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Data Analytics in Education : Current and Future Directions
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Young Ok Kwon (Division of Business Administration, Sookmyung Women’s University)
Vol. 19, No. 2, Page: 87 ~ 100
Bigdata, Analytics, Education Service, Curriculum, Curriculum
Massive increases in data available to an organization are creating a new opportunity for competitive advantage. In this era of big data, developing analytics capabilities, therefore, becomes critical to take advantage of internal and external data and gain insights for data-driven decision making. While, in comparison with business and government, the use of data in education is in its infancy, the potential for data analytics to impact education services is growing. Various information and communication technologies become increasingly embedded in class activities, and significantly more data on individual students are being captured and analyzed. Thus, for example, student academic performance can improve if instructional and curricular decisions are made based on the application of advanced analytics capabilities to student performance data. This paper focuses on education data and, in particular, how the data can be transformed into insights for improved education and can eventually be provided back to students in useful ways. First, in this paper, I survey how universities are currently using education data to improve students’ performance and administrative efficiency, and propose new ways of extending the current use. Some universities provide education programs that can help students choose a major or career path and recommend relevant courses to take to achieve the goal, based on their learning histories. In addition to academic performance data, some individual data from social network services can be used to encourage the involvement in extracurricular activities and as a result affect school performance. Technologies such as mobile phones and tablet computers allow educators and students access to rich online resources. Interactive whiteboards and digital textbooks can also provide multimedia and interactive elements, including video, audio, and images, that can help student understanding. Massive open online courses can be offered in conjunction with traditional classroom-based learning and there are many more opportunities using technologies and education resources available online. I suggest modular or flexible course programs which can suit each student’s interests, learning styles, learning pace, and etc. The best program can be selected for each student, by applying some recommendation algorithms based on the academic performance data of both current and previous students. Visualization technologies can also help students easily track their own progress and let them think of how they can improve and meet their goals by themselves. Academic-log data accumulated in student learning management systems can show each student’s activities on the system and can be used to identify each student’s learning styles and also find students who have difficulties in learning. Technologies and education data can also help to improve administrative functions and operational processes. More personalized supports can be offered to instructors to further their teaching skills, based on individual teaching experiences. Second, while most organizations try to capture the full potential of big data, it has been reported that the shortage of analytics and managerial talent is a significant and pressing problem and CIO survey results show that it is difficult for organizations to find the right people with deep analytical skills, referred to as data scientists. With the data scientist shortage, universities should be able to train professionals with data analytics skills. This paper discusses which skills are valuable to data scientists and introduces various training and certification programs offered by universities and industry. Government, industry, and academia should collaborate to make data science emerge as an academic discipline. I finally conclude the paper by exploring new curriculums where students, by themselves, can learn how to find and use relevant data. Since big data are now everywhere, universities should not only be able to utilize education data that they acquire for better and personalized education services, but also provide practical guidelines for students who want to obtain data analytics skills and foster data driven decision-making in any organization.
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빅데이터를 활용한 맞춤형 교육 서비스 활성화 방안연구
권영옥 (숙명여자대학교 경영학부)
데이터의 급속한 증가로 데이터를 활용한 새로운 가치 창출은 기업뿐 아니라 국가 경쟁력의 중요한 요소로 대두되고 있고, 이에 따라 전 세계적으로 국가 차원에서 빅데이터 활성화를 위한 다양한 노력을 기울이고 있다. 본 연구는 특히 교육 분야에서의 빅데이터 활용 현황을 조사하고 새로운 데이터 기반의 맞춤형 교육 서비스 및 프로그램을 제안한다. 이미 많은 학교에서 학생들의 학업 성과를 높이기 위해서 학생들 개개인의 교육 데이터를 활용한 데이터 기반의 의사 결정을 하고 있다. 먼저 세계 유수 대학에서 시행 중인 대표적인 교육프로그램으로, 개인별 학업 성과 데이터, 진학, 진로 정보 등을 이용한 성적 관리 프로그램, 소셜 네트워크 서비스의 데이터를 활용하여 학업 이외의 학교 생활에도 도움을 주는 교육 프로그램, 모바일 및 정보통신기술을 활용할 수 있는 프로그램들을 소개한다. 그리고, 이를 바탕으로 교육 서비스 및 학교 행정 업무 향상 등 교육환경을 개선할 수 있는 새로운 프로그램도 제시한다. 또한, 빅데이터 활용 능력이 전 산업 분야에서 주요 경쟁요소가 되면서 데이터를 분석하여 통찰력을 제시할 수 있는 데이터 과학자라 불리는 데이터 분석 전문가의 수요가 늘고 있다. 이러한 데이터 분석 전문 인력 양성을 위한 국내외 대학 및 기업의 대표적인 프로그램들을 살펴보고 장기적인 관점에서 데이터 분석 능력을 배양할 수 있는 새로운 교과 과정도 제안한다. 빅데이터 교과 과정은 단순히 관련 지식의 전달보다는 데이터 분석을 위해 필요한 지식이 무엇인지 스스로 찾고 이를 습득할 수 있도록 도와주는데 초점을 두어야 할 것이다. 이러한 훈련을 통하여 제공되는 데이터뿐만 아니라 제공되지 않은 방대한 관련 데이터 중에서 유용한 지식을 찾아내어 이를 분석하고 의미 있는 정보를 도출할 수 있는 기술을 습득할 수 있을 것이다. 정보통신기술을 활용한 스마트 교육으로 교육 데이터는 점점 더 급증하고 있고 이를 통합적으로 관리, 분석하여 새로운 가치를 창출할 수 있는 교육 형태와 패러다임에 대한 연구의 필요성은 더욱 커질 것이다. 본 연구는 다양한 사례를 바탕으로 대학의 교육환경 개선을 위한 교육데이터의 새로운 활용 방안을 모색하였다는데 의의가 있으며, 앞으로 관련 분야에 더 많은 연구가 이루어 질 것으로 기대된다.
Cite this article
& Kwon, Y. O. (2013). Data Analytics in Education : Current and Future Directions. Journal of Intelligence and Information Systems, 19(2), 87-100.

IEEE Style
Young Ok Kwon, "Data Analytics in Education : Current and Future Directions", Journal of Intelligence and Information Systems, vol. 19, no. 2, pp. 87~100, 2013.

ACM Style
& Kwon, Y. O., 2013. Data Analytics in Education : Current and Future Directions. Journal of Intelligence and Information Systems. 19, 2, 87--100.
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author = {Kwon, Young Ok},
title = {Data Analytics in Education : Current and Future Directions},
journal = {Journal of Intelligence and Information Systems},
issue_date = {June 2013},
volume = {19},
number = {2},
month = Jun,
year = {2013},
issn = {2288-4866},
pages = {87--100},
url = { },
doi = {10.13088/jiis.2013.19.2.087},
publisher = {Korea Intelligent Information System Society},
address = {Seoul, Republic of Korea},
keywords = { Bigdata, Analytics, Education Service, Curriculum and Curriculum },
%0 Journal Article
%1 530
%A Young Ok Kwon
%T Data Analytics in Education : Current and Future Directions
%J Journal of Intelligence and Information Systems
%@ 2288-4866
%V 19
%N 2
%P 87-100
%D 2013
%R 10.13088/jiis.2013.19.2.087
%I Korea Intelligent Information System Society