Deep Active Learning

Deep Active Learning

This is the first book to connect the concepts of active learning and deep learning, and to delineate theory and practice through collaboration between scholars in higher education from three countries (Japan, the United States, and Sweden) as well as different subject areas (education, psychology, learning science, teacher training, dentistry, and business).It is only since the beginning of the twenty-first century that active learning has become key to the shift from teaching to learning in Japanese higher education. However, “active learning” in Japan, as in many other countries, is just an umbrella term for teaching methods that promote students’ active participation, such as group work, discussions, presentations, and so on.What is needed for students is not just active learning but deep active learning. Deep learning focuses on content and quality of learning whereas active learning, especially in Japan, focuses on methods of learning. Deep active learning is placed at the intersection of active learning and deep learning, referring to learning that engages students with the world as an object of learning while interacting with others, and helps the students connect what they are learning with their previous knowledge and experiences as well as their future lives.What curricula, pedagogies, assessments and learning environments facilitate such deep active learning? This book attempts to respond to that question by linking theory with practice.

The Impact of Active Learning on Deep Learning Models with Detectron 2

Artificial Intelligence in Environmental Engineering and Ecology: Towards Smart and Sustainable Cities

The ever-growing world population is over-stressing the available resources leading to several social, economic, and environmental issues. The world is facing challenges related to the availability of food, housing, water, and infrastructure. The solutions to sustainability crises require unraveling complex interactions that do not fit neatly into a single discipline. Keeping in view the sustainable development goals which are considered a blueprint for a better and more sustainable future, interdisciplinary research in civil and environmental engineering is of utmost important. The interdisciplinary research tackles the demands of the growing population of urban agglomerates. Designing interdisciplinary solutions for achieving sustainable development goals including Sustainable Cities and Communities; Affordable and Clean Energy; Clean Water and Sanitation; Responsible Consumption and Production; Industry, Innovation, and Infrastructure; Climate Action is the need of the hour. The interdisciplinary research in environmental sustainability can convert real-world complexities such as space dynamics and pressure on cities, sustainable infrastructure, smart transport, smart buildings, climate changes, air pollutant dispersion and pollution, contaminant transport through air water, and soil, ocean dynamics, life below water, and effect of contamination on flora and fauna and more, into predictable models using Artificial Intelligence (AI). The primary objective of this research topic is to consolidate research and application of Artificial Intelligence in environmental engineering, aiming toward smart and sustainable cities. Highlighting AI-based solutions and models across environmental engineering and sustainability, in particular for smart and sustainable cities, is the prime objective of the proposed research topic. The issue will welcome multidisciplinary/ interdisciplinary approaches to provide solutions to current pressing problems of cities from an engineering perspective.