Active Learning

This book discusses the theoretical analysis of active learning and contains practical recommendations for its implementation.

This book brings together leading research on active pedagogy to offer educators practical tools for enhancing student engagement and deepening understanding. The chapters explore diverse strategies, from integrating systems thinking in biological education to applying neuroeducation insights in active learning environments. This collection inspires educators to rethink traditional practices and embrace new, learner-driven approaches for a more engaging and effective educational experience.

This book uncovers the crucial issues in learning technologies in this digital transformation moment, specifically within the COVID-19 umbrella effects. Remote learning, educational technologies, or distance learning are usually used topics by teachers, students, and researchers because the educational context should be transformed and even reinvented itself drastically. Technologies have been used more intensively in the last year than during the last decade. However, what is the effect of these "new" technologies on the teaching and learning methodologies? Are teachers and students fully digital competent to integrate these technologies in their teaching and learning activities? In this book, the authors claim to go forward that the online teaching conception to replicate the face-to-face teaching through a camera. They propose adapting the active methodologies to the online or hybrid context, which is a challenge that must be corroborated with rigorous educational research.

Future epidemics are inevitable, and it takes months and even years to collect fully annotated data. The sheer magnitude of data required for machine learning algorithms, spanning both shallow and deep structures, raises a fundamental question: how big data is big enough to effectively tackle future epidemics? In this context, active learning, often referred to as human or expert-in-the-loop learning, becomes imperative, enabling machines to commence learning from day one with minimal labeled data. In unsupervised learning, the focus shifts toward constructing advanced machine learning models like deep structured networks that autonomously learn over time, with human or expert intervention only when errors occur and for limited data—a process we term mentoring. In the context of Covid-19, this book explores the use of deep features to classify data into two clusters (0/1: Covid-19/non-Covid-19) across three distinct datasets: cough sound, Computed Tomography (CT) scan, and chest x-ray (CXR). Not to be confused, our primary objective is to provide a strong assertion on how active learning could potentially be used to predict disease from any upcoming epidemics. Upon request (education/training purpose), GitHub source codes are provided.

The working model for "helping the learner to learn" presented in this book is relevant to any teaching context, but the focus here is on teaching in secondary and college science classrooms.