Different Cultures Coming Together in Tucson Essay Example for Free

Different Cultures Coming Together in Tucson Essay The Tucson Meet Yourself Festival is a great opportunity to familiarize yourself with different cultures, that live right in our city. You get to experience these great cultures that thrive in our community, by sampling the cultures food and watching and listening to their traditional dances and music. There is everything there from Vietnamese, Greek, Mexican, Native American, Chinese, and many more cultures featured at this event. I was lucky enough to get the opportunity to both experience and dance for this festival. I dance for the most well known Hispanic dance company in Tucson, AZ; Viva Performing Arts Center. We have gotten the opportunity to dance at this wonderful festival for the past two years and the audience is always so appreciative to watch what we have to offer, and soak in the culture through our dances. We usually showcase our well known Mexican Folklore dances as wells as our Samba, Mambo, and Salsa. Just by watching the audiences reaction to our dances not only makes me feel like I’m getting my culture out there in a positive way, but proud to be part of the Hispanic culture. After dancing, as a group we go out together into the festival and explore what this event has to offer. We spent most of the day there. It was great to see how well all the cultures adapt together in one setting. Everyone seems to get along so well and it gives the environment such a friendly and happy feel. All the food booths pull you in with the amazing aromas, that make your mouth water. They are set up right next to each other and its like walking through different countries right in Tucson. My favorite foods were the Vietnamese boba slushies and the Greek gyros. I tried to make as much room for all the different foods I wanted to try, but there was just so many it was impossible! This is definitely what keeps me going back every year. The delicious food from the many different cultures.

Philosophy of Education :: Philosophy of Teaching Statement

Philosophy of Education I knew at an early age that I wanted to be a teacher. As a child, my friends and cousins always played school and of course I always had to be the teacher! We had a cinderblock building that we called the "playhouse" and this is where my teaching career began. We would play for hours doing reading, spelling and writing. I always loved to grade the papers. And most of all I had strict behavior rules. Throughout life, even though my teaching career has been on hold, I have worked in various atmospheres with children from babysitting to different types of leadership roles. Mostly being a youth leader in our church has had a lot of various types of teaching skills. Keeping children's motivation level and interest is a difficult task. I have worked with all different ages from preschool through the sixth grade. I truly believe that sometimes a teacher is the most important role a child comes across. The impact that a teacher makes will last a life time. My elementary experiences were wonderful years that I feel have made me into the person I am today, especially my second grade teacher, Ms. Fezer. She was the greatest teacher of all times. She not only had love for her students but compassion to want them to learn and made sure they that they understood the material. She made learning fun and always had a wonderful smile. I hope to run a classroom in the manner that she did. Another great teacher that stands out is my dad. He is a retired sixth grade teacher. He was very strict, but all the children loved him. He made sure that they were ready for seventh grade. Along with teaching respect and manners and he never failed to help any child in need. These children, now grown up still make a point to speak to him when they see him out. Some even thank him for being so strict and making them work hard that they have told him it sure did pay off later on down the road. As a father, he's the greatest and always willing to help .

Non-reactive Techniques, Observation, and Experimentation

In research, the question, hypothesis, research design, data collection strategy, and data analysis procedures are rooted in previous literatures and identified before the project begins. Any changes in the proposed design while carrying out the research would be seen as weakening the validity of the research finding and, well, just bad research practice. An explanatory, also called classical experimental, design is seen as the most robust, since it follows procedures that meet the criteria for proving causality.It identifies independent and dependent variable, required random assignment of research subjects to experimental and a control group so that both groups are the same, describes procedures for manipulation of the dependent variable(s), and requires development of pretest and posttest instruments and time frames. If this design is implemented then threats to internal validity (proving causality) are removed.Descriptive designs address correlational relationships between indepe ndent and dependent variables, usually through large-scale surveys. Samples are preferably random (representative of the population being studied); however, these samples are not manipulated into control and experimental groups but are surveyed in their own settings using valid and reliable data collection instruments developed in advance of data collection. Such designs do not address threats to internal validity, but they are considered to have stronger external validity (generalizability of findings from the sample to the population of interest) than the explanatory design (Morris, 2006).The â€Å"Classical† Experimental DesignAll experimental designs are variations on the basic classical experimental design, which consists of two groups, an experimental and a control group, and two variables, an independent and a dependent variable. Units to be analyzed (e.g., subjects) are randomly assigned to each of the experimental and control groups. Units in the experimental group r eceive the independent variable (the treatment condition) that the investigator has manipulated. Contributors in the control group do not obtain the independent variable handling. Pretest and Posttest measures are taken on the independent variable(s), and the control group participants are measures at the same time as the experimental group, although no planned change or manipulation has taken place with regard to the independent variable in the control group.Researchers often use this design when they are interested in assessing change from the pretest to the posttest, as a result of a treatment or intervention. This design is also known as â€Å"pretest-posttest† or â€Å"before-after† design, to differentiate it from a posttest-only design in which one group receives a treatment, whereas the other group receives no treatment and serves as a control.The key difference in the posttest-only design is that neither group is pretested, nor only at the end of the study are both groups measured on the dependent variable. Some researchers favor this latter design over the classic two-group pre- and posttest approach because they are concerned that the pretest measures will sensitize participants or that a learning effect might take place that influences individuals’ performance on the posttest (Babbie, 2005).Ascertaining Causality between VariablesResearchers challenge to establish cause-and-effect associations linking independent and dependent variables by experimental studies.An experiment characterizes a set of processes to decide the fundamental nature of the causal association linking independent and dependent variables. â€Å"Systematically changing the value of the independent variable and measuring the effect on the dependent variable characterizes experimentation†(Maxfield & Babbie, 2004). Sometimes, the experiment appraises the outcome of arrangements of independent variable comparative to one or more dependent variables. Not co nsidering the quantity of variables considered, and experiment’s crucial purpose challenges to methodically segregate the result of at least one independent variable connected to at least one dependent variable. Simply when this occurs can one choose which variable(s) truly clarifies the happening (Morris, 2006).To conclude causality, science necessitates that an alteration in the X-variable (independent, influenced variable) go before an adjustment in the Y-variable (dependent, variable predictable for change), with suitable deliberation for scheming other variables that may in reality root the relationship. Perceptive in causal aspects in associations among variables improves one’s perception about experimental data.Controlling all potential factors that influence those effects of the independent variable(s) on the dependent variable(s) requires considerable effort, knowledge about the main factors, and creativity (Lewis-Beck, Bryman, & Liao, 2004).ConclusionIn other words, the fact that a dependent variable and an independent variable are strongly associated cannot always be extended to a logical conclusion that it is the value of the independent variable that is causing the value of the dependent variable to be whatever it is.To achieve causality between variables, one must conduct an experimental study about these variables. Oftentimes, investigational outcome are not constant as they come out. Even though field studies supply purpose insight about probable causes for experiential phenomena, the need of full power innate in such study confines capability to deduce causality. Because neither dynamic treatment of the independent variable by the experimenter nor manage over probable overriding factors happen, no assurance survives that any experiential disparity in the dependent variable essentially resulted from difference in the independent variable (Maxfield & Babbie, 2004).References:Babbie, E. R. (2005). The Basics of Social Research. Belm ont, CA: Thomson Wadsworth.Lewis-Beck, M. S., Bryman, A., & Liao, T. F. (2004). The Sage Encyclopedia of Social Science Research Methods. New York: SAGE.Maxfield, M. G., & Babbie, E. R. (2004). Research Methods for Criminal Justice and Criminology. Belmont, CA: Thomson Wadsworth.Morris, T. (2006). Social Work Research Methods: Four Alternative Paradigms. New York: SAGE. Non-reactive techniques, observation, and experimentation In research, the question, hypothesis, research design, data collection strategy, and data analysis procedures are rooted in previous literatures and identified before the project begins. Any changes in the proposed design while carrying out the research would be seen as weakening the validity of the research finding and, well, just bad research practice. An explanatory, also called classical experimental, design is seen as the most robust, since it follows procedures that meet the criteria for proving causality. It identifies independent and dependent variable, required random assignment of research subjects to experimental and a control group so that both groups are the same, describes procedures for manipulation of the dependent variable(s), and requires development of pretest and posttest instruments and time frames. If this design is implemented then threats to internal validity (proving causality) are removed.Descriptive designs address correlational relationships between indep endent and dependent variables, usually through large-scale surveys. Samples are preferably random (representative of the population being studied); however, these samples are not manipulated into control and experimental groups but are surveyed in their own settings using valid and reliable data collection instruments developed in advance of data collection. Such designs do not address threats to internal validity, but they are considered to have stronger external validity (generalizability of findings from the sample to the population of interest) than the explanatory design (Morris, 2006).The â€Å"Classical† Experimental DesignAll experimental designs are variations on the basic classical experimental design, which consists of two groups, an experimental and a control group, and two variables, an independent and a dependent variable. Units to be analyzed (e.g., subjects) are randomly assigned to each of the experimental and control groups. Units in the experimental group receive the independent variable (the treatment condition) that the investigator has manipulated. Contributors in the control group do not obtain the independent variable handling. Pretest and Posttest measures are taken on the independent variable(s), and the control group participants are measures at the same time as the experimental group, although no planned change or manipulation has taken place with regard to the independent variable in the control group.Researchers often use this design when they are interested in assessing change from the pretest to the posttest, as a result of a treatment or intervention. This design is also known as â€Å"pretest-posttest† or â€Å"before-after† design, to differentiate it from a posttest-only design in which one group receives a treatment, whereas the other group receives no treatment and serves as a control. The key difference in the posttest-only design is that neither group is pretested, nor only at the end of the study a re both groups measured on the dependent variable. Some researchers favor this latter design over the classic two-group pre- and posttest approach because they are concerned that the pretest measures will sensitize participants or that a learning effect might take place that influences individuals’ performance on the posttest (Babbie, 2005).Ascertaining Causality between VariablesResearchers challenge to establish cause-and-effect associations linking independent and dependent variables by experimental studies.An experiment characterizes a set of processes to decide the fundamental nature of the causal association linking independent and dependent variables. â€Å"Systematically changing the value of the independent variable and measuring the effect on the dependent variable characterizes experimentation†(Maxfield & Babbie, 2004). Sometimes, the experiment appraises the outcome of arrangements of independent variable comparative to one or more dependent variables. Not considering the quantity of variables considered, and experiment’s crucial purpose challenges to methodically segregate the result of at least one independent variable connected to at least one dependent variable. Simply when this occurs can one choose which variable(s) truly clarifies the happening (Morris, 2006).To conclude causality, science necessitates that an alteration in the X-variable (independent, influenced variable) go before an adjustment in the Y-variable (dependent, variable predictable for change), with suitable deliberation for scheming other variables that may in reality root the relationship. Perceptive in causal aspects in associations among variables improves one’s perception about experimental data.Controlling all potential factors that influence those effects of the independent variable(s) on the dependent variable(s) requires considerable effort, knowledge about the main factors, and creativity (Lewis-Beck, Bryman, & Liao, 2004).ConclusionIn oth er words, the fact that a dependent variable and an independent variable are strongly associated cannot always be extended to a logical conclusion that it is the value of the independent variable that is causing the value of the dependent variable to be whatever it is.To achieve causality between variables, one must conduct an experimental study about these variables. Oftentimes, investigational outcome are not constant as they come out. Even though field studies supply purpose insight about probable causes for experiential phenomena, the need of full power innate in such study confines capability to deduce causality. Because neither dynamic treatment of the independent variable by the experimenter nor manage over probable overriding factors happen, no assurance survives that any experiential disparity in the dependent variable essentially resulted from difference in the independent variable (Maxfield & Babbie, 2004).References:Babbie, E. R. (2005). The Basics of Social Research. Be lmont, CA: Thomson Wadsworth.Lewis-Beck, M. S., Bryman, A., & Liao, T. F. (2004). The Sage Encyclopedia of Social Science Research Methods. New York: SAGE.Maxfield, M. G., & Babbie, E. R. (2004). Research Methods for Criminal Justice and Criminology. Belmont, CA: Thomson Wadsworth.Morris, T. (2006). Social Work Research Methods: Four Alternative Paradigms. New York: SAGE.

Data Extraction Of Knowledge From High Volume Of Data Essay

Introduction: Data mining is extraction of knowledge from high volume of data. In this data stream mining experiment, I have used â€Å"sorted.arff† dataset contains 540888 instances and 22 attributes. I have tried two single algorithms and two ensemble algorithms, tested the accidents on road for last 15 years. Weka: Data Mining Software Weka (â€Å"Waikato Environment for knowledge Analysis†) is a collection of algorithms and tools used for data analysis. The algorithms can be applied directly or it can be called using java code, an object oriented programming language. It contains tools for pre-processing, classification, regression, clustering, associating, select attributes and visualization on given dataset. The advantages of using WEKA software is, it is freely available and platform independent. It is simple tool and it can be used by non-specialist of data mining. For testing, it doesn’t need any programming code at all. WEKA can identify .arff file format. 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