, R. (2019). Emotions and emotional energy in the science classroom: a discussion of measurement. Cult. Stud. Sci. Educ. 15, 307–315. doi: 10.1007/s11422-019-09929-8
Frenzel, A. C., and Stephens, E. J. (2013). “Emotions,” in Emotion, Motivation, and Self-Regulation: A Handbook for Teachers, eds N. C. Hall and T. Goetz (Bingley: Emerald Group Publishing Limited), 1–56.
Gay, L. R., Mills, G. M., and Airasian, P. (2012). Educational Research: Competencies for Analysis and Applications, 10th Edn. Upper Saddle River, NJ: Pearson.
Goetz, T., Bieg, M., and Hall, N. C. (2016). “Assessing academic emotions via the experience sampling method,” in Methodological Advances in Research on Emotion and Education, eds M. Zembylas and P. Schutz (Berlin: Springer International Publishing), 245–258. doi: 10.1007/978-3-319-29049-2_19
Goetz, T., Frenzel, A. C., Pekrun, R., Hall, N. C., and Lüdtke, O. (2007). Between- and within- domain relations of students? academic emotions. J. Educ. Psychol. 99, 715–733. doi: 10.1037/0022-0663.99.4.715
Goetz, T., and Hall, N. C. (2014). “Academic boredom,” in International Handbook of Emotions in Education, eds R. Pekrun and L. Linnenbrink-Garcia (New York, NY: Routledge), 311–330.
Goetz, T., Hall, N. C., and Krannich. (2019). “Boredom,” in The Cambridge Handbook of Motivation and Learning, eds A. K. Renninger and S. E. Hidi (Cambridge: Cambridge University Press), 465–489. doi: 10.1017/9781316823279
Goetz, T., Keller, M. M., Lüdtke, O., Nett, U. E., and Lipnevich, A. A. (2020). The dynamics of real-time classroom emotions: appraisals mediate the relation between students’ perceptions of teaching and their emotions. J. Educ. Psychol. 112, 1243–1260. doi: 10.1037/edu0000415
Goetz, T., Lüdtke, O., Nett, U. N., Keller, M. M., and Lipnevich, A. A. (2013). Characteristics of teaching and students’ emotions in the classroom: investigating differences across domains. Contemp. Educ. Psychol. 38, 383–394. doi: 10.1016/j.cedpsych.2013.08.001
Goetz, T., Pekrun, R., Hall, N., and Haag, L. (2006). Academic emotions from a social-cognitive perspective: Antecedents and domain specificity of students’ affect in the context of Latin instruction. Br. J. Educ. Psychol. 76, 289–308. doi: 10.1348/000709905X42860
Gottfredson, D. C. (2011). “Schools and delinquency,” in The Oxford Handbook of Juvenile Crime and Juvenile Justice, eds B. C. Feld and D. M. Bishop (New York, NY: Oxford University Press), 203–225.
Graham, S., and Taylor, A. Z. (2014). “An attributional approach to emotional life in the classroom,” in International Handbook of Emotions in Education, eds R. Pekrun and L. Linnenbrink-Garcia (New York, NY: Routledge), 580–606.
Hektner, J. M., Schmidt, J. A., and Csikszentmihalyi, M. (eds) (2007). Experience Sampling Method: Measuring the Quality of Everyday Life. Thousand Oaks, CA: Sage. doi: 10.4135/9781412984201
Hough, H. J., Witte, J., Wang, C., and Calhoun, D. (2021). Evidence-based practices for assessing students’ social and emotional well-being. Los Angeles, CA: Policy Analysis for California Education.
Hu, Y., and Qian, Y. (2021). COVID-19 and adolescent mental health in the United Kingdom. J. Adolesc. Health 69, 26–32. doi: 10.1016/j.jadohealth.2021.04.005
Immordino-Yang, M. H. (2016). “Why Emotions are integral to learning,” in Emotions, Learning, and the Brain: Exploring the Educational Implications of Affective Neuroscience, ed. M. H. Immordino-Yang (New York, NY: W. W. Norton & Company Ltd.), 17–24.
Immordino-Yang, M. H., and Damasio, A. (2016). “We feel, therefore we learn: The relevance of affective and social neuroscience to education,” in Emotions, Learning, and the Brain: Exploring the Educational Implications of Affective Neuroscience, ed. M. H. Immordino-Yang (New York, NY: W. W. Norton & Company Ltd.), 27–42.
Immordino-Yang, M. H., and Sylvan, L. (2016). “Admiration for virtue: Neuroscientific perspectives on a motivation emotion,” in Emotions, Learning, and the Brain: Exploring the Educational Implications of Affective Neuroscience, ed. M. H. Immordino-Yang (New York, NY: W. W. Norton & Company Ltd.), 165–179.
Klem, A. M., and Connell, J. P. (2004). Relationships matter: linking teacher support to student engagement and achievement. J. School Health 74, 262–273. doi: 10.1111/j.1746-1561.2004.tb08283.x
Knudson, J., and Cantor, P. (2020). Ensuring whole-child well-being as a foundation for learning: relationships, routines, and resilience in the time of COVID-19. Los Angeles, CA: Policy and Practice Brief.
Lamb, S., and Huo, S. (2017). Counting the costs of lost opportunity in Australian education (No. 02/2017). Melbourne: Victoria University.
Lamb, S., Huo, S., Walstab, A., Wade, A., Maire, Q., Doecke, E., et al. (2020). Educational opportunity in Australia 2020: Who succeeds and who misses out. Melbourne: Centre for International Research on Education Systems, Victoria University, for the Mitchell Institute.
Lamb, S., Jackson, J., Walstab, A., and Huo, S. (2015). Educational opportunity in Australia 2015: Who succeeds and who misses out. Melbourne: Mitchell Institute.
Larson, R. W., and Richards, M. H. (1991). Boredom in the middle school years: Blaming schools versus blaming students. Am. J. Educ. 99, 418–443. doi: 10.1086/443992
Lazarus, R. (1991). Progress on a cognitive-motivational-relational theory of emotion. Am. Psychol. 46, 819–834. doi: 10.1037/0003-066X.46.8.819
Li, L., Gow, A. D. I., and Zhou, J. (2020). The role of positive emotions in education: a neuroscience perspective. Mind Brain Educ. 14, 220–234. doi: 10.1111/mbe.12244
Magson, N. R., Freeman, J. Y. A., Rapee, R. M., Richardson, C. E., Oar, E. L., and Fardouly, J. (2021). Risk and protective factors for prospective changes in adolescent mental health during the COVID-19 pandemic. J. Youth Adolesc. 50, 44–57. doi: 10.1007/s10964-020-01332-9
Mazer, J. P., McKenna-Buchanan, T. P., Quinlan, M. M., and Titsworth, S. (2014). The dark side of emotion in the classroom: emotional processes as mediators of teacher communication behaviors and student negative emotions. Commun. Educ. 63, 149–168. doi: 10.1080/03634523.2014.904047
McConney, A., and Perry, L. B. (2010). Socioeconomic status, self-efficacy, and mathematics achievement in Australia: a secondary analysis. Educ. Res. Policy Pract. 9, 77–91. doi: 10.1007/s10671-010-9083-4
Meyer, D. K. (2014). “Situating emotions in classroom practices,” in International Handbook of Emotions in Education, eds R. Pekrun and L. Linnenbrink-Garcia (New York, NY: Routledge), 458–472.
Moeller, J., Brackett, M. A., Ivcevic, Z., and White, A. E. (2020). High school students’ feelings: discoveries from a large national survey and an experience sampling study. Learn. Instr. 66, 101–301. doi: 10.1016/j.learninstruc.2019.101301
Nett, U. E., Goetz, T., and Hall, N. C. (2011). Coping with boredom in school: an experience sampling perspective. Contemp. Educ. Psychol. 36, 49–59. doi: 10.1016/j.cedpsych.2010.10.003
Patrick, H., Ryan, A. M., and Kaplan, A. (2007). Early adolescents’ perceptions of the classroom social environment, motivational beliefs, and engagement. J. Educ. Psychol. 99, 83–98. doi: 10.1037/0022-0663.99.1.83
Pekrun, R. (2006). The control-value theory of achievement emotions: assumptions, corollaries, and implications for educational research and practice. Educ. Psychol. Rev. 18, 315–341. doi: 10.1007/s10648-006-9029-9
Pekrun, R. (2016). “Using self-report to assess emotions in education. Introduction to methodological advances in research on emotion in education,” in Methodological Advances in Research on Emotion and Education, eds M. Zembylas and P. A. Schutz (Switzerland, AG: Springer International Publishing), 43–54. doi: 10.1007/978-3-319-29049-2_4
Pekrun, R., and Bühner, M. (2014). “Self-report measures of academic emotions,” in International Handbook of Emotions in Education, eds R. Pekrun and L. Linnenbrink-Garcia (New York, NY: Routledge), 561–579.
Pekrun, R., Goetz, T., Titz, W., and Perry, R. P. (2002). Academic emotions in students? self- regulated learning and achievement: a program of qualitative and quantitative re- search. Educ. Psychol. 37, 91–105. doi: 10.1207/S15326985EP3702_4
Pekrun, R., Hall, N. C., Goetz, T., and Perry, R. P. (2014). Boredom and academic achievement: testing a model of reciprocal causation. J. Educ. Psychol. 106, 696–710. doi: 10.1037/a0036006
Pekrun, R., and Linnenbrink-Garcia, L. (2014a). “Conclusions and future directions,” in International Handbook of Emotions in Education, eds R. Pekrun and L. Linnenbrink-Garcia (New York, NY: Routledge), 659–675. doi: 10.4324/9780203148211
Pekrun, R., and Linnenbrink-Garcia, L. (2014b). “Introduction to emotions in education,” in International Handbook of Emotions in Education, eds R. Pekrun and L. Linnenbrink-Garcia (New York, NY: Routledge), 1–10.
Pekrun, R., Muis, K. R., Frenzel, A. C., and Goetz, T. (2018). Emotions at school. New York, NY: Routledge. doi: 10.4324/9781315187822
Pekrun, R., and Perry, R. P. (2014). “Control-value theory of achievement emotions,” in International Handbook of Emotions in Education, eds R. Pekrun and L. Linnenbrink-Garcia (New York, NY: Routledge), 120–141.
Pekrun, R., and Schutz, P. A. (2007). “Where do we go from here? Implications and future directions for inquiry on emotions in education,” in Emotion in Education, eds P. A. Schutz and R. Pekrun (San Diego, CA: Academic Press), 313–331. doi: 10.1016/B978-012372545-5/50019-8
Perry, L. B. (2018). Educational inequality in Australia. How unequal? Insights into inequality. Committee for Economic Development of Australia. Melbourne: CEDA.
Platell, M., Cook, A., Fisher, C., and Martin, K. (2019). Stopped, delayed or discouraged: What are the barriers for adolescents fully engaging in the mental health system? Int. J. Ment. Health Addict. 18, 1264–1293. doi: 10.1007/s11469-019-00135-9
Prokofieva, V., Kostromina, S., Polevaia, S., and Fenouillet, F. (2019). Understanding emotion related processes in classroom activities through functional measurements. Front. Psychol. 10:2263. doi: 10.3389/fpsyg.2019.02263
Reid, S. C., Kauer, S. D., Dudgeon, P., Sanci, L. A., Shrier, L. A., and Patton, G. C. (2009). A mobile phone program to track young people’s experiences of mood, stress and coping: Development and testing of the mobile type program. Soc. Psychiatry Psychiatr. Epidemiol. 44, 501–507. doi: 10.1007/s00127-008-0455-5
Scherer, K. R. (2005). What are emotions? And how can they be measured? Soc. Sci. Inf. 44, 695–729. doi: 10.1177/0539018405058216
Shuman, V., and Scherer, K. R. (2014). “Concepts and structures of emotion,” in International handbook of emotions in education, eds R. Pekrun, and L. Linnenbrink-Garcia (New York, NY: Routledge), 13-35.
State of Victoria, Department of Education and Early Childhood Development [DEECD] (2010). Pathways to re-engagement through flexible learning options: A policy direction for consultation. Footscray: DEECD.
Stone, A. A., Schwartz, J. E., Neale, J. M., Shiffman, S., Marco, C. A., Hickcox, M., et al. (1998). A comparison of coping assessed by ecological momentary assessment and retrospective recall. J. Pers. Soc. Psychol. 74, 1670–1680. doi: 10.1037/0022-3514.74.6.1670
Uink, B., Modecki, K. L., Barber, B. L., and Correia, H. M. (2018). Socioeconomically disadvantaged adolescents with elevated externalizing symptoms show heightened emotion reactivity to daily stress: an experience sampling study. Child Psychiatry Hum. Dev. 49, 741–756. doi: 10.1007/s10578-018-0784-x
Wang, C., Pan, R., Wan, X., Tan, Y., Xu, L., McIntyre, R. S., et al. (2020). A longitudinal study on the mental health of general population during the COVID-19 epidemic in China. Brain Behav. Immun. 87, 40–48. doi: 10.1016/j.bbi.2020.04.028
Weiner, B. (2007). “Examining emotional diversity in the classroom: An attribution theorist considers the moral emotions,” in Emotion in Education, eds P. A. Schutz and R. Pekrun (San Diego, CA: Academic Press), 75–88. doi: 10.1016/B978-012372545-5/50006-X
Yang, D., Swekwi, U., Tu, C.-C., and Dai, X. (2020). Psychological effects of the COVID-19 pandemic on Wuhan’s high school students. Child. Youth Serv. Rev. 119, 105634–105634. doi: 10.1016/j.childyouth.2020.105634
Keywords: adolescent emotion, real-time emotion, emotion measurement, causes for emotion, classroom emotions
Citation: Bourgeois A, Carroll A and Bower J (2022) Exploring the emotions of disadvantaged adolescents in the classroom: Development of the S2* emotion application. Front. Educ. 7:959673. doi: 10.3389/feduc.2022.959673
Received: 02 June 2022; Accepted: 18 July 2022;
Published: 05 August 2022.
Edited by:
José Antonio Marín-Marín, University of Granada, SpainReviewed by:
Antonio José Moreno Guerrero, University of Granada, SpainJosé María Álvarez Martínez-Iglesias, University of Murcia, Spain
Copyright © 2022 Bourgeois, Carroll and Bower. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Amanda Bourgeois, a.bourgeois@uq.edu.au