Student Profile

My name is Syarifah Nazurah Binti Abd Khalib.

I am 25 years old.

I was born on the 2nd of November 1987.

I am an English teacher at SK Rantau Panjang Klang. 

I am a M.ed Teaching English as a Second Language (ED 770) student in UITM.

My student number is 2011909417.

My IC number is 871102105350.

I love arts, pink and black, travelling and read poetry. 

I love my life.

Reflection

People reflect to see how far he has gone in this life. So am I.

Overall, i am blessed to be given this opportunity to take this subject because initially, I dont really like this subject because I see myself as someone artistic, compared to this subject, very structural and organised. However, i managed to get through it. Alhamdulillah, praise to God. 

This subject has taught me a lot and 'forced' me in a good way to do a lot of academic reading. Of course, for someone who loves to read stories on journey of life and the everlasting subjectivity of life, it was quite hard for me to digest all the methodology terms at first. Throughout the course, i managed to get the hang of it and sail through it as i see the importance of it to expand my professional field. 

I have faced few obstacles for this subject. My health condition and my work business have gotten into the picture for most of the time. I still remember that i have to take the second test on my own in the library due to the sickness that i had during the given test period. Moreover, i struggled a lot in doing my research proposal because my laptops were stolen two times in this semester. One week before i have to submit the research proposal, i had my tonsil removed. Heart-aching i could say, but it paid off today, now, this moment when everything is there at place and Im very proud of myself. 

 The classes were awesome. We managed to discuss on our studies and the progression of the reserach proposal. We had meaningful discussion and healthy environment where everyone helps each other in understanding any given topic. We talk, we discuss, we debate and we exchange ideas. For the better us and the future of education. BENEFICIAL.

The assessments especially the test were a bit harsh on me.  The last time I did any test was during my first year of degree, in 2007. And i had never taken any test few years after that until the first test for this subject. I was never good in test due to i really take time to understand and put everything in my own words. I dont like to memorize but the first test was ok for me. Maybe because i actually spent time read the reading and do the online quiz. The second test was different because I am still not in my good health condition. I really hope that i could get a considerable grade for the second test :p but taking those tests really put me in my students' shoe.

I am happy that everything has come to an end. That does not mean that I am happy that I dont have to attend to classes, but i am happy because I have made it through.

This post comes from my heart. Truly.

THE Research Proposal

At last, im done with it. alhamdulillah.

This is the link for my research proposal : 

THE EFFECTIVENESS OF ICT(INFORMATION AND COMMUNICATION TECHNOLOGY) IN THE LANGUAGE LAB TO ENHANCE THE ENGLISH LANGUAGE TEACHING AND LEARNING FOR PRIMARY STUDENTS

And here are some of the references that I used for this research proposal:

ICT and Classroom 


Factors Affecting School Administrators' Choices in Adopting ICT Tools in Schools – The Case of Malaysian Schools


Language Lab for MBMMBI

alhamdulillah. ;)

Understanding Research Proposal

I have found a webpage which describe what a research proposal is and how to write it. 

How To Write a Research Proposal. 

Enjoy and hope this helps!

Summary - Quantitative Data

Statistics versus Parameters

• A parameter is a characteristic of a population. It is a numerical or graphic way to summarize data obtained from the population.
• A statistic, on the other hand, is a characteristic of a sample. It is a numerical or graphic way to summarize data obtained from a sample.

Types of Numerical Data

• There are two fundamental types of numerical data a researcher can collect. Quantitative data are obtained by determining placement on a scale that indicates amount or degree. Categorical data are data obtained by determining the frequency of occurrences in each of several categories.

Types for Summarizing Quantitative Data

• There are 4 types of summarizing Qualitative Data which are frequency polygon, skewed polygons, histograms and stem leaf plots and the normal curve.

Correlation

• A correlation coefficient is a numerical index expressing the degree of relationship that exists between two quantitative variables. The one most commonly used in educational research is the Pearson r.
• A scatter plot is a graphic way to describe a relationship between two quantitative variables.

Techniques for Summarizing Categorical Data

• Researchers use various graphic techniques to summarize categorical data, including frequency tables, bar graphs, and pie charts.
• A cross break table is a graphic way to report a relationship between two or more categorical variables.

Chapter 11 Inferential Statistics

What Are Inferential Statistics?

• Inferential statistics refer to certain procedures that allow researchers to make inferences about a population based on data obtained from a sample.
• The term probability, as used in research, refers to the predicted relative frequency with which a given event will occur.

Sampling Error

• The term sampling error refers to the variations in sample statistics that occur as a result of repeated sampling from the same population.

The Distribution of Sample Means

• A sampling distribution of means is a frequency distribution resulting from plotting the means of a very large number of samples from the same population.
• The standard error of the mean is the standard deviation of a sampling distribution of means. The standard error of the difference between means is the standard deviation of a sampling distribution of differences between sample means.

Confidence Intervals

• A confidence interval is a region extending both above and below a sample statistic (such as a sample mean) within which a population parameter (such as the population mean) may be said to fall with a specified probability of being wrong.

Hypothesis Testing

• Statistical hypothesis testing is a way of determining the probability that an obtained sample statistic will occur, given a hypothetical population parameter.
• A research hypothesis specifies the nature of the relationship the researcher thinks exists in the population.
• The null hypothesis typically specifies that there is no relationship in the population.

Significance Levels

• The term significance level (or level of significance), as used in research, refers to the probability of a sample statistic occurring as a result of sampling error.
• The significance levels most commonly used in educational research are the .05 and .01 levels.
• Statistical significance and practical significance are not necessarily the same. Even if a result is statistically significant, it may not be practically (i.e., educationally) significant.

Tests of Statistical Significance

• A one-tailed test of significance involves the use of probabilities based on one-half of a sampling distribution because the research hypothesis is a directional hypothesis.
• A two-tailed test, on the other hand, involves the use of probabilities based on both sides of a sampling distribution because the research hypothesis is a nondirectional hypothesis.

Parametric Tests for Quantitative Data

• A parametric statistical test requires various kinds of assumptions about the nature of the population from which the samples involved in the research study were taken.
• Some of the commonly used parametric techniques for analyzing quantitative data include the t-test for means, ANOVA, ANCOVA, MANOVA, MANCOVA, and the t-test for r.

Parametric Tests for Categorical Data

• The most common parametric technique for analyzing categorical data is the t-test for differences in proportions.

Nonparametric Tests for Quantitative Data

• A nonparametric statistical technique makes few, if any, assumptions about the nature of the population from which the samples in the study were taken.
• Some of the commonly used nonparametric techniques for analyzing quantitative data are the Mann-Whitney U test, the Kruskal-Wallis one-way analysis of variance, the sign test, and the Friedman two-way analysis of variance.

Nonparametric Tests for Categorical Data

• The chi-square test is the nonparametric technique most commonly used to analyze categorical data.
• The contingency coefficient is a descriptive statistic indicating the degree of relationship that exists between two categorical variables.

Power of a Statistical Test

• The power of a statistical test for a particular set of data is the likelihood of identifying a difference, when it in fact exists, between population parameters.
• Parametric tests are generally, but not always, more powerful than nonparametric tests.

Chapter 12 – Statistic in Perspectives

Approaches to Research

• A good deal of educational research is done in one of two ways: either two or more groups are compared, or variables within one group are related.
• The data in a study may be either quantitative or categorical.

Comparing Groups Using Quantitative Data

• When comparing two or more groups using quantitative data, researchers can compare them through frequency polygons, calculation of averages, and calculation of spreads.
• We recommend, therefore, constructing frequency polygons, using data on the means of known groups, calculating effect sizes, and reporting confidence intervals when comparing quantitative data from two or more groups.

Comparing Groups When the Data Involved Are Categorical

• When the data are categorical, groups can be compared by reporting either percentages of frequencies in cross break tables.
• It is a good idea to report both the percentage and the number of cases in a crossbreak table, as percentages alone can be misleading.
• Therefore, we recommend constructing crossbreak tables and calculating contingency coefficients when comparing categorical data involving two or more groups.

Comparing Groups Using Categorical Data

• When you are examining relationships among categorical data within one group, we again recommend constructing crossbreak tables and calculating contingency coefficients.

Summary - Qualitative data

Qualitative Data

Qualitative data is extremely varied in nature. It includes virtually any information that can be captured that is not numerical in nature. Here are some of the major categories or types:

• In-Depth Interviews

In-Depth Interviews include both individual interviews (e.g., one-on-one) as well as "group" interviews (including focus groups). The data can be recorded in a wide variety of ways including stenography, audio recording, video recording or written notes. In depth interviews differ from direct observation primarily in the nature of the interaction. In interviews it is assumed that there is a questioner and one or more interviewees. The purpose of the interview is to probe the ideas of the interviewees about the phenomenon of interest.

• Direct Observation

Direct observation is meant very broadly here. It differs from interviewing in that the observer does not actively query the respondent. It can include everything from field research where one lives in another context or culture for a period of time to photographs that illustrate some aspect of the phenomenon. The data can be recorded in many of the same ways as interviews (stenography, audio, video) and through pictures, photos or drawings (e.g., those courtroom drawings of witnesses are a form of direct observation).

• Written Documents

Usually this refers to existing documents (as opposed transcripts of interviews conducted for the research). It can include newspapers, magazines, books, websites, memos, transcripts of conversations, annual reports, and so on. Usually written documents are analyzed with some form of content analysis.

Test 2.

I cant attend the second test due to high fever. I even got an MC for that. 

Not feeling so good now 

:(

Group Presentation

Heyya everyone!

This is the slides for our presentation - Interviews, Checklist and Observations. 

Although everyone is busy, but we had fun doing this group work. We did all the discussions online and via phone due to different geographical area. We divided the task efficiently and did all the reading to ensure all members understand everything that will be presented. We also managed to divide the slides accordingly. Although the three of us have different styles of writing and working, but we managed to pull everything through. Very happy that the presentation went well and all hard work is paid off.

Thank you from us:

Syarifah Nazurah binti Abd Khalib - 2011909417
Wani Nurfahani binti Mohd Sapuan - 2011918621
Rashidah binti Robani - 2011725353

Tell us what do you think, ok?

Summary - Interviews, Checklists, Observations

Hello!

My group members and I are required to present these topics. I will be with Wani and Kak Shidah . I hope it will turn out well.

Gambate!!

Article Review.

For one of our assessment, we have been assigned to do an article review on an article -  ‘Teaching in the Yukon: Exploring teachers’ efficacy beliefs, stress, and job satisfaction in a remote setting'. The article is written by Robert M. Klassen , Rosemary Y. Foster , Sukaina Rajani and Carley Bowmana in 2010. 

It was quite a challenge to myself as after a long break after my degree years, i have to start writing again. It took me about  2 weeks to do this assignment before the due date. Thanks to the technology especially online books and journals because that is how I get my reading from to understand and support my paper.

You can view the article from here: ‘Teaching in the Yukon: Exploringteachers’ efficacy beliefs, stress, and job satisfaction in a remote setting' - Article Review

Have a good weekend!