Educational Web Tools For Graduate School Application For Sustainability

As graduate student I must equip myself with different web tools to be able to compete with today's generation.I was grateful having Prof. Ava as our  mentor because first and foremost I was already left behind by our so called ICT that is because my world evolves in the four walls of my classroom.And because of our work I never had time to manipulate and explore some gadget anymore.


WEB TOOL LINK

SCHOOPY.COM

Schoopy is a social networking site for schools. Schools can sign up and carefully create a networking site for their school that allows the school to communicate with parents, teachers and students online, as well as create "communities" within the school

Statistics as a Platform in Education for Sustainable Development

What is Sustainable Development?

Environmental, economic and social well-being for today and tomorrow

Sustainable development has been defined in many ways, but the most frequently quoted definition is from Our Common Future, also known as the Brundtland Report:^[1]
"Sustainable development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs. It contains within it two key concepts:

• the concept of needs, in particular the essential needs of the world's poor, to which overriding priority should be given; and
  
• the idea of limitations imposed by the state of technology and social organization on the environment's ability to meet present and future needs."
  

All definitions of sustainable development require that we see the world as a system—a system that connects space; and a system that connects time.
When you think of the world as a system over space, you grow to understand that air pollution from North America affects air quality in Asia, and that pesticides sprayed in Argentina could harm fish stocks off the coast of Australia.
And when you think of the world as a system over time, you start to realize that the decisions our grandparents made about how to farm the land continue to affect agricultural practice today; and the economic policies we endorse today will have an impact on urban poverty when our children are adults.
We also understand that quality of life is a system, too. It's good to be physically healthy, but what if you are poor and don't have access to education? It's good to have a secure income, but what if the air in your part of the world is unclean? And it's good to have freedom of religious expression, but what if you can't feed your family?
The concept of sustainable development is rooted in this sort of systems thinking. It helps us understand ourselves and our world. The problems we face are complex and serious—and we can't address them in the same way we created them. But we can address them.

Integration of Statistics with Education for Sustainable Development

Education for Sustainable Development

 ESD encourage people to understand the complexities of, and synergies between, the issues threatening planetary sustainability and understand and assess their own values and those of the society in which they live in the context of sustainability. ESD seeks to engage people in negotiating a sustainable future, making decisions and acting on them. While it is generally agreed on that sustainability education must be customized for individual learners

ESD aims at transformation of social structure and individual lifestyles by establishing a set of values through quality education. ESD centres on the three pillars of society, environment and economics with culture as its underlying dimension. ESD emphasizes "values of respect" ? respect for others including future generations, for diversity and difference, and for environment and resources.

In this manner, ESD is a holistic concept beyond the frameworks of existing Environmental Education and Development Education, with special emphasis on the qualitative aspects of education.

Classtool.net

What is Classtool.net?ClassTools.net is a web-based educational productivity tool. The types of products offered vary widely. Timelines, quizzes, and games are just a few of the products offered free of charge. With a paid subscription, users can access many more features that this website offers.  What Skills or Resources are Needed?  Very little technical skills are needed to get started with this website. Users should be comfortable maneuvering around websites and familiar with how to click on hyperlinks. No email or registration is required for this website.  How Can I Get Started?  Getting started is easy. After arriving at the website, users are presented with a number of tools offered. A menu on the right side of the screen shows a list of the most popular tools. By clicking on any of them, the user is taken directly to the tool. There are also some video tutorials available at the bottom of the home page.  I created a screencast that is a basic overview:  http://screencast.com/t/XSSzifxuIa  Because there are a variety of tools, there is no one way to get started. Depending on what the user needs, there are a variety of template available. A picture of the home page and tool list is below:  classtoolspic  For each tool clicked on, a set of instructions on how to use the tool pops up. There are even some examples of how to use the tool if you roll over certain hyperlinks.  Advantages  The most obvious advantage is that this tool is free. Additionally, no email or registration is required so this allows for anonymity and the possibility of students using it to create templates for their peers. Another advantage of this website is that it does offer a variety of tools and templates that can be saved to their server. When the user is ready to go back and look at their work or play a game previously created, it will be stored for up to 12 months unused or longer than that if it is used continuously.  Disadvantages  There are only a few disadvantages but, unfortunately, they may be sufficient enough to deter users. First, it is a visual mess. There are flashing and zooming graphics all over the homepage. The graphics are old-fashioned and it is probably one of the worst-looking Web 2.0 tools I have ever seen. The website’s homepage is confusing to look at and a turn-off for potential users. There are also many advertisements on the page. A paid subscription offers advertising-free access in addition to other freebees.

Introduction to Statistics

Statistics is a set of tools used to organize and analyze data. Data must either be numeric in origin or transformed by researchers into numbers. For instance, statistics could be used to analyze percentage scores English students receive on a grammar test: the percentage scores ranging from 0 to 100 are already in numeric form. Statistics could also be used to analyze grades on an essay by assigning numeric values to the letter grades, e.g., A=4, B=3, C=2, D=1, and F=0.
Employing statistics serves two purposes, (1) description and (2) prediction. Statistics are used to describe the characteristics of groups. These characteristics are referred to as variables. Data is gathered and recorded for each variable. Descriptive statistics can then be used to reveal the distribution of the data in each variable.
Statistics is also frequently used for purposes of prediction. Prediction is based on the concept of generalizability: if enough data is compiled about a particular context (e.g., students studying writing in a specific set of classrooms), the patterns revealed through analysis of the data collected about that context can be generalized (or predicted to occur in) similar contexts. The prediction of what will happen in a similar context is probabilistic. That is, the researcher is not certain that the same things will happen in other contexts; instead, the researcher can only reasonably expect that the same things will happen.
Prediction is a method employed by individuals throughout daily life. For instance, if writing students begin class every day for the first half of the semester with a five-minute freewriting exercise, then they will likely come to class the first day of the second half of the semester prepared to again freewrite for the first five minutes of class. The students will have made a prediction about the class content based on their previous experiences in the class: Because they began all previous class sessions with freewriting, it would be probable that their next class session will begin the same way. Statistics is used to perform the same function; the difference is that precise probabilities are determined in terms of the percentage chance that an outcome will occur, complete with a range of error. Prediction is a primary goal of inferential statistics.

Descriptive Statistics

Statistics is a set of tools used to organize and analyze data. Data must either be numeric in origin or transformed by researchers into numbers. For instance, statistics could be used to analyze percentage scores English students receive on a grammar test: the percentage scores ranging from 0 to 100 are already in numeric form. Statistics could also be used to analyze grades on an essay by assigning numeric values to the letter grades, e.g., A=4, B=3, C=2, D=1, and F=0.
Employing statistics serves two purposes, (1) description and (2) prediction. Statistics are used to describe the characteristics of groups. These characteristics are referred to as variables. Data is gathered and recorded for each variable. Descriptive statistics can then be used to reveal the distribution of the data in each variable.
Statistics is also frequently used for purposes of prediction. Prediction is based on the concept of generalizability: if enough data is compiled about a particular context (e.g., students studying writing in a specific set of classrooms), the patterns revealed through analysis of the data collected about that context can be generalized (or predicted to occur in) similar contexts. The prediction of what will happen in a similar context is probabilistic.

Variables

Statistics are used to explore numerical data (Levin, 1991). Numerical data are observations which are recorded in the form of numbers (Runyon, 1976). Numbers are variable in nature, which means that quantities vary according to certain factors. For examples, when analyzing the grades on student essays, scores will vary for reasons such as the writing ability of the student, the students' knowledge of the subject, and so on. In statistics, these reasons are called variables. Variables are divided into three basic categories:

• Nominal Variables
• Ordinal Variables
• Interval Variables

Measures of Central Tendency

Central tendency is measured in three ways: mean, median and mode. The mean is simply the average score of a distribution. The median is the center, or middle score within a distribution. The mode is the most frequent score within a distribution. In a normal distribution, the mean, median and mode are identical.

Measures of Variation

Measures of variation determine the range of the distribution, relative to the measures of central tendency. Where the measures of central tendency are specific data points, measures of variation are lengths between various points within the distribution. Variation is measured in terms of range, mean deviation, variance, and standard deviation (Hinkle, Wiersma and Jurs 1988).
The range is the distance between the lowest data point and the highest data point. Deviation scores are the distances between each data point and the mean.
Mean deviation is the average of the absolute values of the deviation scores; that is, mean deviation is the average distance between the mean and the data points. Closely related to the measure of mean deviation is the measure of variance.
Variance also indicates a relationship between the mean of a distribution and the data points; it is determined by averaging the sum of the squared deviations. Squaring the differences instead of taking the absolute values allows for greater flexibility in calculating further algebraic manipulations of the data. Another measure of variation is the standard deviation.
Standard deviation is the square root of the variance. This calculation is useful because it allows for the same flexibility as variance regarding further calculations and yet also expresses variation in the same units as the original measurements

T-Test

A t-test is used to determine if the scores of two groups differ on a single variable. A t-test is designed to test for the differences in mean scores. For instance, you could use a t-test to determine whether writing ability differs among students in two classrooms.
Note: A t-test is appropriate only when looking at paired data. It is useful in analyzing scores of two groups of participants on a particular variable or in analyzing scores of a single group of participants on two variables.

Z-Test

A Z-test is any statistical test for which the distribution of the test statistic under the null hypothesis can be approximated by a normal distribution.^{[dubious – discuss]} Due to the central limit theorem, many test statistics are approximately normally distributed for large samples. Therefore, many statistical tests can be performed as approximate Z-tests if the sample size is large

ANOVA

The ANOVA (analysis of variance) is a statistical test which makes a single, overall decision as to whether a significant difference is present among three or more sample means (Levin 484). An ANOVA is similar to a t-test. However, the ANOVA can also test multiple groups to see if they differ on one or more variables. The ANOVA can be used to test between-groups and within-groups differences. There are two types of ANOVAs:
One-Way ANOVA: This tests a group or groups to determine if there are differences on a single set of scores. For instance, a one-way ANOVA could determine whether freshmen, sophomores, juniors, and seniors differed in their reading ability.
Multiple ANOVA (MANOVA): This tests a group or groups to determine if there are differences on two or more variables. For instance, a MANOVA could determine whether freshmen, sophomores, juniors, and seniors differed in reading ability and whether those differences were reflected by gender. In this case, a researcher could determine (1) whether reading ability differed across class levels, (2) whether reading ability differed across gender, and (3) whether there was an interaction between class level and gender.

Correlation

Correlation tests are used to determine how strongly the scores of two variables are associated – or correlated – with each other. A researcher might want to know, for instance, whether a correlation exists between students' writing placement examination scores and their scores on a standardized test such as the ACT or SAT. Correlation is measured using values between +1.0 and -1.0. Correlations close to 0 indicate little or no relationship between two variables, while correlations close to +1.0 (or -1.0) indicate strong positive (or negative) relationships (Hayes et al. 554).
Correlation denotes positive or negative association between variables in a study. Two variables are positively associated when larger values of one tend to be accompanied by larger values of the other. The variables are negatively associated when larger values of one tend to be accompanied by smaller values of the other (Moore 208).
An example of a strong positive correlation would be the correlation between age and job experience. Typically, the longer people are alive, the more job experience they might have.
An example of a strong negative relationship might occur between the strength of people's party affiliations and their willingness to vote for a candidate from different parties. In many elections, Democrats are unlikely to vote for Republicans, and vice versa.

Regression

Regression analysis attempts to determine the best "fit" between two or more variables. The independent variable in a regression analysis is a continuous variable, and thus allows you to determine how one or more independent variables predict the values of a dependent variable.
Simple Linear Regression is the simplest form of regression. Like a correlation, it determines the extent to which one independent variables predicts a dependent variable. You can think of a simple linear regression as a correlation line. Regression analysis provides you with more information than correlation does, however. It tells you how well the line "fits" the data. That is, it tells you how closely the line comes to all of your data points. The line in the figure indicates the regression line drawn to find the best fit among a set of data points. Each dot represents a person and the axes indicate the amount of job experience and the age of that person. The dotted lines indicate the distance from the regression line. A smaller total distance indicates a better fit. Some of the information provided in a regression analysis, as a result, indicates the slope of the regression line, the R value (or correlation), and the strength of the fit (an indication of the extent to which the line can account for variations among the data points).
Multiple Linear Regression allows one to determine how well multiple independent variables predict the value of a dependent variable. A researcher might examine, for instance, how well age and experience predict a person's salary. The interesting thing here is that one would no longer be dealing with a regression "line." Instead, since the study deals with three dimensions (age, experience, and salary), it would be dealing with a plane, that is, with a two-dimensional figure. If a fourth variable was added to the equations, one would be dealing with a three-dimensional figure, and so on.