Role of Big Data in Education

2022 ◽  
pp. 22-37
Author(s):  
Simin Ghavifekr ◽  
Seng Yue Wong
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Decision Making ◽  
Big Data ◽  
Information Management ◽  
Higher Education Institutions ◽  
Big Data Analytics ◽  
Time Performance ◽  
Teaching Support

Big data has the variety of characteristics, such as real-time performance, timeliness short, and data mining analysis of large value generated. Application of big data in education can be reviewed in various aspects such as 1) providing students with appropriate teaching, 2) providing teaching support to teachers, and 3) providing information management for the administrations. This chapter can serve as a guide for the management of higher education institutions to recognize possible challenges in big data analytics and better prepare for them in future decision making.

Big Data Analytics in Undergraduate Advertising Curricula

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.

scholarly journals On a Certain Research Gap in Big Data Mining for Customer Insights

2021 ◽  
Vol 11 (15) ◽  
pp. 6993
Author(s):  
Maria Mach-Król ◽  
Bartłomiej Hadasik
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Big Data Analytics ◽  
Customer Behavior ◽  
Temporal Dimension ◽  
Big Data Mining ◽  
Analysis And Synthesis ◽  
Research Gap ◽  
Key Phrases

The main purpose of this paper is to provide a theoretically grounded discussion on big data mining for customer insights, as well as to identify and describe a research gap due to the shortcomings in the use of the temporal approach in big data analyzes in scientific literature sources. This article adopts two research methods. The first method is the systematic search in bibliographic repositories aimed at identifying the concepts of big data mining for customer insights. This method has been conducted in four steps: search, selection, analysis, and synthesis. The second research method is the bibliographic verification of the obtained results. The verification consisted of querying the Scopus database with previously identified key phrases and then performing trend analysis on the revealed Scopus results. The main contributions of this study are: (1) to organize knowledge on the role of advanced big data analytics (BDA), mainly big data mining in understanding customer behavior; (2) to indicate the importance of the temporal dimension of customer behavior; and (3) to identify an interesting research gap: mining of temporal big data for a complete picture of customers.

scholarly journals Implementasi Big Data Analytical Untuk Perguruan Tinggi Menggunakan Machine Learning

2021 ◽  
Vol 2 (1) ◽  
pp. 77-88
Author(s):  
Rakhmat Purnomo ◽  
Wowon Priatna ◽  
Tri Dharma Putra
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Data Analysis ◽  
Student Performance ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Process Data ◽  
Grouping Students

The dynamics of higher education are changing and emphasize the need to adapt quickly. Higher education is under the supervision of accreditation agencies, governments and other stakeholders to seek new ways to improve and monitor student success and other institutional policies. Many agencies fail to make efficient use of the large amounts of available data. With the use of big data analytics in higher education, it can be obtained more insight into students, academics, and the process in higher education so that it supports predictive analysis and improves decision making. The purpose of this research is to implement big data analytical to increase the decision making of the competent party. This research begins with the identification of process data based on analytical learning, academic and process in the campus environment. The data used in this study is a public dataset from UCI machine learning, from the 33 available varibales, 4 varibales are used to measure student performance. Big data analysis in this study uses spark apace as a library to operate pyspark so that python can process big data analysis. The data already in the master slave is grouped using k-mean clustering to get the best performing student group. The results of this study succeeded in grouping students into 5 clusters, cluster 1 including the best student performance and cluster 5 including the lowest student performance

Boosting innovation performance through big data analytics:An empirical investigationon the role of firm agility

2021 ◽  
pp. 016555152110474
Author(s):  
Ahad ZareRavasan
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Current Knowledge ◽  
Big Data Analytics ◽  
Innovation Performance ◽  
Data Driven ◽  
Moderating Variables ◽  
Research Gap ◽  
Moderating Role

While past studies proposed the role of big data analytics (BDA) as one of the primary pathways to business value creation, current knowledge on the link between BDA and innovation performance remains limited. In this regard, this study intends to fill this research gap by developing a theoretical framework for understanding how and under which mechanisms BDA influences innovation performance. Firm agility (conceptualised as sensing agility, decision-making agility and acting agility) is used in this research as the mediator between BDA and innovation performance. Besides, this research conceptualises two moderating variables: data-driven culture and BDA team sophistication. This study employs partial least squares (PLS) to test and validate the proposed hypotheses using survey data of 185 firms. The results show that firm agility significantly mediates the link between BDA use and innovation performance. Besides, the results suggest that data-driven culture moderates the relation between sensing agility and decision-making agility. This research also supports the moderating role of BDA team sophistication on the link between BDA use and sensing agility.

Role of Big Data Analytics in Cellular Network Design

2015 ◽  
pp. 110-116
Author(s):  
Pushpa Mannava
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Network Design ◽  
Cellular Network ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Innovative Methods ◽  
Modern Technologies ◽  
Cellular Network Design

Big Data is an arising idea that describes innovative methods and also modern technologies to assess big volume of complicated datasets that are greatly created from numerous sources and with numerous prices. Data mining strategies are offering terrific aid in the location of Big Data analytics, considering that handling Big Data allow challenges for the applications. Big Data analytics is the capability of removing helpful information from such significant datasets. This paper provides an overview of big data analytics.

Sign in / Sign up

Export Citation Format

Share Document