1.Introduction

2. Technical Route

3. System Design

4. Hardware Design

5. Software Design

6. Conclusion

Reference

With the increase in the number and size of school campuses, the issue of campus energy waste has received extensive attention. In order to monitor the environment of the classroom, control the electrical appliances to reduce energy consumption, and analyze the environment and utilization of the classroom, this paper develops an intelligent classroom management system based on the Internet of Things. In this system, the sensor suite of Shanghai Qixiang Technology Co., Ltd. is used as the basic suite of the Internet of Things. The storage model is established by the relational database MySQL and the non-relational database HBase. And the B/S-based website is the user interface. The storage overhead is reduced to a certain extent, and the energy waste in the classroom is effectively reduced.

Introduction

Due to the lack of attention of the leadership and the lack of effective management, the school’s power was wasted. It is not uncommon to leave the classroom without turning off lights, fans, and air conditioners. At the same time, the computers and switches in the equipment room consume a lot of power when they are not in use. However, in the process of automatic control, it is necessary to evaluate a large number of parameters, such as automatically turning off the lights in the room, and need to judge the current external light intensity, whether the classroom is active or not. At the same time, the indoor environment has a direct impact on people’s health, quality of life and work efficiency [1]. With the development of the Internet of Things, people can get the surrounding environment at a lower cost. In order to achieve energy saving and emission reduction in schools, it is necessary to intelligently control the school’s lamps, air conditioners, projections and other electrical equipment. However, in order not to affect the learning and working environment of teachers and students, it is necessary to find a balance that can save energy, reducing the operating costs of schools and meet the daily needs of teachers and students. Therefore, environmental data of classrooms and offices are needed to record the activities of students and teachers, the environmental changes in the room and the use of electrical appliances, and to analyze the data in the later stage to find a balance point. However, the amount of data generated spontaneously by the Internet of Things is very large. Each classroom generates a large amount of environmental data every day. The traditional relational database can no longer meet this demand. There is an urgent need for a new way to store data. Big data technology The emergence of this can solve this problem very well.

Stress is defined as an unpleasant emotional experience linked with specific environmental triggers and associated with feelings of anger, tension, frustration, and anxiety (Kyriacou, 2001). Teacher stress specifically has been associated with such negative outcomes as depression, burnout, physical illness, poor quality of life, and increased staff turnover (Fantuzzo et al., 2012; Yang et al., 2009). In addition, stress can negatively impact teachers’ effectiveness within the classroom while contributing to poor teacher-student rapport (Abel & Sewell, 1999; Kokkinos, 2007). The most prominent and empirically supported model of teacher stress is the JobDemand-Control Support (JDCS) model, where work-related stress develops under perceptions of excessive job demands combined with low control and lack of social support (Payne & Fletcher, 1983; Siegrist, 2002). Satisfaction has been conceptualized as a related but distinct construct from stress (Pelsma, Richard, Harrington, & Burry, 1989). Although satisfaction is less prominent in the literature, high job satisfaction among teachers has been associated with lower anxiety (Ho & Au, 2006), decreased desire to leave one’s job (Johnson, Kraft, & Papay, 2012), and increased overall school effectiveness (Hung, 2012). Teachers in urban schools serving predominantly minority and low income students experience significantly greater stress and lower job satisfaction compared to their colleagues serving students in higher income, suburban, and rural settings (Markow, Moessner, & Horowitz, 2006).

Predictors of Teacher Stress and Satisfaction

A number of contributors to teacher stress have been identified, including personal coping strategies and available social supports (Kyriacou, 2001; Roeser et al., 2013), perceived self-efficacy (Klassen & Chiu, 2010), test-based accountability policies (von der Embse, Pendergast, Segool, Saeki, & Ryan, 2016), and the larger school climate (Grayson & Alvarez, 2008). The most common predictors of teacher satisfaction include student academic success in the classroom (Turner, 2007) and organizational influences such as positive principal leadership styles and a positive school climate (Duyar et al., 2013; Ghavifekr & Pillai, 2016). Not surprisingly, stress and satisfaction often display inverse relationships with similar predictors. For instance, negative teacher-student relationships create stress (Jennings & Greenberg, 2009), while positive teacher-student relationships are associated with greater job satisfaction (Veldman, van Tartwijk, Brekelmans, & Wubbels, 2013). Similarly, perceptions of poor communication and limited connections with colleagues adds stress, while positive communication and collegiality corresponds to higher satisfaction (Kyriacou, 2001). Previous studies have found that organizational factors most consistently predict stress and satisfaction, and are more frequently reported by teachers as significant contributors to stress (Dorman, 2003; Shernoff et al., 2011). Multiple organizational factors come together to form a school’s overall organizational health. A school is considered “healthy” when administrators are perceived as capable of properly educating students and obtaining necessary material supplies, the principal demonstrates both high expectations and concern for the welfare of school staff, students demonstrate a strong academic focus, and teachers feel socially satisfied and connected to both their colleagues and students (Hoy & Woolfolk, 1993).

Beyond academic learning, schools are playing an increasingly central role in cultivating the necessary social, emotional, and ethical skills required to lead meaningful and successful lives. Over the last decade, there has been great interest in bringing mindfulness to educators and students alike ( for reviews, see MeikleJohn et al., 2012; Zenner et al., 2014). Entire books (e.g. Jennings, 2015b; Rechtschaffen, 2016; Hahn and Weare, 2017), journal issues (e.g. Mindfulness, 2016, Volume 7, Issue 1; Childhood Education, 2017, Volume 93, Issue 2), and structured training programs (e.g. Mindful Schools; Learning to BREATHE; MindUP) have been dedicated to the topic of mindfulness and education. More recently, there has been a wave of interest in social-emotional learning (SEL) more specifically. SEL programs tend to cover the concepts related to five core competencies: self-awareness (skills around identifying emotions, accurate self-perception, recognizing strengths, self-confidence, and self-efficacy), self-management (skills around impulse control, stress management, self-discipline, self-motivation, goal setting, and organizational skills), social awareness (skills around perspective taking, empathy, appreciating diversity, and respect for others), relationship skills (skills around communication, social engagement, relationship building, and teamwork), and responsible decision-making (skills around identifying problems, analyzing situations, solving problems, evaluating, reflecting, and ethical responsibility) (Weissberg et al., 2015; Zins et al., 2004). While a recent meta-analysis that reviewed 82 school-based SEL programs suggests that these programs are effective and the benefits are lasting (Taylor et al., 2017), what is noticeably absent from SEL programs is the important construct of compassion – compassion for oneself, compassion for others (known and unknown, liked and disliked, etc.), and receiving compassion from others. To date, while formal, evidenced-based, secular[1], compassion training programs exist for adults (e.g. Gilbert and Proctor, 2006, 2010; Jazaieri et al., 2013; Neff and Germer, 2013; Pace et al., 2009; for a review, see Kirby, 2016) and data from these programs suggest that compassion can indeed be “taught’ ” these compassion programs have not been widely used in the contexts of children, adolescents, or young adults. Given the links between compassion and emotional intelligence (e.g. Heffernan et al., 2010; Şenyuva et al., 2014), and the links between compassion and connection (instead of loneliness and isolation; for a review, see Seppälä et al., 2013), children, adolescents, and young adults seem to be a germane group to examine the construct of compassion within.

In economics teaching, traditional lecturing still takes up the largest share of class time, an estimated 60% (Goffe and Kauper, 2014) to 83% (Watts and Schaur, 2011). This central role of lecturing is under increasing scrutiny as empirical evidence suggests that active learning techniques are more effective than lecturing in promoting learning (see e.g. Freeman et al., 2014). A pedagogical approach that decreases lecturing, thus freeing class time for active learning, is the inverted classroom (Lage et al., 2000) or the classroom flip (Baker, 2000), whereby first exposure to the material is moved outside the classroom usually in the form of lecture videos (Abeysekera and Dawson, 2015). The classroom flip appears to improve learning outcomes as reported in three major reviews of the existing literature (Bishop and Verleger, 2013, Giannakos et al., 2014, O’Flaherty and Phillips, 2015, O’Flaherty et al., 2015) even though reviewers express some concern for the lack of a “robust scientific approach” in evaluating these learning outcomes (O’Flaherty and Phillips, 2015, 89). Recent studies using more robust methods, seem to confirm the existence of improvement in learning outcomes from flipping the classroom, albeit moderate (Anderson and Brennan, 2015, Calimeris and Sauer, 2015). Moving beyond the analysis of how flipping the classroom affects learning outcomes overall, some researchers have begun to investigate more fine-grained questions. Ryan and Reid (2016) asked how flipping the classroom affects the outcomes of weaker students. They found a 56% reduction in Ds and Fs grades and in the withdrawal rate when flipping the classroom even though no improvements in learning emerged at the aggregate level. Touchton (2015) focused on what type of learning flipping affects the most. He found a larger improvement in learning outcomes in the sections which students generally find most challenging even though at the aggregate level the improvements were very small. Olitsky and Cosgrove (2016) examined whether gains in learning outcomes become larger as students adapted to the flip and showed that this was the case: the gains in learning increased as the flipped course progressed and students became better acquainted with the approach. Jensen et al. (2015) raised the question of how much the impact of the flipped classroom on learning outcomes depends on the choice of the control against which the learning outcomes of the flip are evaluated. They found insignificant learning benefits of the flipped classroom compared with a non-flipped, active learning course

Data mining has long been used in marketing, advertising, health, engineering, and information systems. At its core, data mining is an inductive, analytic, and exploratory approach, which is concerned with knowledge discovery through identification of patterns within large sets of data. In the last 10 years, the field of Educational Data Mining (EDM) has emerged as a distinct area of research concerned with using data mining techniques to answer educational questions, such as, “What are the difficulties students encounter during a learning activity?”, “What sequences of computer interactions lead to successful problem-solving performance?”, and “What sequences of actions characterize high performers and low performers in problem-solving activity?” EDM can also provide new insights into “wicked” educational problems, such as, “What are the differences in the ways students experience learning,” and “How can learning designs account for variations in students’ learning experiences?” In particular, EDM is concerned with developing methods for analyzing data from an educational system in order to detect patterns in large datasets that would otherwise be very difficult or even impossible to analyze due to the vast volume of data within which they exist (Romero & Ventura, 2013). Consequently, results from data mining can be used for deciding about how to improve the teaching and learning process as well as how to design or redesign a learning environment (Romero & Ventura, 2007; Ingram, 1999). Data mining techniques have been mostly used within the context of web-based or e-learning education in order to: (a) suggest activities, resources, learning paths, and tasks for improving learners’ performance and adapting learning experience (Tang & McCalla, 2005); (b) provide feedback to teachers and instructional designers in regards to learners’ difficulties with the content and structure of a course, so that revisions can be made to facilitate students’ learning (Merceron & Yacef, 2010; Zaiane & Luo, 2001); (c) predict learners’ performance (Ahmed & Elaraby, 2014); and (d) inform administrators about the effectiveness of instructional programs, so that better planning and allocation of human and material resources can be achieved (Romero & Ventura, 2007).

Games have been a fundamental part of human civilization, and the first written history of human gameplays can be traced back more than three thousand years ago (McGonigal, 2011). In modern society a vast majority of the population is playing games, feeling rewarded by participating and taking action in games and feeding needs that real world is unable to satisfy (McGonigal, 2011; Reeves & Read, 2009). Everyone that has already played some game has felt the sense of being totally focused in something, engaged in every single moment, having a feeling of accomplishment and success (McGonigal, 2011; Reeves & Read, 2009; Yee, 2006). And if this passion is shared with many other people, games also give us the feeling of belonging to a community (McGonigal, 2011). Gamification is present in our daily lives, although sometimes we do not even recognize it. One of the most disseminated examples of the use of gamification with the objective of changing people’s habits is the Piano Staircase at the Odenplan subway in Stockholm. The main idea was to convince people to use the stairs instead of the escalator or elevator, and make the use of stairs a funny thing by turning it into a giant piano (TheFunTheory, 2009). Each step of the staircase would play a musical note when it was stepped on. Another example is Superbetter, aiming at helping people recover from an illness and achieving health goals by belonging to a community and playing a game (McGonigal, 2016). The badges that are given to acknowledge your achievements when you contribute with reviews in Tripadvisor are a tool to convince you to keep contributing with more and more reviews (staying enrolled in the game). Hundreds of millions of people play regularly online massive multiplayer games (Reeves & Read, 2009). About 67% of teenagers regularly play online games, and players aged 18 to 22 play on average around 25 h per week (Williams, Yee, & Caplan, 2008). Students that are now entering university are more and more acquainted with gaming experiences.

ما اثر چندین پارامتر محیط فیزیکی را در یک کلاس درس بر روی تمرکز دانشجویان توصیف و تجزیه و تحلیل می نماییم، در این جا عبارت “مرکز توجه” به احساسات ذهنی دانشجویان در مورد توانایی آنها برای تمرکز بر درس در یک لحظه ی فرضی اشاره دارد.ما چندین پارامتر را در یک محیط کلاس درس واقعی با استفاده از دستگاه های هوشمند کم هزینه اندازه گیری کرده ایم. تحقیقات مبتنی بر مجموعه داده های جمع آوری شده از 14 درس ضبط شده است که 197 دانشجو در آن حضور داشتند. ما 5 پارامتر محیط فیزیکی را اندازه گیری کرده ایم و 22 خصوصیت را از صدای مدرس استخراج کرده ایم. پس از تحلیل اندازه گیری جمع آوری شده، ما هشت پارامتر را شناسایی کرده بودیم که اثبات شده است دارای مقادیر متفاوت آماری برای بخشهای “متمرکز” و “نامتمرکز” هستند. ما از مجموعه داده ی کسب شده برای محک زدن رده بندی کننده های مختلف استفاده نمودیم و رده بندی کننده AdaBoost M1 دارای بهترین دقت شناسایی کلی است (86.78%). پس از انجام سری آزمایشات مکمل، ما سه پارامتر را شناسایی کردیم که میتواند از مجموعه داده اصلی بدون تغییر صحت رده بندی کننده حذف شود، که 5 پارامتر ناهمبسته را ترک کرد که نشان داده شده است دارای اثر معناداری بر تمرکز دانشجو است.