## Chapter 4:

Get downing with the research inquiry, we decide which variables in questionnaires can be analyzed to reply the research inquiries. Based on the figure of variables and a type of measured variables ( categoricalA or uninterrupted 1s ) , it is appropriately decided what statistical analysis technique is used. Tools of the analysis are specifically determined in our statistical bundle ( in this instance, SPSS ) . After making the analysis, with figures and charts, we interpret the end product and draw decisions in relation to the research inquiry.

In proceedings of informations analysis, questionnaires are delivered to pupils and instructors in April and May 2010. Eighty six English pupils, the undergraduates of ITPC of STU, and one hundred 30 of English instructors, the graduate students in Tesol of HCMCOU, are working at high schools, colleges, universities nationwide have participated in the study based on the research aim proposed in the thesis. Class observation conducted in the traditional and practical category is a necessary manner to happen out associated factors in a wider context. Data and information are collected and processed. The content is summarized in the undermentioned tabular arraies.

## 4.1. DATA COLLECTION AND ANALYSIS

## 4.1.1. Class observation

Class observation is a auxiliary study method to assist the research worker qualitatively understand context, status, factors, etc. involved in the research aim.

## 4.1.1.1. Traditional category

The traditional authorship classs organized at ITPC of STU has three categories in which each consists of 30-40 alumnus pupils and is instructed by the physician in English linguistics. First, the teacher presents a theoretical model of English composing accomplishment. Then, pupils discuss the subject in braces and in groups and afterwards, their representatives will demo thoughts, particularly development schemes of the subject. Finally, the lector helps pupils note the raised thoughts, so summarizes the result, recommends the notices and gives the assignment at place. The theoretical account helps pupils interact with the teacher and the equals face-to-face. However, delivered cognition is restricted in the text edition, the course of study is fixed in the prescribed clip, and communicative infinite is limited in a schoolroom. In this instance, traditional category does non play a chief function and is considered as a auxiliary component to online category.

## 4.1.1.2. Online category

The online authorship classs organized at multimedia room of HCMCOU has five categories in which each consists of 30-40 post-graduate pupils and is instructed by the physician in computing machine scientific discipline. First, the teacher introduces a practical acquisition environment and typical distant composing theoretical account of old class that has been wholly established here. Then, pupils are taught to utilize activities and resources integrated in Moodle and the related issues in public-service corporation package. Next, pupils are assigned in a group of two members, pattern by choosing a unit of composing in the coursebook and uploading the papers on the cyberspace. At the terminal of the session, the teacher displays the entry of the groups on the slide screen for pupils to note. Finally, the lector corrects the pupils ‘ results, suggests the betterment, and makes careful recommendations of necessary work to them at place. It is easy to acknowledge that learning and larning in the online environment helps pupils reenforce their attempt. It enables them to research into the job independently and creately, and cooperate with spouses for a peculiar intent to derive cognition and accomplishment. Therefore, e-learning plays a outstanding function in learning and larning that traditional theoretical account did non perchance get the better of as cognition can portion among scholars worldwide. Online acquisition can go on anyplace and anytime.

## 4.1.2. Responses to questionnaires

## 4.1.2.1. Questionnaires for pupils

Questionnaires for pupils comprise four subdivisions: Information about pupils ( 12 inquiries ) , Statements about Online Education ( 12 inquiries ) , Statements about Writing Skill ( 12 inquiries ) and Statements about the web site ( 10 inquiries ) . The entity of surveyed pupils is English pupils of ITPC of STU that have some apprehensions in e-learning.

## 4.1.2.1.1. Informations about pupils

## 4.1.2.1.1.1. Distributed frequence

Table 4.1 shows the informations about of ITPC of STU demonstrated by 12 statements about the gender, age, academic degree, etc. Collected information is organized into the tabular array along with frequence and per centum.

In row Position, there are 121 university pupils ( 100 % ) . In row Gender, the ratio between female and male have non much difference and 65 ( 53.7 % ) pupils are for the former and 35 1s ( 46.3 % ) is for the latter. In row Age, there are 102 ( 84.3 % ) pupils with the age from 18 to 22. The balance has the age above 23. Sing row Old ages of Post-Secondary Schooling, 27 ( 22.3 % ) pupils have been greater than four old ages to analyze English and 22 ( 18.2 % ) for three old ages, 52 ( 43.0 % ) for two old ages and 20 ( 16.5 % ) for 1 twelvemonth. Concerning Academic degree, there are 121 ( 100 % ) undergraduate pupils.

To soundly look into e-learning, it is necessary to see constellation and web of computing machine every bit good as accomplishments of user ‘s computing machine use. In this portion, there are seven inquiries about a computing machine, online classs, internet connexion, etc.

The figure of pupils who has a computing machine available at place and at a topographic point of work histories for really high rate about 118 ( 97.5 % ) and 80 ( 66.1 % ) severally. The figure of on-line classs that is greater than or equal to one is taken with 34 pupils ( 45.5 % ) . The approximative figure of hours from 1 or more they spend per hebdomad for educational intents and multi-purposes histories for 112 ( 92.6 % ) and 61 ( 50.4 % ) severally. The figure of reasonably sufficient computing machines for the acquisition needs in the multimedia lab histories for 78 ( 64.5 % ) . Internet connexion and velocity for pupils to analyze at an mean and above norm history for 77 ( 63.6 % ) .

## Table 4.1: Informations about pupils

## Table 4.1: Informations about pupils ( Cont )

## 4.1.2.1.2. Statements about on-line instruction

## 4.1.2.1.2.1. Distributed frequence

Contingency tabular array of Moodle ‘s characteristics versus pupil ‘s sentiments for 121 indiscriminately selected pupils of STU consists of 12 rows and 5 columns in which contain informations on ascertained frequences and per centums. Particularly, the 13th row along with the 6th column is entire of rows of the column and sum of columns of the row severally.

Statements about on-line instruction include 1s about its characters, activities and impact on participants sing distant preparation. It contains 12 points that are classified into five groups. They are Modular Layout, Attractiveness, Course Management, Assessment Strategies, Cooperative Learning. In each group, there are many statements. Sing a degree from high to low, each inquiry is explored in five degrees: “ Strongly Agree ” , “ Slightly Agree ” , “ Neither Agree nor Disagree ” , “ Slightly Disagree ” , and “ Strongly Disagree ” .

In the group one, “ Modular Layout ” in connexion with classs ‘ layout comprises template-based theoretical accounts to which content must be added with a degree “ Slightly Agree ” accounting for 72 ( 59.5 % ) upper limit.

In the group two, “ Attraction ” is involved with “ Interface ” , “ Activities ” , and “ Active acquisition ” . E-learning ‘s user-friendlly interface with the degree “ Slightly Agree ” histories for 69 ( 57.0 % ) . Varied activities and greater clip flexibleness for pupils with “ Somewhat Agree ” histories for 58 ( 47.9 % ) . Active and dynamic acquisition with “ Somewhat Agree ” histories for 57 ( 47.1 % ) .

In the group three, “ Course Management ” includes “ Education quality ” , “ Pull offing clip ” , “ Re-submitting assignment ” , “ Enrolling a category ” , “ Manipulating activities and resources ” , and “ Manipulating activities and resources ” with the degree “ Slightly Agree ” accounting for 50 ( 41.3 % ) , 57 ( 47.1 % ) , 51 ( 42.1 % ) , 59 ( 48.8 % ) , and 55 ( 45.5 % ) severally.

In the group four, “ Appraisal Schemes ” allows e-learning ‘s classs to execute a broad scope of response types and peer appraisal with the degree “ Slightly Agree ” accounting for 55 ( 45.5 % ) .

In the group five, “ Concerted Learning ” permits pupils to be divided into subgroups ( either seeable or separate ) and interact with each other synchronously in confab activities, or prosecute in asynchronous treatments in Wikis and Forums with the degree “ Slightly Agree ” accounting for 44 ( 36.4 % ) . They can actively join forces with other pupils and communicate with teachers during internet activities with the degree “ Slightly Agree ” accounting for 58 ( 47.9 % ) . ( See Table 4.2 )

## 4.1.2.1.2.2. Cardinal inclination

In this chapter, at subdivision “ On-line Education ” , the pupil ‘s aptitude as “ Strongly Agree ” , “ Slightly Agree ” , “ Neither Agree nor Disagree ” , “ Slightly Disagree ” , and “ Strongly Disagree ” are encoded in the signifier of Numberss 1, 2, 3, 4, 5 severally. These values are considered as group centers of uninterrupted variable. With such encoding, we enter informations in the files *.sav of SPSS, treat them and hold the undermentioned consequences.

Mean, Median and Mode indicate that typical values tend to lie centrally ( “ Strongly Agree ” , “ Slightly Agree ” ) within a set of informations arranged harmonizing to magnitude.

Standard Deviation is a graduated table of the sprinkling of a set of informations from its mean and is computed as the square root of discrepancy. For illustration, with the normal distribution, if the characteristic “ Attractiveness ” has mean = 2.2066, one criterion divergence =0.79623, so 68 % of pupils recognize that Moodle ‘s attraction is between. That means that their 68 % of pick is among the “ 1 ” = “ Strongly Agree ” , “ 2 ” = “ Somewhat Agree ” , “ 3 ” = “ Neither Agree nor Disagree ” .

For ensuing values, positive values of lopsidedness indicates that the distribution is skewed to the right, with a longer tail to the right of the distribution upper limit. The mass of the distribution is concentrated on the left of the figure ( pick ) . In other words, most pupils support on-line instruction. Figures 4.2a. shows the comparative places of the mean, average, and manner for frequence curves skewed to the right.

For ensuing values, positive values of the kurtosis ( leptokurtic: “ Layout ” , “ Management ” , “ Coorperation ” , “ Attractiveness ” , “ Assessment ” ) indicate pointed or peaked distributions. Figure 4.3a. shows the kurtosis with k & gt ; 0.

## Table 4.3: Statisticss on Online Education ( Sts )

Modular Layout

Attraction

Course Management

Appraisal Schemes

Concerted Learning

Nitrogen

Valid

121

363

605

121

242

Missing

484

242

0

484

363

Mean

2.2397

2.2066

2.1570

2.1818

2.1942

Std. Mistake of Mean

.05381

.04179

.03358

.07873

.05268

Median

2.2613a

2.1937a

2.1492a

2.1573a

2.2065a

Manner

2.00

2.00

2.00

2.00

2.00

Std. Deviation

.59196

.79623

.82599

.86603

.81955

Discrepancy

.350

.634

.682

.750

.672

Lopsidedness

-.118

.471

.373

.576

.220

Std. Mistake of Skewness

.220

.128

.099

.220

.156

Kurtosis

-.445

.461

.082

.597

-.062

Std. Mistake of Kurtosis

.437

.255

.198

.437

.312

Sum

271.00

801.00

1305.00

264.00

531.00

a. Calculated from grouped informations.

## 4.1.2.1.2.3. Histograms

In Chart 4.1, the histogram is a graphical representation of a frequence distribution with next rectangles whose breadths show category intervals and whose countries are equal to the corresponding frequences. The tallness of a rectangle is indistinguishable to the frequence divided by the breadth of the interval. The entire country of the histogram lucifers with the figure of informations. The normal curve is besides showed on the histogram.

Looking at the graph, we recognize that frequence distributions fall into columns 1, 2, 3. That means that pupils ‘ sentiment focal points on “ Strongly Agree ” , “ Slightly Agree ” , “ Neither Agree nor Disagree ” . ( See Figure 4.4 )

## Figure 4.3: The kurtosis with k & gt ; 0 and k & lt ; 0.

## B ) Platykurtic ( k & lt ; 0 )

## a ) Leptokurtic ( k & gt ; 0 )

## Figure 4.2: The comparative places of the Mean, Median, and Mode for frequence curves with positive skew and negative skew.

## a ) Positively skewed distribution

## B ) Negatively skewed distribution

## Chart 4.1: The Histogram of frequency distribution of on-line instruction

## 4.1.2.1.2.4. Chi-Square Test online instruction ( Sts )

In the first research inquiry, we recognize the independent variable “ Features ” in association with the dependant variable “ Opinion ” . To operationalize and speculate the variables, we divide the “ Features ” into five factors: “ Modular Layout ” , “ Attractiveness ” , “ Course Management ” , “ Appraisal Schemes ” , “ Concerted Learning ” and categorise “ Opinion ” into five grade: “ Strongly Agree ” , “ Slightly Agree ” , “ Neither Agree nor Disagree ” , “ Slightly Disagree ” , “ Strongly Disagree ” . All are arranged in Table 4.1 with rows and columns. The interception between rows and columns is cells where there are observed and expected frequence.

From questionnaires for pupils, we create eventuality tabular array of Moodle ‘s “ Features ” versus “ Opinion ” for 86 indiscriminately selected pupils, so execute chi-square process.

## Table 4.4: Chi-Square Trials on on-line instruction ( Sts )

Value

df

Asymp. Sig. ( 2-sided )

Pearson Chi-Square

64.785a

44

.022

Likelihood Ratio

72.801

44

.004

Linear-by-Linear Association

.261

1

.609

N of Valid Cases

1452

a. 24 cells ( 40.0 % ) have expected count less than 5. The lower limit expected count is 1.00.

The followers is the process to execute a chi-square independent trial.

## Premises:

24 cells ( 40.0 % ) have expected count less than 5.

The lower limit expected count is 1.00.

## Measure 1: Hypothesiss

Moodle ‘s characteristics and pupil ‘s sentiment are statistically independent.

Moodle ‘s characteristics and pupil ‘s sentiment are statistically dependent.

## Measure 2: Expected frequences

## ,

where R= row entire, C-column sum, and n=sample size. See Table 4.4

Measure 3: Check whether the Expected frequences sastisfy: Premises a and B

## Yes

## Yes

## Measure 4: Significance Degree

I± = 0.05

## Measure 5: The critical value

i??2i?? , df= ( r-1 ) ( c-1 ) = i??2 0.05, df=44= 60.48

## Measure 6: Test Statistic and p-value

## Figure 4.5: Standard for make up one’s minding whether or non to reject the void hypothesis

; p-value =0.022 & lt ; 0.05

## Measure 7: Decision

The value of the Test Statistic is i??2 = 64.785, which falls in the rejection part ( See Figure 4.5 ) . Therefore, we reject H0.

## Measure 8: State decision in words

At the I± = 0.05 important degree, there is grounds that Moodle ‘s characteristics and pupil ‘s sentiment are statistically dependent.

## 4.1.2.1.2.

## Figure 4.2.5. Correlation and arrested development of moodle ‘s characteristic

Using trial-errors and greedy method, we set up tabular array.

## Table 4.5

## Template

## Active_Learning

10.00

20.00

72.00

57.00

39.00

39.00

0.00

2.00

0.00

3.00

50.00

## .

## Table 4.6: Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

T

Sig.

95.0 % Confidence Interval for B

Collinearity Statisticss

Bacillus

Std. Mistake

Beta

Lower Boundary

Upper Bound

Tolerance

VIF

1

( Constant )

4.362

3.895

1.120

.344

-8.035

16.759

Template

.778

.106

.973

7.372

.005

.442

1.114

1.000

1.000

a. Dependent Variable: Active__Learning

## The arrested development equation

From the above end product, the arrested development equation is: Y = 4.362+0.778x

## The coefficient of multiple finding, R2.

## Table 4.7: Model Summaryb

Model

Roentgen

R Square

Adjusted R Square

Std. Mistake of the Estimate

1

.973a

.948

.930

6.57438

a. Forecasters: ( Constant ) , Template

B. Dependent Variable: Active_Learning

The coefficient of multiple finding is 0.948 ; hence, approximately 94.8 % of the Active_Learning is explained by Template. The arrested development equation appears to be really utile for doing anticipations since the value of R2 is near to 1.

## The theoretical account is utile for foretelling Active_Learning.

## ANOVAb

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Arrested development

2349.132

1

2349.132

54.350

.005a

Residual

129.668

3

43.223

Entire

2478.800

4

a. Forecasters: ( Constant ) , Template

B. Dependent Variable: Active_Learning

## Measure 1: Hypothesiss

: I?1 = I?2 = 0 ( Template is non a utile forecaster of Active_Learning )

: at least one I?i a‰ 0 ( Template is a utile forecaster of Active_Learning )

## Measure 2: Significance Degree

I± = 0.05

## Measure 3: Rejection Region

Reject the void hypothesis if p-value a‰¤ 0.05.

## Measure 4: ANOVA Table ( Test Statistic and p-value )

( See Table 4.8 ) F = 54.350, p-value= 0. 005 & lt ; 0.05

## Measure 5: Decision

Since p-value & lt ; 0.05, we shall reject the void hypothesis.

## Measure 6: State decision in words

At the I± = 0.05 important degree, there is grounds that at least one of the forecasters ( Template ) is utile for foretelling Active_Learning ; Therefore, the theoretical account is utile.

## Partial secret plans

## Chart 4.2: Partial Regression Plots

Active_Learning appears to be linearly related to each of the forecaster variables with no seeable possible outliers or influential observations ( no points off from the chief bunch of points ) ; therefore, Assumption 1 appears to be satisfied.

## The variables in inquiries.

We show that the unstandardized remainders ( RESs ) , studentized remainders ( sre ) , the predicted values ( pre ) , the standard mistakes of anticipation ( September ) , lower single assurance interval ( lici ) , upper single assurance ( uici ) , the lower average assurance interval ( lmci ) , and upper mean assurance interval ( umci ) can be found in the information editor window.

## The forecaster variables

## Template

## Step1: Hypothesiss

: I?1 = 0 ( Template is non utile for foretelling Active_Learning )

: I?2 a‰ 0 ( Template is utile for foretelling Active_Learning )

presuming that Template is included in the theoretical account

## Measure 2: Significance Degree

0.05

## Measure 3: Rejection Region

Reject the void hypothesis if p-value a‰¤ 0.05.

## Measure 4: Test Statistic and p-value

( see Table 4.6 ) T = 8.481, p-value=0.003 a‰¤ 0.05

## Measure 5: Decision

Since p-value a‰¤ 0.05, we shall reject the void hypothesis.

## Measure 6: State decision in words

At the I± = 0.05 important degree, there is grounds that the incline of the Template variable is non zero and, therefore, that Template is utile as a forecaster of Active_Learning.

## The inclines, , of the population arrested development line

We are 95 % confident that the incline for Template is someplace between 0.467 and 1.028. In other words, we are 95 % confident that for every single-unit addition in Revising, the mean Merchandise increases between 0.467 and 1.028.

From end product of the informations editor window ( See Table 4.9 ) , we have

Table 4.9: End product of the informations editor window

## Template

## Active Learning

## PRE_1

## RES_1

## SRE_1

## SEP_1

## LMCI_1

## UMCI_1

## LICI_1

## UICI_1

1

10.00

20.00

13.58956

6.41044

1.35123

2.75373

4.82596

22.35316

-5.94377

33.12288

2

72.00

57.00

59.91684

-2.91684

-1.15860

4.87359

44.40690

75.42678

36.56500

83.26868

3

39.00

39.00

35.25877

3.74123

0.79098

2.77814

26.41748

44.10006

15.69046

54.82708

4

0.00

2.00

6.11742

-4.11742

-0.93178

3.25015

-4.22601

16.46084

-14.17388

26.40871

5

0.00

3.00

6.11742

-3.11742

-0.70548

3.25015

-4.22601

16.46084

-14.17388

26.40871

6

50.00

## .

43.47813

3.34431

32.83505

54.12120

23.03246

63.92379

## The point estimation

The point estimation ( PRE_1 ) for the mean Active_Learning, the with 50.00 Template is 43.47813.

## Test the alternate hypothesis

## Measure 1: Hypothesiss

: = 43.40 ( when x = 50.00 )

: & gt ; 43.40 ( when x = 50.00 )

## Measure 2: Significance Degree

i?? = 0.05

## Measure 3: Critical Value ( s ) and Rejection Region ( s )

Critical Value: tI± , df = na?’ ( k+1 ) = t0.05, df = 3 = t90 % CI, df = 3 =2.353

Reject the void hypothesis if T i‚? 2.353 ( or if p-value a‰¤ 0.05 ) .

## Measure 4: Trial Statistic

= 0.02336

; p-value & lt ; 0.05

## Measure 5: Decision

Since 0.02336 & lt ; 2.353, we shall non reject the void hypothesis.

## Measure 6: State decision in words

At the I± = 0.05 important degree, there is non grounds that the average Active_Learning with 50.00 Template is greater than 43.40.

## Assurance interval

We are 95 % confident that the average Active_Learning with 50.00 Template is someplace between 32.83505 ( LMCI_1 ) and 54.12120 ( UMCI_1 ) .

## The predicted Active_Learning

The predicted Active_Learning for my category with 50.00 Template is 43.47813.

## Prediction interval

We are 95 % certain that the Active_Learning with 50.00 Template will be someplace between 23.03246 ( LICI_1 ) and 63.92379 ( UICI_1 ) .

## 4.1.2.1.3. Statements about online authorship accomplishment

## 4.1.2.1.3.1. Distributed Frequency

Statements about Writing Skill in connexion with process-based and communicative authorship comprise 12 inquiries that are divided into four phases: Pre-writing, Writing, Editing and Publishing along with related issues. Each phase is divided into more specific statement that is split in five options harmonizing to realistic background.

Foundation of educational theory to online composing learning are every bit same as traditional one. However, The former has its outstanding maps that offer synergistic and collaborative activities, a broad scope of response and appraisal every bit good as flexibleness in subjecting an assignment and inscribing in a class. Therefore, statements about online authorship are similar to traditional one in the strategy but different in content. ( See Table 4.10. )

## 4.1.2.1.3.2. Cardinal inclination and scattering

With same aforementioned convention and process, we obtain ascertained frequence, per centum of cells in Table 4.11, see the saloon chart of online authorship in Chart 4.3. , and can easy read the informations on cardinal inclination, scattering, Distribution in Table 4.11

## 4.1.2.1.3.3. Histograms

Looking at the graph ( Chart 4.3 ) , we recognize that frequence distributions fall into columns ( 1 ) , ( 2 ) , ( 3 ) . That means that elements of online composing focal point on 1s ( 1 ) , ( 2 ) , ( 3 ) .

## Table 4.10: Online Writing Skill

## Table 4.10: Online Writing Skill

## 4.1.2.1.3.4. Chi-square trials on online authorship accomplishments ( Sts )

In the 2nd research inquiry, we recognize the being of two independent variables “ Online_writing ” and “ Elementss ” . The variable “ Online_writing ” is used to demo a authorship procedure divided into four phases: Pre-writing, Writing, Editing and Publishing. The variable “ Elementss ” is options used to uttered factors involved in composing procedure. These options include five picks in which four 1s are suggested and the 5th one is reserved for readers to show their thoughts. In this portion, questionnaires for pupils are surveyed and a eventuality tabular array of online authorship and its elements for 121 indiscriminately selected pupils is set up to execute chi-square process. See Table 4.12

## Table 4.11: Statisticss on Online Writing ( Sts )

Pre_Writing

Writing

Editing

Printing

Nitrogen

Valid

544

521

273

232

Missing

0

23

271

312

Mean

2.8015

2.0653

2.4908

2.5259

Std. Mistake of Mean

.05347

.05310

.06608

.07200

Median

2.8559a

1.8052a

2.5414a

2.4324a

Manner

4.00

1.00

3.00

2.00

Std. Deviation

1.24714

1.21194

1.09179

1.09672

Discrepancy

1.555

1.469

1.192

1.203

Lopsidedness

-.048

.851

-.028

.510

Std. Mistake of Skewness

.105

.107

.147

.160

Kurtosis

-1.252

-.386

-.916

-.400

Std. Mistake of Kurtosis

.209

.214

.294

.318

Sum

1524.00

1076.00

680.00

586.00

a. Calculated from grouped informations.

## Table 4.12: Chi-Square Trials on online authorship accomplishments ( Sts )

Value

df

Asymp. Sig. ( 2-sided )

Pearson Chi-Square

502.465a

44

.000

Likelihood Ratio

463.047

44

.000

Linear-by-Linear Association

18.265

1

.000

N of Valid Cases

1570

a. 1 cells ( 1.7 % ) have expected count less than 5. The lower limit expected count is 4.92.

At the I± = 0.05 important degree, there is grounds that online authorship and its elements are statistically dependent.

## Chart 4.3: The histogram of frequence distributions of online authorship ( Sts )

## 4.1.2.1.3.

## Figure 4.2.5. Correlation and arrested development of online composing accomplishments ( Sts )

First, we set up the tabular array of relation of the factors of refering “ Online Writing ”