Download or read book Privacy Management for Student Data in Higher Education Learning Analytics Applications written by Stephanie Winkler and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: University students are currently one of the most targeted groups of people for data collection and analytics. Outside of the likelihood of being recruited for research studies, during their time at the university they are required to interact with various information systems which all collect significant amounts of data. Systems used in the classroom, like learning management systems, are usually the most visible to students but other technologies are also passively collecting student information (e.g. student identification cards, educational applications, social media). Learning analytics includes the analysis of this data in order to understand and optimize learning and the learning environment through the use of data science methodologies. While educational data is widely considered to be sensitive, student privacy is not always considered a high priority in the development and deployment of learning analytics applications beyond basic information security. Prior work has also shown that previous assumptions made with privacy management are no longer applicable with widespread ubiquitous data collection practices. This thesis investigates two research questions RQ1: What factors influence student privacy expectations in learning analytics systems? RQ2: How is policy used as a privacy management strategy for learning analytics in higher education? The first question is examined using quantitative vignettes. The second question is addressed through the qualitative analysis of current learning analytics policies at higher education institutions. The results show an expectation for students to grant consent for their data to be used in learning analytics systems. Further, their willingness to grant consent for data to be used is at least partially dependant on the type of data requested. While protecting privacy through policy may be a common strategy in other sectors, it is rare in United States higher education institutions at this time with only a small fraction of the institutions with learning analytics programs adopting a policy to govern the programs.
Download or read book Learning Analytics in Higher Education written by Jaime Lester and published by Routledge. This book was released on 2018-08-06 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning Analytics in Higher Education provides a foundational understanding of how learning analytics is defined, what barriers and opportunities exist, and how it can be used to improve practice, including strategic planning, course development, teaching pedagogy, and student assessment. Well-known contributors provide empirical, theoretical, and practical perspectives on the current use and future potential of learning analytics for student learning and data-driven decision-making, ways to effectively evaluate and research learning analytics, integration of learning analytics into practice, organizational barriers and opportunities for harnessing Big Data to create and support use of these tools, and ethical considerations related to privacy and consent. Designed to give readers a practical and theoretical foundation in learning analytics and how data can support student success in higher education, this book is a valuable resource for scholars and administrators.
Download or read book Big Data and Learning Analytics in Higher Education written by Ben Kei Daniel and published by Springer. This book was released on 2016-08-27 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Learning Analytics in Higher Education written by Jaime Lester and published by John Wiley & Sons. This book was released on 2017-12-21 with total page 155 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning analytics (or educational big data) tools are increasingly being deployed on campuses to improve student performance, retention and completion, especially when those metrics are tied to funding. Providing personalized, real-time, actionable feedback through mining and analysis of large data sets, learning analytics can illuminate trends and predict future outcomes. While promising, there is limited and mixed empirical evidence related to its efficacy to improve student retention and completion. Further, learning analytics tools are used by a variety of people on campus, and as such, its use in practice may not align with institutional intent. This monograph delves into the research, literature, and issues associated with learning analytics implementation, adoption, and use by individuals within higher education institutions. With it, readers will gain a greater understanding of the potential and challenges related to implementing, adopting, and integrating these systems on their campuses and within their classrooms and advising sessions. This is the fifth issue of the 43rd volume of the Jossey-Bass series ASHE Higher Education Report. Each monograph is the definitive analysis of a tough higher education issue, based on thorough research of pertinent literature and institutional experiences. Topics are identified by a national survey. Noted practitioners and scholars are then commissioned to write the reports, with experts providing critical reviews of each manuscript before publication.
Download or read book Adoption of Data Analytics in Higher Education Learning and Teaching written by Dirk Ifenthaler and published by Springer Nature. This book was released on 2020-08-10 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Data for students written by Katherine Brittany Leu and published by RTI Press. This book was released on 2020-03-26 with total page 6 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data analytics and data science can address challenges to student success. Postsecondary institutions have already creatively used analytics to address the problem of college completion through innovations such as academic early warning systems and adaptive learning technologies. A diverse array of data on postsecondary education exists both within and outside of institutions and can be used in analytics to provide a richer view of student success and improve equity. Increased data collection and analysis open up the challenges of data linkage across units and the risk of ethical and privacy violations, which deserve more attention.
Download or read book Data Mining and Learning Analytics written by Samira ElAtia and published by John Wiley & Sons. This book was released on 2016-09-20 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) within educational mandates. Initial series of chapters offer a general overview of DM, Learning Analytics (LA), and data collection models in the context of educational research, while also defining and discussing data mining’s four guiding principles— prediction, clustering, rule association, and outlier detection. The next series of chapters showcase the pedagogical applications of Educational Data Mining (EDM) and feature case studies drawn from Business, Humanities, Health Sciences, Linguistics, and Physical Sciences education that serve to highlight the successes and some of the limitations of data mining research applications in educational settings. The remaining chapters focus exclusively on EDM’s emerging role in helping to advance educational research—from identifying at-risk students and closing socioeconomic gaps in achievement to aiding in teacher evaluation and facilitating peer conferencing. This book features contributions from international experts in a variety of fields. Includes case studies where data mining techniques have been effectively applied to advance teaching and learning Addresses applications of data mining in educational research, including: social networking and education; policy and legislation in the classroom; and identification of at-risk students Explores Massive Open Online Courses (MOOCs) to study the effectiveness of online networks in promoting learning and understanding the communication patterns among users and students Features supplementary resources including a primer on foundational aspects of educational mining and learning analytics Data Mining and Learning Analytics: Applications in Educational Research is written for both scientists in EDM and educators interested in using and integrating DM and LA to improve education and advance educational research.
Download or read book All the Data We Can Get written by and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this dissertation, I explore student privacy perspectives of higher education actors building capacity for learning analytics. Learning analytics is a socio-technical practice that analyzes data captured from information systems and networks students interact with to inform students' educational experience, among other ends. Student privacy concerns are inherently linked to learning analytics due to the comprehensive and sensitive nature of the data and information the technology analyzes. The extant literature indicates that very little is known about what student privacy issues institutions are encountering when they adopt learning analytics; equally little is known about how to resolve privacy problems in practice. The following questions motivated the study: 1) How do institutional actors perceive student privacy issues related to learning analytics technologies, 2) how do they resolve the privacy issues when they emerge, and 3) what contextual factors influence student privacy practices? To answer these questions, I employed an interpretive case study design of two unique public, higher education institutions. Interviews with institutional actors served as the primary data source; I analyzed the data using constructivist grounded theory methods. Findings revealed that powerful actors wish to gather as much students data as possible to develop advanced analytic insights. Current institutional policies and federal privacy law provide colleges and universities freedom to use student data and information with few limitations for learning analytics; also, institutional policy is not advanced enough to handle the emerging privacy problems. Actors revealed that they valued transparency with students about learning analytics and were worried about the negative effects of predictive analytics on students. Yet, the institutions had not created any systematic way to be transparent or reduce harms. Finally, there was notable conflict with regard to whether or not students should be able to control personally identifiable information and data for learning analytics. In the discussion, I use the framework of contextual integrity and conclude that student privacy at the two case sites was under threat, yet in unique ways. I assert that students need greater control over their information by building identity layers into technological systems that can respect privacy preferences.
Download or read book Big Data on Campus written by Karen L. Webber and published by Johns Hopkins University Press. This book was released on 2020-11-03 with total page 337 pages. Available in PDF, EPUB and Kindle. Book excerpt: Webber, Henry Y. Zheng, Ying Zhou
Download or read book Using Data to Improve Higher Education written by Maria Eliophotou Menon and published by Springer. This book was released on 2014-11-26 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent decades, higher education systems and institutions have been called to respond to an unprecedented number of challenges. Major challenges
Download or read book Utilizing Learning Analytics to Support Study Success written by Dirk Ifenthaler and published by Springer. This book was released on 2019-01-17 with total page 341 pages. Available in PDF, EPUB and Kindle. Book excerpt: Students often enter higher education academically unprepared and with unrealistic perceptions and expectations of university life, which are critical factors that influence students’ decisions to leave their institutions prior to degree completion. Advances in educational technology and the current availability of vast amounts of educational data make it possible to represent how students interact with higher education resources, as well as provide insights into students’ learning behavior and processes. This volume offers new research in such learning analytics and demonstrates how they support students at institutions of higher education by offering personalized and adaptive support of their learning journey. It focuses on four major areas of discussion: · Theoretical perspectives linking learning analytics and study success. · Technological innovations for supporting student learning. · Issues and challenges for implementing learning analytics at higher education institutions. · Case studies showcasing successfully implemented learning analytics strategies at higher education institutions. Utilizing Learning Analytics to Support Study Success ably exemplifies how educational data and innovative digital technologies contribute to successful learning and teaching scenarios and provides critical insight to researchers, graduate students, teachers, and administrators in the general areas of education, educational psychology, academic and organizational development, and instructional technology.
Download or read book Transforming Learning with Meaningful Technologies written by Maren Scheffel and published by Springer Nature. This book was released on 2019-09-09 with total page 779 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 14th European Conference on Technology Enhanced Learning, EC-TEL 2019, held in Delft, The Netherlands, in September 2019. The 41 research papers and 50 demo and poster papers presented in this volume were carefully reviewed and selected from 149 submissions. The contributions reflect the debate around the role of and challenges for cutting-edge 21st century meaningful technologies and advances such as artificial intelligence and robots, augmented reality and ubiquitous computing technologies and at the same time connecting them to different pedagogical approaches, types of learning settings, and application domains that can benefit from such technologies.
Download or read book Data Analytics in Higher Education written by Alan Rubel and published by . This book was released on 2017 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt: “Big Data” and data analytics affect all of us. Data collection, analysis, and use on a large scale is an important and growing part of commerce, governance, communication, law enforcement, security, finance, medicine, and research. And the theme of this symposium, “Individual and Informational Privacy in the Age of Big Data,” is expansive; we could have long and fruitful discussions about practices, laws, and concerns in any of these domains. But a big part of the audience for this symposium is students and faculty in higher education institutions (HEIs), and the subject of this paper is data analytics in our own backyards. Higher education learning analytics (LA) is something that most of us involved in this symposium are familiar with. Students have encountered LA in their courses, in their interactions with their law school or with their undergraduate institutions, instructors use systems that collect information about their students, and administrators use information to help understand and steer their institutions. More importantly, though, data analytics in higher education is something that those of us participating in the symposium can actually control. Students can put pressure on administrators, and faculty often participate in university governance. Moreover, the systems in place in HEIs are more easily comprehensible to many of us because we work with them on a day-to-day basis. Students use systems as part of their course work, in their residences, in their libraries, and elsewhere. Faculty deploy course management systems (CMS) such as Desire2Learn, Moodle, Blackboard, and Canvas to structure their courses, and administrators use information gleaned from analytics systems to make operational decisions. If we (the participants in the symposium) indeed care about Individual and Informational Privacy in the Age of Big Data, the topic of this paper is a pretty good place to hone our thinking and put into practice our ideas.
Download or read book Learning Analytics Fundaments Applications and Trends written by Alejandro Peña-Ayala and published by Springer. This book was released on 2017-02-17 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a conceptual and empirical perspective on learning analytics, its goal being to disseminate the core concepts, research, and outcomes of this emergent field. Divided into nine chapters, it offers reviews oriented on selected topics, recent advances, and innovative applications. It presents the broad learning analytics landscape and in-depth studies on higher education, adaptive assessment, teaching and learning. In addition, it discusses valuable approaches to coping with personalization and huge data, as well as conceptual topics and specialized applications that have shaped the current state of the art. By identifying fundamentals, highlighting applications, and pointing out current trends, the book offers an essential overview of learning analytics to enhance learning achievement in diverse educational settings. As such, it represents a valuable resource for researchers, practitioners, and students interested in updating their knowledge and finding inspirations for their future work.
Download or read book Learning With Big Data written by Viktor Mayer-Schönberger and published by HarperCollins. This book was released on 2014-03-04 with total page 63 pages. Available in PDF, EPUB and Kindle. Book excerpt: Homework assignments that learn from students. Courses tailored to fit individual pupils. Textbooks that talk back. This is tomorrow’s education landscape, thanks to the power of big data. These advances go beyond online courses. As the New York Times-bestselling authors of Big Data explain, the truly fascinating changes are actually occurring in how we measure students’ progress and how we can use that data to improve education for everyone, in real time, both on- and offline. Learning with Big Data offers an eye-opening, insight-packed tour through these new trends, for educators, administrators, and readers interested in the latest developments in business and technology.
Download or read book Developing Effective Educational Experiences through Learning Analytics written by Anderson, Mark and published by IGI Global. This book was released on 2016-04-07 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: The quality of students’ learning experiences is a critical concern for all higher education institutions. With the assistance of modern technological advances, educational establishments have the capability to better understand the strengths and weaknesses of their learning programs. Developing Effective Educational Experiences through Learning Analytics is a pivotal reference source that focuses on the adoption of data mining and analysis techniques in academic institutions, examining how this collected information is utilized to improve the outcome of student learning. Highlighting the relevance of data analytics to current educational practices, this book is ideally designed for researchers, practitioners, and professionals actively involved in higher education settings.
Download or read book Contemporary Technologies in Education written by Olusola O. Adesope and published by Springer. This book was released on 2018-11-08 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited volume provides a critical discussion of theoretical, methodological, and practical developments of contemporary forms of educational technologies. Specifically, the book discusses the use of contemporary technologies such as the Flipped Classroom (FC), Massive Open Online Course (MOOC), Social Media, Serious Educational Games (SEG), Wikis, innovative learning software tools, and learning analytic approach for making sense of big data. While some of these contemporary educational technologies have been touted as panaceas, researchers and developers have been faced with enormous challenges in enhancing the use of these technologies to arouse student attention and improve persistent motivation, engagement, and learning. Hence, the book examines how contemporary technologies can engender student motivation and result in improved engagement and learning. Each chapter also discusses the road ahead and where appropriate, uses the current trend to predict future affordances of technologies.
