CHAPTER III RESEARCH METHOD
F. The Technique of Data Analysis
The data get from pre-testand post-test was analyzed through the following steps :
1. Scoring the students’ answer :
𝑺𝒄𝒐𝒓𝒆 = 𝒕𝒉𝒆 𝒄𝒐𝒓𝒓𝒆𝒄𝒕 𝒂𝒏𝒔𝒘𝒆𝒓
𝒕𝒐𝒕𝒂𝒍 𝒏𝒖𝒎𝒃𝒆𝒓 𝒐𝒇 𝒊𝒕𝒆𝒎× 𝟏𝟎𝟎
(Depdikbud in Wafaa, 2017)
Score Classification Criteria
96-100 excellent Students can identify Verb and Noun vocabulary
Students can find out the meaning of vocabulary Verb and Noun
Students can write vocabulary Verb and Noun
Students can memorizing vocabulary Verb and Noun
Students can pronounce vocabulary Verb and Noun
Students can use vocabulary Verb and Noun correctly
86-95 very good Students can identify Verb and Noun vocabulary
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Students can find out the meaning of vocabulary Verb and Noun
Students can write vocabulary Verb and Noun
Students can memorizing vocabulary Verb and Noun
Students can pronounce vocabulary Verb and Noun
Students can not use vocabulary Verb and Noun correctly
76-85 good Students can identify Verb and Noun vocabulary
Students can find out the meaning of vocabulary Verb and Noun
Students can write vocabulary Verb and Noun
Students can memorizing vocabulary Verb and Noun
Students can not pronounce vocabulary Verb and Noun
Students can not use vocabulary Verb and Noun correctly
66-75 fairly good Students can identify Verb and Noun vocabulary
Students can find out the meaning of vocabulary Verb and Noun
Students can write vocabulary Verb and Noun
Students can not memorizing vocabulary Verb and Noun
Students can not pronounce vocabulary Verb and Noun
Students can not use vocabulary Verb and Noun correctly
56-65 Fair Students can identify Verb and Noun vocabulary
Students can find out the meaning of vocabulary Verb and Noun
Students can not write vocabulary Verb and Noun
Students can not memorizing vocabulary Verb and Noun
Students can not pronounce vocabulary Verb and Noun
Students can not use vocabulary Verb and Noun correctly
46-55 poor Students can identify Verb and Noun vocabulary
Students can not find out the meaning of vocabulary Verb and Noun
Students can not write vocabulary Verb and Noun
Students can not memorizing vocabulary Verb and Noun
Students can not pronounce vocabulary Verb and Noun
Students can not use vocabulary Verb and Noun correctly
0-45 very poor Students can not identify Verb and Noun vocabulary
Students can not find out the meaning of vocabulary Verb and Noun
Students can not write vocabulary Verb and Noun
Students can not memorizing vocabulary Verb and Noun
Students can not pronounce vocabulary Verb and Noun
Students can not use vocabulary Verb and Noun correctly
2. The classification of the students’ score calculating the mean score of the students’ vocabulary test by using the following formula :
𝑥 = ∑ 𝑑
𝑁
Notes :
X = mean score
Σd = total of students’ score
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N = the number of students’
(Gay, 2012) 3. From the table classification, the research calculates the value of percentage
gets test through the following formula :
𝑷 = 𝑭
𝑵× 𝟏𝟎𝟎 P = percentage
F = number of correct N = Number of sample
(Arikunto and Cepi, 2010
33 CHAPTER IV
RESEARCH FINDINGS AND DISCUSSION
A. Research Finding
1. Data Description of Data
In this chapter, the researcher presents a description of the data result.
The data used in this research were quantitative. The quantitative data were taken from the test that consisted of pre-test and post-test. The pre-test was given before giving the treatment to the students and the post-test was given in the last meeting. The result of the students' scores can be seen in the following table.
a. Using Hello English Application in Teaching Vocabulary
The description of the results of the data analysis will be included in the research findings. The description of the results of data analysis about enriching students' vocabulary using the Hello English application, where data was collected from 20 students using blank fields and answers to questions. The test was given by the researcher in the form of pre-test and post-test.
This research was conducted for six meetings which were attended by 20 students. Pre-test and post-test material on vocabulary nouns and verbs.
Based on the table the number of the students were 20 students. The total score of pre-test is 1350 and post-test 1625. From the table above, the mean of pre-test is 67.50 and the meanof post-test is 81.25.
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b. The classification and frequency of pre-test and post-test No Categories Pre-test Post-test
Freq % Freq %
1. Excellent 0 0% 0 0%
2. Very good 0 0% 4 20%
3. Good 1 5% 9 45%
4. Fairly Good 9 45% 7 35%
5. Fair 8 40% 0 0%
6. Poor 1 5% 0 0%
7. Very poor 1 5% 0 0%
Total 20 100% 20 100%
Table 4.1. Classification and frequency of students pre-test score
Table 4.1 shows the categories, frequency, and percentage score of the students' vocabulary test where there was a difference between the pre-test and post-test. In the pre-test, almost half of the total students scored in the fairly good category with a percentage of 45%. In addition, there were no student that fall into the very good and excellent categories. Meanwhile, the post-test shows an increase in student scores in the very good category by 4 students (20%), good by 9 students (45%), and no more student scores in the fair, poor, very good categories. However, there are still no students who are in the excellent category. This increasecan be seen more clearly in the following chart.
Figure 4.1. chart of pre-test and post-test 0
4 1
9 9
8 7
1 1 0 0 0
Pre-test Post-test
Very Good Good Fairly Good
Fair Poor Very Poor
Based on the figure d above, it shows that there is a significant difference between the frequency of student rates in the pre-test and post-test.
In the pre-test there were still students in the fair, poor, and very poor categories. The increase in the post-test is that there are no more students in the three categories and the frequency value of increasing student scores is very good and good, although there are still some students in the fairly good category.
c. The total scores of the studentsvocabulary test by using SPSS From the table 2, it can be seen that the learning outcomes the second grade of the students at MTs Bani Rauf Sungguminasaare seen from the total pre-test and post-test scores. After that it was entered into the SPSS application. In this case, to find out the results of students learning vocabulary. Student learning outcomes can be seen from the following table:
Table 4.2. Pretest Learning Outcomes in Mts Bani Rauf Sungguminasa using SPSS Based on the table above, it can be seen that the maximum pretest score obtained the second grade of the students at MTs Bani Rauf Sungguminasawas 85, while the minimum score is 40. The mean obtained is 67,50 with a standard deviation of 9,528. While, the maximum post-test score obtained is 95, while the minimum score is 70. The mean obtained is 81,25 with a standard deviation of 7,048.
Paired Samples Statistics
Mean N Std. Deviation Std. Error Mean PRE-TEST
POST-TEST
67,50 20 9,528 2,131
81,25 20 7,048 1,576
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2. The analysis of data using SPSS a. Normality Test
This test is conducted before calculating the t-test. It purposed to know whether the data is normally distributed or not. The writer used Kolmogorov-Smirnov as presented in Table 7 and 8. The data is normally distributed because the significance is higher than α = 0.05 (5%). The result can be described as below:
Table 4.3 Test Normality using SPSS
From the table above, it can be seen that the significance of the data in the table of Kolmogorov-Smirnov from pre-test was 0.073 and post-test was 0.176. It means that the pre-test data is normally distributed, because the significance score is higher than α = 0.05.
b. Homogeneity Test
After the writer had the data result in normality test, the next step was homogeneity test by using the Levene statistic in order to know whether the data is homogeneous or not. If the result of the data calculation is higher than α = 0.05, it means the data would be homogenous. The result can be seen on table below:
Tests of Normality
Kolmogorov-Smirnova Shapiro-Wilk Statistic Df Sig. Statistic Df Sig.
PRE-TEST POST-TEST
,184 20 ,073 ,919 20 ,094
,162 20 ,176 ,943 20 ,274
a. Lilliefors Significance Correction
Test of Homogeneity of Variance
Levene Statistic df1 df2 Sig.
1,176 1 38 ,285
Table 4.4 Test Homogenity using SPSS
Analysis of data at SPSS using homogeneity calculations, obtained a p-value = 0.285. Conditions that must be met as a condition so that the data comes from a homogeneous population, namely t-value> α, α = 0.05. Because the p-value = 0.285> α = 0.05, based on the results of these calculations it can be concluded that the population variance comes from the same population (homogeneous).
c. Test of hyphothesis
Hypothesis testing has a function to determine the provisional conjecture formulated by the researcher. The following was the hypothesis that the researcher previously set. Based on the homogeneity and normality test, the statistics applied were parametic statistics with paired sample t-tests.
The following was the hypothesis that the authors set earlier:
1. Null Hypothesis (H0): "Hello English" Application doesn’t enrich English vocabulary of the second-grade students of Mts Bani Rauf Sungguminasa.
2. Alternative Hypothesis (Ha): "Hello English" Application enriches English vocabulary of the second-grade students of Mts Bani Rauf Sungguminasa.
According to Singgih Santoso (2014: 265), Guidelines for decision making in the paired sample t-test based on the significance value (Sig.) Of the SPSS output results are as follows.
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1. If the t-value is Sig. (2-tailed) <0.05, then H0 is rejected and Ha is accepted.
2. If the t-value is Sig. (2-tailed)> 0.05, then H0 is accepted and Ha is rejected.
Table 4.5 Paired Sample Test
Based on the "Paired Samples Test" output table above, the Sig. (2- tailed) is 0.000<0.05, then H0 is rejected and Ha is accepted. So it can be denied that there is an mean score difference between the Pre Test and Post Test learning outcomes, which means that there is "Hello English"
Application enriches English vocabulary of the second-grade students of Mts Bani Rauf Sungguminasa.
Apart from comparing the significance p-value (Sig.) With a probability of 0.05, there are other ways that can be done to test the hypothesis in this paired sample t test. Namely by comparing the value of t count with t table. The basic for making decisions is as follows.
1. If the value of t count> t table, then H0 is rejected and Ha is accepted.
2. In fact, if the value of t count <t table, then H0 is accepted and Ha is rejected.
Paired Differences
T df
Sig.
(2- tailed) Mean
Std.
Deviation
Std.
Error Mean
95% Confidence Interval of the
Difference Lower Upper PRE-TEST -
POST-TEST
-13,75000 8,40974 1,88047 -17,68588 -9,81412 -7,312 19 ,000
Based on the output table "Paired Samples Test" above, it is known that t count is negative, which is -7,312. T count is negative because it is because the mean score of the Pre Test was low than the mean score of the Post Test. In the context of cases like this, the negative t count can be accessed by the positive. So that the value of t count becomes 7,312. Next is the stage of finding the t table value, where the t table is searched based on the df value (degrees of freedom or degrees of freedom) and the significance value (α).
From the output above, it is known that the df value is 19 and the value 0.05. the researcher used this value as a reference basic in calculating the t-table value in the distribution of the t-table statistics. Then find the t table value of 1,729. Thus, because the t value is 7,312 > 1,729, then the selection of the basic for decision making above can be rejected, that H0 is rejected and Ha is accepted. So it can be denied that there is an mean score difference between the Pre Test and Post Test learning outcomes, which means that there is "Hello English" Application enriches English vocabulary of the second-grade students of Mts Bani Rauf Sungguminasa.
B. Discussion
From the research results, the researcher found that students who were taught through HEAp could obtain better results in learning English vocabulary. The result of the data was taken from 20 students in a class of pre-test and post-test. It can be seen on the table 2 that has the mean of pre- test that is 67,50 before doing treatments by using “Hello English”
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Application. Then, the mean of post-test score was enrich into 81,25 after doing treatments. Therefore, the students’ mean scores of post-test were highest scores than pre-test.
Based on the "Paired Samples Test" spss, the Sig. (2-tailed) is 0.000 <
0.05, and comparing the t-value and t-table that the df value is 19 and the α = 0.05. the researcher used this value as a reference basic in calculating the t- table value in the distribution of the t-table statistics. Then find the t table is 1,729. Thus, because the t value is 7,312 > 1,729, then the selection of the basic for decision making above can be rejected. So H0 is rejected and Ha is accepted. So it can be denied that there is an mean score difference between the Pre Test and Post Test learning outcomes, which means that there is
"Hello English" Application enriches English vocabulary of the second-grade students of Mts Bani Rauf Sungguminasa.
The vocabulary achievement of students has increased significantly after being taught through HEAp as they could learn vocabulary effectively through an application interesting application. In the app, there were some games and challenges that they could play in the app, such as Quizathon Challenge and the Spellathon Challenge and Spelling Bee Games.In the Quizathon Challenge, the students will play with other students’ that will be detected by the application. They will race to answer questions in 20 seconds.
Students who can answer faster questions will get more coins than their opponents. Almost the same as quizathon, the Spellathon Challenge and Spelling Bee Games also let the students play with the others. The only
difference is that students will find random letters, blanks, and words in Indonesian. They need to arrange letters to translate words from Indonesian into English. Through these games, indirectly, students can enrich their vocabularies, learning words with enjoyables activities.
As the students can learn while playing, the lessons they get will be easier to remember the vocabulary they have learned, as in research by Puspitaloka, Hasanah, and Rahmawati (2017) examined the efforts of the
"Hello English" Application to check to increase the strength of students in knowing English vocabulary for the use of automaton-based learning games.
The other features of the HEApthat should be highlighted are learning materials and reading some vocabulary articles where students’ just click one word the meaning of the word begins to appear. There are also other features of the HEAp which is a Digital Dictionary feature. Sometimes students are lazy to read a dictionary book to look for the meaning of a word, there is a digital dictionary of 10,000 words that students can read differently using their smartphones rather than reading a dictionary book. Then the HEAp can make students more interested in learning because only by means of a smartphone that is familiar to students, they can learn while playing. Liana, Wahyudin, and Hanoum (2018) suggested that the "Hello English"
Application is a medium that helps students in the process of understanding and enhances students' accuracy in learning. According to them, the "Hello English" application is very useful for students because the concept of this medium is learning while playing. This of course will make students feel
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comfortable and will not feel bored or bored when participating in the learning process or the learning process because of it.
From the data analysis above, the researcher can be concluded that
"Hello English" Application can give a significant effect on students’
vocabulary knowledge because the score of vocabulary test after researcher doing treatments is higher than before being taught by "Hello English"
Application. In addition, this application can be applied in all subjects but it depends on students’ grade. It means that using "Hello English" Application showed a positif effect to be implemented for students’ especially for the second-grade students’ of Mts Bani Rauf Sungguminasa.
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CHAPTER V
CONCLUSIONS AND SUGGESTIONS
This chapter discusses the conclusions and suggestions. A detailed explanation of each point is presented below.
A. Conclusions
Based on the results and discussion in the previous part, it can be concluded that there is an increase in students' English vocabulary after taught by using HEAp. This can be seen from the mean value of students in 2 tests, namely the pre-test 67,50 and post-test 81,25. Meanwhile in data analysis, in the table of Kolmogorov-Smirnov from pre-test was 0.073 and post-test was 0.176. It means that the pre-test data is normally distributed, because the significance score is higher than α = 0.05.The p-value = 0,285> α
= 0.05, based on the results of these calculations it can be concluded that the population is homogeneous.
Based on the "Paired Samples Test" output table above, the Sig. (2- tailed) is 0.000 < 0.05, and then find the t value is 7,312 > 1,729. So H0 is rejected and Ha is accepted. Therefore, the application of HEAP in the learning process can improve the vocabulary skills of students.
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B. Suggestions
Based on the conclusion of the study, some suggestions will be directed toward the English teacher and other researchers.
1. To English Teacher
The English teacher can use the Hello English Application as media in the learning process. Because based on the result of the research, the use of Hello English Application aslearning media to enriching the students‟
English vocabulary. It also can support the teaching-learning process so that the students will be more interested in learning English.
2. To the Other Researcher
This study is mainly intended to describe how Hello English Application was implemented to enrich vocabulary of the second-grade students at MTs Bani Rauf Sungguminasa. Theother researchers may follow this study in different contexts to find moreactions to enrich the students’
English vocabulary. This study may be used asone of the resources before the researchers do action research related toenrich students’ English vocabulary.
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