Emergent Practices Of Learning Analytics In K 12 Classrooms

Emergent Practices of Learning Analytics in K-12 Classrooms

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 Kavaklı Ulutaş 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.

EMERGENT PRACTICES OF LEARNING ANALYTICS IN K-12 CLASSROOMS.

Real-World Applications of AI Innovation

Artificial intelligence (AI) innovation works to transform various sectors, driving efficiency, enhancing decision-making, and creating new opportunities for growth. From healthcare and finance to agriculture and entertainment, real-world applications of AI are demonstrating its potential to solve complex problems and improve everyday life. As these technologies continue to evolve, further exploration into the integration of AI into different fields may allow for a more efficient, sustainable, and innovative future. Real-World Applications of AI Innovation explores the latest advancements and practical applications of artificial intelligence across various domains. It delves into cutting-edge AI methodologies, algorithms, and technologies, providing readers with a deep understanding of the current landscape and future trends in AI research and development. This book covers topics such as smart farming, machine learning, and deep neural networks, and is a useful resource for computer engineers, scientists, medical professionals, agriculturalists, educators, researchers, academicians, and business owners.