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| data in higher education: Big Data on Campus Karen L. Webber, Henry Y. Zheng, 2020-11-03 Webber, Henry Y. Zheng, Ying Zhou |
| data in higher education: You Are a Data Person Amelia Parnell, 2023-07-03 Internal and external pressure continues to mount for college professionals to provide evidence of successful activities, programs, and services, which means that, going forward, nearly every campus professional will need to approach their work with a data-informed perspective.But you find yourself thinking “I am not a data person”.Yes, you are. Or can be with the help of Amelia Parnell.You Are a Data Person provides context for the levels at which you are currently comfortable using data, helps you identify both the areas where you should strengthen your knowledge and where you can use this knowledge in your particular university role.For example, the rising cost to deliver high-quality programs and services to students has pushed many institutions to reallocate resources to find efficiencies. Also, more institutions are intentionally connecting classroom and cocurricular learning experiences which, in some instances, requires an increased gathering of evidence that students have acquired certain skills and competencies. In addition to programs, services, and pedagogy, professionals are constantly monitoring the rates at which students are entering, remaining enrolled in, and leaving the institution, as those movements impact the institution’s financial position.From teaching professors to student affairs personnel and beyond, Parnell offers tangible examples of how professionals can make data contributions at their current and future knowledge level, and will even inspire readers to take the initiative to engage in data projects.The book includes a set of self-assessment questions and a companion set of action steps and available resources to help readers accept their identity as a data person. It also includes an annotated list of at least 20 indicators that any higher education professional can examine without sophisticated data analyses. |
| data in higher education: The Analytics Revolution in Higher Education Jonathan S. Gagliardi, Amelia Parnell, Julia Carpenter-Hubin, 2023-07-03 Co-published with and In this era of “Big Data,” institutions of higher education are challenged to make the most of the information they have to improve student learning outcomes, close equity gaps, keep costs down, and address the economic needs of the communities they serve at the local, regional, and national levels. This book helps readers understand and respond to this “analytics revolution,” examining the evolving dynamics of the institutional research (IR) function, and the many audiences that institutional researchers need to serve.Internally, there is a growing need among senior leaders, administrators, faculty, advisors, and staff for decision analytics that help craft better resource strategies and bring greater efficiencies and return-on-investment for students and families. Externally, state legislators, the federal government, and philanthropies demand more forecasting and more evidence than ever before. These demands require new and creative responses, as they are added to previous demands, rather than replacing them, nor do they come with additional resources to produce the analysis to make data into actionable improvements. Thus the IR function must become that of teacher, ensuring that data and analyses are accurate, timely, accessible, and compelling, whether produced by an IR office or some other source. Despite formidable challenges, IR functions have begun to leverage big data and unlock the power of predictive tools and techniques, contributing to improved student outcomes. |
| data in higher education: Big Data and Learning Analytics in Higher Education Ben Kei Daniel, 2016-08-27 This book focuses on the uses of big data in the context of higher education. The book describes a wide range of administrative and operational data gathering processes aimed at assessing institutional performance and progress in order to predict future performance, and identifies potential issues related to academic programming, research, teaching and learning. Big data refers to data which is fundamentally too big and complex and moves too fast for the processing capacity of conventional database systems. The value of big data is the ability to identify useful data and turn it into useable information by identifying patterns and deviations from patterns. |
| data in higher education: Adoption of Data Analytics in Higher Education Learning and Teaching Dirk Ifenthaler, David Gibson, 2020-08-10 The book aims to advance global knowledge and practice in applying data science to transform higher education learning and teaching to improve personalization, access and effectiveness of education for all. Currently, higher education institutions and involved stakeholders can derive multiple benefits from educational data mining and learning analytics by using different data analytics strategies to produce summative, real-time, and predictive or prescriptive insights and recommendations. Educational data mining refers to the process of extracting useful information out of a large collection of complex educational datasets while learning analytics emphasizes insights and responses to real-time learning processes based on educational information from digital learning environments, administrative systems, and social platforms. This volume provides insight into the emerging paradigms, frameworks, methods and processes of managing change to better facilitate organizational transformation toward implementation of educational data mining and learning analytics. It features current research exploring the (a) theoretical foundation and empirical evidence of the adoption of learning analytics, (b) technological infrastructure and staff capabilities required, as well as (c) case studies that describe current practices and experiences in the use of data analytics in higher education. |
| data in higher education: Cultivating a Data Culture in Higher Education Kristina Powers, Angela E. Henderson, 2018 Higher education institutions have experienced a sharp increase in demand for accountability. To meet the growing demand by legislators, accreditors, consumers, taxpayers, and parents for evidence of successful outcomes, this important book provides higher education leaders and practitioners with actionable strategies for developing a comprehensive data culture throughout the entire institution. Exploring key considerations necessary for the development of an effective data culture in colleges and universities, this volume brings together diverse voices and perspectives, including institutional researchers, senior academic leaders, and faculty. Each chapter focuses on a critical element of managing or influencing a data culture, approaches for breaking through common challenges, and concludes with practical, research-based implementation strategies. Collectively, these strategies form a comprehensive list of recommendations for developing a data culture and becoming a change agent within your higher education institution. |
| data in higher education: Data Strategy in Colleges and Universities Kristina Powers, 2019-10-16 This valuable resource helps institutional leaders understand and implement a data strategy at their college or university that maximizes benefits to all creators and users of data. Exploring key considerations necessary for coordination of fragmented resources and the development of an effective, cohesive data strategy, this book brings together professionals from different higher education experiences and perspectives, including academic, administration, institutional research, information technology, and student affairs. Focusing on critical elements of data strategy and governance, each chapter in Data Strategy in Colleges and Universities helps higher education leaders address a frustrating problem with much-needed solutions for fostering a collaborative, data-driven strategy. |
| data in higher education: Cultivating a Data Culture in Higher Education Kristina Powers, Angela E. Henderson, 2018-05-25 Higher education institutions have experienced a sharp increase in demand for accountability. To meet the growing demand by legislators, accreditors, consumers, taxpayers, and parents for evidence of successful outcomes, this important book provides higher education leaders and practitioners with actionable strategies for developing a comprehensive data culture throughout the entire institution. Exploring key considerations necessary for the development of an effective data culture in colleges and universities, this volume brings together diverse voices and perspectives, including institutional researchers, senior academic leaders, and faculty. Each chapter focuses on a critical element of managing or influencing a data culture, approaches for breaking through common challenges, and concludes with practical, research-based implementation strategies. Collectively, these strategies form a comprehensive list of recommendations for developing a data culture and becoming a change agent within your higher education institution. |
| data in higher education: Using Data to Improve Higher Education Maria Eliophotou Menon, Dawn G. Terkla, Paul Gibbs, 2014 In recent decades, higher education systems and institutions have been called to respond to an unprecedented number of challenges. Major challenges emerged with the phenomenal increase in the demand for higher education and the associated massive expansion of higher education systems. In response universities were called to adopt planning and research methods that would enable them to identify and address the needs of a larger, more diverse student body. Higher education institutions began to place greater emphasis on planning and marketing, seeking to maintain their position in an increasingly competitive higher education market. Under the current economic downturn, universities are under pressure to further cut costs while maintaining their attractiveness to prospective students.As a result educational policy makers and administrators are called to select the 'right' alternatives, aiming for both efficiency and effectiveness in delivered outcomes. This book provides insights into the use of data as an input in planning and improvement initiatives in higher education. It focuses on uses (and potential abuses) of data in educational planning and policy formulation, examining several practices and perspectives relating to different types of data. The book is intended to address the need for the collection and utilization of data in the attempt to improve higher education both at the systemic and the institutional level. |
| data in higher education: Demographics and the Demand for Higher Education Nathan D. Grawe, 2018 The economics of American higher education are driven by one key factor--the availability of students willing to pay tuition--and many related factors that determine what schools they attend. By digging into the data, economist Nathan Grawe has created probability models for predicting college attendance. What he sees are alarming events on the horizon that every college and university needs to understand. Overall, he spots demographic patterns that are tilting the US population toward the Hispanic southwest. Moreover, since 2007, fertility rates have fallen by 12 percent. Higher education analysts recognize the destabilizing potential of these trends. However, existing work fails to adjust headcounts for college attendance probabilities and makes no systematic attempt to distinguish demand by institution type. This book analyzes demand forecasts by institution type and rank, disaggregating by demographic groups. Its findings often contradict the dominant narrative: while many schools face painful contractions, demand for elite schools is expected to grow by 15+ percent. Geographic and racial profiles will shift only slightly--and attendance by Asians, not Hispanics, will grow most. Grawe also use the model to consider possible changes in institutional recruitment strategies and government policies. These what if analyses show that even aggressive innovation is unlikely to overcome trends toward larger gaps across racial, family income, and parent education groups. Aimed at administrators and trustees with responsibility for decisions ranging from admissions to student support to tenure practices to facilities construction, this book offers data to inform decision-making--decisions that will determine institutional success in meeting demographic challenges-- |
| data in higher education: The Race between Education and Technology Claudia Goldin, Lawrence F. Katz, 2009-07-01 This book provides a careful historical analysis of the co-evolution of educational attainment and the wage structure in the United States through the twentieth century. The authors propose that the twentieth century was not only the American Century but also the Human Capital Century. That is, the American educational system is what made America the richest nation in the world. Its educational system had always been less elite than that of most European nations. By 1900 the U.S. had begun to educate its masses at the secondary level, not just in the primary schools that had remarkable success in the nineteenth century. The book argues that technological change, education, and inequality have been involved in a kind of race. During the first eight decades of the twentieth century, the increase of educated workers was higher than the demand for them. This had the effect of boosting income for most people and lowering inequality. However, the reverse has been true since about 1980. This educational slowdown was accompanied by rising inequality. The authors discuss the complex reasons for this, and what might be done to ameliorate it. |
| data in higher education: Data Science in Education Using R Ryan A. Estrellado, Emily Freer, Joshua M. Rosenberg, Isabella C. Velásquez, 2020-10-26 Data Science in Education Using R is the go-to reference for learning data science in the education field. The book answers questions like: What does a data scientist in education do? How do I get started learning R, the popular open-source statistical programming language? And what does a data analysis project in education look like? If you’re just getting started with R in an education job, this is the book you’ll want with you. This book gets you started with R by teaching the building blocks of programming that you’ll use many times in your career. The book takes a learn by doing approach and offers eight analysis walkthroughs that show you a data analysis from start to finish, complete with code for you to practice with. The book finishes with how to get involved in the data science community and how to integrate data science in your education job. This book will be an essential resource for education professionals and researchers looking to increase their data analysis skills as part of their professional and academic development. |
| data in higher education: Higher Education Policy Analysis Using Quantitative Techniques Marvin Titus, 2021-05-14 This textbook introduces graduate students in education and policy research to data and statistical methods in state-level higher education policy analysis. It also serves as a methodological guide to students, practitioners, and researchers who want a clear approach to conducting higher education policy analysis that involves the use of institutional- and state-level secondary data and quantitative methods ranging from descriptive to advanced statistical techniques. This book is unique in that it introduces readers to various types of data sources and quantitative methods utilized in policy research and in that it demonstrates how results of statistical analyses should be presented to higher education policy makers. It helps to bridge the gap between researchers, policy makers, and practitioners both within education policy and between other fields. Coverage includes identifying pertinent data sources, the creation and management of customized data sets, teaching beginning and advanced statistical methods and analyses, and the presentation of analyses for different audiences (including higher education policy makers). |
| data in higher education: Using Data in Schools to Inform Leadership and Decision Making Alex J. Bowers, Alan R. Shoho, Bruce G. Barnett, 2014-11-01 Our fifth book in the International Research on School Leadership series focuses on the use of data in schools and districts as useful information for leadership and decision making. Schools are awash in data and information, from test scores, to grades, to discipline reports, and attendance as just a short list of student information sources, while additional streams of data feed into schools and districts from teachers and parents as well as local, regional and national policy levels. To deal with the data, schools have implemented a variety of data practices, from data rooms, to data days, data walks, and data protocols. However, despite the flood of data, successful school leaders are leveraging an analysis of their school’s data as a means to bring about continuous improvement in an effort to improve instruction for all students. Nevertheless, some drown, some swim, while others find success. Our goal in this book volume is to bring together a set of chapters by authors who examine successful data use as it relates to leadership and school improvement. In particular, the chapters in this volume consider important issues in this domain, including: • How educational leaders use data to inform their practice. • What types of data and data analysis are most useful to successful school leaders. • To what extent are data driven and data informed practices helping school leaders positively change instructional practice? • In what ways does good data collection and analysis feed into successful continuous improvement and holistic systems thinking? • How have school leadership practices changed as more data and data analysis techniques have become available? • What are the major obstacles facing school leaders when using data for decision making and how do they overcome them? |
| data in higher education: Big Data in Education: Pedagogy and Research Theodosia Prodromou, 2021-10-04 This book discusses how Big Data could be implemented in educational settings and research, using empirical data and suggesting both best practices and areas in which to invest future research and development. It also explores: 1) the use of learning analytics to improve learning and teaching; 2) the opportunities and challenges of learning analytics in education. As Big Data becomes a common part of the fabric of our world, education and research are challenged to use this data to improve educational and research systems, and also are tasked with teaching coming generations to deal with Big Data both effectively and ethically. The Big Data era is changing the data landscape for statistical analysis, the ways in which data is captured and presented, and the necessary level of statistical literacy to analyse and interpret data for future decision making. The advent of Big Data accentuates the need to enable citizens to develop statistical skills, thinking and reasoning needed for representing, integrating and exploring complex information. This book offers guidance to researchers who are seeking suitable topics to explore. It presents research into the skills needed by data practitioners (data analysts, data managers, statisticians, and data consumers, academics), and provides insights into the statistical skills, thinking and reasoning needed by educators and researchers in the future to work with Big Data. This book serves as a concise reference for policymakers, who must make critical decisions regarding funding and applications. |
| data in higher education: Choosing College Michael B. Horn, Bob Moesta, 2019-09-11 Cut through the noise and make better college and career choices This book is about addressing the college-choosing problem. The rankings, metrics, analytics, college visits, and advice that we use today to help us make these decisions are out of step with the progress individual students are trying to make. They don't give students and families the information and context they need to make such a high-stakes decision about whether and where to get an education. Choosing College strips away the noise to help you understand why you’re going to school. What's driving you? What are you trying to accomplish? Once you know why, the book will help you make better choices. The research in this book illustrates that choosing a school is complicated. By constructing more than 200 mini-documentaries of how students chose different postsecondary educational experiences, the authors explore the motivations for how and why people make the decisions that they do at a much deeper, causal level. By the end, you’ll know why you’re going and what you’re really chasing. The book: Identifies the five different Jobs for which students hire postsecondary education Allows you to see your true options for what’s next Offers guidance for how to successfully choose your pathway Illuminates how colleges and entrepreneurs can build better experiences for each Job The authors help readers understand not what job students want out of college, but what Job students are hiring college to do for them. |
| data in higher education: Advancing the Power of Learning Analytics and Big Data in Education Azevedo, Ana, Azevedo, José Manuel, Onohuome Uhomoibhi, James, Ossiannilsson, Ebba, 2021-03-19 The term learning analytics is used in the context of the use of analytics in e-learning environments. Learning analytics is used to improve quality. It uses data about students and their activities to provide better understanding and to improve student learning. The use of learning management systems, where the activity of the students can be easily accessed, potentiated the use of learning analytics to understand their route during the learning process, help students be aware of their progress, and detect situations where students can give up the course before its completion, which is a growing problem in e-learning environments. Advancing the Power of Learning Analytics and Big Data in Education provides insights concerning the use of learning analytics, the role and impact of analytics on education, and how learning analytics are designed, employed, and assessed. The chapters will discuss factors affecting learning analytics such as human factors, geographical factors, technological factors, and ethical and legal factors. This book is ideal for teachers, administrators, teacher educators, practitioners, stakeholders, researchers, academicians, and students interested in the use of big data and learning analytics for improved student success and educational environments. |
| data in higher education: Productivity in Higher Education Caroline M. Hoxby, Kevin Stange, 2019-11-22 How do the benefits of higher education compare with its costs, and how does this comparison vary across individuals and institutions? These questions are fundamental to quantifying the productivity of the education sector. The studies in Productivity in Higher Education use rich and novel administrative data, modern econometric methods, and careful institutional analysis to explore productivity issues. The authors examine the returns to undergraduate education, differences in costs by major, the productivity of for-profit schools, the productivity of various types of faculty and of outcomes, the effects of online education on the higher education market, and the ways in which the productivity of different institutions responds to market forces. The analyses recognize five key challenges to assessing productivity in higher education: the potential for multiple student outcomes in terms of skills, earnings, invention, and employment; the fact that colleges and universities are “multiproduct” firms that conduct varied activities across many domains; the fact that students select which school to attend based in part on their aptitude; the difficulty of attributing outcomes to individual institutions when students attend more than one; and the possibility that some of the benefits of higher education may arise from the system as a whole rather than from a single institution. The findings and the approaches illustrated can facilitate decision-making processes in higher education. |
| data in higher education: Research Anthology on Big Data Analytics, Architectures, and Applications Information Resources Management Association, 2022 Society is now completely driven by data with many industries relying on data to conduct business or basic functions within the organization. With the efficiencies that big data bring to all institutions, data is continuously being collected and analyzed. However, data sets may be too complex for traditional data-processing, and therefore, different strategies must evolve to solve the issue. The field of big data works as a valuable tool for many different industries. The Research Anthology on Big Data Analytics, Architectures, and Applications is a complete reference source on big data analytics that offers the latest, innovative architectures and frameworks and explores a variety of applications within various industries. Offering an international perspective, the applications discussed within this anthology feature global representation. Covering topics such as advertising curricula, driven supply chain, and smart cities, this research anthology is ideal for data scientists, data analysts, computer engineers, software engineers, technologists, government officials, managers, CEOs, professors, graduate students, researchers, and academicians. |
| data in higher education: From Equity Talk to Equity Walk Tia Brown McNair, Estela Mara Bensimon, Lindsey Malcom-Piqueux, 2020-01-22 A practical guide for achieving equitable outcomes From Equity Talk to Equity Walk offers practical guidance on the design and application of campus change strategies for achieving equitable outcomes. Drawing from campus-based research projects sponsored by the Association of American Colleges and Universities and the Center for Urban Education at the University of Southern California, this invaluable resource provides real-world steps that reinforce primary elements for examining equity in student achievement, while challenging educators to specifically focus on racial equity as a critical lens for institutional and systemic change. Colleges and universities have placed greater emphasis on education equity in recent years. Acknowledging the changing realities and increasing demands placed on contemporary postsecondary education, this book meets educators where they are and offers an effective design framework for what it means to move beyond equity being a buzzword in higher education. Central concepts and key points are illustrated through campus examples. This indispensable guide presents academic administrators and staff with advice on building an equity-minded campus culture, aligning strategic priorities and institutional missions to advance equity, understanding equity-minded data analysis, developing campus strategies for making excellence inclusive, and moving from a first-generation equity educator to an equity-minded practitioner. From Equity Talk to Equity Walk: A Guide for Campus-Based Leadership and Practice is a vital wealth of information for college and university presidents and provosts, academic and student affairs professionals, faculty, and practitioners who seek to dismantle institutional barriers that stand in the way of achieving equity, specifically racial equity to achieve equitable outcomes in higher education. |
| data in higher education: Utilizing Educational Data Mining Techniques for Improved Learning: Emerging Research and Opportunities Bhatt, Chintan, Sajja, Priti Srinivas, Liyanage, Sidath, 2019-08-02 Modern education has increased its reach through ICT tools and techniques. To manage educational data with the help of modern artificial intelligence, data and web mining techniques on dedicated cloud or grid platforms for educational institutes can be used. By utilizing data science techniques to manage educational data, the safekeeping, delivery, and use of knowledge can be increased for better quality education. Utilizing Educational Data Mining Techniques for Improved Learning: Emerging Research and Opportunities is a critical scholarly resource that explores data mining and management techniques that promote the improvement and optimization of educational data systems. The book intends to provide new models, platforms, tools, and protocols in data science for educational data analysis and introduces innovative hybrid system models dedicated to data science. Including topics such as automatic assessment, educational analytics, and machine learning, this book is essential for IT specialists, data analysts, computer engineers, education professionals, administrators, policymakers, researchers, academicians, and technology experts. |
| data in higher education: The Agile College Nathan D. Grawe, 2021-01-12 Following Grawe's seminal first book, this volume answers the question: How can a college or university prepare for forecasted demographic disruptions? Demographic changes promise to reshape the market for higher education in the next 15 years. Colleges are already grappling with the consequences of declining family size due to low birth rates brought on by the Great Recession, as well as the continuing shift toward minority student populations. Each institution faces a distinct market context with unique organizational strengths; no one-size-fits-all answer could suffice. In this essential follow-up to Demographics and the Demand for Higher Education, Nathan D. Grawe explores how proactive institutions are preparing for the resulting challenges that lie ahead. While it isn't possible to reverse the demographic tide, most institutions, he argues persuasively, can mitigate the effects. Drawing on interviews with higher education leaders, Grawe explores successful avenues of response, including • recruitment initiatives • retention programs • revisions to the academic and cocurricular program • institutional growth plans • retrenchment efforts • collaborative action Throughout, Grawe presents readers with examples taken from a range of institutions—small and large, public and private, two-year and four-year, selective and open-access. While an effective response to demographic change must reflect the individual campus context, the cases Grawe analyzes will prompt conversations about the best paths forward. The Agile College also extends projections for higher education demand. Using data from the High School Longitudinal Study, the book updates prior work by incorporating new information on college-going after the Great Recession and pushes forecasts into the mid-2030s. What's more, the analysis expands to examine additional aspects of the higher education market, such as dual enrollment, transfer students, and the role of immigration in college demand. |
| data in higher education: Data Science in Higher Education Jesse Lawson, 2015-09-06 Be the Change your Institution Needs What are leaders in research saying about Data Science in Higher Education? Where has this book been all these years? This is THE starting point for researchers looking for a leg up in today's college environment. Two parts discussion, one part methodology, and one part witty humor. I love it! Buy this book for your analysts. They and your college will thank you. This is the only book on data science specific for higher education research that covers both theory and practice. I'm not a programmer at all, and I found this book very enjoyable. You wont regret it -- I know I don't! When our department was tasked with coming up with a predictive 'machine-learning' model, we hired Jesse to help us. His charisma and knowledge are unmatched, and this book only helps to breathe fresh life into issues in research today that are all too often swept under the rug. Discover the tools to take your institution to the next level! Data Science in higher education is the process of turning raw institutional data into actionable intelligence. With this introduction to foundational topics in machine learning and predictive analytics, ambitious leaders in research can develop and employ sophisticated predictive models to better inform their institution's decision-making process. You don't need an advanced degree in math or statistics to do data science. With the open-source statistical programming language R, you'll learn how to tackle real-life institutional data challenges (with actual institutional data!) by going step-by-step through different case studies. Topics include: Simple, Multiple, & Logistic Regression Techniques, and Naive Bayes Classifiers Best Practices for Data Scientists in Higher Education Narrative-style stories, gotchas, and insights from actual data science jobs at colleges and universities Forget the textbooks. This is a book on data science written for institutional researchers *by* an institutional researcher. You need this book.------------------------------------------ Data Science is the art of carefully picking through that pile of book pages and putting together a complete book. It's the art of developing a narrative for your data, so that all the raw information that your institution warehouses and reports in bar charts and histograms is replaced with actionable intelligence. Here's what we know: Data science can and should be an integral part of college and university operations. Institutional effectiveness should be working side-by-side with faculty and educators to collect, clean, and mine through data of current and past students' behaviors in order to better empower counseling and advisement services (whether virtual or otherwise). Data itself should be considered an asset to an institution, and the data mining process a necessary function of institutional operations. So how do we do it? It starts with a solid perspective and great research tools. With Data Science in Higher Education you'll learn about and solve real-world institutional problems with open-source tools and machine learning research techniques. Using R, you'll tackle case studies from real colleges and develop predictive analytical solutions to problems that colleges and universities face to this day. |
| data in higher education: No BS (Bad Stats) Ivory A. Toldson, 2019-04-09 A Brill | Sense Bestseller! What if everything you thought you knew about Black people generally, and educating Black children specifically, was based on BS (bad stats)? We often hear things like, “Black boys are a dying breed,” “There are more Black men in prison than college,” “Black children fail because single mothers raise them,” and “Black students don’t read.” In No BS, Ivory A. Toldson uses data analysis, anecdotes, and powerful commentary to dispel common myths and challenge conventional beliefs about educating Black children. With provocative, engaging, and at times humorous prose, Toldson teaches educators, parents, advocates, and students how to avoid BS, raise expectations, and create an educational agenda for Black children that is based on good data, thoughtful analysis, and compassion. No BS helps people understand why Black people need people who believe in Black people enough not to believe every bad thing they hear about Black people. |
| data in higher education: Using Data to Improve Higher Education , 2014-01-01 In recent decades, higher education systems and institutions have been called to respond to an unprecedented number of challenges. Major challenges emerged with the phenomenal increase in the demand for higher education and the associated massive expansion of higher education systems. In response universities were called to adopt planning and research methods that would enable them to identify and address the needs of a larger, more diverse student body. Higher education institutions began to place greater emphasis on planning and marketing, seeking to maintain their position in an increasingly competitive higher education market. Under the current economic downturn, universities are under pressure to further cut costs while maintaining their attractiveness to prospective students. As a result educational policy makers and administrators are called to select the ‘right’ alternatives, aiming for both efficiency and effectiveness in delivered outcomes. This book provides insights into the use of data as an input in planning and improvement initiatives in higher education. It focuses on uses (and potential abuses) of data in educational planning and policy formulation, examining several practices and perspectives relating to different types of data. The book is intended to address the need for the collection and utilization of data in the attempt to improve higher education both at the systemic and the institutional level. |
| data in higher education: Student Retention and Success in Higher Education Mahsood Shah, Sally Kift, Liz Thomas, 2021-09-15 This book draws together international research to assess the quality of successful efforts to retain students. The editors and contributors unite diverse global research from countries who have led student retention and success projects at national, institutional, faculty or program level with positive outcomes. The book is underpinned by the philosophy that a more diverse student population requires higher education institutions to fundamentally change, in order to facilitate the success of all students. All of humanity, its economies and societies, are being pummelled by waves of pandemic-induced crises in tandem with globalisation and demographic shifts. Ultimately, this book acts as a clarion to higher education institutions to better support and retain their students, in order to create a more stable learning environment. |
| data in higher education: A Hands-On Introduction to Data Science Chirag Shah, 2020-04-02 An introductory textbook offering a low barrier entry to data science; the hands-on approach will appeal to students from a range of disciplines. |
| data in higher education: Student Success in College George D. Kuh, Jillian Kinzie, John H. Schuh, Elizabeth J. Whitt, 2011-01-07 Student Success in College describes policies, programs, and practices that a diverse set of institutions have used to enhance student achievement. This book clearly shows the benefits of student learning and educational effectiveness that can be realized when these conditions are present. Based on the Documenting Effective Educational Practice (DEEP) project from the Center for Postsecondary Research at Indiana University, this book provides concrete examples from twenty institutions that other colleges and universities can learn from and adapt to help create a success-oriented campus culture and learning environment. |
| data in higher education: Data Science for Undergraduates National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Science Education, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Computer Science and Telecommunications Board, Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, 2018-11-11 Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field. |
| data in higher education: Handbook of Research on Emerging Trends and Applications of Machine Learning Solanki, Arun, Kumar, Sandeep, Nayyar, Anand, 2019-12-13 As today’s world continues to advance, Artificial Intelligence (AI) is a field that has become a staple of technological development and led to the advancement of numerous professional industries. An application within AI that has gained attention is machine learning. Machine learning uses statistical techniques and algorithms to give computer systems the ability to understand and its popularity has circulated through many trades. Understanding this technology and its countless implementations is pivotal for scientists and researchers across the world. The Handbook of Research on Emerging Trends and Applications of Machine Learning provides a high-level understanding of various machine learning algorithms along with modern tools and techniques using Artificial Intelligence. In addition, this book explores the critical role that machine learning plays in a variety of professional fields including healthcare, business, and computer science. While highlighting topics including image processing, predictive analytics, and smart grid management, this book is ideally designed for developers, data scientists, business analysts, information architects, finance agents, healthcare professionals, researchers, retail traders, professors, and graduate students seeking current research on the benefits, implementations, and trends of machine learning. |
| data in higher education: Critical Theory and Qualitative Data Analysis in Education Rachelle Winkle-Wagner, Jamila Lee-Johnson, Ashley N. Gaskew, 2018-07-04 Critical Theory and Qualitative Data Analysis in Education offers a path-breaking explanation of how critical theories can be used within the analysis of qualitative data to inform research processes, such as data collection, analysis, and interpretation. This contributed volume offers examples of qualitative data analysis techniques and exemplars of empirical studies that employ critical theory concepts in data analysis. By creating a clear and accessible bridge between data analysis and critical social theories, this book helps scholars and researchers effectively translate their research designs and findings to multiple audiences for more equitable outcomes and disruption of historical and contemporary inequality. |
| data in higher education: The Real World of College Wendy Fischman, Howard Gardner, 2022-03-22 Why higher education in the United States has lost its way, and how universities and colleges can focus sharply on their core mission. For The Real World of College, Wendy Fischman and Howard Gardner analyzed in-depth interviews with more than 2,000 students, alumni, faculty, administrators, parents, trustees, and others, which were conducted at ten institutions ranging from highly selective liberal arts colleges to less-selective state schools. What they found challenged characterizations in the media: students are not preoccupied by political correctness, free speech, or even the cost of college. They are most concerned about their GPA and their resumes; they see jobs and earning potential as more important than learning. Many say they face mental health challenges, fear that they don’t belong, and feel a deep sense of alienation. Given this daily reality for students, has higher education lost its way? Fischman and Gardner contend that US universities and colleges must focus sharply on their core educational mission. Fischman and Gardner, both recognized authorities on education and learning, argue that higher education in the United States has lost sight of its principal reason for existing: not vocational training, not the provision of campus amenities, but to increase what Fischman and Gardner call “higher education capital”—to help students think well and broadly, express themselves clearly, explore new areas, and be open to possible transformations. Fischman and Gardner offer cogent recommendations for how every college can become a community of learners who are open to change as thinkers, citizens, and human beings. |
| data in higher education: Smart Sensors at the IoT Frontier Hiroto Yasuura, Chong-Min Kyung, Yongpan Liu, Youn-Long Lin, 2017-05-29 This book describes technology used for effective sensing of our physical world and intelligent processing techniques for sensed information, which are essential to the success of Internet of Things (IoT). The authors provide a multidisciplinary view of sensor technology from materials, process, circuits, to big data domains and they showcase smart sensor systems in real applications including smart home, transportation, medical, environmental, agricultural, etc. Unlike earlier books on sensors, this book provides a “global” view on smart sensors covering abstraction levels from device, circuit, systems, and algorithms. |
| data in higher education: The Datafication of Education Juliane Jarke, Andreas Breiter, 2020-05-21 This book attends to the transformation of processes and practices in education, relating to its increasing digitisation and datafication. The introduction of new means to measure, capture, describe and represent social life in numbers has not only transformed the ways in which teaching and learning are organised, but also the ways in which future generations (will) construct reality with and through data. Contributions consider data practices that span across different countries, educational fields and governance levels, ranging from early childhood education, to schools, universities, educational technology providers, to educational policy making and governance. The book demonstrates how digital data not only support decision making, but also fundamentally change the organisation of learning and teaching, and how these transformation processes can have partly ambivalent consequences, such as new possibilities for participation, but also the monitoring and emergence/manifestation of inequalities. Focusing on how data can drive decision making in education and learning, this book will be of interest to those studying both educational technology and educational policy making. The chapters in this book were originally published in Learning, Media and Technology. Chapter 4 of this book is freely available as a downloadable Open Access PDF at http://www.taylorfrancis.com under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license. |
| data in higher education: Developing Writers in Higher Education Anne Ruggles Gere, 2019-01-02 For undergraduates following any course of study, it is essential to develop the ability to write effectively. Yet the processes by which students become more capable and ready to meet the challenges of writing for employers, the wider public, and their own purposes remain largely invisible. Developing Writers in Higher Education shows how learning to write for various purposes in multiple disciplines leads college students to new levels of competence. This volume draws on an in-depth study of the writing and experiences of 169 University of Michigan undergraduates, using statistical analysis of 322 surveys, qualitative analysis of 131 interviews, use of corpus linguistics on 94 electronic portfolios and 2,406 pieces of student writing, and case studies of individual students to trace the multiple paths taken by student writers. Topics include student writers’ interaction with feedback; perceptions of genre; the role of disciplinary writing; generality and certainty in student writing; students’ concepts of voice and style; students’ understanding of multimodal and digital writing; high school’s influence on college writers; and writing development after college. The digital edition offers samples of student writing, electronic portfolios produced by student writers, transcripts of interviews with students, and explanations of some of the analysis conducted by the contributors. This is an important book for researchers and graduate students in multiple fields. Those in writing studies get an overview of other longitudinal studies as well as key questions currently circulating. For linguists, it demonstrates how corpus linguistics can inform writing studies. Scholars in higher education will gain a new perspective on college student development. The book also adds to current understandings of sociocultural theories of literacy and offers prospective teachers insights into how students learn to write. Finally, for high school teachers, this volume will answer questions about college writing. Companion Website Click here to access the Developing Writers project and its findings at the interactive companion website. Project Data Access the data from the project through this tutorial. |
| data in higher education: The European Higher Education Area Adrian Curaj, Liviu Matei, Remus Pricopie, Jamil Salmi, Peter Scott, 2015-10-12 Bridging the gap between higher education research and policy making was always a challenge, but the recent calls for more evidence-based policies have opened a window of unprecedented opportunity for researchers to bring more contributions to shaping the future of the European Higher Education Area (EHEA). Encouraged by the success of the 2011 first edition, Romania and Armenia have organised a 2nd edition of the Future of Higher Education – Bologna Process Researchers’ Conference (FOHE-BPRC) in November 2014, with the support of the Italian Presidency of the European Union and as part of the official EHEA agenda. Reuniting over 170 researchers from more than 30 countries, the event was a forum to debate the trends and challenges faced by higher education today and look at the future of European cooperation in higher education. The research volumes offer unique insights regarding the state of affairs of European higher education and research, as well as forward-looking policy proposals. More than 50 articles focus on essential themes in higher education: Internationalization of higher education; Financing and governance; Excellence and the diversification of missions; Teaching, learning and student engagement; Equity and the social dimension of higher education; Education, research and innovation; Quality assurance, The impacts of the Bologna Process on the EHEA and beyond and Evidence-based policies in higher education. The Bologna process was launched at a time of great optimism about the future of the European project – to which, of course, the reform of higher education across the continent has made a major contribution. Today, for the present, that optimism has faded as economic troubles have accumulated in the Euro-zone, political tensions have been increased on issues such as immigration and armed conflict has broken out in Ukraine. There is clearly a risk that, against this troubled background, the Bologna process itself may falter. There are already signs that it has been downgraded in some countries with evidence of political withdrawal. All the more reason for the voice of higher education researchers to be heard. Since the first conference they have established themselves as powerful stakeholders in the development of the EHEA, who are helping to maintain the momentum of the Bologna process. Their pivotal role has been strengthened by the second Bucharest conference. Peter Scott, Institute of Education, London (General Rapporteur of the FOHE-BPRC first edition) |
| data in higher education: Data-Driven Science and Engineering Steven L. Brunton, J. Nathan Kutz, 2022-05-05 A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®. |
| data in higher education: Higher Education Research Methodology Ben Kei Daniel, Tony Harland, 2017-12-15 This book is for anyone who wishes to improve university teaching and learning through systematic inquiry. It provides advice, but also a constructive critique of research methods and, in turn, the authors also make a contribution to the theories of research methodology. Topics covered include ontology, epistemology and engagement with academic literature, as well as research design approaches and methods of data collection. There is a keen focus on quality in both the analysis and evaluation of research and new models are proposed to help the new researcher. The authors conclude by examining the challenges in getting work published and close with some words on quality of thought and action. The ideas in the book come from the authors’ extensive experience in teaching research methods courses in higher education, health and the corporate sector, as well as several empirical research projects that have helped provide a methodology for higher education. It will be of particular interest to postgraduate students, academic developers and experienced academics from a wide variety of disciplines. |
| data in higher education: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala |
| data in higher education: Data Wise, Revised and Expanded Edition Kathryn Parker Boudett, Elizabeth A. City, Richard J. Murnane, 2020-08-26 Data Wise: A Step-by-Step Guide to Using Assessment Results to Improve Teaching and Learning presents a clear and carefully tested blueprint for school leaders. It shows how examining test scores and other classroom data can become a catalyst for important schoolwide conversations that will enhance schools’ abilities to capture teachers’ knowledge, foster collaboration, identify obstacles to change, and enhance school culture and climate. This revised and expanded edition captures the learning that has emerged in integrating the Data Wise process into school practice and brings the book up-to-date with recent developments in education and technology including: The shift to the Common Core State Standards. New material on the “ACE Habits of Mind”: practices that prioritize Action, Collaboration, and Evidence as part of transforming school culture. A new chapter on “How We Improve,” based on experiences implementing Data Wise and to address two common questions: “Where do I start?” and “How long will it take?” Other revisions take into account changes in the roles of school data teams and instructional leadership teams in guiding the inquiry process. The authors have also updated exhibits, examples, and terminology throughout and have added new protocols and resources. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
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