We examine four important considerations in the development of covariate adjustment methodologies for indirect treatment comparisons. First, we consider potential advantages of weighting versus outcome modeling, placing focus on bias-robustness. Second, we outline why model-based extrapolation may be required and useful, in the specific context of…

In today's digital world, higher education institutions collect and use more data than ever. However, institutional silos create barriers for stakeholders who need data for daily operations and strategy. This article presents a vision of a unified, collaborative future for data governance and actionable steps stakeholders can take.

There is an increasing demand to introduce Introductory Business Analytics (IBA) courses into undergraduate business education. Many real-world business contexts require predictive analytics to understand the determinants of a dichotomous outcome; hence, IBA courses should include binary logistic regression analysis. This article provides our…

Cloud computing, internet of things (IoT), artificial intelligence, and big data are four very different technologies that are already discussed separately. The use of the four technologies is required to be more and more necessary in the present day in order to make them important components in today's world technology. In this paper, the authors…

The General Data Protection Regulation (GDPR) in the European Union contains directions on how user data may be collected, stored, and when it must be deleted. As similar legislation is developed around the globe, there is the potential for repercussions across multiple fields of research, including educational data mining (EDM). Over the past two…

This study explores an integrated approach for sustainable urban early childhood education and development within the context of urban planning and preschool infrastructure in rapidly urbanizing areas. It focuses on a case study conducted in both private and public preschools in Addis Ababa. The study adopts a qualitative case study design,…

A reproducible analysis is one in which an independent entity, using the same data and the same statistical code, would obtain the exact same result as the previous analyst. Reproducible analyses utilize script-based analyses and open data to aid in the reproduction of the analysis. A reproducible analysis does not ensure the same results are…

Amidst ongoing technological and social change, this article explores the implications for critical education that result from a data-driven model of digital governance. The article argues that traditional notions of critique which rely upon the deconstruction and analysis of texts are increasingly redundant in the age of datafication, where the…

This response to Neil Selwyn's paper, 'What's the problem with learning analytics?', relates his work to the ethical challenges associated with learning analytics and proposes six ethical challenges for the field.

This paper reports on the challenges faced by two researchers during data collection and translation of data and analysis in two public sector universities in Pakistan. Data collection from each institute involved different procedures and a different set of issues, including negotiating access with gatekeepers and participants, dealing with…

Quality education is a prerequisite and essence of human development. At present, India has improved a lot, but, in some cases, it still needs some improvement like retention and enrollment of students in education. The drop-out problem is a major in India. During this pandemic situation, drop-out problems become a significant issue. Researchers…

Purpose: The primary goal of this commentary was to consider the future directions that researchers dealing with levels and regulation of strategies and with approaches to learning may wish to pursue in the years to come. Procedure: In order to accomplish this goal, the first step was to look for any common ground shared by authors contributing to…

We briefly review the emergence of internet research ethics (IRE) since 2000 across three stages, showing how the last, IRE 3.0, focuses on ethical challenges and issues evoked by Big Data. We explore specific examples of IRE 3.0 as occasioned by requirements for informed consent -- including Big Data analyses of a closed Facebook group -- as…

Interdisciplinarity is becoming increasingly important in education. With the rapidly evolving job market, an interdisciplinary education can prepare students for the flexibility and broad knowledge base required to adapt. At the University of Pisa, we recognized the value of an interdisciplinary educational environment during our participation in…

Data analytics is an integral part of planning and decision making in business. Priorities have shifted to hiring skilled employees to support a company's analytics requirements. The authors discuss the background of big data and data analytics, demand for trained professionals, and information on the development of a data analytics curriculum.…