http://www.iaeme.com/IJMET/index.asp 1974 editor@iaeme.com
International Journal of Mechanical Engineering and Technology (IJMET)
Volume 10, Issue 03, March 2019, pp. 1974–1981, Article ID: IJMET_10_03_199
Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=3
ISSN Print: 0976-6340 and ISSN Online: 0976-6359
© IAEME Publication Scopus Indexed
THE BIG DATA USABILITY’S TRENDS IN
EDUCATION: A SYSTEMATIC LITERATURE
REVIEW
Devyano Luhukay, Meyliana, Achmad Nizar Hidayanto, Harjanto Prabowo
Information Systems Department, School of Information System,
Bina Nusantara University, Jakarta, Indonesia 11480
ABSTRACT
Technology innovation nowadays makes life easier than previous decades. In
Education sector, technology has been used to enhance the learning experience such as
Learning Management Systems, e-Learning, etc. Another innovation in database is Big
Data. This paper uses Systematic Literature Review (SLR) to find the trends of usability
of Big Data in education sector. The research found that there are mostly 2 types of
trends of Big Data usability in Education they are Conceptual Trends and Research
Trends. Conceptual Trends affects Functional Area such as: Curriculum; Education
Management and Learning Activities. Research Trends affects: Learning Activities and
Curriculum.
Key words: Big Data, Education, Learning Analytics, Big Data Trends
Cite this Article: Devyano Luhukay, Meyliana, Achmad Nizar Hidayanto,
Harjanto Prabowo, The Big Data Usability’s Trends in Education: A Systematic
Literature Review, International Journal of Mechanical Engineering and Technology
10(3), 2019, pp. 1974–1981.
http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=10&IType=3
1. INTRODUCTION
Technology innovation nowadays makes life easier than previous decades. All technologies
that applied in many fields will improve quality in many ways. One of the countries in Asia,
Korea as a country with the highest number of internet users in the world according to [1], in
the past 10 years their life relies on digital products in much areas which directly increase the
amount of digital data exponentially. The development of technology could be observed from
several kinds of software used in various fields such as software or an application for supporting
education. In China, specifically in the education sector there are so much learning management
systems that has been developed which lead to the very large data on learner and the process of
learning [2]. The quantity of the information and data flow through the application required a
big medium storage. According to [3], conventional medium storage, “Traditional Databases:
Traditional data sources are the existing relational databases, data warehouses, data marts, or
any other information system producing structured data. A conventional medium storage where
data that is stored only in the form of text and numbers are not able to receive enough to storing
Devyano Luhukay, Meyliana, Achmad Nizar Hidayanto, Harjanto Prabowo
http://www.iaeme.com/IJMET/index.asp 1975 editor@iaeme.com
and processing the data when these might include video and pictures”. According to [4], “Big
data means that a variety of data is now available faster. Big data have been around for years”.
[5] In his research found 7 important factors for the implementation of big data that can be used
to increase the value added in accreditation of education institutions in Saudi Arabia. [6] says
that “The primary role of modeling in education has varied as data collection and analysis
traverse the contexts of testing, tutoring, and online instruction at scale.” According to [7], Big
Data mining for insights in higher education can be done through enabling a new level of
evidence-based research into learning and teaching. For example, in nurse education, the Data
types include data about teaching, learning and assessment, e-portfolios, electronic medical
records and social media, academic progress reports, class attendance lists, scholarship and
research [8]. Big Data research is needed also in a global health workforce to anticipate the
risk’s increasing of pandemic as the countries now getting more interconnected [9]. This study
tries to answer research question: what kind of Big Data usability trend generally implemented
in education sector. Further the aim of this research is to know which area of education that still
have possibility to be enhanced using Big Data Technology.
2. METHODOLOGY
The methodology used to conduct this research is Study Literature Review (SLR) that includes
some stages: the determination of information source, the determination of searching keywords,
criteria for initiation of inclusion and exclusion, data extraction, and analysis to define research
question.
2.1. Search Process
The Source of Literature used on this research are: EBSCOhost (https://search.ebscohost.com/),
Emerald(http://www.emeraldinsight.com/), Google Scholar (https://scholar.google.co.id/),
Proquest (www.proquest.com), SAGE (www.sage.com), ScienceDirect
(www.sciencedirect.com), Springer(www.springer.com). Four keyword pattern used to answer
research question formed with the Boolean operators “OR” and “AND”, a combination of
keywords right of: 1. ((big AND data) AND (higher AND education) OR (higher AND
institution) OR (education)); 2. ((critical AND success AND factor) AND (big AND data) AND
(higher AND education) OR (higher AND institution) OR (education)); 3. ((big AND data)
AND (management OR implementation) AND (higher AND education) OR (higher AND
institution) OR (education)); 4. ((big AND data AND analytic) AND (higher AND education)
OR (higher AND institution) OR (education)). The criteria to validate the sources as follows:
Paper Published in 2014 – 2017, Paper have a complete structure consist of journal/conference,
author identity and any other information, Duplicate papers are removed from the source list.
2.2. Data Extraction
This research analyzes 169 papers as Founded paper from various sources, then from that 169
papers there will be chosen 64 papers after analysis based on the title and abstract. These 64
papers are the Candidate Paper that some of them later will be selected as main paper to be
discussed. The process continues by search deeper into all paper contents that has possibility
to answer the research question. The result from that process is 27 papers Selected paper.
Table 1. Data Extraction with inclusion criteria
The Big Data Usability’s Trends in Education: A Systematic Literature Review
http://www.iaeme.com/IJMET/index.asp 1976 editor@iaeme.com
Source
Founded
Candidate
Selected
EBSCOhost
53
18
6
Emerald
6
5
3
Google Scholar
3
0
0
Proquest
13
7
3
SAGE
12
9
1
Science Direct
75
19
11
Springer
7
6
3
TOTAL
169
64
27
3. RESULT AND DISCUSSION
This research aims to determine the trend in the usage of the big data on education. Big Data
on education consists of the application on the curriculum to the management of education
itself. This study will provide demographics and characteristic of the chosen paper including
sources and year of publication, researchers by publicizing a big data, researchers discipline,
background, former university and state, and the trend of using big data on education, but
limited to the pages submitted, this paper excluded some tables.
3.1. Demographic and trend characteristic
3.1.1. Publishing Outlets
There are many paper about the application of Big Data on education, some of paper posted on
the journal or certain seminar. Several journal or seminar referred to: Journal TechTrends (#3),
Procedia Computer Science (#3), Procedia-Social and Behavioral Science (#1) and much
journal or other seminar publication. Further from the result, there are 23 sources of publication,
group of Journal publication provide 20 papers, and 3 papers from conference group source. As
displayed on the table, 1 Journal and 1 Procedia with largest number of publication, they are
TechTrends Journal and Computer Science Procedia. TechTrends provide 3 papers about Big
Data on Education sector while Computer Science Procedia with 3 Paper. Another 21 sources
provide 1 paper with the same percentage that is 3.70%
3.1.2. Most Prolific Author
From the authors, the result represents each researcher on time data collected, have same
number of publication contribution from all 27 papers that is 1 publication each with 1.27%.
This show that Big Data topic is something new on education sector.
3.1.3. Most Productive Institution
From 27 papers found, there are 38 institutions where the author comes from. The type of
institution is University, Non-Profit Organization (NPO), and Zoo. From Table 4 below, type
of institution that has largest number of paper produced is University with as All institution
gives the same contribution of publication that is 1 paper each. Even though only publish 1
paper, from the table can be seen that number of institution is 38 that is larger than number of
papers, this thing shows that to compose a paper about Big Data in Education sector
collaboration between more than one institution is needed. For example, on [3], there are
collaboration between Al-Imam Mohammed Ibn Saud Islamic University and University of
Leipzig to produce paper that aims to determines challenge of Big Data implementation in
learning process. Form the published time, period of collecting the data is from 2014 – 2017.
With number of papers on 2014 is 2, then on 2015 growth until 7 papers. This number continues
to growth until in 2017 it become 10 papers. We can conclude that number of papers published
about Big Data on education is growing fast, this happened because almost all sector aware of
Big Data technology and the implementation to improve quality of each, specially education
sector. Continuing see the result by the country of each source institution, 15 countries of the
Devyano Luhukay, Meyliana, Achmad Nizar Hidayanto, Harjanto Prabowo
http://www.iaeme.com/IJMET/index.asp 1977 editor@iaeme.com
paper published comes from almost all continental which are Asia, America, Australia and
Europe. Country with the largest number of published papers about Big Data on Education is
USA with 9 Papers, followed by Australia, China, India, Saudi Arabia all with 2 publications
each. This shows that Big Data penetration already reach more than a half of the world. Now
the publication from Africa Continent is not exists yet, but maybe in the next month or year it
will be.
3.1.4. Trends
Table 2 describes trend of Big Data usability from 27 papers found.
Table 2. Trends
Title
Type of
Trends
Trends
Functional Area
A Study on…. [1]
Concept
Learning Analytics
Learning Activities
Analysis of Online
Learning…[2]
Research
Learning Analytics,
Reflection of
Teaching
Learning Activities
Big Data Analytics….[3]
Concept
Behavioral
analytics
Learning Activities
Big Data Challenges…[4]
Concept
Challenges of big
data
Education
Management
Big data for accreditation…[5]
Concept
Education
Accreditation
Education
Management
Big data in education…[6]
Research
Education
Modeling
Curriculum
Big Data in Higher
Education…[7]
Concept
Learning Journey
Education
Management
Big Data in Nurse…[8]
Concept
Improve Teaching
and Learning
Experiences
Curriculum
Big Data Knowledge…[9]
Research
Implementation of
Big Data in Global
Health
Curriculum
Big Data: Applications…[10]
Concept
Improve
curriculum,
Learning and
Teaching Methods
Education
Management
Big Opportunities…[11]
Concept
learning analytics
and educational
policy
Learning Activities
Can conservation…[12]
Concept
Internal decision-
making processes
Curriculum
Developing the role…[13]
Concept
Data Analytics for
services
Education
Management
Discovering Big Data…[14]
Concept
Data Analytics
using Monggo DB
& MapReduce
Learning Activities
Visual analytics in…[15]
Concept
Curriculum
Mapping
Curriculum
From Big Data…[16]
Concept
competitive
advantage in higher
education
institutions
Education
Management
The Big Data Usability’s Trends in Education: A Systematic Literature Review
http://www.iaeme.com/IJMET/index.asp 1978 editor@iaeme.com
Title
Type of
Trends
Trends
Functional Area
Impacting Big Data…[17]
Concept
Graduate
Competency
Education
Management
Introduction: Big data…[18]
Concept
Moral Challenge
Education
Management
Learning Analytics…[19]
Concept
Learning Analytic,
personalizing
students learning
experiences
Learning Activities
Mining theory-based…[20]
Concept
Learning Analytic
Learning Activities
Perspectives on a Big
Data…[21]
Concept
Curriculum
Curriculum
Primary Education…[22]
Concept
Assessment of
Basic Education
Quality
Education
Management
Research on the
Construction…[23]
Concept
Online Learning
Curriculum
The role of big data…[24]
Concept
Collaborating
between
Academician and
Industry
Education
Management
The skinny on big data…[25]
Concept
Learning Analytic
Education
Management
Toward integration…[26]
Concept
Curriculum
Curriculum
Using Big Data…[27]
Concept
Model of Learning
Education
Management
Further the table shows the impact of that trends to Education’s functional area. All trends
grouped into 2 major type they are Conceptual Trend and Research Trend. Conceptual trends
defined a paper that use Big Data as a concept in education sector, it can be a model of
implementation or important factors to implement Big Data on education or any other concepts.
While Research Trend defined as papers that use Big Data as a research approach to find
information or conclusion about education. Trends that grouped into Conceptual Trends are:
Learning Activities; Behavioral Analytics; Challenge of Big Data; Education Accreditation;
Learner Journey; Improve Teaching and Learning Experience; Improve Curriculum, Learning
and Teaching Methods; Learning Analytics and Educational Policy; Internal Decision-making
process; Data Analytics for Services; Data Analytics using Monggo DB & Map Reduce;
Curriculum Mapping; Competitive Advantage in Higher Educations; Graduate Competency;
Moral Challenge; Learning Analytic, Personalizing Student Learning Experiences; Learning
Analytic; Curriculum; Assessment of Basic Education Quality; Online Learning; Collaborating
between academician and industry; Learning Analytic; Curriculum; Model of Learning. Trends
that grouped into Research Trends are: Learning Analytics, Reflection of Teaching; Education
Modeling; Implementation of Big Data in Global Health.