Best Practices for Using Data Analytics Tools in Universities : State ‐ of ‐ play (original) (raw)
Related papers
Data Analytics in Higher Education: An Integrated View
J. Inf. Syst. Educ., 2020
Data analytics in higher education provides unique opportunities to examine, understand, and model pedagogical processes. Consequently, the methodologies and processes underpinning data analytics in higher education have led to distinguishing, highly correlative terms such as Learning Analytics (LA), Academic Analytics (AA), and Educational Data Mining (EDM), where the outcome of one may become the input of another. The purpose of this paper is to offer IS educators and researchers an overview of the current status of the research and theoretical perspectives on educational data analytics. The paper proposes a set of unified definitions and an integrated framework for data analytics in higher education. By considering the framework, researchers may discover new contexts as well as areas of inquiry. As a Gestalt-like exercise, the framework (whole) and the articulation of data analytics (parts) may be useful for educational stakeholders in decision-making at the level of individual s...
Data Analytics: Data Analytics in Higher Education
2017
Data analytics (DA) is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions and by scientists and researchers to verify or disprove scientific models, theories and hypotheses. (Rouse and Stedman, 2016). This project will seek to discover the applicability of DA within higher education and specifically, the usability of DA to the University of the West Indies (UWI). By using DA, the UWI can achieve many business outcomes and be more responsive to their target market.
Turkish Online Journal of Distance Education, 2021
Data science is a very important factor in the development of educational environments and in institutions, especially in higher education, to make sustainable studies. Higher education institutions can benefit from educational data mining and learning analytics by adopting different data analytics strategies to improve recommendations for the future of the institutions. From a perspective supporting the issue, this book “Adoption of Data Analytics in Higher Education Learning and Teaching” edited by Dirk Ifenthaler and David Gibson presents valuable insights and contributions to higher education institutions regarding the adoption of learning analytics and educational data mining. This book also examines case studies that describe current practices and experiences about the use of data analytics in higher education.
International Journal of Innovative Business Strategies, 2018
Changes are constantly sweeping many higher education institutions across the globe. They are more rapid than was ever projected for the twenty first century higher education. These institutions are out of sync with the demands of the twenty first century economy. Consequently, current college graduates are facing very difficult times landing a meaningful entry level job within the workforce. The inability to find jobs makes them unable to have financial freedom following graduation. This trend is real and ongoing because the graduates are lacking basic job ready skills. The endless list of many problems that have been chronicled to be wrong with higher education include but are not limited to lack of accountability, the constant budget woes, the ever-increasing declining trend in enrollment, retention, cost, and deficient basic job ready skills for graduates. Data analytics as a management tool as applied in industries holds promise in addressing many higher education issues. As a way out, transcripts are gradually being promoted as credentials in their own right. This paradigm shift has led many experts to predict the proliferation of badges, and expanded transcripts in place of diplomas for the professions and fields that are not easily certified. This study examined the spectrum of data analytics as a management tool in higher education: recruitment/retention, budgetary issues, degrees vs. badges and credentials, student success, supportive infrastructure, customer service, cybersecurity, predictive models/algorithms, and achievement of institutional effectiveness/goals. Further, it addressed advantages of data analytics, getting the right data at the right time, data informed learning design and key changes in higher education for the future.
1 In this chapter, we outline the importance of data usage for improving policy-making (at the system level), management of educational institutions and pedagogical approaches in the classroom. We illustrate how traditional data analyses are becoming gradually substituted by more sophisticated forms of analytics, and we provide a classification for these recent movements (in particular learning analytics, academic analytics and educational data mining). After having illustrated some examples of recent applications, we warn against potential risks of inadequate analytics in education, and list a number of barriers that impede the widespread application of better data use. As implications, we call for a development of a more robust professional role of data scientists applied to education, with the aim of sustaining and reinforcing a positive data-driven approach to decision making in the educational field.
A Systematic Review Of The State-Of-Art Of Big Data And Analytics In Higher Education, 2022
The higher education sector is facing several challenges. The challenges include reduced government funding, increased accountability from various stakeholders, diverse students, changes brought by the pandemic, and the increasing introduction of new digital learning technologies. In order to make better decisions to tackle these challenges, those in the decision-making should use data and analytics to answer some of the difficult or complex questions that the higher education sector is facing. Therefore, the goal of this report is to explore different ways and/or techniques that can be used to address some of these challenges that higher institutions of education are facing. The decision-makers in higher education need to know the conception of big data to explore how big data and analytics can be applied in higher education. Furthermore, the understanding of the current systems and applications used in supporting the idea of big data and analytics in higher education. Achieving these insights and information from data requires not a single report from a single system but rather the ability to access, harvest, link, share, and explore institution-wide data that can be transformed into meaningful insights. The utilisation of Big Data and analytics offer opportunities to support students, faculty, senior leadership to use evidence in making sound decisions. The study employs a systematic review using a newly developed tripartite model for conducting and presenting literature review reports. The model approaches the literature review process systematically and three phases for the critical examination of literature: These phases include; description, synthesis, and critique. The current review focuses on the use of big data and analytics in higher institutions. Relevant articles published between 2012-2021 were obtained from the Scopus, IEEE, Google Scholars, and Web of Science databases. Three core themes were identified: Lack of data or evidenced-based decision making, Lack of expertise in data mining and management, Lack of involvement of stakeholders-communities as they know which areas need to be focused on and improved. The review examines contextual variation and modalities of applying big data and analytics and the challenges and opportunities. It should be noted that using multiple datasets and different methodological approaches can provide further insights into decision makers-management staff of higher education to engage in data-based decision making. This approach to measuring big data and analytics, opportunities, and challenges in higher education can authenticate findings and most likely provide additional insights into big data analytics in the area of higher education.
Improving Students’ Success in 21st Century Higher Education Through Data Analytics
International Journal for Cross-Disciplinary Subjects in Education, 2018
Changes are constantly sweeping many higher education institutions across the globe. They are more rapid than was ever projected for the twenty first century higher education. These institutions are out of sync with the demands of the twenty first century economy. Consequently, current college graduates are facing very difficult times landing a meaningful entry level job within the workforce. The inability to find jobs makes them unable to have financial freedom following graduation. This trend is real and ongoing because the graduates are lacking basic job ready skills. The endless list of many problems that have been chronicled to be wrong with higher education include but are not limited to lack of accountability, the constant budget woes, the ever increasing declining trend in enrollment, retention, cost, and deficient basic job ready skills for graduates. Data analytics as a management tool as applied in industries holds promise in addressing many higher education issues. This study examined the spectrum of data analytics as a management tool in higher education: student success, equity and institutional performance, promotion of equity and generational involvement, projectory role of millennials and data analytics, and five projections for facilitated teaching and learning towards the future. Further, it addressed advantages of data analytics, getting the right data at the right time, data informed learning design and key changes in higher education for the future.