The big data usability’s trends in education: a systematic literature review

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This paper uses Systematic Literature Review (SLR) to find the trends of usability of Big Data in education sector.

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Nội dung Text: The big data usability’s trends in education: a systematic literature review

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 http://www.iaeme.com/IJMET/index.asp 1974 editor@iaeme.com
Devyano Luhukay, Meyliana, Achmad Nizar Hidayanto, Harjanto Prabowo 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 http://www.iaeme.com/IJMET/index.asp 1975 editor@iaeme.com
The Big Data Usability’s Trends in Education: A Systematic Literature Review 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 http://www.iaeme.com/IJMET/index.asp 1976 editor@iaeme.com
Devyano Luhukay, Meyliana, Achmad Nizar Hidayanto, Harjanto Prabowo 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 Type of Title Trends Functional Area Trends A Study on…. [1] Concept Learning Analytics Learning Activities Learning Analytics, Analysis of Online Research Reflection of Learning Activities Learning…[2] Teaching Behavioral Big Data Analytics….[3] Concept Learning Activities analytics Challenges of big Education Big Data Challenges…[4] Concept data Management Education Education Big data for accreditation…[5] Concept Accreditation Management Education Big data in education…[6] Research Curriculum Modeling Big Data in Higher Education Concept Learning Journey Education…[7] Management Improve Teaching Big Data in Nurse…[8] Concept and Learning Curriculum Experiences Implementation of Big Data Knowledge…[9] Research Big Data in Global Curriculum Health Improve curriculum, Education Big Data: Applications…[10] Concept Learning and Management Teaching Methods learning analytics Big Opportunities…[11] Concept and educational Learning Activities policy Internal decision- Can conservation…[12] Concept Curriculum making processes Data Analytics for Education Developing the role…[13] Concept services Management Data Analytics Discovering Big Data…[14] Concept using Monggo DB Learning Activities & MapReduce Curriculum Visual analytics in…[15] Concept Curriculum Mapping competitive advantage in higher Education From Big Data…[16] Concept education Management institutions http://www.iaeme.com/IJMET/index.asp 1977 editor@iaeme.com
The Big Data Usability’s Trends in Education: A Systematic Literature Review Type of Title Trends Functional Area Trends Graduate Education Impacting Big Data…[17] Concept Competency Management Education Introduction: Big data…[18] Concept Moral Challenge Management Learning Analytic, personalizing Learning Analytics…[19] Concept Learning Activities students learning experiences Mining theory-based…[20] Concept Learning Analytic Learning Activities Perspectives on a Big Concept Curriculum Curriculum Data…[21] Assessment of Education Primary Education…[22] Concept Basic Education Management Quality Research on the Concept Online Learning Curriculum Construction…[23] Collaborating between Education The role of big data…[24] Concept Academician and Management Industry Education The skinny on big data…[25] Concept Learning Analytic Management Toward integration…[26] Concept Curriculum Curriculum Education Using Big Data…[27] Concept Model of Learning 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. http://www.iaeme.com/IJMET/index.asp 1978 editor@iaeme.com
Devyano Luhukay, Meyliana, Achmad Nizar Hidayanto, Harjanto Prabowo # Big Data Trend & Education Functional Area 15 12 10 6 6 5 2 1 0 Learning Activities Education Management Curriculum Functional Area Concept Research Figure 2. Big Data Trend & Education Functional Area There are 24 Conceptual trends and 3 Research trends from the 27 papers founded. Functional area affected of Big Data trend on Education are: Curriculum 7 papers, Education Management 13 papers and Learning Activities 8 papers. Learning Activities Functional Area, 6 papers grouped into Conceptual Trends while 1 paper grouped into Research Trends. In Conceptual Trends, [1] purpose concept of the environment for the learning analytics but they found that it is lack of standard. While [3] focusing on monitoring student’s behavior, predict whether some of them prone to deviant ideologies. Another paper in Learning Activities Functional Area is [11], in trend of learning analytics and educational policy, examines opportunities and concern over big data in education. [14] in their research creating model of education using monggo DB & mapReduce. [19] and [20] research have trend learning analytic. Where [20] contributions include identification of the six most frequent interaction sequent patterns of student when using MOOC, Differentiate interaction sequence patterns between learners, Identification of learner profile, and association of observed interaction sequence patterns. Paper with trend type research on functional area learning activities, [2] has implication on new form of reflection on learning and teaching. [2] concluded that Learning Analytics is effective in supporting reflection of tutor on interactive online teaching and learning. Educational Management Functional Area with 12 concept types of trends. All the papers from this functional area focusing Big Data usability on Educational Management with different field of study such as Medical, Health and general education. [5] in their research identify factors that required to implement Big Data and how to enhances the factors to support university accreditation. [17] combine six sigma principles and Big Data Technique to improve graduate competency. [27] also create model of learning to use Big Data technologies on e-learning. Curriculum Functional Area has 6 conceptual trends and 2 research trends. On Conceptual trends, many fields of studies have been using Big Data such as Nurse Education, Conservations Education, Healthcare Education, Vocational colleges and Accounting. Big Data be considered to embedded into curriculum of specific field, [8] synthesize the current literature on big data and apply it in nurse education. The implication of the research of [8] to bring revolution into health professional education. [15] evaluates and verify ongoing medical education toward desired learning outcomes the big picture of the curriculum. [21] stated that cloud computing and big data offer additional benefits together. http://www.iaeme.com/IJMET/index.asp 1979 editor@iaeme.com
The Big Data Usability’s Trends in Education: A Systematic Literature Review 4. CONCLUSSION AND IMPLICATIONS The study of Big Data on Education using SLR from the 27 papers 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; Curriculum and Services. This research has both theory and practical implication on Usability of Big Data on education sector. Practical impact of this research is this paper can be used to identify which area of the education sector that has possibility of development using Big Data. The Area that possible to develop such as: Learning Activities that include learning activity, learning session, learning media, etc., Education Management that include administration of student, grading, and student graduation, Curriculum area where Big Data can be a streaming or specific course to facilitate student to the new Big Data profession like Big Data Specialist, Data Scientists and any other. On Service area, Big Data can be used to help institution to provide information services, academic services or any other operational things. As a theory implications, this research can be used as a reference to improve Big Data implementation on Education Sector. 5. LIMITATION & FUTURE RESEARCH This research has limitation in a matter of search sources, and any other research field that related with Big Data on education sector. For the future, there are still many improvements needed to do research on Big data for Education, on Learning Activity, Education Management, and Curriculum Functional Area. On Learning Activity Big Data can be developed further by capturing the behavior of the user (student and teacher) when using Learning Management Systems, the data can be used to profile user and provide customized looks or interaction of the LMS, so it will help user to engage more with the system. Big Data on Education Management Functional Area has possibility to learn and help internal decision making by knowing stakeholder preference and needs. On Curriculum Functional Area, Big Data can be blended into many fields of study as the technology can provide more information or interpretation from structured and unstructured data. REFERENCES [1] Kim Y. H, Ahn J. H., A Study on the Application of Big Data to the Korean College Education System, Procedia Computer Science, 2016 [2] Han, Y., Wei, S, Zang, S., Analysis of Online Learning Behavior from a Tutor Perspective: Reflections on Interactive Teaching and Learning in the Big Data Era, Asian Association of Open Universities Journal, 2015 [3] Baig, A. R., Jabeen, H., Big Data Analytics for Behavior Monitoring of Students, Procedia Computer Science, 2016 [4] Wang, S., Wang, H., Big Data Challenges for Management of Teaching and Learning, International Journal of Arts & Sciences, 2015 [5] Ahmed, A., I., Big data for accreditation: A case study of Saudi universities, Journal of Theoretical and Applied Information Technology, 2016 [6] Pardos, Z., A., Big data in education and the models that love them, Current Opinion in Behavioral Sciences, 2017 [7] Gibson, D., Big Data in Higher Education: Research Methods and Analytics Supporting the Learning Journey, 2017 [8] Schwerdtle, P., Bonnamy, J., Big Data in Nurse Education, Nurse Education Today, 2017 [9] Olayinka, O., Kekeh, M., Sheth-chandra, M., Akpinar-Elci, M., Big Data Knowledge in Global Health Education, Annals of Global Health, 2017 http://www.iaeme.com/IJMET/index.asp 1980 editor@iaeme.com
Devyano Luhukay, Meyliana, Achmad Nizar Hidayanto, Harjanto Prabowo [10] Leteef, F., A., Big Data: Applications in Healthcare and Medical Education, Education in Medicine Journal, 2016 [11] Wang, Y., Big Opportunities and Big Concerns of Big Data in Education, TechTrends, 2016 [12] Moss, A., Can conservation education learn anything from a Big Data? International Zoo Yearbook, 2016 [13] Ellaway, R., H.., Pusic., M., V., Galbraith, R., M., Cameron, T., Developing the role of big data and analytics in health professional education, Medical Teacher, 2014 [14] Wassan, J., T., Discovering Big Data Modelling for Educational World, Procedia - Social and Behavioral Sciences, 2015 [15] Vaitsis, C., Nilsson, G., Zary., N., Visual analytics in healthcare education: exploring novel ways to analyze and represent big data in undergraduate medical education, PeerJ, 2014 [16] Chaurasia, S., S., Frieda, R., A., From Big Data to Big Impact: analytics for teaching and learning in higher education, Industrial and Commercial Training, 2017 [17] Laux, C., Li, N., Seliger, C., Springer, J., Impacting Big Data analytics in higher education through Six Sigma techniques, International Journal of Productivity and Performance Management, 2017 [18] Ben-Porath, S., Shahar, T., H., B.,, Introduction: Big data and education: ethical and moral challenges, Theory and Research in Education, 2017 [19] Aguilar, S., J., Learning Analytics: at the Nexus of Big Data, Digital Innovation, and Social Justice in Education, TechTrends, 2017 [20] Maldonado-Mahauad, J., P'erez-Sanagustn, M., Kizilcec, R., F., Morales, N., Munoz-Gama, J., Mining theory-based patterns from Big data: Identifying self-regulated learning strategies in Massive Open Online Courses, Computers in Human Behavior, 2017 [21] Erturk, M., Jyoti, K., Perspectives on a Big Data Application: What Database Engineers and IT Students Need to Know, Engineering, Technology & Applied Science Research, 2015 [22] Graca, T., Cristian, J., Machado, F., Vieira, B., P., Primary Education Evaluation in Brazil using Big Data and Cluster Analysis, Procedia Computer Science,2015 [23] Wang, Z., Research on the Construction of Metal Materials and Heat Treatment "" Network Course to Higher Vocational Colleges under the Background of Big Data"", Applied Mechanics and Materials, 2016 [24] Coccoli, M, Maresca, P., Stanganelli, L., The role of big data and cognitive computing in the learning process, Journal of Visual Language and Computing, 2016 [25] Reyes, J., A., The skinny on big data in education: Learning analytics simplified, TechTrends, 2015 [26] Sledgianowsko, D., Gomma, M., Tan., C., Toward integration of Big Data, technology and information systems competencies into the accounting curriculum, Journal of Accounting Education, 2017 [27] Logica, B., Magdalena, R., Using Big Data in the Academic Environment, Procedia Economics and Finance, 2015" [28] UNESCO (2016). Out in the Open: Education sector responses to violence based on sexual orientation and gender identity/expression (PDF). Paris, UNESCO. p. 54. ISBN 978-92-3- 100150-5. http://www.iaeme.com/IJMET/index.asp 1981 editor@iaeme.com