Social Networks among Teachers

Social Networks among Teachers CHAPTER 1 The Social Fabric of Elementary School Teams: How Network Content Shapes Social Networks [1] ABSTRACT Background. Social networks among teachers are receiving increased attention as a vehicle to support the implementation of educational innovations, foster teacher development, and ultimately, improve school achievement. While researchers are currently studying a variety of teacher network types for their impact on educational policy implementation and practice, knowledge on how various types of networks are interrelated is limited. Moreover, studies that examine the dimensionality that may underlie various types of social networks in schools are scarce. Purpose. The goal of this chapter was to increase our understanding of how network content shapes social network structure in elementary school teams. The study examines the extent to which various work-related (instrumental) and personal (expressive) social networks among educators are related. In addition, we explore a typology of social networks in schools and investigate whether the common distinction between instrumental and expressive social networks could be validated in the context of elementary school teams. Method. Social network data were collected among 775 educators from 53 elementary schools in a large educational system in the Netherlands. The interrelatedness of seven social networks was assessed using the Quadratic Assignment Procedure (QAP) correlations. Multidimensional Scaling (MDS) was used to discern underlying dimensions that may explain the observed similarities. Finally, we describe and visualize the seven networks in an exemplary sample school. Conclusions. Findings suggest small to moderate similarity between the social networks under study. Results support the distinction between instrumental and expressive networks in school teams and suggest a second dimension of mutual in(ter)dependence to explain differences in social relationships between educators. The social fabric of elementary school teams; How network content shapes social networks INTRODUCTION The rapidly growing interest in social networks can be characterized as one of the major trends in social science research. According to scientific databases (ERIC, Picarta, and Web of Science), the number of publications in social sciences using the word ‘social network(s) in the title, keywords, or abstract, has increased exponentially over the last two decades (Borgatti Foster, 2003) (see 1). Evidence of this trend in education is exhibited by an increasing number of articles focusing on the intersection of social networks and education in a growing variety of settings and areas of emphasis. The thesis that ‘relationships matter is currently inspiring educational researchers around the world to study social networks in school teams (Daly, in press; Daly Finnigan, 2009; Daly et al., in press; McCormick, Fox, Carmichael, Procter, in press; Penuel, Riel, Krause, Frank, 2009) (see also 1). An important prerequisite for gaining insights in the potential of social networ ks for schools is the emergence of social network studies that provide a deepened understanding of the structure and content of teachers professional relationships (Coburn Russell, 2008). Social network scholars emphasize that social networks are shaped by the content or purpose of the social resources that are exchanged in the network (Burt, 1992; Coleman, 1990; Lin, 2001; Putnam, 2000; Scott, 2000; Wasserman Faust, 1997). Studies suggest that the distribution of resources in a network may depend on the content of the network (Haines Hurlbert, 1992; Raider Burt, 1996). For instance, a social network that is maintained for the purpose of exchanging work related knowledge and expertise may look significantly different from a social network that is created for personal support. Even though both social networks contain social resources that may be accessed and leveraged, both networks may be shaped quite differently. Several scholars have therefore voiced the need to examine multiple relationships simultaneously (Friedkin, 2004; Ibarra Andrews, 1993; McPherson, Smith-Lovin, Cook, 2001; Mehra, Kilduff, Brass, 1998; Monge Contractor, 2003; Pustejovsky Spillane, 200 9; Wasserman Faust, 1997). Yet, few studies have been conducted into the ways in which social networks are shaped differently depending on the content of their ties (Hite, Williams, Baugh, 2005; Moolenaar, Daly, Sleegers, in press). The goal of this chapter is to examine the extent to which multiple social networks among educators are shaped differently depending on their content. We will address this goal by exploring the similarity between multiple social networks in school teams and working towards a typology of social networks in school teams according to underlying dimensions. Our enquiry is guided by social network theory and the social network concept of ‘network multiplexity. In short, network multiplexity is concerned with the ‘overlap between social networks that transfer different content among the same individuals. With this chapter, we aim to contribute to recent knowledge on the nature of social networks in school teams by comparing and contrasting different networks (e.g., friendship, advice) in 53 Dutch elementary schools located in a single district. We will start with an overview of social network theory and network multiplexity as these provide the conceptual background to the stud y. THEORETICAL FRAMEWORK Social network theory A growing body of educational research points to the potential of social networks to affect teachers instructional practice, and ultimately, benefit student achievement (Coburn Russell, 2008; Daly et al., in press; Penuel, Frank, Krause, 2007; Penuel Riel, 2007). Building on social network theory, these studies examine the extent to which the pattern of relationships among teachers and the exchange of resources within these relationships may support or constrain school functioning and improvement. An important feature of social network theory is the focus on both the individual actors and the social relationships linking them (Wasserman Galaskiewicz, 1994). Through social interaction among educators, social relationships develop into a patchwork of ties that knit the social fabric of school teams (Field, 2003; Putnam, 2000). Social network theory argues that the quality and denseness of this social fabric eventually determines the speed, direction and flow of resources through a social network (Burt, 1992). In turn, it is through the flow and use of social resources that collective action may be facilitated and organizational goals may be achieved (Lin, 2001; Lochner, Kawachi, Kennedy, 1999). For instance, strong social relationships are suggested to facilitate joint problem solving, lower transaction costs, and support the exchange of complex, tacit knowledge among network members (Hansen, 1999; Putnam, 1993; Uzzi, 1997). Studies into social networks among educators have focused on various types of social networks that connect teachers within and between schools, such as discussion about curricular issues (content, teaching materials, planning), communication around reform, seeking advice, and friendship among teachers (Coburn Russell, 2008; Cole Weinbaum, 2007; Daly Finnigan, 2009, Hite, Williams, Baugh, 2005; Pustejovsky Spillane, 2009). While some studies focus on a single relationship (Coburn Russell, 2008), others include and contrast multiple relationships (Cole Weinbaum, 2007; Pustejovsky Spillane, 2009), although not for the purpose of explicating their similarities or differences per se. Therefore, what is less clear is whether educators social networks are shaped by the content that defines their ties (Hite, Williams, Hilton, Baugh, 2006; Podolny Baron, 1997). Insights in the way network content shapes collegial relationships is important for understanding the extent to which teach ers professional relationships may affect educational practice. As Little (1990) marks: ‘It is precisely such â€Å"content† that renders teachers collegial affinities consequential for pupils. This insight can be provided by investigating network multiplexity and exploring a typology of social networks in school teams. Network multiplexity In social network terms, multiplex relationships are relationships that serve multiple interests or are characterized by a multiplicity of purposes (Gluckman, 1955, 1965). In other words, multiplexity focuses on the extent to which there is overlap between different social relationships, for instance advice and friendship. Many studies focus on multiplex exchanges within a single relationship, for instance, whether a relationship between two individuals is characterized by the exchange of both work related advice and friendship (De Klepper, Van de Bunt, Groenewegen, 2007; Hansen, Mors, Lovas, 2005; Hite et al., 2006; Hite, Williams, Baugh, 2005, Koehly Pattison, 2005; Lazega Pattison, 1999; Lomi, 2002). Less attention has been paid to the issue of multiplexity in regard to whole networks. To advance social network theory in this direction, this chapter therefore focuses on multiplexity of whole networks. Meaning, we will examine the overlap between whole networks among the same s et of individuals that are characterized by a multiplicity of purposes. Multiplex relationships that serve multiple purposes are suggested to be stronger than relationships that only serve a single purpose, and individuals who are connected through multiplex networks will have greater success in accessing and mobilizing resources (Kapferer, 1969; Doreian, 1974). Multiplex, or multi-dimensional social networks have been studied outside education to validate name generator questions (Ruan, 1998), to examine the pattern of relationships among lawyers (Lazega Pattison, 1999), to differentiate between different types of support networks (Bernard et al., 1990) and advice networks (Cross, Borgatti, Parker, 2001). Yet, knowledge on the extent to which social networks in school teams can be differentiated is scarce. Towards a typology of social networks in school teams Teacher-to-teacher exchange can be captured by a variety of references that all refer to some form of collegiality (Little, 1990; Rosenholtz, 1989), such as sharing, giving advice, discussing work, and collaborating. Little (1990) argues that these exchanges are not just a straightforward collection of activities, but rather ‘phenomenologically discrete forms that vary from one another in the degree to which they induce mutual obligation, expose the work of each person to the scrutiny of others, and call for, tolerate, or reward initiative in matters of curriculum and instruction (p. 512). Little (1990) places various collegial forms on a dimension of mutual interdependence, with storytelling as an example of collegiality that entails low mutual interdependence, and joint work as an example of collegiality that involves high interdependence. She poses that a shift on this dimension toward increased interdependence relates to changes in the frequency and intensity of teachers in teractions and the likelihood of mutual influence. Moreover, increased interdependence poses rising demands for collective autonomy and teacher-to-teacher initiative (Little, 1990). While this dimension of mutual interdependence could serve as a valuable guide in typifying various forms of social relationships in school teams, it has not yet received much empirical attention. Given the popularity of social network studies in education, the question in which forms the amorphous concept of ‘collegiality permeates teachers daily practice is more relevant than ever before. Another useful dimensionality of social relationships that has become common practice in social network research is the distinction between instrumental and expressive relationships (Ibarra, 1993, 1995). These distinct relationships are believed to provide different kinds of support and transfer unique knowledge and information (Erickson, 1988). Instrumental relationships encompass social interactions that are ultimately aimed at achieving organizational goals, such as work related advice or collaboration. Instrumental ties are believed to be ‘weak ties through which work related information and knowledge is exchanged between experts and people who seek information (Granovetter, 1973). Expressive relationships are formed through social interaction that is not directly aimed at work related issues, that often places the individuals interest above that of the organization (Burt, 1997), and that is mostly characterized by an affective component, such as personal support and friend ship. In general, expressive ties are believed to be stronger, more durable and trustworthy, and offer greater potential to exert social influence (Granovetter, 1973; Ibarra, 1993; Marsden, 1988; Uzzi, 1997). Increased understanding of a typology of social networks in school teams is indicated as social network studies often examine various types of networks without specifically addressing differences between the social networks under investigation[2]. By exploring multiple social networks this chapter not only aims to deepen our insights in the social fabric of school teams, but also addresses the validity of the common instrumental-expressive distinction in the context of education. The boundaries between instrumental and expressive relationships are fuzzy and often tend to overlap (Borgatti Foster, 2003). In addition, recent research has suggested that one type of relationship can in part determine or reinforce another type of relationship (Casciaro Lobo, 2005). Since a systematic investigation of multiple networks in school teams is missing, this chapter is one of the earliest to explore a typology of social networks in school teams. In addition to advancing social network theory, t he study thereby offers a unique insight in the social fabric of Dutch elementary schools. METHOD Context We conducted a survey study at 53 elementary schools in south of The Netherlands. The schools resided under a single district board that provided the schools with IT, financial, and administrative support. The schools participated in the study as part of a district-wide school improvement program focused on school monitoring and teacher development. The 53 sample schools were located in rural as well as urban areas and served a student population ranging from 53 to 545 students in the age of 4 to 13. While the schools differed slightly regarding students SES and ethnicity, the schools student population can be considered as rather homogeneous in comparison to the Dutch average. Sample All principals and teachers were asked to participate in the survey study. A total of 51 principals and 775 teachers responded to this call, reflecting a return rate of 96.8 %. Of the sample, 72.9 % was female and 52.5 % worked full-time (32 hours or more). The age of educators in the sample ranged from 21 to 63 (M = 45.7, sd = 10.7). Additional sample characteristics are included Table 1 and 2. Instruments Social networks. To discern common types of interaction among teachers in elementary education, we interviewed seventeen elementary school teachers, two principals and one coach[3] who volunteered in reaction to a canvas call among the personal social network contacts of the principal researcher. We asked the educators to describe a regular work week and give examples of the types of social interaction they had with their colleagues. The hour-long interviews were audio-recorded and conducted using a semi-structured interview guide (Patton, Table 1. Sample demographics of schools and educators (N = 53, n = 775) Individual level Gender Male 210 (27.1 %) Female 565 (72.9 %) Working hours Part time (less than 32 hours) 368 (47.5 %) Full time (32 hours or more) 407 (52.5 %) Experience 1-3 years 152 (19.6 %) at school 4-10 years 256 (33.0 %) > 11 years 367 (47.4 %) Grade level[4] Lower grade (K 2) 353 (45.4 %) Upper grade (3 6) 422 (54.5 %) School level Team experience 6 months to 2 years More than 2 years 20 (37.8 %) 33 (62.2 %) Table 2. Sample demographics of schools and educators (N = 53, n = 775) N Min. Max. M Sd Individual level Age 775 21 63 45.7 10.7 School level Gender ratio[5] 53 57.0 100.0 76.8 10.7 Average age 53 35.4 52.8 45.3 3.7 Number of students 53 53 545 213 116.6 Team size 53 6 31 14.8 6.8 Socio-economic status (SES) [6] 53 0.4 47.3 7.9 9.5 1990; Spradley, 1980). We analyzed the interview data using a constant comparative analysis method (Boeije, 2002; Glaser Strauss, 1967). We compared perspectives of educators with different formal roles and at different grade levels, grouped different forms of social interaction mentioned by the educators, and checked and rechecked emerging types of social interaction (Miles Huberman, 1994). From this preliminary analysis, we deduced seven social networks that capture the forms of social interaction as described by the interviewed educators. As a member-check procedure (Miles Huberman, 1994), these social networks were then shared with a new group of educators. This group comprised eleven principals and six teachers who formed a pilot sample to establish face validity of the social network questions. Based on their comments, slight adjustments were made that resulted in the final questions to assess social networks of educators in elementary school teams (see Table 3). We include discussing work as social interaction concerning the discussion of work related issues. The nature of teaching requires the accumulation, transfer and exchange of ideas, experiences, expertise, and knowledge, all which can be shared through the discussing of work with colleagues (Monge Contractor, 2003). Discussing work can be regarded a general form of resource exchange related to work and can pertain to various topics, such as instruction, planning, or use of teaching materials. Collaboration refers to joint work among educators who are collectively responsible for the product of collaboration, and as such, collaborative relationships address collective action among teachers (Little, 1990). Interaction through collaboration may offer valuable opportunities for the exchange of knowledge and ideas, and the alignment of shared goals and expectations. Given the nature of schools as ‘loosely coupled systems (Weick, 1976) and the relative autonomy that teachers have in their classrooms (Lortie, 2002), collaboration in Dutch elementary schools often follows formal task hierarchy and is prescribed by formal roles, such as coaches or social support specialists. However, collaboration may also be voluntary, such as participating in a committee for a specific event. Asking for advice is of interest to the study of teacher networks since receiving advice may be part of ongoing teacher development and may facilitate the adoption and implementation of reform and innovation in schools (Moolenaar, Daly Sleegers, in press). Asking for advice addresses the issue of ‘who seeks out whom for work-related advice and thereby, in contrast to the previous types of instrumental interaction, implies an interdependence of knowledge, expertise, or information between the advice-seeker and the advice-giver. For the advice-giver, advice relationships are a powerful tool to gain social control as they convey information and disclose vulnerability and risk-taking on the part of the advice-seeker. Research has indicated than advice-seekers often seek advice from people with a higher status than the advice-seeker (Blau, 1964; Lazega Van Duijn, 1997). Table 3. The seven social network questions to assess social networks in Dutch elementary school teams Social network questions (in Dutch) Met welke collegas kunt u goed over uw werk praten? Met welke collegas werkt u het liefst samen? Aan welke collegas vraagt u meestal advies over uw werk? Met welke collegas brengt u graag pauzes door? Met welke collegas heeft u wel eens meer persoonlijke gesprekken? Met welke collegas spreekt u wel eens buiten het werk? Welke collegas beschouwt u als vrienden? English equivalent of the original Dutch question Whom do you turn to in order to discuss your work? With whom do you like to collaborate the most? Whom do you go to for work related advice? With whom do you like to spend your breaks? Whom do you go to for guidance on more personal matters? Who do you sometimes speak outside work? Who do you regard as a friend? Network Discussing work Collaboration Asking advice Spending breaks Personal guidance Contact outside work Friendship The interviewed educators mentioned spending breaks as another important form of social interaction. During breaks, teachers may exchange many types of resources, both work related and personal. Relationships based on spending breaks may be seen as mostly expressive since, according to the interviewed educators, breaks imply ‘off the job moments in which teachers may discuss personal issues or social conversation more easily than during formal meetings. Another social relationship among educators involves going to a colleague for personal guidance and to discuss personal matters. This form of interaction explicitly addresses the informal, personal nature of relationships. A relationship around personal guidance and the discussion of personal matters implies a certain level of trust between the people involved in the relationship. Such a personal bond is believed to be more strong and durable than work related relationships such as work related collaborative exchange (Granovetter, 1973). Whereas ‘spending breaks and ‘personal guidance may be described as ‘friendly relationships, the next two relationships tap into interaction that more specifically addresses ‘friendship (Kurth, 1970). The next social relationship, according to the interviewed educators, entails having contact outside work. When teachers have frequent contact with one another outside school, this may indicate a relationship that is built on more personal grounds than work. Therefore, having contact outside work may be a good indicator of some sort of friendship or strong bond, even though both individuals may not define the relationship as a friendship relationship (Ibarra, 1992; Zagenczyk, Gibney, Murrell Boss, 2008). The final social relationship addresses friendship. Friendship is included in many social network studies as the prototypical expressive relationship (e.g., Cole Weinbaum, 2007; Lazega Pattison, 1999) as friendship expresses personal affect and social support (Gibbons, 2004). Individuals depend on friends for counseling and companionship (Krackhardt Stern, 1988), and friendship ties facilitate open and honest communication that may boost organizational change (Gibbons, 2004). These seven social network questions were included in a social network survey to assess social relationships among educators. Respondents were provided with a school specific appendix that contained the names of the school team members of their school, accompanied by a letter combination for each school team member (e.g., Mr. Jay Hoffer[7] = AB). They were asked to answer each social network question by writing down the letter combination(s) of the coworker(s) they would like to indicate as being a part of their social network as specified by the question. The number of colleagues a respondent could answer was unlimited. Data analysis Social network analysis. The data were examined using social network analysis. Social network analysis is a technique to systematically analyze patterns of relationships in order to understand how individual action is situated in structural configurations (Scott, 2000; Valente, 1995). We first constructed matrices for each network question for each school. The matrices were compiled following the same procedure, namely if educator i nominated educator j as an advice relationship, a 1 was entered in cell Xij. If educator i did not nominate educator j, a 0 was entered in cell Xij. This procedure resulted in an asymmetric matrix that summarized all directed relationships among the educators within a single school. To explore and describe the networks, several social network properties at both the individual and school level were calculated based on the matrices using software package Ucinet 6.0 (Borgatti, Everett, Freeman, 2002; Borgatti, Jones Everett 1998; Burt, 1983). Individual level properties include raw and normalized scores for out-degree and in-degree, and ego-reciprocity. Out-degree depicts the number of people nominated by the respondent, and can therefore be interpreted as a measure of individual activity. In-degree represents the number of people by whom the respondent was nominated, and can be read as a measure of individual popularity. The raw scores of in- and out-degree encompassed the actual number of educators that were named by the respondents. Because the average in-degree is the same as the average out-degree (each out-going relationship for one educator also implies an in-coming relationship for another educator), we only report the average in-/out-degree. The standard deviations of the out- and in-degrees reflect the variability among educators in the amount of out-going and in-coming relationships, and may thus be different for the out-degrees and in-degrees. For instance, educators may vary greatly in the number of relationships they indicate to have, but there may be less variability in the number of relationships that educators receive. The range of the average raw scores varies from 0 to 14.8 since this is the average team size of the sample schools. Besides these raw scores, we also report normalized scores for out-degree and in-degree to facilitate comparisons among schools with different team sizes . The normalized scores can be interpreted as the percentage of relationships of the whole network that an educator maintains. The normalized out- and in-degrees range from 0 (the educator has no relationships) to 100 (the educator has a relationship with all of his/her team members). Again, the average percentage of out-going relationships is the same as the average percentage of in-coming relationships. The standard deviations of the normalized out- and in-degrees mirror the variability among educators in the percentage of relationships that are sent (out-going) or received (in-coming). Ego-reciprocity is a measure of reciprocity at the individual level. Ego-reciprocity is calculated as the number of reciprocal relationships in which in educator is involved, divided by the total number of his/her relationships. Ego-reciprocity thus reflects the percentage of ties of an educator that is reciprocated. Ego-reciprocity ranges from 0 (none of the individuals relationships are reciprocated) to 100 (all of the individuals relationships are reciprocated). At the school level, we calculated the network measures of density, reciprocity, and centralization. Density represents the concentration of relationships in a social network, and is calculated by dividing the number of observed relationships by the total number of possible relationships in a given network. This means that the greater the proportion of social relationships between school staff members, the more dense the social network. The density of a schools network may range from 0 (there are no relationships in the school team) to 1 (all school team members have indicated to maintain a relationship with each other). The density of a network can be thought of as a measure of cohesion (Blau, 1977). A dense network is believed to be able to move resources more quickly than a network with fewer ties (Scott, 2000). Reciprocity captures the extent to which the relationships in a social network are reciprocal, and is calculated as the number of reciprocal relationships in a team, divided by the total possible number of reciprocal relationships. Higher levels of reciprocity have been associated with complex knowledge exchange and higher organizational performance (Kilduff Tsai, 2003). The reciprocity of a schools network may range from 0 (none of the relationships in the school team are reciprocated or mutual) to 1 (all of the relationships in the school team are reciprocated or mutual). In-centralization was included to examine the central tendency of the social networks. This measure assesses whether the relationships in a given network are evenly dispersed in a network, or whether the relationships are centralized around one (or a few) very central people, who receive many nominations. In-centralization is based on the variability of in-degrees within a given team. High in-centralization reflects a high variability in the school team between educators who are often nominated and educators who are seldom nominated. As such, centralization of a social network refers to the difference between one or a few highly central person(s) and other (more peripheral) people in the network. Centralization ranges from 0 (no variability all members of the network are chosen for advice as frequently) to 1 (maximum variability every educator in a network only nominates a single person in the network, while these educators themselves are not nominated at all). The more centralized the social network is, the more resources are disseminated by a single or a few influential people to the rest of the network. In contrast, relationships and resources in a decentralized social network are much more evenly shared among all school team members. Examining multiplexity To determine the similarity between the seven social networks within each school, we estimated a series of Quadratic Assignment Procedure (QAP) correlations in Ucinet (Borgatti, Everett, Freeman, 2002; Hanneman Riddle, 2005; Krackhardt, 1987). The QAP is a procedure to calculate correlations between social networks. When conducting social network research, statistical assumptions of independence are violated because relations between individuals are nested and embedded within the same network. Social network data are often interdependent, thus limiting the use of ‘conventional statistical techniques such as Pearson correlations. The QAP was designed as a variation on conventional correlational analyses for the use with social network data. The QAP follows a specific process. First, a Pearson correlation coefficient is calculated for two corresponding cells of two matrices that contain network data. Then, it randomly permutes the rows and columns of one of the matrices hundreds of times (each time computing a new correlation coefficient), and compares the proportion of times that these random correlations are larger than or equal to the original observed correlation. A low proportion (p

Closed Loop Motor Speed Control

INTRODUCTION 1. 1 In order to be able to build a closed loop controller, you need some mean of gaining information about the rotation of the shaft like the  number of revolutions executed per second, or even the precise angle of the shaft. This source of information about the shaft of the motor is called â€Å"feed-back† because it sends back information from the controlled actuator to the controller. 1. 2 METHODOLOGY: LASER BASED SPEED SENSOR The methodology of our project is shown in the block diagram in fig 1. 1 . LCD DISPLAY MOTOR DRIVER MOTOR INPUTS MCU MOTOR CONTROLLER Fig 1. 1: Block Diagram of DC Motor Speed Control 1.INPUTS: User defined or user desirable speed are fed to the MCU block or microcontrller through numerical keyboard. 2. MCU: The main controlling unit which takes as well as sends signal to control the speed of the motor and displays the speed as well. 3. MOTOR CONTROLLER: It takes the reference signal from MCU and convert it into required voltage. 4. MO TOR DRIVER: The required power to drive the motor will be fed by the motor driver. 5. MOTOR: The motor now runs at the desired speed as given. 6. LASER BASED SPEED SENSOR: Here laser speed sensor circuit is used to drive the speed of the motor and feeds the equivalent signal to the MCU. . LCD DISPLAY: Displays the obtained speed. 1. 3 CIRCUIT DIAGRAM: 1. 4 WORK PLAN: In this semester we are planning to design the optical tachometer and learn the basic operation of the microcontroller. START Doing some literature survey and studying the project Design the block diagram of the whole circuit Design the circuitry for each blocks Testing the hardware for the blocks Make changes to the circuitry. Is the hardware working properly NO Test the changes Integrating different block together YES Testing the assembled circuit Make some modification in the circuit Is the model working properly STOP Documentation NO YESFig 1. 4: Flowchart Showing the Methodology of the Project. 1. 7 CONCLUSION In t his project we learn about optical tachometer using microcontroller, its uses and advantages. We can implement this knowledge in further application of using microcontroller. Through this microcontroller we can arrange a pattern of speed. This can be easily done with microcontroller but through other technique it becomes difficult because using microcontroller we just have to change the software to change the speed. But in other technique the complete hardware has to be changed. The speed of the motor can be changed by using duty cycle. REFERENCE: 1] Peter Nawfel,†Laser Based Non Contact Speed Sensor Reduces Breaks On High Speed Unwind†,Paper Age,Nov/Dec 2003. [2] Priya Duggal,†Microcontroller Based Speed control of DC Motor Through RS-232 Interface With PC. †International Journal of Advances in Electronics Engineering. [3] Azim Fauza Bin Md khair,† The Motor Speed System Using Infrared System †,May 2008, http//www. google. com [4] R. Arulmozhiyal ,etal. ,† Space Vector Pulse Width Modulation Based Speed Control of Induction Motor using Fuzzy PI Controller†, International Journal of Computer and Electrical Engineering, Volume. 1, No. 1, April 2009. [5] Rohit Gupta,etal. †Thyristor Based Speed Control Techniques of DC Motor: A Comparative Analysis†,International Journal of Scientific and Research Publications, Volume 2, Issue 6, June 2012. [6] Atul Kumar Dewangan,etal. ,† PWM Based Automatic Closed Loop Speed Control of DC Motor†, International Journal of Engineering Trends and Technology,Volume 3,Issue 2,2012. [7] Ashok Kusagur,etal. ,† Modelling of Induction Motor & Control Of Speed Using Hybrid Controller Technology†, Journal of Theoretical and Applied Information Technology. [8] Sanjeev Kumar,etal. ,† Speed Control Of Separately Excited DC Motor Using Neuro Fuzzy Technique†,http//www. oogle. com,2009-2010. [9] Sarat Kumar Sahoo,etal. ,† Touch Screen based Sp eed Control of Single Phase Induction Motor†, International Journal of Engineering and Technology, Volume 2 (6), 2010, 392-396. [10] Sharanbasappa. B. Belamgi,etal. ,† Speed Control Of AC/DC Motor Using Single Phase Line-Interactive Power Supply†, World Journal of Science and Technology, 2011. [11] Amir Faizy,etal. ,† DC Motor Control Using Chopper†, http//www. google. com. [12] Hussein TH. Khamees,†Design and Implementation of speed control Induction Machine by Using Interface programming†, International Journal of Modern Engineering Research (IJMER), Vol. , Issue. 3, May-June 2012 pp-1334-1338. [13] M. V. Ramesh,etal. † Speed Control Of Brushless DC Motor By Using Fuzzy Logic PI Controller†, ARPN Journal of Engineering and Applied Sciences,Volume 6, NO. 9, September 2011. [14] Donato Pasquariello,etal. , â€Å"Remote-Touch: A Laser Input User–Display Interaction Technology†, JournalL of Display Technology, Volume 4, NO. 1, March 2008. [15] Mehdi Ghazavi Dozein,etal. , â€Å"Speed Control of DC Motor Using Different Optimization Techniques Based PID Controller†, Journal of Basic and Applied Scientific Research,2(7)6488-6494, 2012. [16] V. Bhaskar,etal. â€Å"GSM Based Motor Monitoring and Speed Control†, International Journal of Mechanical and Industrial Engineering (IJMIE), ISSN No. 2231 –6477, Volume-1, Issue-2, 2011. [17] G. Gurusamy,etal. ,† Design of Speed Controller for Speed Control of Converter Fed DC Motor Drive using Model Order Reduction Technique†, European Journal of Scientific Research, ISSN 1450-216X Vol. 76 No. 2 (2012), pp. 288-299. [18] Ahmed Rhif ,â€Å"Stabilizing Sliding Mode Control Design And Application For a DC Motor: Speed Control†, International Journal of Instrumentation and Control Systems (IJICS), Vol. 2, No. 1, January 2012. [19] M. Dechrit,etal. † Wheelchair Stabilizing by Controlling the Speed Control of its DC Mot or†, World Academy of Science, Engineering and Technology 58 2011. [20] R. P. Dhobale,etal. ,† FPGA Implementation of Three-Phase Induction Motor Speed Control Using Fuzzy Logic and Logic Based PWM Technique†, 1st International Conference on Recent Trends in Engineering & Technology, Mar-2012 Special Issue of International Journal of electronics, Communication & Soft Computing Science & Engineering, ISSN: 2277-9477. [21] â€Å"Internal Peripheral Of AVR MCUs†,Extreme Electronics. [22] â€Å"Pin Configuration of AT mega 16†, Extreme Electronics.

Njhs Essay Samples Reviews & Guide

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A Dolls House The Analysis of Nora and Her Case of...

In Henrik Ibesens play A Doll House, Nora Helmer struggles with telling her husband, Torvald Helmer, the truth about a loan she receives for them to go to Italy when he was sick. Consequently, when Torvald learns of the news he instantly insults Nora and declares that she has ruined [his] happiness (Ibesen 93). However, when Torvald tries to dismiss his insults after receiving a note that her contract was revoked, she does not accept his apologizes and decides to leave Torvald and her children to make sense of [her]self and everything around [her] (Ibesen 100). Her selfish decision to leave makes her a bad wife and mother, but she there are a few more characteristics that makes her a bad wife. The characteristics that Nora shows in†¦show more content†¦Unfortunately, there are two players to this game, and Nora decides to betray her husband and borrow money anyhow, not only to help her aide him during his illness, but also use it to an advantage: Years from now, when I†™m no longer pretty †¦ Then it might be useful to have something up my sleeve† (Ibesen 18). Fabienne Oguer states that Torvalds motivation to play Trust is that â€Å"he believes Nora would feel guilty if she betrayed his trust (Oguer 86). Unfortunately, it only makes her feel an obligation to be an individual, separate from martial control. Nora is motivated throughout the story to be according to her free, however, she does not only want to be free of the loan she is owning to Krogstad, she wants to be free from her father and husbands control. Throughout the story, Nora feels as if she has always been treated like a doll child first by her father and then by her husband and is never given the opportunity to evolve as an individual and become a woman who has the potential to be independent and forceful (Yuehua 83). The perfect example her attempt to fulfill her potential as a woman is when she first borrows the loan from Krogstad by forging her dying fathers signa ture. Although she knows her act is wrong and against the law, she still goes on with a naà ¯ve challenge to Krogstad during their encounter about the forged signatureShow MoreRelatedA Critics Opinion of a Dolls House1743 Words   |  7 PagesOpinion of A Doll’s House In Henrik Ibsen’s A Doll’s House many views could be seen from both sides of the gender world. Critics will argue about the true meaning of the story and why Ibsen wrote the story. The main points of the play that critics discuss are sexuality i.e. feminism, the wrong doing of the father figure, and spiritual revolution. I believe these critics are each right in their own way from my understanding of the play and their ideas about the play. Sexuality or, in the case of A Doll’sRead MoreAnalysis Of A Doll s House 1005 Words   |  5 PagesA Doll’s House is an iconic play focusing on the themes of respect and reputation, lies and deceit, men and masculinity, and women and femininity. It emphasizes physical aspects of women, while excluding influence on the men s appearance, giving insight into the time period. It shows how society judged others while presenting information about how reputation influenced relationships and marriage, leaving the audience pondering what would have happened to the Helmer family if Nora had been unattractiveRead MoreA Dolls House Feminism Essay1763 Words   |  8 Pagesfeminist issues without realizing it, such as Henrik Ibsen. 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Nora talks joyfully about her love for TorvaldRead MoreCriticle Analysis of a Dolls House2519 Words   |  11 PagesCritical Analysis of A Dolls House by Henrik Ibsen Henrik Ibsen’s A Doll’s House was a controversial play for its time because it questioned societys basic rules and norms. Multiple interpretations can be applied to the drama, which allows the reader to appreciate many different aspects of the play. This paper examines how both Feminist and Marxist analyses can be applied as literary theories in discussing Ibsens play because both center on two important subject matters in the literary work:Read MoreA Doll House by Henrik Ibsen7379 Words   |  30 PagesMa. Jennifer S. Yap Dr. Sherwin Perlas World Literature January 14, 2012 A Doll House by Henrik Ibsen Translated by Rolf Fjelde I. Introduction During the late nineteenth century, women were enslaved in their gender roles and certain restrictions were enforced on them by a male dominant culture. Every woman was raised believing that they had neither self-control nor self-government but that they must yield to the control of a stronger gender. John Stuart Mill wrote in his essay, â€Å"The SubjectionRead MoreHumanities Test4641 Words   |  19 PagesShakespeares Falstaff questions the value of: honor 1.   ( T or F ) Aristotle considered plot more important than character or thought. True 2.   ( T or F ) Aristotle was both a theater critic and a philosopher. True 3.   Aristotles famous analysis of tragedy is found in a work called: The Poetics 4.   Aristotles work on comedy is how long? 5 acts long 5. What did the Commedia dellearte specialize in? 6.   What is satire? a literary genre or form, although in practice it is also found