Wearable mobile internet devices involved in big data solution for education

Big Data research has rapidly developed in the last 10 years, making it an interesting topic to investigate to understand the trends and developments of Big Data in Education. This study aims to analyze source documents and state contributions, and visualize research trends on the subject of Big Data in education during the last 10 years and identify potential research topics related to Big Data in Education in the future. This study uses a literature review and Meta-Analyses (PRISMA) method associated with bibliometric analysis using the Scopus and VOSViewer databases, through which 1,076 documents were obtained. The results of the bibliometric analysis show that Big Data research in education has increased significantly in the last 10 years. However, in the last year, it has decreased, this is the next challenge, and an opportunity for future research. The most common types of documents are conference papers, the source of most documents is conference proceedings, and the country that contributes the most is China. English is the most widely spoken language, and the authors with the highest contribution are Daniel, B. K. Further research related to Big Data can be used and implemented in the business world for Big Data analysis in education. Big Data can also be integrated with Science, technology, engineering, and mathematics (STEM) which can be a further research opportunity that can be applied to education because analyzing Big Data requires STEM learning skills. Thus, this research recommends finding updates in the study of Big Data in education by integrating STEM in education because analyzing Big Data again requires STEM learning expertise. This study has limitations in using only one database, namely Scopus, to obtain research data. Therefore, it is recommended that Big Data in Education research be conducted using other databases besides Scopus to obtain more extensive data.

Big Data refers to large and disparate volumes of data generated by people, applications and machines. It is gaining increasing attention from a variety of domains, including education. What are the challenges of engaging with Big Data research in education? This paper identifies a wide range of critical issues that researchers need to consider when working with Big Data in education. The issues identified include diversity in the conception and meaning of Big Data in education, ontological, epistemological disparity, technical challenges, ethics and privacy, digital divide and digital dividend, lack of expertise and academic development opportunities to prepare educational researchers to leverage opportunities afforded by Big Data. The goal of this paper is to raise awareness on these issues and initiate a dialogue. The paper was inspired partly by insights drawn from the literature but mostly informed by experience researching into Big Data in education.

Due to technology advancements, the education sector has undergone major changes, particularly with the adoption of e-learning platforms and web services for student interactions. As a result, as many people log into these programmes, a large amount of data is being generated. Most academic institutions, however, have not yet completely tapped into this data, frequently merely handling the information connected to user queries while ignoring the crucial web behavioural inputs. This research attempts to provide a paradigm for using big data technologies to improve teaching and learning in educational institutions in order to address this issue.The document has three distinct goals. To begin with, list the advantages of big data technology in education, emphasising the potential gains for educational institutions. Second, to outline several big data applications in education and show how they might be applied to enhance educational procedures and results. The paper's final goal is to present specialised big data models that are designed for the educational system and provide a useful implementation framework.The research uses both survey and modelling methodologies to accomplish these goals. The survey's component uses review papers to describe the advantages and uses of big data in education while drawing conclusions from the body of existing research. The modelling approach, on the other hand, uses the Unified Modelling Language (UML) to offer a descriptive framework for comprehending the connections between little data and big data in the educational system. Big data covers the full learning process, including both students and teachers, while small data concentrates on conventional input about student performance alone.

Along with the rapid development of technology, individuals today are required to align every aspect of their lives with the technological developments of the industrial revolution 4.0, consisting of artificial intelligence, the internet of things, and big data presented in society. Notably, it was related to education, including Islamic education, which frequently stereotyped about delays in responding to globalization's challenges. This preliminary study aims to encourage empirical research that is still lacking by exploring the role of big data in Islamic education and combining data from general education that has similar core. The study focused on using the scoping review method as a part of a literature review. As a result of this study, there are four impacted factors for strengthening the usage of big data: the performance and behavior factors of learning; the storage of education data; the update in the education system; and the use of big data in the education curriculum. Future studies should begin empirical research to elaborate more on these four impacted factors practically.

Chapter 13 - Big Data in Education: New Efficiencies for Recruitment, Learning, and Retention of Students and Donors

The availability of big data sets on the different learning modes has stimulated the applications of big data technology in education. This article is to introduce relevant application examples of education data mining to show how big data in education help to solve education issues. Firstly it lists the groups of target users and participants involved in education data mining, and the objectives to use data information of each group. It then goes on to illustrate the groups and their objectives further by given examples and finally conclude how big data help to solve education issues through reaching the objective of each group. The examples show that for students big data can generate personalized activities and offer academic advising, for teachers big data can reveal students' characteristics and predict future performance and offer assistance, for educational organizations big data can make scientific decisions.

Big data in education relate closely to a wealth of activities from the teachers, students and parents, as well as a substantial resources of knowledge that can be represented in hierarchical structures. The activities have characteristic of geolocation which can be projected onto a map, while the resources of knowledge can also be converted into the map. A map-based management and visual analysis method will largely benefit the users and the researchers from taking advantages of the big data in education. In this paper, we propose a novel map based method to manage and analyze the big data of mobile learning in education. With this method, the activities of users scattered among the space are reorganized on a geographic map with location changes in time series, and the resources are geo-tagged with the information from the developers or adopters, which are converted to a map style according to their hierarchical structures even when the users' information are unavailable. We first present the basic framework to organize the data by a map-based technology, and then a platform is proposed to perform the visual analysis. The method is adapt to the construction of massive online learning system and the mobile learning system of Central China Normal University to serve a national wide big data cloud learning program.

In recent years, research on Educational Data Mining (EDM) has developed rapidly. However, most researches focus on data source issues, and ignore the importance of data preprocessing and data mining algorithms. This paper has studied EDM, with a special focus on educational big data mining algorithms. Firstly, it analyzed the relevant elements of EDM and introduces big data technology based on the requirements of educational data application. Then it introduced the common educational big data mining algorithms and their applications, and finally discussed the development trend of educational big data mining algorithms.

Big data, characterized by rich information, wide coverage, high value and universality, is applied in all fields including education. Educational researchers are focused on how to apply big data technology to the educational domain rationally. This paper examines the research status quo by reporting an analysis of research literature related to the application of big data in the fields of education, searched on the WOS (Web of Science). The analyses included the amount of literature, areas of distribution, journals in which the articles were published, research hotspots and research trends, using bibliometric analysis software CiteSpace and NoteExpress. The paper summarizes the research results, discusses the advantages and disadvantages of current research, and puts forward some suggestions that can be used as reference in later research of big data in education.

The development of mobile Internet, Internet of Things, and cloud computing has contributed to the unprecedented growth of information data. Big data plays a very important role in education. Currently, the literature review and in-depth research on big educational data are not very extensive, mainly involved in two fields: education mining and learning analysis. For a perfect research about education big data, this paper comprehensively reviewed three major aspects (Predictive Analytics, Learning Analytics, and Recommendation Systems) of educational data analytics for an intensive investigation and analysis: (1) Predictive Analytics: It predicts students’ learning performance by tracking students’ learning information and then analyzes students’ learning competence to build an academic early warning system; teachers can be allowed to intervene in students in time and adopts different teaching ways for different students. Therefore, both students’ learning and ability can be individualized and improved; (2) Learning Analytics: This part can identify the learners’ behavior patterns and obtain more implicit learner characteristics by studying the hidden meaning behind learning behaviors and strategies; (3) Recommendation Systems: It can match the needs of learners and recommend appropriate learning resources through different methods. All the above proved that the application of big data technology in education provides powerful data support for the development of education.

Dropout early warning systems for high school students using machine learning

The article discusses the advanced and promising Big Data technology in the field of higher education at the present stage of development. The authors of the article determine the methodological aspects of the implementation of the technology of operating with big data in education, which is aimed at the development of educational systems through the identification of formed patterns in the education system. In this case, the basic approach is the processes of mathematical modeling, which describe the development of the educational system with the inclusion of regulatory measures based on the analysis of the identified patterns. As a result of the study, important aspects of the application and development of Big Data technology in education are characterized, conclusions are drawn about the need to change the approaches used or actions taken for the development of educational systems. The advantages of using Big Data at the university for students, teachers and administration are described. The impact of Big Data on the practice of higher education is assessed. It is recommended to use the conceptual diagram of the managerial decision-making process at the university based on Big Data.

Big data has become a new driving force for national innovation and entrepreneurship. Although colleges and universities, which are responsible for cultivating high-quality entrepreneurship and innovation talents, have achieved certain results, there are still many problems in practice, which need to be driven by big data. Big data and the practice of innovation and entrepreneurship education in colleges and universities have certain inherent commonality. The integration of big data into the practice of innovation and entrepreneurship education in colleges and universities needs to be improved and strengthened in terms of top-level design, data environment, and educational concepts. Educational big data has become a hot topic and trend in educational research. College students’ entrepreneurship is a kind of entrepreneurial process in which special groups of college students and graduate students are the main body of entrepreneurship. With the recent transformation process of our country and the increasing pressure of social employment, entrepreneurship has gradually become a career choice for college students and graduates. Through data mining, statistical analysis, model construction, and comprehensive reasoning of educational big data are realized, and positive suggestions and countermeasures are provided for education and teaching. On the basis of the pattern analysis of educational data mining process, the concrete application of educational data mining is analyzed, and the correlation and rules between educational phenomena are found, so as to provide educational prediction and educational decision support for further optimization of teaching.

With the rapid development of information technology such as artificial intelligence and cloud computing, big data of higher education has received a lot of attention. This article uses the visual software Citespace from 2013 to 2022 to research higher education in big data in the Web of Science database. Through visual analysis, and uses of the keyword co-occurrence function and cluster analysis function, this article clarifies the main research directions, key documents, hotspots, and limitations in the field of big data in higher education, education big data technology, learning behavior analysis, learning style exploration, education resource management, and personalized teaching reform are the development direction and research hotspots. This article can provide readers and other researchers with some references and help.

The paper focuses on the degree of influence of Big Data on building the educational process at the Russian Law University during the COVID-19 pandemic. We use the sociological method as the primary research method. The result shows the influence of the collected information on administrative decision-making. Additionally, we describe the educational process within the selected university. The paper demonstrates student attitude toward Big Data in various aspects, including awareness of the technology and its significance for the university's management. Weconclude that Big Data has not yet been widely used in Russian higher education. Therefore, we identify the reasons preventing the use of Big Data in education. This research novelty lies in pointing to Big Data's impact on the architecture of the university educational process in different periods of the COVID-19 pandemic when higher education institutions implemented distance or mixed learning and online communications.