Research Summary On Big Data In Education
Significance of Big Data:
In recent years, the rapid development of Internet, Internet of Things, and Cloud Computing have led to the explosive growth of data in almost every industry and business area. Big data has rapidly developed into a hot topic that attracts extensive attention from academia, industry, and governments around the world. In this position paper, we first briefly introduce the concept of big data, including its definition, features, and value. We then identify from different perspectives the significance and opportunities that big data brings to us. Next, we present representative big data initiatives all over the world. We describe the grand challenges (namely, data complexity, computational complexity, and system complexity), as well as possible solutions to address these challenges. Finally, we conclude the paper by presenting several suggestions on carrying out big data projects.
Big data has made a strong impact in almost every sector and industry today. In this paper, we have briefly reviewed the op-portunities and significance of big data, as well as some grand challenges that big data brings us. We close by a few suggestions on how to make a big data project successful.
The successful applications of big data in industry point to the following necessary conditions for a big data project to be suc-cessful. Firstly, there must be very clear requirements, regardless of whether they are technical, social, or economic. Secondly, to efficiently work with big data, we will need to explore and find the kernel structure or kernel data to be processed. Finding kernel data and structures, which are small enough and yet can charac-terize the behavior and properties of the underlying big data, is non-trivial because it is very domain-specific. Thirdly, a top-down management model should be adopted. Although a bottom-up ap-proach may allow us to solve some niche problems, the isolated solutions often cannot be put together into a complete solution. Finally, the goal should be to solve the entire problem by an in-tegrated solution, rather than striving for isolated successes in a few aspects. In short, an integrated engineering approach should be employed in managing a big-data project.
Big Data in Education:
One of most important Aspect of big data is in Education, there are many challenges in Educational field that how to improve the performance of the students, how to give them maximum knowledge to improve their educational performance. Data with high volume, velocity, variety and veracity brings the new experience curve of analytics. Big data in higher education comes from different sources that include blogs, social networks, student information systems, learning management systems, research, and other machine-generated data. Once the data is analysed it promises better student placement processes; more accurate enrolment forecasts, and early warning systems that identify and assist students at-risk of failing or dropping out. Big data is becoming a key to creating competitive advantages in higher education. Like with any organization, traditional data processing and analysis of structured and unstructured data using RDBMS and data warehousing no longer satisfy big data challenges. The lack of adequate conceptual architectures for big data tailored for institutions of higher education has led to many failures to produce meaningful, accessible, and timely information for decision making. Therefore, this calls for the development of conceptual architectures for big data in higher education. This paper presents an architecture for big data analytics in higher education. Applying data mining in education is an emerging interdisciplinary research field also known as educational data mining (EDM). It is concerned with developing methods for exploring the unique types of data that come from educational environments. Its goal is to better understand how students learn and identify the settings in which they learn to improve educational outcomes and to gain insights into and explain educational phenomena. Educational information systems can store a huge amount of potential data from multiple sources coming in different formats and at different granularity levels. Each particular educational problem has a specific objective with special characteristics that require a different treatment of the mining problem. The issues mean that traditional DM techniques cannot be applied directly to these types of data and problems. As a consequence, the knowledge discovery process has to be adapted and some specific DM techniques are needed. This paper introduces and reviews key milestones and the current state of affairs in the field of EDM, together with specific applications, tools, and future insights.
Higher education institutions are often very curious to know about the success rate of the students throughout their study. For this reason, they need to use several methods like physical examination, Statistical methods and currently prevailing data mining techniques for the prediction of student’s performance. An upcoming area of research which uses techniques of data mining is known as Educational Data Mining. It involves machine learning algorithms and statistical techniques to help the user for interpretation of student’s learning habits, their academic performance and further improvement if required. In this paper we will discuss various techniques of data mining which are useful for predicting performance level of students.
As the data involved in education becomes larger, the applications of Big Data techniques become more and more necessary in learning environments. MOOCs are good examples of learning environments that were resource hungry and raised the need for data mining in education. The recent trends in the published papers in EDM indicate the growth in data mining in education field. Apart from EDM which we saw in this study, other communities are also involved in researching this field. Exploring those communities will provide greater insights in the field. Educational Data Mining is sure to reshape the way in which the forthcoming generations would learn. This study revealed two major trends from articles located from top 45 search results of a Google scholar search on “Educational Data Mining”. The articles were mainly from journals and conferences related to Educational Data Mining. The major trends were identified as “Introduction to the concepts of applying Data Mining in Education” and “Prediction or measurement of Student Performance using Data Mining”. As the former trend also involved articles on performance prediction, we can conclude that the biggest focus is on “Performance Prediction using Data Mining”.
The growth in the emerging fields of educational data mining and learning analytics can be seen from the availability of literature from 2010 until present. In the 45 articles selected for review in “Educational Data Mining”, 18 were focusing on exploring the ways in which the data mining techniques can be applied in education, while another 18 focused on evaluating or predicting student’s performance. A few articles focused on specific data mining algorithms and pedagogical analysis. This clearly shows that researchers are focusing mainly on the top two themes, the application of data mining techniques in education and prediction of student performance. The 4 themes were selected as they clearly distinguish the articles into 4 groups and help identify the theme most used by the researchers.
References:
- V. Mayer-Schonberger, K. Cukier, Big Data: A Revolution That Will Transform How We Live, Work, and Think, Houghton Mifflin Harcourt, 2013.
- R. Huebner, “A survey of educational data mining research”, Research in Higher Education Journal, 2012.
- Chitra Jalota ; Rashmi Agrawal Analysis of Educational Data Mining using Classification” , Published in: 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing.
- S. Ayesha, T. Mustafa, A. Sattar and M. Khan, “Data mining model for higher education system”, European Journal of Scientific Research, Vol.43, no.1, pp.24-29., 2010.
