Below is the link of my survey on the perception of the people on the 17 principles of sustainability formulated by The Rio Declaration on Environment and Development.
Friday, July 08, 2011
Statistics as a Platform in Education for Sustainable Development
The world today is in great need of people who will totally eradicate the main culprit that brought poverty as the biggest problem in the world. Illiteracy as experienced by most number of people hinders the progress and the development of the needy world.
Many would agree that the answer is still the quality and good education which caters the needs of learners. Good access to education, enhanced by the good innovations and with the aid of multimedia instructions will really bring a big change in the lives of the people.
Statistics as a platform in education will bring out indicators, on what have been achieved and what needs to be achieved for the development of our educational system. It will present the effects of social variables on the economy such as education and training.
Education enables people to enter the workforce which promotes sustainable development that is environmentally-sound, sociably -equitable, culturally-sensitive and economically-just. Education systems prepare learners to become more responsible, effective and efficient, for a peaceful and sustainable society.
Introduction
The blogging technology gives us chance to share our thoughts and ideas to the world without the hassle. Many blogs provide commentary or news on a particular subject; others function as more personal online diaries. A typical blog combines text, images, and links to other blogs, Web pages, and other media related to its topic. The ability of readers to leave comments in an interactive format is an important part of many blogs. Most blogs are primarily textual, although some focus on art (art blog), photographs (photoblog), videos (video blogging), music (MP3 blog), and audio (podcasting). Microblogging is another type of blogging, featuring very short posts.
This E-Portfolio is another way of using the technology in education. It gives us the way to communicate with the class after class hours. It is an innovation of written assignments that can be checked right away. Everybody can make the blog for a great purpose. Let’s enjoy the comfort of the blogging technology now!
I. Objectives:
- Records ongoing learning in Statistics and facilitates reflection and evaluation.
- Creates enthusiasm for responsible public writing and communication.
- Shares important statistical knowledge and ideas to other bloggers.
II. Scope of the blog
The List of Content in Educ 213
Chapter 1 Introduction to Statistics
Chapter 3 Measures of Central Location
Chapter 4 Measures of Variation
Chapter 5 Simple Correlation
Chapter 6 Simple Regression
Chapter 7 The Normal Probability Distribution
Chapter 8 Statistical Estimation & Sampling Theory
Chapter 9 Statistical Inference
Chapter10 Z-Test of One Sample Means
Chapter11 T-Test of Significance
Chapter12 ANOVA
III. My Blog OUTPUTS
1. Powerpoint Presentation
1. Powerpoint Presentation
2. Variance and Coefficient of Variation
IV. Multimedia
1. Videos Teaching Statistics
b. Train Race
c. Data Grapher
d. Linear Regression
Defining E-Portfolio
An electronic portfolio, also known as an e-portfolio or digital portfolio, is a collection of electronic evidence assembled and managed by a user, usually on the Web. Such electronic evidence may include inputted text, electronic files, images, multimedia, blog entries, and hyperlinks. E-portfolios are both demonstrations of the user's abilities and platforms for self-expression, and, if they are online, they can be maintained dynamically over time. Some e-portfolio applications permit varying degrees of audience access, so the same portfolio might be used for multiple purposes.
An e-portfolio can be seen as a type of learning record that provides actual evidence of achievement. Learning records are closely related to the Learning Plan, an emerging tool that is being used to manage learning by individuals, teams, communities of interest, and organizations. To the extent that a Personal Learning Environment captures and displays a learning record, it also might be understood to be an electronic portfolio.
Students have been taught to create digital identities using presentation software or tools to create web pages. More recently the use of virtual learning environments (VLEs) in schools and universities has led to an increased activity in the creation of e-portfolios for a variety of reasons. Most of these e-portfolios, however, are retained within the VLE and are not easily accessed outside the VLE. This results in problems of exporting data and related interoperability issues. An alternative approach is to use a system externally hosted to any institution. This permits transition through the various stages of education and employments and even into retirement.
E-portfolios, like traditional portfolios, can facilitate students' reflection on their own learning, leading to more awareness of learning strategies and needs.[1] Results of a comparative research, by M. van Wesel and Prop, between paper based portfolios and electronic portfolios in the same setting, suggest use of an electronic portfolio leads to better learning outcomes.[2](wikipedia.com)
Module 1 - Introduction to Educational Statistics
We educators sometimes find it hard to understand and interpret statistical data that we encounter in our field of work. That is the reason why we sometimes refrain from using statistics in our educational activities. We often think that statistics is very difficult to integrate in our field of work. Well, not now, for I have found out that we have a lot of statistical tools that we can easily use in interpreting data, especially on pupil's performance. This was made possible with the help of our very competent and knowledgeable professor, Dr. Ava Clare Marie O. Robles. She opened our once sleepy thoughts on the different topics in educational statistics.
Module 2 - Variables: The Subject Matter of Statistics and Research
Variables are things that we measure, control, or manipulate in research. They differ in many respects, most notably in the role they are given in our research and in the type of measures that can be applied to them. I have previous knowledge about this subject matter, but it's only now that I fully understood it. At first, I really find it hard to classify variables as to ratio, nominal, interval, and ordinal. With constant practice and focus, I've finally familiarize the different classifications of variables and its uses. Now, who says statistics is a very difficult subject to learn? Well, not me...
Module 3 - Integration of Innovation in Statistics
Do you have any idea about what a blog is? That was the question posted by Dr. Ava that gave empty result from my mind. Honestly, it was only that moment that I have learned about a blog. Not until that Dr. Ava let us create our own blog, our very own space in the net where we can post our ideas and thoughts about Educational Statistics. From then on, a multitude of ideas flowed out from our minds on the things to post on our blog. What you are reading now is one concrete proof on what came out of my mind regarding this post. This is one of the innovations in statistics that Dr. Ava introduced to us. I'm very thankful that I can now have my own space here to share what I have learned. Thank you so much Dr. Ava!
Module 4 - Measures of Central Location
That was a very fine day, the day when the first group of reporters will showcase their report. It was also the day that I was put in a great trouble, you know why? I was late that day, but I have a considerable reason to be late. Anyway, the reporters gave their best that day, they delivered their report pretty well considering the little time given for the preparations. They gave us a clear insight on the peculiarity of each measure of central tendency. They gave us also the specific uses of each measure including the hint on the most reliable measure to use. In summary, it was a very commendable report.
Module 5 - Measures of Variation
It was our turn to report, together with my groupmates Sir Reden and Maam Karen. We were all ready to report that day, but sad to say I was not able to report due to the unavailability of time. I should be thankful for I still have much time to revise and improve my report, but then my agony of thinking how I may deliver my report well was extended for another week.
Our group was tasked to report on range, average deviation, quartile deviation, standard deviation, variance, and coefficient of variation. In God's mercy, we were all able to deliver it good and sufficiently for our classmates to comprehend. The names of these measures seems to suggest a difficult topic, but it's only a name because in reality, these are all easy to comprehend and to solve. That is, if you only focus well and practice more often. A copy of my report is being linked below.
Module 6 - Measures of Correlation
In this topic, Dr. Ava made us understand the relationship between two given variables. We were made to solve problems and interpret the degree of relationship between the given variables. It was easy to solve but take extra careful in entering the numbers in your calculator, one false move and everything else fails. That's the wonder of correlation, if you commit mistake along the way, you might end up with an erroneous interpretation.
On the other hand, Dr. Ava also taught us how to use the Microsoft Excel in solving for the correlation (can be found in her book Parametric Statistics Made Easy Using MS Excel, pages 62-64). The beauty of it is that we have the choice on what to use to solve for the correlation. This is another innovation introduced by Dr. Ava.
Module 7 - The Normal Probability Distribution
Normal probability distribution? If there's a normal probability distribution, is there an abnormal probability distribution too? What a silly question, but that really crossed my mind. All these questions were given answers when Dr. Ava started to explain to us what normal probability distribution is. I have learned that normal probability distribution is considered the most prominent probability distribution in statistics. There are many reasons for this: First, the normal distribution is very tractable analytically, that is, a large number of results involving this distribution can be derived in explicit form. Second, the normal distribution arises as the outcome of the central limit theorem, which states that under mild conditions the sum of a large number of random variables is distributed approximately normally. Finally, the "bell" shape of the normal distribution makes it a convenient choice for modelling a large variety of random variables encountered in practice.
Guess what.... There really exist an abnormal distribution. The term used for this is Skewness, a distribution can be positively or negatively skewed. Another term for not normal distribution is Kurtosis, a distribution that is more peaked than the normal is leptokurtic while one that is flatter than the normal is called platykurtic.
Module 8 - Sampling Theories and Hypothesis Testing
Most of us find it hard to determine our sample population to be used in conducting our research. We hardly identity how many of the total population shall we take as our sample. Worry no more, for there is a very reliable way to solve for the sample size. This is what I have learned when Dr. Ava explained to us the topic on Sampling Theories and hypothesis testing. We can use the Slovin's Formula in determining the sample size of the population.
Dr. Ava also emphasized to us the proper way of testing the hypothesis. Before, I really don't understand what a null hypothesis is. I've heard a lot about it but I don't know what it is. Since Dr. Ava explained to us about it, I can now easily test hypothesis based on given data and situations. Reject or accept? Well, it depends on the result of the tcomputed and ttabled. If tcomputed is lesser than ttabled, then accept the null hypothesis. If the tcomputed is greater than the ttabled, then reject the null hypothesis.
Module 9 - The T-Test, Z-Test and Chi Square
T-Test? Z-Test? Chi Square? What are those? Well, these are all inferential statistical tools. T-test is used to determine the significant difference between two means of independent samples that is less than 30. Z-test is used to determine the significant difference between the sample mean and the perceived population mean. The variables are more than 30. Chi square is an inferential statistical tool which determines the observed and expected frequencies of independent variables.
In applying these tools, we used both the manual computation and the use of MS Excel. This made the learning more meaningful for we were able to compare the results from both methods.
Module 10 - Analysis of Variance (ANOVA)
In statistics, analysis of variance (ANOVA) is a collection of statistical models, and their associated procedures, in which the observed variance in a particular variable is partitioned into components attributable to different sources of variation. In its simplest form ANOVA provides a statistical test of whether or not the means of several groups are all equal, and therefore generalizes t-test to more than two groups. Doing multiple two-sample t-tests would result in an increased chance of committing a type I error. For this reason, ANOVAs are useful in comparing two, three or more means.
The reporters gave us a bird's eye view of the topic which we easily understood. They explained each step in a way that we could easily comprehend.
Module 11 - Linear and Multiple Regression Analysis
According to Dr. Ava, this is the most complicated and certainly the most difficult topic in parametric statistics because of its complicated formula. That is why, she taught us how to solve for this using the MS Excel to arrive at a very reliable answer. According to her, we might encounter mistakes along the way if we solve for it manually. Dr. Ava was correct, it is really very complicated. Despite that, I still manage to understand it little by little. Hopefully, I will be able to master solving it manually.
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