A Proposed Framework for Big Data Analytics in Higher Education

Data science or specifically data analytics systems have become an emerging trend in information technology and have attracted many organizations, including higher education. Higher Education Systems (HES) involve very active entities (students, faculty members, researchers, employers) who generate and require large volumes of data that go beyond the structured data stored in the house. The collection, analysis, and visualization of such big data present a huge challenge for HES. Big data analysis could be the solution to this challenge. However, the rationale and decision process for the adoption of big data analytics can be difficult. Such a knowledge-driven process requires a multitude of technical and organizational aspects that must be accounted for to ensure informed decisions are made. Existing research and development indicates that the decision to adopt, although systematic research with a theoretical background is rare and none of the existing studies have considered diffusion of innovation (DOI) theory. This paper aims to support HES, by providing a systematic analysis of the determinants for the decision to adopt big data analytics. An integrated framework referred to as the Technology Organization Environment (TOE) framework is proposed. The proposed framework is validated using structural equation modeling. Eleven determinants are confirmed that influence the TOE-driven framework for data analytics in HES. The result is expected to contribute to on-going research that attempts to address the complex and multidimensional challenge that relates to data science and analytics implementation in HES.

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Big Data Analytics in Undergraduate Advertising Curricula

10.4018/978-1-6684-3662-2.ch095 ◽

2022 ◽

pp. 1958-1973

Author(s):

Kenneth C. C. Yang ◽

Yowei Kang

Keyword(s):

Higher Education ◽

Big Data ◽

Data Analytics ◽

Higher Education Institutions ◽

Big Data Analytics ◽

Past Research ◽

Data Driven ◽

The World ◽

Tools And Techniques ◽

Personalized Advertising

The rapid ascent of data-driven advertising practices has allowed advertising professionals to develop highly-targeted and personalized advertising campaigns. The success of data-driven advertising relies on if future professionals are proficient with basics of Big Data analytics. However, past research of undergraduate advertising curricula around the world has shown that higher education institutions tend to fall behind in offering the most up-to-dated training for advertising students. Findings have shown that undergraduate advertising programs have slowly taken advantage of the potential of the data analytics tools and techniques. This trend is observed among higher education institutions around the world. Practical, research, and pedagogical implications are discussed.

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Impacting Big Data analytics in higher education through Six Sigma techniques

International Journal of Productivity and Performance Management ◽

10.1108/ijppm-09-2016-0194 ◽

2017 ◽

Vol 66 (5) ◽

pp. 662-679 ◽

Cited By ~ 8

Author(s):

Chad Laux ◽

Na Li ◽

Corey Seliger ◽

John Springer

Keyword(s):

Higher Education ◽

Big Data ◽

Student Success ◽

Research Design ◽

Six Sigma ◽

Data Analytics ◽

Design Methodology ◽

Big Data Analytics ◽

Content Type ◽

Conceptual Research

Purpose The purpose of this paper is to develop a framework for utilizing Six Sigma (SS) principles and Big Data analytics at a US public university for the improvement of student success. This research utilizes findings from the Gallup index to identify performance factors of higher education. The goal is to offer a reimagined SS DMAIC methodology that incorporates Big Data principles. Design/methodology/approach The authors utilize a conceptual research design methodology based upon theory building consisting of discovery, description, explanation of the disciplines of SS and Big Data. Findings The authors have found that the interdisciplinary approach to SS and Big Data may be grounded in a framework that reimagines the define, measure, analyze, improve and control (DMAIC) methodology that incorporates Big Data principles. The authors offer propositions of SS DMAIC to be theory tested in subsequent study and offer the practitioner managing the performance of higher education institutions (HEIs) indicators and examples for managing the student success mission of the organization. Research limitations/implications The study is limited to conceptual research design with regard to the SS and Big Data interdisciplinary research. For performance management, this study is limited to HEIs and non-FERPA student data. Implications of this study include a detailed framework for conducting SS Big Data projects. Practical implications Devising a more effective management approach for higher education needs to be based upon student success and performance indicators that accurately measure and support the higher education mission. A proactive approach should utilize the data rich environment being generated. The individual that is most successful in engaging and managing this effort will have the knowledge and skills that are found in both SS and Big Data. Social implications HEIs have historically been significant contributors to the development of meritocracy in democratic societies. Due to a variety of factors, HEIs, especially publicly funded institutions, have been under stress due to a reduction of public funding, resulting in more limited access to the public in which they serve. Originality/value This paper examines Big Data and SS in interdisciplinary effort, an important contribution to SS but lacking a conceptual foundation in the literature. Higher education, as an industry, lacks penetration and adoption of continuous improvement efforts, despite being under tremendous cost pressures and ripe for disruption.

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