CHAPTER III RESEARCH METHODOLOGY
3.7 Criteria of Testing Hypothesis
∑ D = The total scores of difference between pre-test and post-test (X1 – X2)
n = Total sample
5. Finding out the difference of the mean score of difference between pre-test and post-test by calculate the t-test value using the following formula:
t = D
√∑ D2− (∑ D)2n
n(n−1)
Where:
T : The test of significance
D : The mean score of difference (X1-X2)
∑ D : The sum of the total score difference
∑ D2 : The square of the sum score of difference n : The total sample39
CHAPTER IV
FINDINGS AND DISCUSSION
This chapter consists of two sections, namely the research finding and the discussion of the research. The finding of the research covers the description of the result of data collected through a test that could be discussed in the section below.
4.1 Research Finding
4.1.1 Description of the Research
The researcher had given a test for student. The test was a speaking test and it consists of pre-test and post-test. The pre-test gave before treatment to know the students’ intensive speaking skill. The post-test gave after treatment to know the improvement students’ intensive speaking skill after using cooperative script learning model. Pre-test and post-test gave to know the answer of the problem statement: “Is there any Difference Students’ Intensive Speaking Skill before and after Using Cooperative Script Learning Model?”. The researcher also gave observation for teacher to know this cooperative script can be effective to increase students’ intensive speaking skill at SMK DDI Parepare. The data collected from students’ pre-test and students’ post-test at two classes, in which class XI TKJ and class XI PMR.
4.1.2 Finding though the Test
4.1.2.1 The Students’ Speaking Skill by Using Pre-test
The researcher had given pre-test to find out extent the students’ intensive speaking skill before treatment by applying cooperative script learning model. The researcher gave some question to the students’ as the pre-test to know the students’
intensive speaking skill. Every student got the questions and answered it. Then, the researcher recorded the students’ answers. After giving the pre-test, the researcher
found out the result of students’ intensive speaking skill based on the criteria of speaking skill which are pronunciation, vocabulary, fluency, and comprehension before giving treatment. The result was shown in the following table:
Table 4.1 The Students’ Score in Pre-test
No Student
Speaking Scoring
Sum Average
Classification
Pronunciation Vocabulary Fluency Comprehension
1 AD 5 6 6 6 23 5.75 Fair
2 A 6 5 5 4 20 5 Poor
3 ES 6 3 3 5 17 4.25 Poor
4 H 6 7 7 8 28 7 Good
5 HW 3 3 5 5 16 4 Poor
6 IB 3 2 4 5 14 3.5 Very Poor
7 JAA 8 7 7 8 30 7.5 Good
8 MB 7 6 6 6 25 6.25 Fair
9 MA 6 3 3 5 17 4.25 Poor
10 MR 2 2 3 4 11 2.75 Very Poor
11 R 5 6 5 6 22 5.5 Poor
12 AP 8 5 7 7 27 6.75 Good
13 MZ 2 3 2 5 12 3 Very Poor
14 MF 6 7 6 7 26 6.5 Fair
15 AA 5 6 8 7 26 6.5 Fair
16 GA 7 5 7 7 26 6.5 Fair
17 MIA 2 2 2 6 12 3 Very Poor
18 MA 5 5 7 7 24 6 Fair
19 WRL 7 8 8 8 31 7.75 Good
20 AAN 8 8 8 8 32 8 Very Good
21 FAA 2 6 5 6 19 4.75 Poor
22 E 5 7 7 7 26 6.5 Fair
∑ 484 121
(Data Source: The students’ score in pre-test)
Found on the table above about students’ score in pre-test. From 22 students showed that the sum score in pre-test was 484 and the average score in pre-test were 121. After knowing the students’ score in pre-test based on criteria of speaking skill which are pronunciation, vocabulary, fluency, and comprehension. The following table below was to know students’ intensive speaking score in pre-test:
Table 4.2 The Students’ Intensive Speaking Score in Pre-test No Students
Pre-test of Students
Total Score (X1) (X1)2 Classification
1 AD 5.75 33.06 Fair
2 A 5 25 Poor
3 ES 4.25 18.06 Poor
4 H 7 49 Good
5 HW 4 16 Poor
6 IB 3.5 12.25 Very Poor
7 JAA 7.5 56.25 Good
8 MB 6.25 39.06 Fair
9 MA 4.25 18.06 Poor
10 MR 2.75 7.56 Very Poor
11 R 5.5 30.25 Poor
12 AP 6.75 45.56 Good
13 MZ 3 9 Very Poor
14 MF 6.5 42.25 Fair
15 AA 6.5 42.25 Fair
16 GA 6.5 42.25 Fair
17 MIA 3 9 Very Poor
18 MA 6 36 Fair
19 WRL 7.75 60.06 Good
20 AAN 8 64 Very Good
21 FAA 4.75 22.56 Poor
22 E 6.5 42.25 Fair
∑ 121 719.73
(Data Source: the students’ intensive speaking score in pre-test)
Based on the result of pre-test analysis in the table above, it showed that there are four students got very poor, there are six students got poor, there are seven students got fair, there are four students got good, and there are one student got very good. However, the average score was 121 from the overall students achieved of their speaking. It described that the students’ intensive speaking skill was still poor before getting a treatment. Found on the table above about students’ intensive speaking score in pre-test. We knew the frequency of the classification score by looking the following table:
Table 4.3 The Rate Percentage of the Frequency of the Pre-test
No Classification Score Frequency Percentage (%)
1 Very good 8.0 – 10 1 4.54%
2 Good 6.6 – 7.9 4 18.18%
3 Fair 5.6 – 6.5 7 31.81%
4 Poor 4.0 – 5.5 6 27.27%
5 Very poor ≤ 4.0 4 18.18%
Total 22 100%
(Data Source: the rate percentage of the frequency of the pre-test)
The table above described that one student classified into very good with rate percentage (4.45%), there were four students classified into good with rate percentage (18.18%), there were seven students classified into fair with rate percentage (31.81%), there were six students classified into poor with rate percentage (27.27%), and there were four students classified into very poor with rate percentage (18.18%).
4.1.2.2 The mean score and the standard deviation in the pre-test
The following are the process of calculating to find out the mean score and standard deviation based on the calculating of student’s score in the pre-test.
Calculating the mean score of pre-test as follow:
Χ̅ = ∑ χ n In which:
Χ̅ = Mean Score
∑ χ = Total Score
n = The Total Number of Students Answer:
Χ̅ = 121 22 Χ̅ = 5.5
So, the mean score of pre-test (X1) is 5.5
After determining the mean score of pre-test was 5.5. It could be seen that student’s intensive speaking skill was in poor category. From that analyzing, it had shown that almost of the 22 student skills in speaking was still low because most of the students got fair, poor, and very poor. The total score in pre-test was still low.
Standard derivation of pre-test as follow:
SS = ∑χ2− (∑ χ)2 n SS = 719.73 − (121)2
22 SS = 719.73 − 14641
22 SS = 719.73 − 665.5 SS = 54.2
So, the sum of square is 54.2
SD = √ SS n − 1
SD = √ 54.2 22 − 1
SD = √54.2 21 SD = √0.25 SD = 0.5
Thus, the standard deviation (SD) of pre-test is 0.5
After determining the mean score (X1) of pre-test was 5.5 and the standard deviation (SD) of pre-test was 0.5. It had shown that student’s intensive speaking skill was in poor category.
4.1.2.3 The Students’ Speaking Skill by Using Post-test
The researcher had given post-test to know the improvement students’
intensive speaking skill after using cooperative script learning model for six meetings. Most of them were better than before. They could speak English fluenly with a good pronunce. The result was shown in the following table:
Table 4.4 The Students’ Score in Post-test
No Student
Speaking Scoring
Sum Average
Classification
Pronunciation Vocabulary Fluency Comprehension
1 AD 7 7 7 7 28 7 Good
2 A 8 7 6 7 28 7 Good
3 ES 6 6 5 6 23 5.75 Fair
4 H 8 8 8 9 33 8.25 Very Good
5 HW 5 6 5 7 23 5.75 Fair
6 IB 5 6 5 6 22 5.5 Poor
7 JAA 9 8 8 9 34 8.5 Very Good
8 MB 7 7 8 7 29 7.25 Good
9 MA 7 6 6 8 27 6.75 Good
10 MR 5 5 4 6 20 5 Poor
11 R 8 7 7 8 30 7.5 Good
12 AP 9 7 8 8 32 8 Very Good
13 MZ 5 5 5 6 21 5.25 Poor
14 MF 9 8 8 8 33 8.25 Very Good
15 AA 9 7 9 8 33 8.25 Very Good
16 GA 8 8 8 8 32 8 Very Good
17 MIA 6 5 5 6 22 5.5 Poor
18 MA 7 6 8 8 29 7.25 Good
19 WRL 9 8 9 8 34 8.5 Very Good
20 AAN 9 8 9 9 35 8.75 Very Good
21 FAA 5 6 6 7 24 6 Fair
22 E 6 7 7 8 28 7 Good
∑ 620 155
(Data Source: the students’ score in post-test)
Found on the table above about students’ score in post-test. From 21 students showed that the sum score in post-test was 620 and the average score in post-test were 155. After knowing the students’ score in pre-test based on criteria of speaking skill which are pronunciation, vocabulary, fluency, and comprehension. The following table below was to know students’ intensive speaking score in post-test:
Table 4.5 The Students’ Intensive Speaking Score in Post-test No Students
Post-test of Students
Total Score (X2) (X2)2 Classification
1 AD 7 49 Good
2 A 7 49 Good
3 ES 5.75 33.06 Fair
4 H 8.25 68.06 Very Good
5 HW 5.75 33.06 Fair
6 IB 5.5 30.25 Poor
7 JAA 8.5 72.25 Very Good
8 MB 7.25 52.56 Good
9 MA 6.75 45.56 Good
10 MR 5 25 Poor
11 R 7.5 56.25 Good
12 AP 8 64 Very Good
13 MZ 5.25 27.56 Poor
14 MF 8.25 68.06 Very Good
15 AA 8.25 68.06 Very Good
16 GA 8 64 Very Good
17 MIA 5.5 30.25 Poor
18 MA 7.25 52.56 Good
19 WRL 8.5 72.25 Very Good
20 AAN 8.75 76.56 Very Good
21 FAA 6 36 Fair
22 E 7 49 Good
∑ 155 1122.35
(Data Source: the students’ intensive speaking score in post-test)
Based on the result of the post-test analysis in the table above, it showed that there are eight students got very good, there are seven students got good, there are three students got fair, and there are four students got poor category. However, the average score is 155 from the overall students achieved of their speaking. It described that the quality of the students’ intensive speaking skill was good. There was improvement after getting treatment by using cooperative script learning model.
Found on the table above about students’ intensive speaking score in post-test. We knew the frequency of the classification score by looking the following table:
Table 4.6 The Rate Percentage of the Frequency of the Post-test
No Classification Score Frequency Percentage (%)
1 Very good 8.0 – 10 8 36.36%
2 Good 6.6 – 7.9 7 31.81%
3 Fair 5.6 – 6.5 3 13.63%
4 Poor 4.0 – 5.5 4 18.18%
5 Very poor ≤ 4.0 0 0
Total 22 100%
(Data Source: the rate percentage of the frequency of the post-test)
The table above described that eight student classified into very good with rate percentage (36.36%), there were seven students classified into good with rate percentage (31.81%), there were three students classified into fair with rate percentage (13.63%), and there were four students classified into poor with rate percentage (18.18%). The following are the process of calculating to find out the mean score and standard deviation based on the calculating of student’s score in the post-test.
4.1.2.4 The Mean Score and the Standard Derivation in Post-test
The following are the process of calculating to find out the mean score and standard deviation based on the calculating of student’s score in the post-test.
Calculating the mean score of post-test as follow:
Χ̅ = ∑ χ n In which:
Χ̅ = Mean Score
∑ χ = Total Score
n = The Total Number of Students Answer:
Χ̅ = 155 22 Χ̅ = 7
So, the mean score of post-test (X2) is 7
After determining the mean score of post-test was 7. It could be seen that student’s intensive speaking skill was in a good category.
Standard derivation of post-test as follow:
SS = ∑χ2− (∑ χ)2 n
SS = 1122.35 − (155)2 22 SS = 1122.35 − 24025
22 SS = 1122.35 − 1092 SS = 30.35
So, the sum of square is 30.35 SD = √ SS
n − 1
SD = √30.35 22 − 1
SD = √30.35 21 SD = √1.44 SD = 1.2
Thus, the standard deviation (SD) of post-test is 1.2
After determining the mean score (X2) of post-test was 7 and the standard deviation (SD) of post-test was 1.2. It had shown that student’s intensive speaking skill was in a good category.
4.1.3 The Overall Result of Pre-test and Post-test
The comparison of the gained score between pre-test and post-test can be illustrated as follow:
Table 4.7 The Comparison Between Pre-test and Post-test Result
No Student The Students’ Score
Pre-test Post-test
1 AD 5.75 7
2 A 5 7
3 ES 4.25 5.75
4 H 7 8.25
5 HW 4 5.75
6 IB 3.5 5.5
7 JAA 7.5 8.5
8 MB 6.25 7.25
9 MA 4.25 6.75
10 MR 2.75 5
11 R 5.5 7.5
12 AP 6.75 8
13 MZ 3 5.25
14 MF 6.5 8.25
15 AA 6.5 8.25
16 GA 6.5 8
17 MIA 3 5.5
18 MA 6 7.25
19 WRL 7.75 8.5
20 AAN 8 8.75
21 FAA 4.75 6
22 E 6.5 7
∑ 121 155
Mean Score 5.5 7
Max Score 8 8.75
Min Score 2.75 5
(Source: primary data processing)
The table above showed that the students got improvement by gaining score before and after treatment. It proved that the students got improvement in their intensive speaking skill by using cooperative script learning model. The improvement could be measured by presenting the minimum and maximum score of pre-test and post-test. The minimum score of pre-test was 2.75 and the maximum was 8, beside
that the minimum score of post-test was 5 and the maximum score of post-test was 8.75. The mean of pre-test was 5.5 and the mean of post-test was 7. Before treatment the students got poor category but after doing treatment by cooperative script learning model the students got good cetegory, it means that there was improvement with students’ intensive speking skill.
The result of pre-test and the result of post-test were presented in the following:
Table 4.8 The Mean Score and Standard Derivation of Pre-test and Posttest
Test Mean Score ( Χ̅ ) Standard Derivation (SD)
Pre-test 5.5 0.5
Post-test 7 1.2
(Data’ Source: the mean score and standard derivation of pre-test and post test)
The data in table 4.2 and 4.3 showed that the mean score pre-test (X1) was 5.5 while the mean score post-test (X2) was 7. The standard derivation of pre-test was 0.5 while the standard derivation of post-test was 1.2. As the result at this item was the mean score pre-test greater than the mean score post-test. It means that there was improvement students’ intensive speaking skill after using cooperative script learning model.
4.1.4 Data Analysis
In analyzing the data, t-test was used to make it easier to test the hypotheses.
The formula of the t-test is as follows:
t = D
√∑ D2− (∑ D)2n
n(n−1)
Before analyzing the data by using the t-test formula, there were several steps that should be done as follows.
4.1.4.1 T-test Value
T-test used to ensure that students got an improvement after giving treatment.
The following is the table to find out the difference of the mean score between pre- test and posttest.
Table 4.9 The Worksheet of the Calculating of the Score on Pre-test and Posttest
No Pre-test Post-test (D) (D)2
1 5.75 7 1.25 1.56
2 5 7 2 4
3 4.25 5.75 1.5 2.25
4 7 8.25 1.25 1.56
5 4 5.75 0.75 0.56
6 3.5 5.5 2 4
7 7.5 8.5 1 1
8 6.25 7.25 1 1
9 4.25 6.75 2.5 6.25
10 2.75 5 2.25 5
11 5.5 7.5 2 4
12 6.75 8 1.25 1.56
13 3 5.25 2.25 5
14 6.5 8.25 1.75 3
15 6.5 8.25 1.75 3
16 6.5 8 1.5 2.25
17 3 5.5 2.5 6.25
18 6 7.25 1.25 1.56
19 7.75 8.5 0.75 0.56
20 8 8.75 0.75 0.56
21 4.75 6 1.25 1.56
22 6.5 7 0.5 0.25
∑ 121 155 33 56.73
Mean
Score 5.5 7
(Data Source: the worksheet of the calculating on pre-test and post-test)
In the other to see the student’s score, the following is t-test was statically applied:
1. Calculating the mean score of difference between pre-test and post-test by using the following formula:
D = ∑ D n D = 33 22 D = 1.5
So, the mean score of difference is 1.5
2. Determining the standard deviation by applying this formula:
SS = ∑χ2− (∑ χ)2 n SS = 56.73 − (33)2
22 SS = 56.73 − 1089
22 SS = 56.73 − 49.5 SS = 7.23
So, The sum of square is 7.23 SD = √ SS
n − 1
SD = √ 7.23 22 − 1
SD = √7.23 21 SD = √0.34 SD = 0.58
So, The standard deviation is 0.58
3. Determining the difference of the mean score of difference between pre-test and post-test by calculate the t-test value using the following formula:
The calculation the t-test value
t = D
√∑ D2− (∑ D)2n
n(n−1)
t = 1.5
√56.73− (33)222
22(22−1)
t = 1.5
√56.73− (33)222
22(22−1)
t = 1.5
√56.73− (33)222
22(21)
t = 1.5
√56.73−
1089 22 22(21)
t = 1.5
√56.73− 49.5 462
t = 1.5
√7.23
462
t = 1.5
√0.015 t = 1.5
0.125 t = 12.5
So, The t-test value is 12.5 4.1.4.2 Test of Significant
In order to know whether the means score of the pre-test and the means score of the post-test was significantly different, the researcher used t-test. The result of t- test was t-test = 12.5. To find out the degree of freedom (𝒅𝒇) the researcher used following formula:
𝒅𝒇 = n – 1 𝒅𝒇 = 22 – 1 𝒅𝒇 = 21
After obtaining the degrees of freedom, looking at t-table (tt) at the degree of freedom 21 in significant degrees of 0.05 (5%), the t-table (tt) was 1.721. Then, the value of the t-test was 12.5. The value of the t-test was greater than the t-table (12.5 >
1.721). It means that there was difference students’ intensive speaking skill before and after using cooperative script learning model at SMK DDI Parepare.
4.1.5 The Improvement Student’s Intensive Speaking Skill after Using Cooperative Script Learning Model
The post-test aimed to know how was the improvement student’s intensive speaking skill after using cooperative script learning model. After using cooperative script learning model, the students’ intensive speaking skill had increased. Based on the result of mean score in post-test had increased from the mean score was 5.5 became the mean score was 7. It had shown that student’s intensive speaking skill had changed from poor category became good category.
4.1.6 The Difference Students’ Intensive Speaking Skill Before and After Using Cooperative Script Learning Model
The pre-test and the post-test gave to know the difference students’ intensive speaking skill before and after using cooperative script learning model. Measuring the students’ intensive speaking skill before and after being taught by using cooperative script learning model could be seen at students’ score in pretest and posttest. It could be said that the implementation of cooperative script learning model could be effective to increase students’ intensive speaking skill if the students’ score of posttest was higher than the students’ score pretest. By looking at the research finding, found that the mean score of pretest was 5.5 and the mean score of posttest was 7.
From that finding, it could be interpreted that students’ intensive speaking skill before being taught by using cooperative script learning model was lower if it compared with the students’ intensive speaking skill after being taught by using cooperative script learning model. It implicated that using cooperative script learning model could be effective to increase students’ intensive speaking skill. It means that there was difference students’ intensive speaking skill before and after using cooperative script learning model.
Furthermore, to make a conclusion about the effectiveness of cooperative script learning model to increase students’ intensive speaking skill at SMK DDI Parepare, it could be done by analyzing the data using to and compared it with the t- table. The result of the data analyzes showed that to (12.5) > tt (1.721). It means that there was difference students’ intensive speaking skill before and after using cooperative script learning model at SMK DDI Parepare.
4.2 Discussion
This section provides the discussion about the finding that showed in previous section. The discussion of this research provides insight about the implementation of cooperative script learning model to increase students’ intensive speaking skill.
4.2.1 Data Interpretation
Based on data analysis, if to (t-test) is higher than tt (t-table), (12.5 > 1.721), the null hypothesis (H0) was rejected. It should be concluded that there was difference students’ intensive speaking skill before and after using cooperative script learning model at SMK DDI Parepare.
4.2.2 The Implementation of Cooperative Script Learning Model to Increase Students’ Intensive Speaking Skill
In the treatment process, the researcher took eight meetings include pre-test and post-test in teaching cooperative script learning model at the class (XI TKJ) and the class (XI PMR) to increase students’ intensive speaking skill. As the theory in chapter II, the researcher did the treatment by following the procedure in teaching cooperative script learning model.
The first meeting before the researcher gave treatment that was conducted on Wednesday October 30th, 2019 which in the class of XI TKJ and XI PMR, the students were given the pre-test to measure their intensive speaking skill. After the researcher opened the meeting, the researcher gave some test to the students’ as the pre-test to know extent the students’ intensive speaking skill. Before the researcher gave the test, the researcher asked for every student about do you ever speak English?
The type of test is interview about introduction yourself. Every student got the question and answered it.
The second meeting was conducted on Tuesday November 5th, 2019. This meeting was a first treatment after giving the pre-test. The researcher greets the student. The student take a prayer before the student study. After that, the researcher explained expression of relief, pain, and pleasure and the student listen to model of pronunciation and the student repeated what the researcher said. Then, the researcher explained about cooperative script learning model and the researcher has divided the students in some group, every group consist of two students. After that, the researcher gave a manuscript in the form of dialogue completion task to every group. Then, the researcher told stage of cooperative script teaching, they are: mood, understand, recall, detect, elaborate, and review. The researcher gave time to complete the dialogue. After that, every group rose in front of the class to tell the results of the group. Then, the researcher concluded the material from the student. Finally, the researcher closed the class.
The third meeting was conducted on Wednesday November 6th, 2019. The researcher greets the student. The student take a prayer before the student study. After that, the researcher explained expression of agreement and expression of disagreement. Then, the researcher mentioned the name of student according to the absence then the student mentioned the pronunciation of the word indicated by the researcher. Then, students do cooperative script learning model as the previous session and the researcher has divided the students in some group, every group consist of two students. After that, the researcher gave a manuscript in the form of dialogue completion task to every group. Then, the researcher told stage of cooperative script teaching, they are: mood, understand, recall, detect, elaborate, and review. The researcher gave time to complete the dialogue. After that, every group