source for text data for qualitative study. Documents can be in the form of public and private documents. Minutes from meeting, officials memo records, and archival
material in libraries belongs to public documents. Meanwhile, private documents consist of personal journals and diaries, letters, personal notes, and jottings
individuals write to themselves. In this study, the researcher documented syllabus and materials of English Class
for the first semester midwifery students of ‘Aisyiyah Health Sciences College of Yogyakarta. The documents were the syllabus of English for Midwives 1 and the text
book used in teaching English for the midwifery students in that college.
4. Data Collecting Technique
The data of this research were in the form of qualitative and quantitative data. The qualitative data were collected by conducting unstructured questionnaire and
interviews. The qualitative data were gathered in the form of opinions, suggestions, comments and expectation related to the evaluation of the learning model.
Meanwhile, the quantitative data were collected as the results of the structured questionnaires in the experts and users validations.
5. Data Analysis Technique
The gathered data were analyzed in quantitative and qualitative way. The data from the questionnaires were analyzed using some statistical calculations.
Meanwhile, the data from open-ended questionnaire and interview were interpreted in written forms.
The data from material and media evaluation were obtained through structured questionnaires in the form of scores. The scores in the questionnaire were divided
into some classification. The classification was adapted from Sukardjo 2006 in Rohmah 2014. There are five agreements are applied as follows:
Strongly Agree = 5
Agree = 4
Not Sure = 3
Disagree = 2
Strongly Disagree = 1
The results of the structured questionnaire were then converted for quantitative data analysis and categorized into a scale of five. Martanti 2015
proposes three steps to analyze data from questionnaires. The steps are 1 collecting raw data, 2 converting and scoring the data for quantitative analysis,
3 categorizing the scores into a scale of five. The results of the questionnaire were then presented using the following table.
Table 3.2 The Description of the Validation Questionnaire Result No.
Statements Converted
Score Mean
Category
Total Score Mean
In order to have a clear general thoughts or opinions from the participants, the raw scores were then converted into some scores. The converted scores were
adapted from Best 1977 in Maharani 2013 as presented in table 3.3.
Table 3.3 The Conversion Table of The Questionnaires Results Raw Scores
Meaning of Scores Converted Scores
5 Strongly Agree
2 4
Agree 1
3 Not Sure
2 Disagree
-1 1
Strongly Disagree -2
The gathered scores were then classified using Criterion Reference Evaluation CRE to get the score ranges for data interpretation. The five scales
data of CRE based on formula quoted from Sukardjo 2006.
Table 3.4. The Data Classification Using CRE Scores
Criteria
X X
i
+ 1.80 SD Very High Very Good
X
i
+ 0.60 SD X X
i
+ 1.80 SD High Good
X
i
– 0.60 SD X X
i
+ 0.60 SD Fair
X
i
– 1.80 SD X X
i
– 0.60 SD LowPoor
X i – 1.80 SD Very LowVery Poor
Notes: X
i
Ideal Score Average = maximum score + minimum score SD Standard Deviation = maximum score – minimum score
X = Actual Score The maximum score refers to the highest converted score i.e. 2, while the
maximum score is the lowest converted score i.e. -2. After the data converted to quantitative data, the data were then analyzed by
finding the average score or value. The average score of the product’s evaluations were calculated as follow:
i = Notes:
X
i
= Average Score ∑
= Total Score N = Number of participants
Based on Table 3.4., if the mean score is considered in a very good category, it means that it does not need any revision. Then, if the score is considered as
good category, it means that the revision is optional. If the score is classified into fair category, it means that it is necessary to conduct more exploration on the
design. If it belongs to poor category, it is recommended to make revisions or improvements to the parts of the product. Moreover, if the score is considered
very poor, it means that revisions or improvements are highly recommended. Meanwhile, the qualitative data gathered from open-ended questionnaire and
interview were selected as necessary and interpreted in the form of written paragraph. The data were used to support the quantitative data from the
questionnaire results. The interpretation were based on the theory of teaching speaking cycles by Burns 2011 and teaching speaking by Nunan 2003.
CHAPTER IV DEVELOPMENT PROCESS AND FINAL PRODUCT
This chapter presents the research findings and discussions that answer the research question as the result of this study. What the android application model to
learn speaking for midwifery students looks like is the question where the researcher began this research. In this chapter, the researcher presents the process of developing
the application and also the description and the accountability of the application to enhance the students’ speaking competence.
A. Process of Developing the Speak App
Process is an important part in developing an educational product. A good process will result a good product. In this part, the researcher shows the process of developing
the product. The researcher employed ADDIE model in the process of developing the application. There were five stages of ADDIE model. They are Analyze, Design,
Develop, Implement, and Evaluation. In order to give clearer explanation about the process of designing the android application model, the researcher would elaborate
the process through these five stages.
1. Analyze
According to Taylor, analysis is the phase where the problem is identified, defined, and solutions posed. In this research, the purpose of this phase was to
determine the problems in the teaching and learning English at Health Sciences Colleges and to find out the learning materials as the sources of the problems’
