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.

In the world of education, technology has revolutionized teaching and learning practices globally. However, the widespread adoption of technology, especially in response to Covid-19, has led to a deluge of data from students' digital footprints. The challenge lies in effectively using this data through learning analytics (LA) to optimize K-12 classroom practices. Emergent Practices of Learning Analytics in K-12 Classrooms, edited by Dr. Nurdan Kavakl? Uluta? and Dr. Devrim Höl offers a comprehensive solution to these challenges. This book gathers academic scholars to explore practical perspectives on applying learning analytics in K-12 classrooms, combining qualitative and quantitative methodologies. Addressing topics such as predictive analytics, ethical considerations, and future directions, it empowers educators to make data-driven decisions, creating engaging learning experiences for improved student outcomes. By embracing the insights and recommendations presented in this book, academic scholars can confidently navigate the realm of learning analytics and shape the future of K-12 education.

Inventory Analytics provides a comprehensive and accessible introduction to the theory and practice of inventory control – a significant research area central to supply chain planning. The book outlines the foundations of inventory systems and surveys prescriptive analytics models for deterministic inventory control. It further discusses predictive analytics techniques for demand forecasting in inventory control and also examines prescriptive analytics models for stochastic inventory control. Inventory Analytics is the first book of its kind to adopt a practicable, Python-driven approach to illustrating theories and concepts via computational examples, with each model covered in the book accompanied by its Python code. Originating as a collection of self-contained lectures, Inventory Analytics will be an indispensable resource for practitioners, researchers, teachers, and students alike.

Due to rapid technological advancements, evolving workplace structures, and a shift towards remote work, the nature of work is undergoing transformations influencing communication, work dynamics, and learning processes. Improving the skills of future workforces is now a necessity as the growth of work-skill misalignment and employability skills gaps becomes prevalent. Educational institutions must prepare students for the digital world by providing them with advanced knowledge and essential skills in modern technology. To positively shape the future workforce, effective solutions for skill revitalization should be explored. Revitalizing Student Skills for Workforce Preparation explores the intricacies of evolving and complex professional landscapes, including the challenges and opportunities of student skill development. The pivotal role of skill development in education is emphasized while delving into how effective skill education will shape the future workforce. This book covers topics such as digital technology, professional development, and emotional intelligence, and is a useful resource for educators, business professionals, and policymakers, academicians, researchers, and administrators.

Examines the issues raised by the ACER Research Conference 2005. Analyses conference papers, distils essence of conference 'conversations' and contextualises them in the light of Australian and international literature.

This book presents original research on analytics and context awareness with regard to providing sophisticated learning services for all stakeholders in the eLearning context. It offers essential information on the definition, modeling, development and deployment of services for these stakeholders. Data analysis has long-since been a cornerstone of eLearning, supplying learners, teachers, researchers, managers and policymakers with valuable information on learning activities and design. With the rapid development of Internet technologies and sophisticated online learning environments, increasing volumes and varieties of data are being generated, and data analysis has moved on to more complex analysis techniques, such as educational data mining and learning analytics. Now powered by cloud technologies, online learning environments are capable of gathering and storing massive amounts of data in various formats, of tracking user-system and user-user interactions, and of delivering rich contextual information.

Assessment methods can largely determine what and how students learn, so it is vital that our assessment methods are appropriate to our true educational purposes. This book examines the issues underlying assessment procedures, such as truth, fairness, trust, humanity and social justice and goes on to consider the five key dimensions of assessment: * why assess? * what to assess? * how to assess? * How to interpret? * How to respond? Having guided us through the many conceptual and terminological traps, the book ends constructively with seventeen proposals for making assessment work in the best interests of our students.

Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified “white box” approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets. Data Mining and Predictive Analytics: Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language Features over 750 chapter exercises, allowing readers to assess their understanding of the new material Provides a detailed case study that brings together the lessons learned in the book Includes access to the companion website, www.dataminingconsultant, with exclusive password-protected instructor content Data Mining and Predictive Analytics will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives.

This timely book takes stock of the wide range of developments in society, education and assessment and offers conclusions and strategies that are necessary for the future of educational assessment. Drawing on examples from the UK, Europe and USA, the book will dissect cultural, political, psychological and ideological ideas on society, education and assessment and foreground pressing issues relating to artificial intelligence, social justice and climate change. Acknowledging its predominantly Western perspective and providing context on the evolution of educational assessment, the book will bridge the gap between theory and practice to progress debate and discourse on creating a culture of assessment fit for the future and rethinking strategies for the path ahead. Ultimately, the book will provide insights and key takeaways for the field of educational assessment along with an evidence-based agenda that will be relevant for education professionals, the assessment industry and policymakers interested in higher education, international and comparative education and testing.