Active Learning With Ai
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Active Learning with AI
Artificial Intelligence (AI) has immense potential to enhance teaching and learning in countless ways. However, for educators and course designers, this potential can seem overwhelming, confusing and even frightening. In this book, researcher and educational innovator Stephen M. Kosslyn demystifies AI and its relationship to education, and he provides a wealth of step-by-step illustrations and examples to help instructors and course designers both understand AI concepts and use them to build stimulating active learning exercises and lesson plans. Whether you teach a course in a traditional classroom, online, or with a hybrid approach, this book will inform and empower you to create more engaging, personalized, and scalable learning experiences for your students.
Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance
Over the last two decades, researchers are looking at imbalanced data learning as a prominent research area. Many critical real-world application areas like finance, health, network, news, online advertisement, social network media, and weather have imbalanced data, which emphasizes the research necessity for real-time implications of precise fraud/defaulter detection, rare disease/reaction prediction, network intrusion detection, fake news detection, fraud advertisement detection, cyber bullying identification, disaster events prediction, and more. Machine learning algorithms are based on the heuristic of equally-distributed balanced data and provide the biased result towards the majority data class, which is not acceptable considering imbalanced data is omnipresent in real-life scenarios and is forcing us to learn from imbalanced data for foolproof application design. Imbalanced data is multifaceted and demands a new perception using the novelty at sampling approach of data preprocessing, an active learning approach, and a cost perceptive approach to resolve data imbalance. Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance offers new aspects for imbalanced data learning by providing the advancements of the traditional methods, with respect to big data, through case studies and research from experts in academia, engineering, and industry. The chapters provide theoretical frameworks and the latest empirical research findings that help to improve the understanding of the impact of imbalanced data and its resolving techniques based on data preprocessing, active learning, and cost perceptive approaches. This book is ideal for data scientists, data analysts, engineers, practitioners, researchers, academicians, and students looking for more information on imbalanced data characteristics and solutions using varied approaches.
Active Learning
Author: Sílvio Manuel Brito
language: en
Publisher: BoD – Books on Demand
Release Date: 2019-10-02
Active learning is now a form of learning that accompanies the knowledge evolution that challenges the learner to promote it, but also encourages him to investigate and become emotionally involved in the task. The great key to obtaining this behavior successfully depends, therefore, on the subject's involvement and ability to undertake, so that active learning becomes emotional entrepreneurial learning that generates new ideas and new forms of knowledge. From memorization, we move on to inquiry, from questioning to constructive participation, from hypostasis to problem-solving, from generalization to critical thinking. When we look at this book, we see real examples, concrete, and senses, from the most important act of human nature: learning!