Big data analysis education
199580366
Creating Value for Education through Big Data Analysis Education Programs
Wooje Cho Mi rim Yu
202791486
Problems of Big Data Analysis Education and Their Solutions
Choi Do-sik
59565385
Toward Clarifying Human Information Processing: A Case Study of Big Data Analysis in Education
Our purpose of this paper is to make language processing more clearly by finding out differentiation among individual traits, depending on various kinds of materials, such as context (emotional and nonemotional) and media (sound voice and text) We have carried out comparative experiments, using questionnaires of personality testing and measured the decision time of ninety eight female university students. The result of those data analysis has shown that the members of Type X have sustained learning more effectively than Type Y ones. It is implied that the relationship between higher order and language information processing might be implemented depending on individual differences and diversity of materials. Those views of this study would continuously help teachers to predict students' behavior and plan their instructions so that they can make teaching and learning effectively.
IHSI
Keiko Tsujioka
85502020
Construction and Application of Big Data Analysis Platform for Ideological and Political Education in Colleges
To meet the large data analysis needs of ideological and political education in colleges, this paper designs an assistant expert analysis system based on Hadoop. It integrates the functions of data acquisition, data storage, data analysis and data visualization, and provides a friendly human computer interaction interface to shield the details of large data analysis algorithm, to help teaching staff use large data analysis technology simply and efficiently for high concurrency and rapid query. We classify and design the basic operation model based on MapReduce for users to configure complex data processing logic freely. The system can also add reusable and extensible computing and analysis module to the platform according to the field experience, to expand the data analysis and processing capabilities of the system, which facilitates users to explore iterative and incremental data.
2019 International Conference on Intelligent Transportation, Big Data Smart City (ICITBS)
Tiemei Irene Zhang
59153809
Social media (Twitter) Data analysis using maximum entropy classifier on big data processing framework (Case study: Analysis of health condition, education status, states of business)
Most of the people aren't aware about their health situation, and they don't interest which level has been stand by their nation in case of business and health states. These factors are considered as important things to be improved each nation. Therefore, it is needed to focus these things not only citizens but also the authorities of each country. But, it can be difficult to focus these stated factors without using the modern computer technology. Nowadays, most of the people friendly used social media and people have started expressing their feelings and activities on it. And so, social media is a valuable source to analyze these things by using data mining techniques. Therefore, social media (Twitter) data analysis system is developed to know about health condition, education status, and states of business which are good, fair, or bad based on the data that they post on the Twitter. Maximum Entropy classifier is used to perform sentiment analysis on their tweets to suggest these stated conditions. It is interacting with Twitter data (big data environment) and so, big data processing framework is built to efficiently handle large amount of Twitter data
Hein Zaw Htet Yi Yi Myint
24343696
Research on architecture of education big data analysis system
As the big data Era, the amount of data in education shows explosive growth. Big data analysis system architecture intends to provide a reference for large educational data analysis. Through literature analysis and network investigation, This paper reviewed the existing mature system of large educational data analysis system. From two aspects of generality and difference to compare, this paper proposed research framework from several aspects, including analysis of the common thinking, the generality of open source thought, analysis of the common areas, technology framework, the core technology and special functions. This paper compares 3 mainstream data analysis system, summarizes the enlightenment for the analysis of large data, puts forward a kind of intelligent analysis system of education big data, and sums up the development prospect of big data analysis system.
2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA)
Jinhua Chen Jing Tang Qin Jiang Yuxin Wang Chunmei Tao Xu Zhang Jingya Liao
23253579
Establishment of Business Intelligence and Big Data Analysis for Higher Education
The applications on business intelligence and big data analysis to extract useful information are getting more mature, but the development and operations in higher education institutions are still be lack. This study aims to explore how HEIs employ business intelligence to analysis and mining student learning and HEIs' operation data from database. The outcomes are benefit for universities to support the management of decision making.
ICBIM 2017
Michael Yao-Ping Peng Sheng-Hwa Tuan Feng-Chi Liu
46697255
A Big Data Analysis Method for Online Education
Online education is combined by internet technology and traditional education. It grows rapidly recently. Big data has the characteristics of massiveness, diversification, low value and rapidity, which is deeply influencing and reconstructing the online education. The study on the educational technology matching big data analysis, and the exploration of the development trend and law of online education can help to successfully get out of the dilemma facing with the online education, and to realize the individualized development strategy with the promoted educational technology and educational thinking based on big data. How to use the big data technology in this field? Here give one method and give one example to illustrate and discuss.
2017 10th International Conference on Intelligent Computation Technology and Automation (ICICTA)
Shidong Yu Dong-Sheng Yang Xue Feng
157960284
Comparison of Graduates' and Academics' Perceptions of the Skills Required for Big Data Analysis: Statistics Education in the Age of Big Data
There is currently a shortage of graduates with the necessary skills for jobs in data analytics and "Big Data" Recently many new university degrees have been created to address the skills gap, but they are mostly computer science based with little coverage of statistics. In this chapter, the perceptions of graduates and academics about the types of expertise and the types of software skills required for this field are documented based on two online surveys in Australia and New Zealand. The results showed that Statistical Analysis and Statistical Software Skills were the most necessary type of expertise required. Graduates in industry identified SQL as the most necessary software skill while academics teaching in relevant disciplines identified R programming as the most necessary software skill for Big Data analysis. The authors recommend multidisciplinary degrees where the appropriate combination of skills in statistics and computing can be provided for future graduates. Comparison of Graduates' and Academics' Perceptions of the Skills Required for Big Data Analysis: Statistics Education in the Age of Big Data
Busayasachee Puang-Ngern Ayse Aysin Bilgin Timothy Kyng
64032951
The research of approaches of applying the results of big data analysis in higher education
This article briefly discusses the approaches to the use of Big Data in the educational process of higher educational institutions. There is a brief description of nature of big data, their distribution in the education industry and new ways to use Big Data as part of the educational process are offered as well. This article describes a method for the analysis of the relevant requests by using Yandex.Wordstat (for laboratory works on the processing of data) and Google Trends (for actual pictures of interest and preference in a higher education institution)
Olexii Kochetkov I V Prokhorov