Considering the rise of implementation of big data analytics (BDA) in Saudi Arabian higher education institutions but with relatively lesser optimal performance, the study investigated the causality of organisational culture (OC) and BDA's social and technical subsystems, following the Socio-Technical Systems theory, with the strategic…

Firms in industry continue to be attracted by the benefits of Big Data Analytics. The benefits of Big Data Analytics projects may not be as evident as frequently indicated in the literature. The authors of the study evaluate factors in a customized methodology that may increase the benefits of Big Data Analytics projects. Evaluating firms in the…

This research shares the challenges of bringing in real-world big business data into the classroom so students can experience how today's business decisions can improve with the strategic use of data analytics. Finding a true big data set that provides real world business transactions and operational data has been a challenge for academics…

The benefits of Big Data Analytics are cited frequently in the literature. However, the difficulties of implementing Big Data Analytics can limit the number of organizational projects. In this study, the authors evaluate business, procedural and technical factors in the implementation of Big Data Analytics, applying a methodology program. Focusing…

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…

Business leaders around the world are using emerging technologies to capitalize on data, to create business value and to compete effectively in a digitally driven world. They rely on data analytics to accelerate time to insight and to gain a better understanding of their customers' needs and wants. However, big data and data analytics solutions in…

"Education data science" is an emerging methodological field which possesses the algorithm-driven technologies required to generate insights and knowledge from educational big data. This article consists of an analysis of the Lytics Lab, Stanford University's laboratory for research and development in learning analytics, and the Center…

The Intelligent Research and Training Platform (IRTP) of the National Center for Educational Technology (NECT) is an application designed to integrate AI technology and teacher education in response to the "Artificial Intelligence + Teacher Education" strategy, in order to provide teacher professional development and power the…

This qualitative study identified those factors that influence the perceived effectiveness of traditional IA control frameworks. The key factors examined in this study are risk management, governance, access control, privacy protection, integrity, availability, reliability, and usability. The researcher endeavored to determine how the…

In the context of the hype, promise and perils of Big Data and the currently dominant paradigm of data-driven decision-making, it is important to critically engage with the potential of Big Data for higher education. We do not question the potential of Big Data, but we do raise a number of issues, and present a number of theses to be seriously…

"Starting Research in Clinical Education" is written by a global team of experienced and emerging clinical education researchers who have a wealth of knowledge designing rigorous research projects and expertise in contemporary methods. Covering a broad spectrum of methods used by clinical education researchers, the book is split into…

Purpose: The purpose of this paper is to demonstrate the utility of combining event-centred and variable-centred approaches when analysing big data for higher education institutions. It uses a large, university-wide data set to demonstrate the methodology for this analysis by using the case study method. It presents empirical findings about…

The article addresses the question of how the assessment process with large-scale data derived from online learning environments will be different from the assessment process without it. Following an explanation of big data and how it is different from previously available learner data, we describe three notable features that characterize…

We propose that machine-learned computational models (MLCMs), in which the model parameters and perhaps even structure are learned from data, can complement extant approaches to the study of text and discourse. Such models are particularly useful when theoretical understanding is insufficient, when the data are rife with nonlinearities and…

Most distance learning institutions collect vast amounts of learning data. Making sense of this 'Big Data' can be a challenge, in particular when data are stored at different data warehouses and require advanced statistical skills to interpret complex patterns of data. As a leading institute on learning analytics, the Open University UK instigated…