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The Implementation of Google Classroom as the E-Learning Platform for
Teaching Non-Parametric Statistics during COVID- 19 Pandemic in Indonesia
Article · January 2020
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International Journal of Advanced Science and Technology
Vol. 29, No. 4, (2020), pp. 5793 - 5803
The Implementation of Google Classroom as the E-Learning
Platform for Teaching Non-Parametric Statistics during COVID19 Pandemic in Indonesia
Georgina Maria Tinungki1*, Budi Nurwahyu2
1
Department of Statistics, Hasanuddin University, Makassar, Indonesia
Department of Mathematics, Hasanuddin University, Makassar Indonesia
1
georgina@unhas.ac.id (*corresponding author), 2 budinurwahyu@unhas.ac.id
2
Abstract
This research was done to support teaching and learning activities from home, as
suggested by the Indonesian government to stop the COVID-19 pandemic. This
experimental research employed development stages that result in the recommendation of
e-learning using Google classroom, which implementation was then assessed based on
the criteria of the model quality, namely validity, practicality, and effectiveness. The
subjects in this study were 33 students majoring in Statistics who attended the nonparametric statistics course, consisting of 19 females and 14 males. Data were collected
through observation and written tests. A set of essay test was used as the data collection
instrument. The results showed that all students and educators could carry out teaching
and learning activities from home. The use of Google classroom during the planning has
been considered quite effective with a percentage of 79%. The Learning Implementation
was much in line with the Semester Learning Plan, showing the percentage of 86.7%. The
accessibility of Lecturers and Students to Google classroom was considered appropriate
(77.2%). The fulfillment of the standards of process and content also complied with the
National Education Standards and the Standards of the relevant tertiary institution
(76.3%). The learning aspect that has been documented were regarded as appropriate
(78.2%). Therefore, the implementation of Google Classroom as the e-learning platform,
as seen from learning outcomes has been categorized good and students’ responses to the
implementation were also good.
Keywords: COVID-19, effective category, E-learning, Google Classroom, Nonparametric Statistics.
1. INTRODUCTION
The COVID-19 pandemic struck and almost paralyzed all countries in terms of social
and economic activities, including Indonesia [1]. At present, the Indonesian government
has imposed warnings and prohibitions on leaving homes, working, or going to school.
Several new terms have emerged, including work from home or learn from home. Elearning is a system or concept of education that utilizes information technology in
teaching and learning, where learning is arranged using an electronic or computer system
to support the learning process [2]. Google Classroom is one of the other e-learning
platforms in which the number of users significantly increased during the COVID-19
pandemic. These regulations undoubtedly affect the learning process. However, students
still have to keep learning even though it is done online [3]. For the lecturers, one of the
essential goals in teaching in the class to help students to enrich their learning goals [4].
Google Classroom is a management system of teaching and learning activities that can be
used and accessed via a desktop at classroom.google.com site [5].
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2. LITERATURE REVIEW
2.1 COVID-19
Coronavirus or severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a
virus that attacks the respiratory system. This disease is caused by a viral infection called
COVID-19. Coronavirus can cause mild symptoms to the respiratory system, severe lung
infections, to death. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2),
better known as the Coronavirus, is a new type of Coronavirus that is transmitted to
humans. Although it is more aggressive to the elderly, this virus can affect anyone,
including infants, children, to adults, including pregnant women and nursing mothers [6].
Coronavirus infection called COVID-19 (Corona Virus Disease 2019) was first
detected in Wuhan city, China, at the end of December 2019. This virus has been
transmitted very rapidly and has spread to almost all countries, including Indonesia,
within just a few months. Many states implement policies to impose lockdowns to delay
the spread of Coronavirus. In Indonesia, Large-Scale Social Restrictions (PSBB) policy
has been imposed to suppress the spread of this virus [7, 8].
Coronavirus is a collection of viruses that can infect the respiratory system. In many
cases, this virus only causes mild respiratory infections, such as flu. However, this virus
can also cause severe respiratory disease, such as lung infections (pneumonia).
In addition to the SARS-CoV-2 virus or Coronavirus, viruses that are also included in
this group are the viruses that cause Severe Acute Respiratory Syndrome (SARS) and
similar virus that causes Middle-East Respiratory Syndrome (MERS). Although this
disease is caused by viruses from the same group, namely coronaviruses, COVID-19 has
some differences from SARS and MERS, and others in terms of the speed of spread and
severity of symptoms [6].
The initial symptoms of Coronavirus infection or COVID-19 can resemble flu
symptoms, namely fever, runny nose, dry cough, sore throat, and headache. After that, the
symptoms can disappear and heal or even aggravate. Patients with severe symptoms can
experience high fever, cough with phlegm and even bleeding, shortness of breath, and
chest pain. These symptoms appear when the body reacts against the Coronavirus.
There are three general symptoms that can indicate Coronavirus infection, including
fever (body temperature above 38 degrees Celsius), cough, and difficulties to breathe. The
symptoms of COVID-19 generally appear within two days to two weeks after being
exposed to the Coronavirus [9, 10].
2.2 Google Classroom
Google Classroom is a foyer of blended learning applications that can be used for free
[11]. Lecturers can create their own classes and share the class code or invite students.
Google Classroom is intended to help students find or overcome learning difficulties,
share lessons, and create assignments without having to attend a face to face class. The
primary purpose of Google Classroom is to streamline the process of file sharing between
lecturers and students. Google Classroom combines Google Drive for assignment creation
and distribution, Google Docs, Sheets, Slides for writing, Gmail for communication, and
Google Calendar for scheduling. Students can be invited to join classes via private codes
or automatically imported from the school domain [12].
Each class can be given a separate folder in each user's drive, where students can
submit work to be assessed by the lecturer. This application is available for iOS and
Android devices, allowing users to take photos and attach assignments, share files from
other applications, and access information offline. Lecturers can monitor the progress of
each student's learning, and after being assessed, lecturers can return their works [13].
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2.3 Google classroom application
To start using Google classroom application, users can download the application from
the Play Store and the App Store. For desktop use, this application can be accessed at
classroom.google.com. Users can log in on the system using a Gmail account. After that,
lecturers will be given the option to create classes that are flexible and adjustable. After
the class is created, lecturers can invite students to join the class by sharing a unique code,
which is a combination of letters and numbers [14].
One of the main features of Google classroom is the feature to insert a number of main
files as on the main page. Google Classroom provides a place for lecturers to describe the
profile of the class. In this section, lecturers can also insert syllabi, class rules, or other
guidelines. In addition, Google Classroom also provides Announcement features [15].
Through this feature, lecturers can easily share important announcements for all students.
In the assignment feature, Google Classroom also makes it easy for lecturers to give
assignments. Lecturers can also complete the assignment with other information such as
description, deadline, and insert a number of images, instructions, and videos [16].
Google Classroom provides four options that can be selected according to needs. The
first option is a stream or a forum. Through this classroom option, the teacher and
students can interact directly to conduct discussions. The next option is Classwork.
Through this class, teachers and students will find it easier to distribute tasks.
Furthermore, Google Classroom also presents the Grade section, which is used to
recapitulate students’ assessments. Through all the features in Google Classroom,
lecturers and students can continue to carry out teaching and learning activities without
face to face meetings. To note, through Google Classroom, teachers and students can
easily convey information, assignments, and conduct discussions [17].
2.4 Google Classroom features
Google Classroom has many conveniences such as Google Drive, Google Docs, Sheets
and Slides, and Gmail, which will help educational institutions to more easily teach
without physical material such as classes, whiteboards, and stationery. Here are some
features that really support this online learning [18]:
2.4.1
Assignments
Each downloaded task will be saved and assessed in the Google productivity suite of
applications that have made this online collaboration possible. Instead of just sharing
documents that are in a student's Google Drive with the teacher, the file is hosted on the
student's Drive and then sent for assessment. The teacher can choose the file as a
template, allowing each student to edit their own copy and submit it to be assessed.
Hence, all students can view, copy, or edit the same document. Students can also choose
to attach additional documents from their Drive to an assignment.
2.4.2
Rating (Grading)
Google Classroom supports different assessment methods. Lecturers can monitor the
progress each student makes on every assignment and they can also send comments and
edit. Modified assignments can be graded by the teacher and returned with comments to
allow students to revise the assignment and return it. After being assessed, the assignment
can only be edited by the teacher unless the teacher returns the assignment.
2.4.3
Smooth communication
Announcements can be posted by lecturers to the class stream, which can be
commented on by students allowing two-way communication between lecturers and
students. Students can also post to class but will not be as high a priority as an
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announcement by the lecturer and can be moderated. Various types of media from Google
products such as YouTube videos and Google Drive files can be attached to
announcements and posts for sharing content. Gmail also provides an email option for
lecturers to send emails to one or more students in the Google Classroom interface.
Classes can be accessed on the web or through the Android and iOS Class mobile apps.
2.5 Non-Parametric Statistics
Non-parametric statistics are statistics that are used when the normal distribution of
data is neglected. Some experts define non-parametric statistics as Statistics for
qualitative data and which have free distribution K [2].
Why do we use non-parametric statistics? Because parametric statistics cannot be used
since the data were not normally distributed, qualitative, and data can only be measured
on a nominal or ordinal scale. The advantages or benefits of non-parametric statistics are
the probability of the analysis result can be obtained with certainty and it can be used to
analyze with a small number of samples (samples) (minimum limit = 6). It can be used to
analyze data obtained from different populations, data measured in the nominal or ordinal
scale. The method of analysis is relatively easy in the form of simple algebra that is easy
to learn and use.
Although non-parametric statistics are much more flexible because they do not require
variety of fairly stringent requirements such as parametric statistics, the use of parametric
statistics is still preferred. When research data are normally distributed, or of type ratio, or
is in large numbers, parametric statistics must take precedence.
The weaknesses of non-parametric statistics are: if normal assumptions can be met,
then the conclusions of the analysis obtained may be biased. Non-parametric statistics
cannot be used to measure interactions, and because it cannot be used for regression
analysis, non-parametric statistical practice is not for predicting.
Weaknesses or shortcomings of non-parametric statistical procedures are precisely
related to their strengths, because they can be used even with a minimal process to
process data, the conclusions drawn with non-parametric procedures will be weaker than
the ones of parametric procedures [19].
3. RESEARCH METHODS
This experimental research was conducted through development stages, assessing the
implementation of e-learning using Google classroom based on the criteria of model
quality, namely validity, practicality, and effectiveness. This research involved students
majoring in Statistics study programs who attended the non-parametric statistics course.
Those criteria were determined since the non-parametric statistics course was delivered
amid the COVID-19 pandemic. Assignments that promote a balance portion in knowledge
and skills required tools or media that can facilitate students to do the assignments and
answer questions given by the instructor through Google classroom. Implementation
standards used in the evaluation included quality standards for implementing e-learning
that has been developed by the number of universities. Samples were taken by
purposively from the population. The determination of the sample size of students from
the population was done using Slovin formula as follows:
where :
N = population size
n = sample size
d = estimation error
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The variables examined in this study include aspects of Google classroom learning
management consisting of learning planning variables, design and creation of materials,
delivery of learning, learning interactions, and evaluation of the implementation of
Google classroom learning. Analysis of data described in this study is intended to
describe and interpret the effectiveness of Google classroom as a learning medium in nonparametric statistics courses.
The variables examined in this study included Google classroom learning management
aspects consisting of learning planning variables, design and creation of materials,
delivery of learning, learning interactions, and evaluation of the implementation of
Google classroom learning. Data analysis was intended to analyse and interpret the
effectiveness of Google classroom as a learning medium in non-parametric statistics
courses, as shown in Table 1.
Table 1. Effectiveness Assessment Criteria for Descriptive Analysis
No.
1
2
3
4
Sources: [20, 21].
Formula
Classification
Effective
Fairly effective
Ineffective
Very ineffective
Information:
= average ideal = ½ (Ideal Maximum Score + Ideal Minimum Score)
= deviation = 1/6 (Ideal Maximum Score - Ideal Minimum Score)
= Empirical score
4. RESULTS AND DISCUSSION
The virtual class was started by the lecturer directing all students attending the nonparametric statistics course to download the Google classroom application through the
Play Store and App Store. For use on the desktop, this application can be accessed
through the class.google.com site. Furthermore, students logged into the system using
Gmail accounts. After that, students were given some options to take a class. After the
class is formed, the lecturer invited students to join the class after using a unique code:
wsc6prg as shown in Figure 1 below:
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Figure 1: The lecturer invited students to join the non-parametric statistics
class using a code: wsc6prg.
At the beginning of the learning process, the lecturer missed the attendance list of each
student, yet most of the students were already present. The lecturer explained the material
to be discussed, as shown in Figure 2.
Figure 2: The discussion between lecturer and students.
Figure 2 above shows, the discussion between lecturers and students, between students
and students about the definition of non-parametric statistics, as well as the advantages
and disadvantages. Learning continued, where students were very happy and motivated
to understand better the concepts of the material discussed until it ended.
The third meeting was attended by 35 students, as shown in Figure 3.
Figure 3: Students’ activities during the e-learning lecture using Google
classroom application.
Figure 3 Students’ activities in e-learning lectures using google classroom application,
where students were given individual assignments.
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Figure 4: Individual assignments.
Based on Figure 4 above, it appears that the lecturer gave practice questions done
within 20 minutes. After 20 minutes, there was a discussion between students and
lecturers. Students were very enthusiastic and could understand the concepts of the
material being discussed.
In the fourth meeting, an evaluation was carried out through an assessment that each
student had to finish in their respective room, as shown in Figure 5.
Figure 5 : Students answered the questions uploaded to their respective
rooms.
Figure 5 shows that students have answered the questions given in their respective
rooms in the form of pdf format and Microsoft word format. All of those file types were
acceptable. The lecturer gave feedback for every student's answer.
In the 5th meeting, the lecturer delivered the teaching material as shown in Figure 6.
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Figure 6: Teaching material uploaded by lecturers as material to be
discussed.
Based on Figure 6 above, we can see The Kruskal-Wallis One-Way Analysis of
Variance by Ranks teaching material uploaded by lecturers. After lecturing for 7
meetings, and in the 8th meeting a midterm exam is held, as shown in Figure 7 below:
Figure 7: Sample answer of midtest exam
Based on Figure 7 above, a student answered the questions correctly in a respective
room as expected in the learning process.
Based on several lecture meetings and interactions that occur during the lecture
process, the tendency for the effectiveness of learning using Google classroom is
presented in Table 2.
Mi = 1/2 (Highest ideal score + Lowest ideal score) = 1/2 (86 + 35) = 59,5
SDi = 1/6 (Highest ideal score - Lowest ideal score) = 1/6 (86 - 35) = 8,5
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Table 2. Distribution of trends in the effectiveness of Google classroom
learning as a learning medium
No.
1
2
3
Formula
4
TOTAL
Frequency Percentage
5
19%
27
79%
3
2,0%
0
35
Qualification
Effective
Fairly effective
Ineffective
0% Very Ineffective
100%
Table 2 shows that the implementation of Google classroom learning as a learning
medium as a whole has been quite effective with a trend level of 79%. The effectiveness
of Google classroom as an e-learning platform for each variable is effective. The
Effectiveness of Learning Implementation according to RPS is 86.7%, the Lecturer and
Student component has access to Google classroom 73.27%, the Lecturer component has
access to the e-learning learning development facility by 76.3%, the learning interaction
component is 66.10 %, and components available for access to Google classroom
facilities at 78.2.01%. The criteria for assessing Google classroom learning for planning
indicators can be seen in Table 3:
Table 3: Assessment criteria of teaching planning using google classroom
No.
Description
1 Implementation of Learning according to
the Semester Learning Plan.
2 Lecturers and Students have access to
Google classroom
3 The fulfillment of process standards and
standards for the content of education
implementation in accordance with the
National Education Standards and
Standards of the relevant tertiary
institutions
4 This learning is documented and can be
accounted for.
Percentage
86,7%
77.2 %
Qualification
Very
appropriate
Appropriate
76,3%
Appropriate
78,2%
Appropriate
Table 4 presents that the Implementation of Learning according to RPS (86.7%) has
met the qualifications. Whereas for lecturers and students having access to google
classroom is appropriate (77.2%), and the fulfillment of the process standards and the
content standards of the implementation of education based on the National Education
Standards and Standards of the relevant tertiary institutions has been appropriate (76.3%).
Furthermore, learning can be documented and can also be accounted for accordingly
(78.2%). Thus, the urgency level of learning using use google classroom media is quite
good. Practitioners are recommended to further improve the effectiveness of e-learning
learning planning using google classroom by emphasizing on the main aspects of
implementation planning as a first step in using google classroom as a learning medium.
5. CONCLUSION AND RECOMMENDATION
5.1 Conclusion
Based on the results of the analysis of the effectiveness of e-learning using Google
classroom, it can be concluded that the platform successfully supported the
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recommendation for learning issued by the Indonesian government. All students and
educators could carry out teaching and learning activities from home to suppress the
spread of COVID-19 pandemic. Google classroom learning planning aspects are
categorized quite effective, with a percentage of 79%. The Learning Implementation
aspect is very much in accordance with the Semester Learning Plan to 86.7%. The aspects
of Lecturers and Students having access to Google classroom are appropriate (77.2%).
The process standards and content standards of the implementation of education based on
the National Education Standards and Standards of the relevant tertiary institution has
been fulfilled (76.3%). Learning aspects have been documented and are considered
appropriate (78.2%).
5.2 Recommendation
The e-learning use Google classroom is very suitable to be applied to support one of
the Indonesian government's appeals. Then all students and educators can execute
teaching and learning activities from home in order to suppress the pandemic COVID-19.
Acknowledgment
The authors thank to Mr. Powell Gian Hartono for his contributions to the medical
theory aspect.
References
[1]
AHK Indonesien, “Covid-19 developments in Indonesia”, (2020). Available from:
https://indonesien.ahk.de/id/infocenter/berita/berita/covid-19-developments-in-indonesia
[2]
M. Grant, “Nonparametric Statistic”, (2019). Available from:
https://www.investopedia.com/terms/n/nonparametric-statistics.asp
[3]
R. Piatek, “Student Response System: Student Activation Towards Better Learning in Large Classes. A
Practical Guide”, (2014). Available from:
https://samf.ku.dk/pcs/english/forteachers/tlhe/projects/Remi_Piatek_ TLHE_Project.pdf
[4]
B. Nurwahyu and G.M. Tinungki, “Concept image and its influence on beliefs: Case study on
undergraduate engineering students in solving of calculus concept problems”, International Journal of
Advanced Science and Technology, vol. 29, no. 5, (2020), pp. 2227 – 2243. Available from:
http://sersc.org/journals/index.php/IJAST/article/view/10990/5837
[5]
A.G.K Triune, “Teachers discuss e-learning situation”, (2020). Available from:
https://www.kokomotribune.com/news/covid-19/teachers-discuss-e-learning-situation/article_0601fb8672b3-11ea-bb58-7f2a4f9bf87b.html
[6]
M.D.C. Pane, “Virus Corona (COVID-19)”, (2020). Available from:
https://www.alodokter.com/virus-corona
[7]
D.W. Anthony, “Gubernur SULSEL Minta Warga Disiplin saat PSBB Makassar: Jangan Sampai Ada
Keliaran!” (2020). Available from: https://news.detik.com/berita/d-4979146/gubernur-sulsel-mintawarga-disiplin-saat-psbb-makassar-jangan-sampai-keliaran
[8]
H. Lu, C.W. Stratton, and Y. Tang, “Outbreak of Pneumonia of Unknown Etiology in Wuhan China: the
Mystery and the Miracle,” Journal of Medical Virology,
vol.
92, no. 4,
(2020). https://doi.org/10.1002/jmv.25678
[9]
World Health Organization, “Novel Corona Virus: Q-Q for public”, (2020). Available from:
https://www.who.int/indonesia/news/novel-coronavirus/qa-for-public
[10] H.A. Rothan and S.N. Byrareddy, “The epidemiology and pathogenesis of coronavirus disease
(COVID-19)
outbreak”,
Journal
of
Autoimmunity,
vol,
109,
(2020).
https://doi.org/10.1016/j.jaut.2020.102433
[11] B. Schoon, “Google Classroom is the most popular education app on Android and iOS amid
coronavirus”, (2020). Available from: https://9to5google.com/2020/03/28/google-classroomcoronavirus-downloads/
ISSN: 2005-4238 IJAST
Copyright ⓒ 2020 SERSC
5802
International Journal of Advanced Science and Technology
Vol. 29, No. 4, (2020), pp. 5793 - 5803
[12] E. Dabbour, “Quantifying the Effects of Using Online Student Response Systems in an Engineering
Ethics Course”, Journal of Professional Issues in Engineering Education and Practice, vol. 142, no. 2,
04015010, (2016). https://doi.org/10.1061/(asce)ei.1943-5541.0000260
[13] C-M. Mork, “Benefits of Using Online Student Response Systems in Japanese EFL Classrooms”, The
JALT CALL Journal, vol. 10, (2014), pp. 127–137. Available from:
https://files.eric.ed.gov/fulltext/EJ1107921.pdf
[14] M.G. Rae, and D. A’Malley, “Using an online student response system, Socrative, to facilitate active
learning of Physiology by first year graduate entry to medicine students: a feasibility study”,
MedEdPublish, vol. 6, no. 1, (2017). . https://doi.org/10.15694/mep.2017.000004
[15] P. Wash, “Taking advantage of mobile devices: Using Socrative in the classroom”, Journal of Teaching
and Learning With Technology, vol. 3, no. 1, (2014), pp. 99-101.
https://doi.org/10.14434/jotlt.v3n1.5016
[16] S. Muir, L. Tirlea, B. Elphinstone, and M. Huynh, “Promoting classroom engagement through the use
of an online student response system: A mixed methods analysis”, Journal of Statistics Education, vol.
28, no. 1, (2020), pp. 25 – 31. https://doi.org/10.1080/10691898.2020.1730733
[17] A.R. Trees, and M.H. Jackson, “The learning environment in clicker classrooms: student processes of
learning and involvement in large university‐level courses using student response systems”, Learning,
Media and Technology, vol. 32, no. 1, (2007), pp. 21 – 40. https://10.1080/17439880601141179
[18] H.S. Mokhtar, “Teachers explore online teaching methods during MCO”, (2020). Available from:
https://www.nst.com.my/education/2020/03/579992/teachers-explore-online-teaching-methods-duringmco
[19] M. Hollander, D.A. Wolfe, and E. Chicken, “Nonparametric Statistical Methods, Third Edition, John
Wiley & Sons, Inc., Wiley Series in Probability and Statistics, United States of America, (2015).
Available from: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119196037
[20] S. Loeb, S. Dynarski, D. McFarland, P. Morris, S. Reardon, and S. Reber, “Descriptive analysis in
education: A guide for researchers” (NCEE 2017–4023). U.S. Department of Education, Institute of
Education Sciences, National Center for Education Evaluation and Regional Assistance, Washington,
D.C., (2017).
[21] E.P. Widoyoko, “Evaluasi Program Pembelajaran: Panduan Praktis bagi Pendidik dan Calon Pendidik”
Pustaka Pelajar, Yogyakarta, (2013).
AUTHORS
Georgina Maria Tinungki is an Associate Professor at the Department
of Statistics, Faculty of Mathematics and Natural Sciences,
Hasanuddin University, Makassar city, Indonesia. She is Head of the
MSc program in Statistics, Hasanuddin University. She got her
undergraduate degree (doctoranda / Dra.) in Mathematics from
Hasanuddin University in 1986, MSc in Statistics from Bogor
Agricultural University in 2000, PhD in Coastal and Marine Resource Management from
Bogor Agricultural University in 2005, and PhD in Mathematics Education from the
Indonesia University of Education in 2016.
Budi Nurwahyu is an Associate Professor at the Department of
Mathematics, Faculty of Mathematics and Natural Sciences,
Hasanuddin University, Makassar city, Indonesia. He is Managing
Editor of the Journal of Mathematics, Statistics, and Computer
Science (Jurnal Matematika, Statistika, Komputasi), Hasanuddin
University. He got his undergraduate degree (doctorandus / Drs.) in
Mathematics from Gadjah Mada University in 1982, MSc in Mathematics from Bandung
Institute of Technology in 1987, and PhD in Mathematics Education from the State
University of Surabaya in 2016.
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