using the Spreading Activation Network Model, for class B, they did the post- test based on what they know. After they have finished doing it, he gave the
scores, and made comparison from these two classes’ score to find which one was better, the one using the spreading activation network model or not
3.6 Data analysis
For the students in Don Bosco Senior High School, Semarang, most of the students feels that writing is one of the problems. They think it is hard to
create the idea of the title, and also deliberate it into the form of writing. It happens because they think it is hard to find the correct theme that connects
perfectly with the title. To complete this article, the researcher compared two classes, one class used the spreading activation network model, and the other
one did not. After that the researcher teaches about the spreading activation network for the class which used it in their writing task.
In the end, the researcher received two results of their writing test. With these two results, from the class who used the spreading activation network
model and the class who did not use it, the researcher compared the scores, to find out which one has the better mean score. For this part, the researcher
used Scoring rubrics by Brown and t-Test. Scoring rubric is used to scoring the students’ writing result of the pre-test and post test for each class,
meanwhile T-test is to calculate the mean score of each class, which class is
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better. Based on Mustafid’s book 2003, “statistika elementer” page 83, T- test formula is:
Before we use the T-test, we have to find the v first. V formula is:
t
is used to find which class who has the best mean point, before we do that we must find the v.
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CHAPTER 4 FINDINGS AND DISCUSSION
This chapter the results of pretest and post-test, the analysis and the calculation result of the comparison between class A and class B pretest and post-
test result, to find the mean score, which class is better
4.1 Findings
In a way to know the score for each student, the researcher used two tests, the pre-test and the post test. The subjects were divided into two classes,
the class that used the spreading activation network model and the class that did not use the spreading activation network model.
In doing the research, the researcher had a role as a teacher too, he also gave help to the students who did not understand, for example, translated the
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Indonesian word into English. The researcher gave a task for all of the students, a writing task. In doing the task, he gave the students 15 minutes to
finish the task; he also gave no boundaries for the students to choose their ideas. The sum of meeting for each class was same, for class that used the
spreading activation network model, which was class A, contains 3 meetings. Meanwhile for the class that did not use the spreading activation network,
which is class B, contain 3 meetings also. The difference was only in the second meeting. For class A, he explained their mistakes in the pretest and
taught the Spreading Activation Network Model, for class B, he only explained their mistakes.
After he had the tests from every class, the researcher gave score based on the rubric scoring by Douglas Brown. In Douglas Brown’s scoring rubric the
score is between 1-20, because the KTSP’s scoring standard maximum is 10, so he divided the Douglas Brown’s scoring rubric into two.
For example: if the student gets 18 in Douglas Brown’s rubric scoring, the score he uses will be 9.
a First meeting: Pretest result
The researcher gave the pre test to know how far the students of both classes knew about writing and their writing skill. In this first meeting,
most of the students had problems in writing, especially to find the right vocabularies. The students who had 5 and 6 dominated the students’ score
list
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b Second Meeting
For this meeting, the researcher differentiated for each class. For class A, The researcher explained their mistakes in pretest, and also taught the
Spreading Activation Network Model. The first model that was used by the researcher was the Spreading Activation Network Model made by
Collins and Loftus 1975 Diagram 1:
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Animal
Bird
Insect Airplane
Can fly
Has engine Breathe
s
Aardvark Canary
The researcher explained the model. Animal node is divided into several nodes; they are insect, canary, bird, aardvark, and canary. Animal
also breathes, that’s why there is node of breath. Canary and bird is related, because canary is one of kinds of bird, that’s why node canary and bird is
connected. Bird also breathes same like animal. Airplane model is made based on bird body, which is why they are connected. Airplane can fly, so
can bird, their nodes are connected. In order to fly, an airplane has to have an engine, their nodes are connected.
The researcher also give another model, because, he taught the model made by Collins and Loftus was hard for them.
Diagram 2:
────────── Teacher ↑
Don Bosco Father ↑ ↑
Student Me Family→Mother→Lecturer ↓
Brother ↓
College Boy UNDIP ↓
English Faculty ──────
By making a simpler model, the researcher had helped the students to understand how to use the Spreading Activation Network Model. The
researcher explained the model. There is a family of 4, father mother, brother and me. Father works as a teacher. He teaches in Don Bosco
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Senior High School. Mother works as a lecturer. He lectures in UNDIP. Brother is a college boy, he studies in English Faculty of UNDIP. Me, is a
student. He studies in Don Bosco Senior High School. Meanwhile, for class B, the researcher only explained their mistakes they
had made in the pretest. He emphasized that in making writing task, from first paragraph until the last paragraph must be connected.
c Third meeting: Post-test result
The researcher gave the post-test to know were there any improvement from both classes. For class A, they did the writing task with
full of confident. They did not ask. Most of them finished it before the time was up. They managed to make their own model, although still a
simple form. Their writing result also showed an improvement, they have made a good writing result. For class B, they finished the writing task in a
nick of time. They still have a hard time in finishing the task. Below are the lists of scores for class A and class B
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Table 1:
Class that used Spreading Activation Network Model
Name Score
Pre-Test Final Test
1. Bella C. 2. Cahya
3. Ryan 4. Doma
5. Bagus 6. Rebecca
7. Anastasia 8. Bella B.
9. Margaretha
10. Apfel 11. Adamas
12. Ralisna 13. Nidya
14. Mega 15. Nyoman
16. Igansius 17. Stefi
18. Mitza 19. Florentina
20. Fernando 21. Silvia
22. Andreas 23. Fx. Enrico
24. Yudha 25. Gresilia
26. M. Bagie 27. Sangwiku
28. Bastian 29. Desy
6 5
5 5
6 6
6.5 6
6 6
6 6
7 7
7
6.5 6
5 5
5 6
6 6
6
5.5 5
6 6
6 6.5
5 4.5
6 5
7 7
6.5 6.5
6 6.5
6.5 7.5
7.5 7.5
7 6.5
6 5
6
6.5 6.5
4.5
3 6.5
5 6
7
6.5
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30. Yulianto 5
6.5 TOTAL
175.5 184
The tabel above was the table for class A, experimental class. In the pre-test, Nidya, Mega and Nyoman’s score was the highest. They got 7 for the
pre-test. Meanwhile for the final test, Nidya, Mega and Nyoman’s score was also the highest. They improved their score by 0.5. The total score for pre-test
was 175.5. By adding all of the score from the students, the researcher got the total score. The total score for final test was 184.
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Table 2:
Class that did not use Spreading Activation Network Model
Name Score
Pre-Test Final Test
1. Gemma 2. Febrina
3. Boby 4. Felicitas
5. Ayub 6. Dominicus
7. Gregorius 8. Dita
9. Julius
10. Cozmos 11. Vinsensius
12. Anastasya 13. Bondan
14. Laurensius 15. Eduardus
16. Alpinta 17. Stefanus
18. Dersyanto 19. Dwiki
20. Heronimus 21. Damanika
22. Maria 23. Valentina
24. Hayyu 25. Della
26. Sondhy 27. Christine
28. Andreas 29. Dionisius
6.5 6
6.5 6
6.5 6
6 6
5.5 5.5
5.5
6 6
5.5 5.5
6 6
5.5 5
6 6
6
6.5 6
6 6.5
7.5 7.5
6 6.5
6 6.5
6 6.5
6.5 6
6 6
6
5.5 5.5
4.5
6 6
5 6
6 6
6 6
6
6.5 6.5
6 6.5
7.5 7.5
6
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TOTAL 175.5
177 The tabel above was the table score list for class B, controlled class. In
the pre-test, Christine and Andreas’ score was the highest. They got 7.5 for the pre-test. Meanwhile for the final test, By adding all of the score from the
students, the researcher got the total score. The total score for final test was 177. Again, by adding all of the score from the students, the researcher got the
total score. Christine and Andreas’ score was also the highest. But they did not
improve at all, the score was still 7.5. Based on the pretest score from both classes, it can be stated that they
still had difficulties in doing the writing task. The researcher still found most of them did not finish the writing. It happened because they still confused on
the vocabularies, what were the right words that related with their ideas. From the post –test result from class A, there were a significant improvement with
the scores. With the help of the Spreading Activation Network Model, they could finish their writing and gained a better score, although there were still a
few students who did not make an improvement. Based on the researcher’s point of view, this happened because, these students were too confident with
their writing, they did not check their writing result again when there was still more time. For class B, they did not make any improvement; most of the
students’ scores were decreasing. This happened because they still had a hard time to finish their writing.
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By using these scores, he used t-test in finding which class that has the bigger mean score. But before that, he has to find the change from the pre
test’s score to the post test’s score for each class.
Table 3:
Case Processing Summary
29 100,0
,0 29
100,0 30
100,0 ,0
30 100,0
kategori WITHOUT SANM
WITH SANM perubahan
N Percent
N Percent
N Percent
Valid Missing
Total Cases
Based on the table, the sum of the sample N for class B without SANM was 29, there was no missing score. So was for class A with
SANM, the sum of the sample N was 30, there was no missing score. Missing score means that all of the students have their scores for pretest and
post-test.
Table 4:
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Descriptives
,2586 ,07286
,1094 ,4079
,2126 ,0000
,154 ,39235
,00 1,50
1,50 ,50
1,606 ,434
2,415 ,845
,6833 ,10850
,4614 ,9052
,6204 ,5000
,353 ,59427
,00 3,00
3,00 ,50
2,123 ,427
7,162 ,833
Mean Lower Bound
Upper Bound 95 Confidence
Interval for Mean 5 Trimmed Mean
Median Variance
Std. Deviation Minimum
Maximum Range
Interquartile Range Skewness
Kurtosis Mean
Lower Bound Upper Bound
95 Confidence Interval for Mean
5 Trimmed Mean Median
Variance Std. Deviation
Minimum Maximum
Range Interquartile Range
Skewness Kurtosis
kategori WITHOUT SANM
WITH SANM perubahan
Statistic Std. Error
Based on the data’s description, mean score for class B without SANM was 0.258, the median score was 0.000, the minimum score was 0.00
and the maximum score was 1.50. Meanwhile, the mean score for class A with SANM was 0.6833, the median score was 0.500, the minimum score
was 0.00, and the maximum score was 3.00. It can be concluded that the data
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for class A with SANM had a higher mean score than the data for class B without SANM.
Table 5:
Tests of Normality
,366 29
,000 ,690
29 ,000
,288 30
,000 ,761
30 ,000
kategori WITHOUT SANM
WITH SANM perubahan
Statistic df
Sig. Statistic
df Sig.
Kolmogorov-Smirnov
a
Shapiro-Wilk
Lilliefors Significance Correction a.
The table above was the normality test for knowing the data distribution, the result for the sig. point for class B without SANM was
0.000 0.05 5 and the sig point for class A with SANM was 0.000 0.05 5. It can be concluded that the data distribution for class B without
SANM and class A with SANM was not normal because the sig point was smaller than 0.05
Table 6:
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kategori
WITH SANM WITHOUT SANM
p er
u b
ah an
3.00 2.50
2.00 1.50
1.00 0.50
0.00 53
13
The graph above was boxplot for normality test. From the picture we saw that there was a score outside the boxplot, 13 for class B without
SANM and 53 for class A with SANM. It showed that the data had an abnormal distribution to support the previous test. The statistic test for an
abnormal distribution data which the sample was free and only two categories, the researcher used Mann Whitney test.
NPar Tests
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Mann-Whitney Test
Table 7:
Ranks
29 22,59
655,00 30
37,17 1115,00
59 kategori
WITHOUT SANM WITH SANM
Total perubahan
N Mean Rank
Sum of Ranks
Based on the data above, the mean rank for class A data with SANM was 37.17. Meanwhile, for class B data without SANM was 22.59. The
mean rank for class A data was higher than the mean rank for class B.
Table 8:
Test Statistics
a
220,000 655,000
-3,479 ,001
Mann-Whitney U Wilcoxon W
Z Asymp. Sig. 2-tailed
perubahan
Grouping Variable: kategori a.
4.2 Discussion
