Effective Approach for Raising the Efficiency of Happiness Essay Example

studies benefit consulting practices like clinical psychology, psychiatry, and life coaching [3],[4],[5].

The text examines various elements that affect human happiness, including finances, employment, social and cultural factors, and social media. It also investigates the impact of technology on happiness and identifies both its advantages and disadvantages. Ultimately, a combination of these factors appears to contribute to the highest levels of human happiness (figure 1) [6],[7],[8],[9].

Figure 1: Factors that contribute to the happiness of individuals

Related Work

There are numerous publications on the topic of happiness, with some focusing on education and others on areas such as business and health. This section highlights several of these works that may be beneficial for the approach being implemented. See below for a list of these works:

Peter Hills and Michael Argyle (2002) developed the Oxford Happiness Questionnaire (OHQ), incorporating elements from the Oxford Happiness Inventory (OHI). The OHI comprises 29 elements, with four options for each. The OHQ adopts a similar approach, presenting each element as a statement to rate on a six-point Likert scale. This revised tool is both robust and user-friendly, making it applicable in various contexts. Sequential analysis of the OHQ factors identified a single higher order factor, suggesting that well-being measurement is one-dimensional [10].

Peter Michael Hills and Argyle (2008) conducted a study that analyzed the way young individuals with autism perceive semi-dynamic facial sense and visual language. The study involved comparing the abilities of 25 individuals with autism spectrum disorder and 25 typically developing individuals in recreating sequences of four dynamic emotional facial expressions (happiness, sadness, disgust, and fear) and four spoken words (with bath, thumb, and watch), using six images related to a video sequence. The

results showed that typically developing adults were better at recreating the dynamic properties of emotional expressions compared to facial expressions, whereas the autism group exhibited the opposite pattern [11].

Peter Sheridan Dodds and Christopher M. Danforth (2009) stressed the significance of measuring the type and intensity of emotions on a population level. They want to comprehend the how, when, and why behind people's emotions in order to facilitate the development of improved public policies, successful organizations, and a deeper understanding of economic and social phenomena. In this study, they integrate direct human evaluation words to assess levels of happiness across a wide range of textual sources such as song titles and lyrics, weblogs, and addresses from the State of the Union. This method is clear, refined, and capable of efficiently analyzing large amounts of text on the Internet, surpassing previous approaches based on general categorization [12].

In their 2011 study, Nader Soleimani and Elaheh Tebyanian utilized two instruments for collecting data. They used a self-designed questionnaire to measure the creativity and happiness of the school. The data collected was then analyzed using correlation techniques, T-tests, ANOVA, and multiple regression. The study concluded that principals have the ability to introduce new ideas and innovations to overcome boundaries, creating a happy environment for students. The study also found that creative thinking led to the breaking of rules and traditions, emphasizing proactive planning. Ultimately, the results showed a positive relationship between the principal's creativity and academic happiness [13].

The article by Vezzetti E. and Marcolin F. (2012) utilizes an algorithm or manual placement on the faces to extract important points. These points are then used to calculate measurements and extract geometric

features for the study. The goal of the article is to collect and explain these features in order to create a structured database of potential users for parameters and features. It begins by defining and contextualizing facial marks, implementing various morphometric measurements, and presenting some results. Finally, the article compares the most important measurements in order to select the best method for a specific application of face recognition [14].

Aleix Martinez and Shichuan Du (2012) developed a model that incorporates various continuous spaces, enabling the recognition of multiple emotions by combining facial distances in a linear manner. The dimensions of these distances can be customized as needed. Instead of focusing on recognition, this model emphasizes the precise classification of facial expressions by detecting specific facial markers. Additionally, it provides an overview of relevant literature, demonstrates how the resulting model can be utilized for developing algorithms to recognize facial expressions of emotion, and suggests avenues for future research in the areas of automatic learning and artificial vision. This serves to engage scientists and researchers in these fields [15].

Deepak Ghimire and Joonwhoan Lee (2013) introduced a novel approach to automatically identify facial expressions in sequences of facial images. This method captures facial features in consecutive video frames through movements based on an elastic chart, which compares the estimated displacement. The achieved recognition accuracy is 95.17% with the Ada Boost multi-class technique and 97.35% with support vector machines [16].

In a study conducted by Maria Jose Rodriguez-Araneda (2014), the social representation of happiness and the factors that contribute to happiness were qualitatively analyzed. The study focused on the perspectives of socializing agents in the welfare and quality of life. Additionally,

the research examined whether these perspectives aligned with the findings of positive psychology. The study followed a non-experimental, cross-sectional, intercultural, and qualitative research design. The non-probabilistic sample consisted of students and professionals in health and education from Chile and Italy. Open-ended questions were posed to psychology, obstetrics, and related field students aged 18-38 [17].

Richard G. Booth (2015) conducted a study on nursing students' use of Twitter to discuss various aspects of their education. The study collected tweets about courses, conferences, and clinical examinations in October 2011 and identified five main themes. Overall, 498 tweets were gathered over a span of six days, with 189 tweets falling into these thematic categories. The findings revealed that nursing students use social media to talk about different elements of their training, such as positive or exciting events and situations. The students also expressed feelings of stress and boredom when discussing their education. Additionally, the study highlighted that some tweets contained offensive language or derogatory remarks related to nursing education guidelines. Most of the collected tweets consisted of informal conversations about nursing education, including requests for information and comments on specific events [18].

In a study conducted by Ahmed M. Abdel-Khalek and David Lester (2017), 702 Arab Muslim students were involved. They were asked to complete four scales: a comprehensive self-assessment of religiosity, a self-assessment of happiness, the Arab scale of self-efficacy, and the mental health Arabic scale. The findings revealed that male students scored higher on both self-efficacy and mental health compared to female students. Additionally, all Pearson correlations between the scales used in the study were statistically significant and positive for both genders [19].

The existing literature on classroom

happiness primarily focuses on administering questionnaires and analyzing results. However, this proposed work offers a more efficient approach to address the issues identified in previous studies. Moreover, it aims to overcome hardware and software limitations within a specific timeframe to enhance student happiness in the classroom.

Competition in the Classroom

Competition in the classroom is influenced by various factors and primarily involves the professor and the students. It is essential to occasionally break away from scientific lectures to stimulate understanding and comprehension. Human competition can be defined as a contest where typically only one or a few participants emerge as winners while others do not. While some individuals have had positive experiences with student competition, others have encountered painful or unpleasant encounters. Adults often adopt the concept of competition from their student years and apply it to teaching students and children [20],[21].

When determining whether a competitive position is more or less advantageous in the classroom, certain principles need to be taken into account [22].

Happiness Analysis

In order for educators to prioritize happiness and human flourishing, it is necessary for there to be fundamental changes in how we comprehend, approach, and structure education [23],[24].

Firstly, going beyond the classroom and teaching environment, prioritizing happiness in education involves offering additional opportunities and experiences. Educational institutions should have a genuine concern for students' well-being by providing various extracurricular activities and involvement in community life.

Secondly, it includes participating in informal education, community learning, and other educational methods that encourage dialogue.

Thirdly, it involves removing significant portions of national and state curricula, or even the curricula as a whole. This is done to prioritize approaches and subjects that foster inclusivity instead of isolation.

Fourthly, it is

crucial to have easy access to counseling and pastoral care in order to attain happiness in education. This enables individuals who are encountering difficulties to develop self-awareness and comprehend their situations.

The analysis of facial expressions

Facial features are crucial in determining happiness and anger, as well as stability. Therefore, studying and analyzing facial features are essential in any research or study related to this topic [25].

The study of facial expressions is a powerful method for identifying human emotions. This approach involves different techniques and tools, each targeting specific factors. Consequently, there is strong evidence confirming the presence of seven universal facial expressions: anger, contempt, disgust, fear, joy, sadness, and surprise. Further support from various sources also confirms that emotional facial expressions have biological and genetic roots [26].

The science of people plays a crucial role in our daily lives and social interactions, with various applications. The face serves as a powerful indicator of emotions, enabling others to gain insights into our thoughts through changes in key facial features such as the eyes, eyebrows, eyelids, nose, and lips when we laugh or cry. Understanding microexpressions is essential for comprehending non-verbal behavior and analyzing individuals. Microexpressions are brief and involuntary facial expressions that reflect human emotions. Unlike deliberate facial expressions, faking a microexpression is challenging. There are seven universally recognized microexpressions: disgust, anger, fear, sadness, happiness, surprise, and contempt. These microexpressions rely on specific facial details and often occur within a fraction of a second—as fast as 40 to 60 milliseconds [27],[28].

The microexpression of happiness, as shown in figure 2, depends on various factors such as [29],[30].

Corners of the lips


teeth exposed


wrinkle runs


Cheeks


Lower lid


Crow’s

feet

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Fig. 2 depicts the various factors that contribute to the expression of happiness.

Methodology

The emotional facial expressions vary greatly depending on the age group. Therefore, we have chosen a specific age group to test our idea. The success of this project relies on the ability to understand and recognize emotions. There are seven or more emotions that can be seen on people's faces as reflected reactions. Our approach focuses specifically on the emotion of happiness during classroom activities. A typical class session is usually around 50 minutes, but it can be extended to 90 minutes. To keep your students engaged and motivated, you can use interactive recognition of emotional facial expressions to excite them. Throughout the class, you can periodically highlight interactive faces every ten minutes.

Now we attempt to initiate the thought process on how to stimulate or produce happiness. This concept includes the following steps as depicted in figure 3:

Our goal is to ascend to the peak by progressing from explanation to clarification, and from clarification to action, and from action to construction.

Fig. 3 presents the various stages of achieving happiness.

Before constructing the proposed system for recognizing emotional facial expressions, we categorized our measures based on color levels. The color level signifies the brightness or darkness of the color.

Our approach utilizes three main paint colors (red, green, and blue) to depict happiness levels. These colors are divided into three sublevels each, resulting in a total of nine colors used to represent happiness levels. Figure 4 provides an illustration of these colors.

The recognition system first detects the faces of the students and then continuously tracks these

faces. This method relies on an interactive feedback cycle to recognize any interactive actions. The process of detecting, tracking, and interacting contributes to the increase in happiness. Therefore, the continuous cycling of this process encourages students to interact, which is reflected on the screen as shown in Figure 5.

Fig. 4 represents the different levels of color divisions for happiness, while Fig. 5 shows the increasing trend of happiness.

Easy accessibility is crucial for happiness in education, and the implemented system comprises both hardware and software components. Implementing the hardware part of the system is simple, as illustrated in figure 6. It involves a high resolution digital camera positioned in front of students, a high resolution LCD screen with a specific size (approximately 50?), and an acceptable processor. These devices are connected via two interfaces: one for input and another for output.

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Fig. 6 depicts the hardware components of the implemented system.

The Raspberry Pi is a system with real-time operational capabilities, and it has been produced in different generations. The first generation includes the Raspberry Pi 1G (Model A and Model B), which was released in February 2012. The second generation is the Raspberry Pi 2G, which was released in February 2015 and has increased RAM. Lastly, there is the Raspberry Pi 3 Model B, released in February 2016, which comes with built-in WiFi and Bluetooth.

The Raspberry Pi, created at Cambridge University [31], is an embedded computer system utilized in schools for teaching IT [32]. It features a 700 MHz ARM11 co-processor [33] and comes in two models: the B+ with 512 MB RAM and

the A with 256 MB RAM. Additionally, it includes a Broadcom video core IV [34]. With its usage of open source operating systems like Raspbin, the Raspberry Pi is extensively employed in science. Its powerful capabilities enable real-time operations for detection, capturing, and tracking through specified algorithms. Moreover, it can simulate various applications in everyday life. For a visual illustration of the Raspberry Pi's architectural design, please see Figure 7 [34].

The hardware of the implemented system using Raspberry Pi is illustrated in Figure 8. This system allows for real-time interaction, control, and work. All operations, including image processing operations, can be executed in real time without any delays stated.

A A

Fig. 7 displays the architecture of the Raspberry Pi, while Fig. 8 shows the system connections.

The software component of the system involves the ability to detect emotional facial expressions, as depicted in figure 9. This algorithm is separated into the subsequent steps.

Face and lips detection


Face and lips recognition


Face and lips tracking


Face interaction


Happiness measures


Coloring levels

Fig. 9: Approach to recognize emotional facial expressions

Results and Analysis

Various pre-tests are utilized to evaluate the happiness level within the classroom setting. Ultimately, we have determined that facial expressions and emotions play a crucial role in this aspect. These factors can be gauged by observing the visibility of teeth. The degree of happiness is categorized into three groups: low (Blue), medium (Green), and high (Red). In the low (Blue) category, most students exhibit disinterest towards ongoing events. The ideal state lies in the medium (Green) level, as it indicates moderate excitement among students while aiding their memory retention capabilities. However, reaching the high (Red)

level may lead to student disruptions and negatively impact overall lectures, potentially resulting in unruliness. To ensure representative sampling from our student population, this approach primarily focuses on fourth-year undergraduate students enrolled in the computer science department (refer to figure 10).

Fig. 10 depicts a group of unhappy students in the classroom.

At first, the camera focuses on a specific face and tracks it. The tracked face then undergoes a direct process to determine its level of happiness. The size of the tracked faces is 46*46 pixels, while the size of the tracked lips is 16*31 pixels. The faces and lips of the tracked student go through a series of measurements to evaluate their happiness based on specific criteria. Figure 11 displays the tracked faces and lips of the students, indicating that they are initially sad, as evident from the assigned blue color on the scale. These results are obtained by calculating various factors and analyzing the histogram of the lips.

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Fig. 11 shows the tracking of faces and lips that are not happy.

The monitoring and tracking system causes some students to gradually smile more, which leads to an increase in happiness. This increase is shown by a rising color indicator, as seen in Figure 12 where the green color on the scale represents the slight rise in happiness observed from the students' smiles. The histogram clearly displays a shift towards higher values, indicating an overall boost in happiness.

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Fig. 12 shows the tracking of faces

and lips, specifically during a happy expression.

Figure 13 displays a significant increase in student happiness, evident from their wide smiles. The histogram also indicates a rightward shift, indicating higher values which are represented by the red color scale. This scenario may suggest that the students are becoming louder, possibly disrupting the lecture. Therefore, it would be beneficial to establish a balance and maintain control in the classroom.

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Fig. 13 tracking faces and lips (full happy)

Conclusions

The classroom is an important part of real life, where students and teachers have a direct and private interaction. It is widely acknowledged that students often lose focus soon after a lesson begins. Therefore, it is crucial to use effective methods that capture students' attention and involve them in the learning process. Various techniques, tools, and procedures are used to engage students and help them understand the material being taught.

At the beginning of a lecture, students pay attention to both the teacher and what is being presented on the board. However, after a few minutes they tend to get distracted and lose interest. To bring their focus back to the lesson and increase their enjoyment, it is essential to find ways of making the material more interesting.

This study aims to improve students' satisfaction during classroom sessions by implementing interactive image processing technologies that include face detection and tracking approaches.

The implemented system comprises a camera, monitor, and face tracking algorithm. It is compatible with any processor, although we have chosen to use the Raspberry Pi card in this instance. The objective of this system is to enhance student happiness by actively

monitoring their faces and assessing their happiness levels during class. By encouraging students to engage with the screen, we aim to boost their overall happiness. Moreover, the system has been designed for quick processing and analysis of microexpressions, completing the task in approximately 40 milliseconds. The color scale transitions from blue to green to red depending on the level of happiness detected in the classroom.

References

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[2] Martin, P. (2005) Making Happy People. The nature of happiness and its origins in childhood, London: Fourth Estate.

Argyle, M., & Hills, P. (2000). Religious experiences and their relations with happiness and personality. International Journal for the Psychology of Religion, 10, 157-172.

The Hebrew translation of the Oxford Happiness Inventory, conducted by Francis and Katz (2000), demonstrated internal consistency reliability and validity (Psychological Reports, 87, 193-196).

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[18] In a pilot study titled "Happiness, stress, a bit of vulgarity, and lots of discursive conversation: A pilot study examining nursing students' tweets about nursing education posted to Twitter," Richard G. Booth explores the tweets of nursing students related to their education. The study was published in Nurse Education Today in 2015 (pp. 322-327).

The paper titled "The association between religiosity, generalized self-efficacy, mental health, and happiness in Arab college students" by Ahmed M. Abdel-Khalek and David Lester was published in Personality and Individual Differences (2017) with the reference number [19].

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The following text is a citation and it should be kept in a paragraph with the :

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The publication titled "Changing the world with a Raspberry Pi" by J. D. Brock, R. F. Bruce, and M. E. Cameron was published in the Journal of Computer Science College in December 2013 with volume 29, issue 2, pages 151-153 [31].The article titled "Affordable and Energy-" by P. Abrahamsson, S. Helmer, N. Phaphoom, L. Nicolodi, N. Preda, L. Miori, M. Angriman, J. Rikkila, X. Wang, K. Hamily, and S. Bugoloni discusses the topic of affordable and energy-efficient solutions.