Big Data In Higher Education Research Articles

Big Data in Higher Education: Bibliometric Analysis

Big Data in Higher Education: Bibliometric Analysis

MODEL OF STUDENT PREPARATION IN HIGHER EDUCATION IN THE CONTEXT OF BIG DATA

Developing a robust educational system across all domains is essential for providing contemporary educational programs. The aim of our research work is to identify and practically implement the theoretical foundations of preparing students in the field of big data in higher education, which is the cornerstone of training amodern, competitive future specialist.Ensuring the high-quality organization of fundamental disciplines is crucial in developing specific competencies for future educators. Therefore, the main focus of the proposed article is on shaping an educated individual with a broad outlook and high culture of thought in preparing IT specialists at the L.N. Gumilyev Eurasian National University. The article provides a comprehensive definition of the model of student preparation in higher education in the context of big data, outlines and implements components, and discusses the model for achieving positive outcomes. Before considering the model for improving student preparation in the context of big data, let us briefly define big data and examine the concept of a model.

Mathematical Modeling and Multi-Objective Optimization Methods for Big Data in Higher Education

Mathematical Modeling and Multi-Objective Optimization Methods for Big Data in Higher Education

A decade of research into the application of big data and analytics in higher education: A systematic review of the literature

The need for data-driven decision-making primarily motivates interest in analysing Big Data in higher education. Although there has been considerable research on the value of Big Data in higher education, its application to address critical issues within the sector is still limited. This systematic review, conducted in December 2021 and encompassing 75 papers, analysed the applications of Big Data and analytics in higher education. The focus was on their usage in supporting learning, teaching and administration as reported in papers indexed in SCOPUS, Web of Science and IEEE Xplore. The key findings from the review revealed that Big Data and analytics are predominantly used to support learning and, to a lesser extent, guide teaching and informing administrative decision-making processes. The review also identified a set of studies focused on supporting student well-being. Further, we extend the use of Big Data in higher education to include the well-being of students and staff. This paper contributes to the growing debate on the practical use of Big Data and analytics to provide valuable insights for solving systemic challenges facing high education in the twenty-first century.

Problems and Prospects for the Art Education Development in Higher Educational Institutions Based on Big Data Technologies and Digital Platforms

In order to build an effective system of providing educational services in the art direction, it is necessary to possess an assessment of the results of Big Data initial implementation. Previously conducted studies on the specifics of implementing digital platforms in the educational art space are incomplete and insufficient. Most universities of different countries introduce these systems independently, and, therefore, there are no methods and principles for implementing such measures in the educational process. The purpose of the present research is to assess the role of Big Data and digital platforms in improving the quality and efficiency of art education. An analysis of assessing the implementation and functioning of digital systems in the leading higher educational institutions of the art direction in Poland, the Czech Republic, and Ukraine was carried out. This made it possible to generalize the experience gained and identify the main trends of this process. This made it possible to generalize the experience gained and identify the main trends of this process. In particular, 70% of students have a positive attitude towards using digital platforms that allow them to expand their awareness and informativeness, and 73% note these platforms as a source of obtaining available information. This made it possible to generalize the experience gained and identify the main tendencies of this process. As a result, modelling of the concept of using Big Data in higher education in the art direction has been presented. The main methods and examples of using Big Data in art education have been defined and characterized. Prospective directions for further application and implementation of innovative digital systems have been indicated. The use of research results creates opportunities for more flexible expansion of existing digital systems and the formation of new directions for subsequent implementation in the educational process. The research pointed to the most significant problem of comprehensive using Big Data by students due to ignorance and lack of awareness of the potential of Big Data in terms of planning, forecasting behavioral actions both in the process of learning, and in future professional activities. Further development of the research topic should focus on the quantitative and qualitative assessment of existing systems and the formation of a detailed methodology for the introduction of the latest educational services in the art education system.

Big Data in Higher Education for Student Behavior Analytics (Big Data-HE-SBA System Architecture)

Big data is an important part of innovation that has recently attracted a lot of interest from academics and practitioners alike. Given the importance of the education industry, there is a growing trend to investigate the role of big data in this field. Much research has been undertaken to date in order to better understand the use of big data in many sectors for diverse reasons. Big data in higher education, however, still lacks a complete examination. Thus, the purposes of the research were (1) to design the system architecture of big data in higher education for student behavior analytics and (2) to evaluate the system architecture of big data in higher education for student behavior analytics. The research procedure was divided into two phases. The first phase is designing a system architecture for big data in higher education for student behavior analytics, and the second phase is the architecture evaluation by experts. Purposive sampling was used to select ten experts in big data and student behavior analytics. Data collection tools were the system and the assessment of an appropriate model with a five-level rating scale. The statistics used in the data analysis were means and standard deviation. The results showed that the system architecture of big data in higher education for student behavior analytics consists of four elements: a) Big Data Sources for Behavioral Analytics; b) Big Data Sources for Behavioral Analytics Sub-Domains; c) Big data capture and storage for behavioral analytics; and d) big data behavioral analysis. The experts' opinions on the system architecture were at the most appropriate level.

Understanding students’ effective use of data in the age of big data in higher education

ABSTRACT With the advancement of digital technologies, big data and learning analytics have become prevalent in the higher education. Various student-facing systems increased the amount of data available to students, and whether students can use big data and learning analytics effectively will affect their academic success. Most studies, however, have focused on how teachers and administrative personnel use student data to make data-driven instruction and management decisions. As a result, little attention has been given to students' use of relevant data that generated by big data and learning analytics to promote their own learning and growth. This study explored using social cognitive theory to identify possible environmental, personal, and behavioural factors that affect students' data use. We used an online questionnaire that collected 242 completed surveys from Chinese university students. Partial Least Squares (PLS) path modelling was used to analyse the data. The initial findings support the conclusion that university students could be encouraged to effectively use data in three ways: (1) through the promotion of university-wide cultures of data use and sustained improvements in data quality, (2) through the professional development of student data literacy, and (3) through the support of student data autonomy, student data reflectiveness, and students' digital identities.

Big Educational Data & Analytics: Survey, Architecture and Challenges

The proliferation of mobile devices and the rapid development of information and communication technologies (ICT) have seen increasingly large volume and variety of data being generated at an unprecedented pace. Big data have started to demonstrate significant values in higher education. This paper gives several contributions to the state-of-the-art for Big data in higher education and learning technologies research. Currently, there is no comprehensive survey or literature review for Big educational data. Most literature reviews from a few authors have focused on one of these fields: educational mining, learning analytics with discussions on one or two aspects such as Big data technologies without educational focus, social media data in education, etc. Most of these literature reviews are short and insufficient to provide more inclusive reviews for Big educational data. In this paper, we present a comprehensive literature review of the current and emerging paradigms for Big educational data. The survey is presented in five parts: (1) The first part presents an overview and classification of Big education research to show the full landscape in this field, which also gives a concise summary of the overall scope of this paper; (2) The second part presents a discussion for the various data sources from education platforms or systems including learning management systems (LMS), massive open online courses (MOOC), learning object repository (LOR), OpenCourseWare (OCW), open educational resources (OER), social media, linked data and mobile learning contributing to Big education data; (3) The third part presents the data collection, data mining and databases in Big education data; (4) The fourth part presents the technological aspects including Big data platforms and architectures such as Hadoop, Spark, Samza and Big data tools for Big education data; and (5) The fifth part presents different approaches of data analytics for Big education data. This part provides a more inclusive discussion on data analytics which is beyond traditional forms of learning analysis in higher education. This includes predictive analytics, learning analytics including collaborative, behavior, personal learnings and assessment, followed by recommendation systems, graph analytics, visual analytics, immersive learning and analytics, etc. The final part of the paper discusses social (e.g. privacy and ethical issues) and technological challenges for Big data in education. This part also illustrates the technological challenges faced by giving an example for utilizing graph-based analytics for a cross-institution learning analytics scenario.

Adoption of Big Data in Higher Education for Better Institutional Effectiveness

Big data provides many opportunities to broaden existing insights in different fields and higher education also can use the big data for the better institutional performance. This study examines the conceptual models to adopt big data for institutional effectiveness on higher education system. Based on the three relevant entities including institution, faculty, and student, this research provides the relationship between them and construct the negotiable environment as well as the characteristics of relevant indicators. This research suggests the conceptual model that provide how relevant campus members pursuit the better institutional performance and what the individual targeting is. Conceptual model indicates the three main stakeholders exchange specific values under certain type of negotiable interactions and the trade makes better outcomes for institutional effectiveness in the long run. This relationships between the stakeholders generate potential connections with other players outside and the relation creates another chance to broaden range of institutional growth in the higher education system.

From Big Data to Big Impact: analytics for teaching and learning in higher education

PurposeThe purpose of this paper is to examine the applicability of Big Data in higher education institutions.Design/methodology/approachA qualitative research approach using semi-structured interviews was employed to get insights from 23 experts from the Indian higher education sector. Respondents included higher education specialists from information technology, administration and academicians from public and private funded institutions.FindingsBased on competitive advantage and data complexity, four major application areas were identified for the use of Big Data in higher education. These application areas are reporting and compliance; analysis and visualization; security and risk mitigation; and predictive analytics.Research limitations/implicationsQualitative methodology is suitable to explain constructs and relationships between constructs, but it does not explain the magnitude of the relationships. The lack of Big Data experts in higher education constrained the ability of this research by leading to repeated themes. Finally, including participants from other countries would have assisted further in generalizing the findings.Originality/valueAs both interest and reluctance persists about Big Data, it calls for the application across industries and cost-benefit analyses. A number of researchers have studied the use of Big Data in various fields associated with the applicability, the data availability, the cost, the competence, the privacy, the relevance and the ownership. Very few publications explicitly address the integrative use of Big Data in higher education. So the current study examines the applicability of Big Data analytics in higher education institutions.

Big Data in Higher Education: Research Methods and Analytics Supporting the Learning Journey

Big Data in Higher Education: Research Methods and Analytics Supporting the Learning Journey

Inferring Learning from Big Data: The Importance of a Transdisciplinary and Multidimensional Approach

The use of big data in higher education has evolved rapidly with a focus on the practical application of new tools and methods for supporting learning. In this paper, we depart from the core emphasis on application and delve into a mostly neglected aspect of the big data conversation in higher education. Drawing on developments in cognate disciplines, we analyse the inherent difficulties in inferring the complex phenomenon that is learning from big datasets. This forms the basis of a discussion about the possibilities for systematic collaboration across different paradigms and disciplinary backgrounds in interpreting big data for enhancing learning. The aim of this paper is to provide the foundation for a research agenda, where differing conceptualisations of learning become a strength in interpreting patterns in big datasets, rather than a point of contention.

Business Intelligence and Big Data Analytics for Higher Education: Cases from UK Higher Education Institutions

With the rise of big data, its impact has becoming apparent even in the Higher Education sector. The strategic use and applications of big data in higher education would lead to higher educational quality and better student and staff experience. Using the output from UK JISC’s Business Intelligence projects, this paper reviews and outlines eleven cases of big data analytics in the UK Higher Education institutions, followed by presenting one of the projects to demonstrate the application of big data analytics on student engagement. This leads to further research implications for future big data research in Higher Education.

Business Intelligence and Big Data in Higher Education: Status of a Multi-Year Model Curriculum Development Effort for Business School Undergraduates, MS Graduates, and MBAs

Business Intelligence and Big Data in Higher Education: Status of a Multi-Year Model Curriculum Development Effort for Business School Undergraduates, MS Graduates, and MBAs

The Ethics of Big Data in Higher Education

Data mining and predictive analytics—collectively referred to as “big data”—are increasingly used in higher education to classify students and predict student behavior. But while the potential benefits of such techniques are significant, realizing them presents a range of ethical and social challenges. The immediate challenge considers the extent to which data mining’s outcomes are themselves ethical with respect to both individuals and institutions. A deep challenge, not readily apparent to institutional researchers or administrators, considers the implications of uncritical understanding of the scientific basis of data mining. These challenges can be met by understanding data mining as part of a value-laden nexus of problems, models, and interventions; by protecting the contextual integrity of information flows; and by ensuring both the scientific and normative validity of data mining applications.