The research described in this book searches for the answers on how learners learn in todays open and networked learning environments and how learners, educators, institutions, and researchers can best support this process. There is sufficient data available on virtual learning environments, provided by learning analytics, on student and teacher behaviour and performance, but there is no common practice among teachers in higher education for using this data to improve the learning and teaching process. Learning analytics and data may inform and improve open and online learning from the point of view of teacher and learner awareness about their behaviour and their learning and teaching methods. The idea of describing learning analytics as a metacognitive tool, suggesting a development of metacognitive decision-making skills in teacher education, and focusing on learning design in higher education by using data from learning analytics served as the main focus of this research. The aim of the research was to create the model of application of learning analytics method as a metacognitive tool to enhance student success. The aim of the research was reached through theoretical and empirical objectives, namely: describing the learning analytics method as a metacognitive tool; revealing teacher metacognitive practices in application of learning analytics in teaching and learning, as well as learning design; and creating the model of application of learning analytics as a metacognitive tool to enhance student success. This research study is the result of the research project "Open Online Learning for Digital and Networked Society (3.3-LMT-K-712-01-0189)". The project is funded by the European Social Fund according to the activity "Improvement of researchers' qualification by implementing world-class R&D projects" of Measure No. 09.3.3-LMT-K-712.

Learning Analytics in the Classroom presents a coherent framework for the effective translation of learning analytics research for educational practice to its practical application in different education domains. Highlighting the real potential of learning analytics as a way to better understand and enhance student learning and with each chapter including specific discussion about what the research means in the classroom, this book provides educators and researchers alike with the tools and frameworks to effectively make sense of and use data and analytics in their everyday practice. This volume is split into five sections, all of which relate to the key themes in understanding learning analytics through the lens of the classroom: broad theoretical perspectives understanding learning through analytics the relationship between learning design and learning analytics analytics in the classroom and the impact it can and will have on education implementing analytics and the challenges involved. Bridging the gap between research, theory and practice, Learning Analytics in the Classroom is both a practical tool and an instructive guide for educators, and a valuable addition to researchers' bookshelves. A team of world-leading researchers and expert editors have compiled a state-of-the-art compendium on this fascinating subject and this will be a critical resource for the evolution of this field into the future.

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.

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.

This book is about practicable learning analytics, that is able to become a successful part of practice, ultimately leading to improved learning and teaching. The aim of the book is to shift our perspective on learning analytics creation and implementation from that of “designing of” technology to that of “designing for” a system of practice. That is, any successful implementation of learning analytics requires a systematic approach, which the book explains through the lens of the Information Systems Artefact, constituting of the three interdependent artefacts: “technical”, “information” and “social”. The contributions of this book go beyond a consideration of particular humans such as teachers and students, and their individual activities to consider the larger systems of activity of which analytics become part of. The chapters included in this book present different cases of learning analytics implementation across countries, and the related opportunities and challenges related to generalizability of the results. The book is written for designers, students and educators of learning analytics who aim to improve learning and teaching through learning analytics.

Online learning has increasingly been viewed as a possible way to remove barriers associated with traditional face-to-face teaching, such as overcrowded classrooms and shortage of certified teachers. While online learning has been recognized as a possible approach to deliver more desirable learning outcomes, close to half of online students drop out as a result of student-related, course-related, and out-of-school-related factors (e.g., poor self-regulation; ineffective teacher-student, student-student, and platform-student interactions; low household income). Many educators have expressed concern over students who unexpectedly begin to struggle and appear to fall off track without apparent reason. A well-implemented early warning system, therefore, can help educators identify students at risk of dropping out and assign and monitor interventions to keep them on track for graduation. Despite the popularity of early warning systems, research on their design and implementation is sparse. Early Warning Systems and Targeted Interventions for Student Success in Online Courses is a cutting-edge research publication that examines current theoretical frameworks, research projects, and empirical studies related to the design, implementation, and evaluation of early warning systems and targeted interventions and discusses their implications for policy and practice. Moreover, this book will review common challenges of early warning systems and dashboard design and will explore design principles and data visualization tools to make data more understandable and, therefore, more actionable. Highlighting a range of topics such as curriculum design, game-based learning, and learning support, it is ideal for academicians, policymakers, administrators, researchers, education professionals, instructional designers, data analysts, and students.

Digital age learners come to the science classroom equipped with a wide range of skills and a wealth of information at their fingertips. Although science and technology have enjoyed a symbiotic relationship, the ubiquity of information technologies requires teachers to modify instruction and experiences for K-12 science learners. Environmental and societal changes have impacted how and when students acquire and synthesize knowledge. These changes compel us to modify and adjust to improve the practice of teaching science to meet the unique needs of students who are growing up in a society dominated by connected digital devices, constant communication, and the ubiquity of information. Theoretical and Practical Teaching Strategies for K-12 Science Education in the Digital Age disseminates theory-informed practices for science teachers that increase their instructional effectiveness in teaching digital age learners. It communicates how to increase science educators’ understandings of the needs of digital age learners, develops theoretical and practical teaching strategies that align with science content, and integrates technologies for learning with fidelity. Covering topics such as design-based inclusive science, project-based learning, and science instruction, this premier reference source is an excellent resource for administrators and science educators within K-12 education, pre-service teachers, teacher educators, librarians, researchers, and academicians.

The Open Access version of this book, available at https://www.taylorfrancis.com/books/e/9781351049139, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license. This volume offers an exhaustive look at the latest research on metacognition in language learning and teaching. While other works have explored certain notions of metacognition in language learning and teaching, this book, divided into theoretical and empirical chapters, looks at metacognition from a variety of perspectives, including metalinguistic and multilingual awareness, and language learning and teaching in L2 and L3 settings, and explores a range of studies from around the world. This allows the volume to highlight a diverse set of methodological approaches, including blogging, screen recording software, automatic translation programs, language corpora, classroom interventions, and interviews, and subsequently, to demonstrate the value of metacognition research and how insights from such findings can contribute to a greater understanding of language learning and language teaching processes more generally. This innovative collection is an essential resource for students and scholars in language teaching pedagogy, and applied linguistics.

Maximizing student outcomes in education presents a significant challenge, as traditional assessment methods often fall short in providing actionable insights for improvement. Perspectives on Learning Analytics for Maximizing Student Outcomes addresses this challenge by offering a comprehensive solution. Edited by esteemed scholars Gürhan Durak and Serkan Çankaya, this book provides innovative knowledge and practical experiences on emerging technologies and processes in learning analytics. It covers topics such as data collection, visualization, predictive analytics, and ethical considerations, serving as a guide for academic scholars, technology enthusiasts, and educational institutions. This book empowers professionals and researchers to leverage learning analytics effectively, enabling data-informed decision-making, improved teaching practices, and tailored educational programs. By presenting best practices and future directions, it equips readers with the necessary tools to optimize learning environments and drive student success. With a focus on the transformative potential of learning analytics, this book propels education toward a more efficient and effective system that prioritizes student outcomes.

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.