Big Data Analytics in Higher Education

2018 ◽  
pp. 38-61 ◽  
Author(s):  
Tanveer H. Shah
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Big Data ◽  
Big Data Analytics ◽  
Executive Leadership ◽  
State Universities ◽  
Decision Making Process ◽  
Business Analytics ◽  
Executive Leaders ◽  
Future Direction

This chapter reviews the literature on the use of business analytics in higher education. Universities have large datasets available to predict future direction and generate actionable information. An important type of analytics used to improve management processes and to make informed decisions is big data business analytics. State university executive leaders may improve the effectiveness of their decisions by integrating business analytics in the decision-making models. However, there is a need to examine the use of big data business analytics in the decision-making process at the executive leadership level of the selected state universities. Especially in the context of how descriptive, predictive, prescriptive, decisive and basic analytics, and data collection influence the decision-making process at the executive leadership level of the state universities in terms of student retention and graduation rates.

Big Data Analytics in Higher Education

2022 ◽  
pp. 1275-1293
Author(s):  
Tanveer H. Shah
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Big Data ◽  
Big Data Analytics ◽  
Executive Leadership ◽  
State Universities ◽  
Decision Making Process ◽  
Business Analytics ◽  
Executive Leaders ◽  
Future Direction

This chapter reviews the literature on the use of business analytics in higher education. Universities have large datasets available to predict future direction and generate actionable information. An important type of analytics used to improve management processes and to make informed decisions is big data business analytics. State university executive leaders may improve the effectiveness of their decisions by integrating business analytics in the decision-making models. However, there is a need to examine the use of big data business analytics in the decision-making process at the executive leadership level of the selected state universities. Especially in the context of how descriptive, predictive, prescriptive, decisive and basic analytics, and data collection influence the decision-making process at the executive leadership level of the state universities in terms of student retention and graduation rates.

scholarly journals A functional framework based on big data analytics for smart farming

2021 ◽  
Vol 24 (3) ◽  
pp. 1772
Author(s):  
Loubna Rabhi ◽  
Noureddine Falih ◽  
Lekbir Afraites ◽  
Belaid Bouikhalene
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Internet Of Things ◽  
Data Analytics ◽  
New Products ◽  
Big Data Analytics ◽  
Decision Making Process ◽  
Time Production ◽  
Smart Farming ◽  
The Future

Big <span>data in agriculture is defined as massive volumes of data with a wide variety of sources and types which can be captured using internet of things sensors (soil and crops sensors, drones, and meteorological stations), analyzed and used for decision-making. In the era of internet of things (IoT) tools, connected agriculture has appeared. Big data outputs can be exploited by the future connected agriculture in order to reduce cost and time production, improve yield, develop new products, offer optimization and smart decision-making. In this article, we propose a functional framework to model the decision-making process in digital and connected agriculture</span>.

scholarly journals Predictive Analysis in Business Analytics: Application of Decision Tree in Business Decision Making

2021 ◽  
Vol 26 (1) ◽  
pp. 1-29
Author(s):  
Chee Sun Lee ◽  
Peck Yeng Sharon Cheang
Keyword(s):  
Decision Making ◽  
Decision Tree ◽  
Big Data Analytics ◽  
Predictive Analysis ◽  
Business Analytics ◽  
Late 20Th Century ◽  
Business Decision ◽  
And Performance ◽  
Future Direction ◽  
Classification Tool

Business Analytics was defined as one of the most important aspects of combinations of skills, technologies and practices which scrutinize a corporation’s data and performance to transpire a data driven decision making analysis for a corporation’s future direction and investment plans. In this paper, much of the focus will be given to the predictive analysis which is a branch of business analytics which scrutinize the application of input data, statistical combinations and intelligence machine learning (ML) statistics on predicting the plausibility of a particular event happening, forecast future trends or outcomes utilizing on hand data with the final objective of improving performance of the corporation. Predictive analysis has been gaining much attention in the late 20th century and it has been around for decades, but as technology advances, so does this technique and the techniques include data mining, big data analytics, and prescriptive analytics. Last but not least, the decision tree methodology (DT) which is a supervised simple classification tool for predictive analysis which be fully scrutinized below for applying predictive business analytics and DT in business applications

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

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.

scholarly journals Big Data Driven Decision Making Model: A case of the South African banking sector

2021 ◽  
Vol 33 (2) ◽  
Author(s):  
Komla Pillay ◽  
Alta Van der Merwe
Keyword(s):  
Decision Making ◽  
Big Data ◽  
South African ◽  
Data Analytics ◽  
Banking Sector ◽  
Big Data Analytics ◽  
Data Driven ◽  
Decision Making Process ◽  
Data Driven Decision Making ◽  
Decision Making Model

The quest to develop a Big Data Driven Decision Making framework to support the incorporation of big data analytics into the decision-making process resulted in the development of a decision making model. The study was conducted within the banking sector of South Africa, with participants from three leading South African banking institutions. The conducted research followed the design science research process of awareness, suggestion, development, evaluation and conclusion. This study developed a theoretical Big Data Driven Decision Making model which illustrates the decision-making process in banking using big data. The study further determined the organizational supports that need to be in place to support the big data analytics decision-making process.

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