Thoughts on Recent Trends and Future Research Perspectives in Big Data and Analytics in Higher Education

2016 ◽  
pp. 7-17 ◽  
Author(s):  
Jay Liebowitz
Keyword(s):  
Higher Education ◽  
Big Data ◽  
Future Research ◽  
Research Perspectives ◽  
Recent Trends

Integrating Open and Big Data via ‘e-Oman’: prospects and issues

2016 ◽  
Vol 9 (4) ◽  
pp. 607-621 ◽  
Author(s):  
Stuti Saxena
Keyword(s):  
Higher Education ◽  
Big Data ◽  
Case Studies ◽  
Open Data ◽  
Future Research ◽  
The Public ◽  
Online Portal ◽  
Higher Education Admissions ◽  
The Government ◽  
Government Portal

While ‘e-Oman’ is a repository of Open Data, its significance in terms of being a potent source for Big Data deserves attention. This paper seeks to underscore how important is the integration of Big and Open Data in e-Oman – the e-government portal of Oman. Drawing evidence from four case studies based on the Higher Education Admissions Center (HEAC) ‘e-Portal’ – an online portal meant for the payment of electricity bills, traffic fines and visa applications – the paper lends support to the implementation of integration of Big and Open Data which, for a number of purposes, could be better harnessed. Thus, while the paper identifies the opportunities entailed in achieving the integration of Big and Open Data in the context of the case studies chosen for the study, there are concomitant challenges impacting this integration that need to be addressed. Specifically, e-Oman needs to be updated with Open Data and the government needs to take steps to build and maintain a robust physical, human and information infrastructure for harnessing the potential of integrating Open and Big Data in the public sector. The paper concludes with directions for future research.

scholarly journals Big data for sustainable agri‐food supply chains: a review and future research perspectives

2021 ◽  
Author(s):  
Abderahman Rejeb ◽  
Karim Rejeb ◽  
Suhaiza Zailani
Keyword(s):  
Big Data ◽  
Supply Chains ◽  
Food Supply ◽  
Future Research ◽  
Plant Management ◽  
The Sustainable Development ◽  
Food Supply Chains ◽  
Animal Management ◽  
Research Perspectives ◽  
Future Research Directions

AbstractResearch on agri-food supply chains (AFSCs) has attracted significant attention in recent years due to the challenges associated with sustainably feeding the global population. The purpose of this study is to review the potentials of big data for sustainable AFSCs. One hundred twenty-eight (128) journal articles were selected to identify how big data can contribute to the sustainable development of AFSCs. As part of our focus, a framework was developed based on the conceptualization of AFSCs in the extant literature to analyse big data research in the context of AFSCs and to provide insights into the potentials of the technology for agri-food businesses. The findings of the review indicate that there is a noticeable growth in the number of studies addressing the applications of big data for AFSCs. The potentials of big data for AFSC sustainability were synthesized in a summary framework, highlighting the primary resources and activities that are ready for improvement with big data. These include soil, water, crop and plant management, animal management, waste management and traceability management. The challenges of big data integration in AFSCs, the study’s implications, contributions, and the future research directions are highlighted in detail.

AI: new source of competitiveness in higher education

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Erin Hannan ◽  
Shuguang Liu
Keyword(s):  
Higher Education ◽  
Design Methodology ◽  
Enrollment Management ◽  
Learning Experience ◽  
Future Research ◽  
Future Directions ◽  
Content Type ◽  
Early Adopters ◽  
Recent Trends ◽  
Applications Of Ai

Purpose This paper aims to survey the current landscape of artificial intelligence (AI) applications in higher education institutions (HEIs) and recommend future directions. Design/methodology/approach This paper reviews the recent trends, showcases the applications and provides future directions through a review of current uses of AI in HEIs. Findings The results of this study highlight successful applications of AI technologies in three main areas of college operation: student learning experience; student support; and enrollment management. Research limitations/implications This review has important implications for early adopters of AI by HEIs in providing a competitive advantage. The limitation lies in the scope of the review. It is not comprehensive and does not cover other areas of college operations. Originality/value This is the first review about AI in higher education. It is of value in building future research and serving as a framework for AI applications in HEI.

scholarly journals Contemporary Privacy Theory Contributions to Learning Analytics

2014 ◽  
Vol 1 (1) ◽  
pp. 140-149 ◽  
Author(s):  
Jennifer Heath
Keyword(s):  
Higher Education ◽  
Big Data ◽  
Information Systems ◽  
Student Voice ◽  
Learning Analytics ◽  
Higher Education Institutions ◽  
Future Research ◽  
Potential Contribution

With the continued adoption of learning analytics in higher education institutions, vast volumes of data are generated and “big data” related issues, including privacy, emerge. Privacy is an ill-defined concept and subject to various interpretations and perspectives, including those of philosophers, lawyers, and information systems specialists. This paper provides an overview of privacy and considers the potential contribution contemporary privacy theories can make to learning analytics. Conclusions reflect on the suitability of these theories towards the advancement of learning analytics and future research considers the importance of hearing the student voice in this space.

Research perspectives on fully homomorphic encryption models for cloud sector

2021 ◽  
pp. 1-26
Author(s):  
Sonam Mittal ◽  
K.R. Ramkumar
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Homomorphic Encryption ◽  
Data Encryption ◽  
Cloud Services ◽  
Future Research ◽  
Public Cloud ◽  
Fully Homomorphic Encryption ◽  
Research Perspectives ◽  
Learning With Errors

As there is a continuous delivery of big data, the researchers are showing interest in the applications of cloud computing concerning privacy, and security. On the other hand, many researchers and experts of cybersecurity have commenced on a quest for improving the data encryption to the models of big data and applications of cloud computing. Since many users of the cloud become public cloud services, confidentiality turns out to be a more compound problem. To solve the confidentiality problem, cloud clients maintain the data on the public cloud. Under this circumstance, Homomorphic Encryption (HE) appears as a probable solution, in which the information of the client is encrypted on the cloud in such a process that it permits few manipulation operations without decryption. The main intent of this paper is to present the systematic review of research papers published in the field of Fully Homomorphic Encryption (FHE) over the past 10 years. The encryption scheme is considered full when it consists of plaintext, a ciphertext, a keyspace, an encryption algorithm, and a decryption algorithm. Hence, the review mostly concentrates on reviewing more powerful and recent FHE. The contributions using different algorithms in FHE like Lattice-based, integer-based, Learning With Errors (LWE), Ring Learning With Errors (RLWE), and Nth degree Truncated polynomial Ring Units (NTRU) are also discussed. Finally, it highlights the challenges and gaps to be addressed in modeling and learning about competent, effectual, and vigorous FHE for the cloud sector and pays attention to directions for better future research.

Integrating Big Data Analytics Into Retail Services Marketing Management

2020 ◽  
pp. 205-222
Author(s):  
Eleonora Pantano ◽  
Simona Giglio ◽  
Charles Dennis
Keyword(s):  
Big Data ◽  
Research Agenda ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Shopping Center ◽  
Future Research ◽  
Related Services ◽  
Future Research Agenda ◽  
Recent Trends ◽  
Retail Services

This chapter aims at exploring the extent to which the recent trends in digitalization of marketing and related services are leading to a massive amount of consumers' information (big data) in order to suggest possible solutions and recommendations. To this end, the chapter will focus on the case of a large shopping center in London (UK) as meaningful example of how retailers might exploit big data analytics such as sentiment and image analytics to get useful consumers' insights to be successfully integrated into marketing strategies. Finally, the chapter discusses the implications for scholars and practitioners and proposes a future research agenda.

Big data academic and learning analytics

2018 ◽  
Vol 32 (6) ◽  
pp. 1099-1117 ◽  
Author(s):  
Sushil S. Chaurasia ◽  
Devendra Kodwani ◽  
Hitendra Lachhwani ◽  
Manisha Avadhut Ketkar
Keyword(s):  
Higher Education ◽  
Big Data ◽  
Learning Analytics ◽  
Predictive Analytics ◽  
Business Value ◽  
Sufficient Evidence ◽  
Future Research ◽  
Content Type ◽  
Potential Benefits ◽  
Value Model

Purpose Although big data analytics (BDA) have great benefits for higher education institutions (HEIs), due to lack of sufficient evidence on how BDA investment can pay off, it is tough for HEIs practitioners to realize value from such adoption. The purpose of this paper is to propose a big data academic and learning analytics enabled business value model to explain BDA potential benefits and business value which can be obtained by developing such analytics capabilities in HEIs. Design/methodology/approach The study examined 47 case descriptions from 26 HEIs to investigate the causal association between the BDA current and potential benefits and business value creation path for big data academic and learning analytics success in HEIs. Findings The pressure of compliance with all legal and regulatory requirements and competition had pushed HEIs hard to adopt BDA tools. However, the study found out that application of risk and security and predictive analytics to higher education fields is still in its infancy. Using this theoretical model, the results provide new insights to higher education administrators on ways to create BDA capabilities for HEIs transformation and suggest an empirical foundation that can lead to more thorough analysis of BDA implementation. Originality/value A distinctive theoretical contribution of this study is its conceptualization of understanding business value from BDA in the typical setting of higher education. The study provides HEIs with an all-inclusive understanding of BDA and gives insights on how it helps to transform HEIs. The new perspectives associated with the big data academic and learning analytics enabled business value model will contribute to future research in this area.

Big-data business models: A critical literature review and multiperspective research framework

2020 ◽  
Vol 35 (1) ◽  
pp. 66-91 ◽  
Author(s):  
Martin Wiener ◽  
Carol Saunders ◽  
Marco Marabelli
Keyword(s):  
Big Data ◽  
Business Model ◽  
Business Models ◽  
Theory Development ◽  
Big Data Analytics ◽  
Future Research ◽  
Central Research ◽  
Research Perspectives ◽  
Model Research ◽  
Critical Literature

The emergence of “big data” offers organizations unprecedented opportunities to gain and maintain competitive advantage. Trying to exploit the strategic business potential embedded in big data, many organizations have started to renovate their business models or develop new ones, giving rise to the phenomenon of big-data business models. Although big-data business model research is still in its infancy, a significant number of studies on the topic have been published since 2014. We thus suggest it is time to perform a critical review and assessment of the literature at the intersection of business models and big data (analytics), thereby responding to recent calls for further research on and sustained analysis of big-data business models. In particular, our review uses three major criteria (big-data business model types, dimensions, and deployment) to assess the state of the big-data business model literature and identify shortcomings in this literature. On this basis, we derive and discuss five central research perspectives (supply chain, stakeholder, ethics, national, and process), providing guidance for future research and theory development in the area. These perspectives also have practical implications on how to address the current big-data business model deployment gap.

scholarly journals Applying Big Data Analytics in Higher Education

2021 ◽  
Vol 17 (3) ◽  
pp. 29-51
Author(s):  
Adel Alkhalil ◽  
Magdy Abd Elrahman Abdallah ◽  
Azizah Alogali ◽  
Abdulaziz Aljaloud
Keyword(s):  
Higher Education ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Research Direction ◽  
Future Research ◽  
Systematic Mapping Study ◽  
Mapping Study ◽  
Ongoing Research ◽  
Motivation Factors

Higher education systems (HES) have become increasingly absorbed in applying big data analytics due to competition as well as economic pressures. Many studies have been conducted that applied big data analytics in HES; however, a systematic review (SR) of the research is scarce. In this paper, the authors conducted a systematic mapping study to address this deficiency. The qualitative and quantitative analysis of the mapping study resulted in highlighting the research progression over the last decade, and identification of three major themes, 12 subthemes, 10 motivation factors, 10 major challenges, three categories of tools and support techniques, and 16 models for applying big data analytics in higher education. This result contributes to the ongoing research on applying big data analytics in HES. It provides a better understanding of the level of contribution to research as well as identifies gaps for future research direction.

Touchy-Feely History: The ‘Stuff’ of American Culture

2015 ◽  
Vol 4 (1) ◽  
pp. 4-18
Author(s):  
Lauren Rebecca Sklaroff
Keyword(s):  
Material Culture ◽  
Cultural History ◽  
Race And Ethnicity ◽  
The Body ◽  
Special Issue ◽  
Historical Significance ◽  
Research Perspectives ◽  
Economic Trends ◽  
Recent Trends ◽  
New Research

This state of the field essay examines recent trends in American Cultural History, focusing on music, race and ethnicity, material culture, and the body. Expanding on key themes in articles featured in the special issue of Cultural History, the essay draws linkages to other important literatures. The essay argues for more a more serious consideration of the products within popular culture, less as a reflection of social or economic trends, rather for their own historical significance. While the essay examines some classic texts, more emphasis is on work published within the last decade. Here, interdisciplinary methods are stressed, as are new research perspectives developing by non-western historians.

Sign in / Sign up

Export Citation Format

Share Document