About: Active learning is a(n) research topic. Over the lifetime, 42301 publication(s) have been published within this topic receiving 1120603 citation(s).
01 Jan 1999-
Abstract: New developments in the science of learning science of learning overview mind and brain how experts differ from novices how children learn learning and transfer the learning environment curriculum, instruction and commnity effective teaching - examples in history, mathematics and science teacher learning technology to support learning conclusions from new developments in the science of learning.
03 Sep 1991-
Abstract: 1. Making Meaning: The Dynamics of Learning. 2. Meaning Perspectives: How We Understand Experience. 3. Intentional Learning: A Process of Problem Solving. 4. Making Meaning Through Reflection. 5. Distorted Assumptions: Uncovering Errors in Learning. 6. Perspective Transformation: How Learning Leads to Change. 7. Fostering Transformative Adult Learning.
01 Dec 2014-
TL;DR: This chapter discusses the emergence of learning activity as a historical form of human learning and the zone of proximal development as the basic category of expansive research.
Abstract: 1. Introduction 2. The emergence of learning activity as a historical form of human learning 3. The zone of proximal development as the basic category of expansive research 4. The instruments of expansion 5. Toward an expansive methodology 6. Epilogue.
TL;DR: A critical review of the nature of the problem, the state-of-the-art technologies, and the current assessment metrics used to evaluate learning performance under the imbalanced learning scenario is provided.
Abstract: With the continuous expansion of data availability in many large-scale, complex, and networked systems, such as surveillance, security, Internet, and finance, it becomes critical to advance the fundamental understanding of knowledge discovery and analysis from raw data to support decision-making processes. Although existing knowledge discovery and data engineering techniques have shown great success in many real-world applications, the problem of learning from imbalanced data (the imbalanced learning problem) is a relatively new challenge that has attracted growing attention from both academia and industry. The imbalanced learning problem is concerned with the performance of learning algorithms in the presence of underrepresented data and severe class distribution skews. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently into information and knowledge representation. In this paper, we provide a comprehensive review of the development of research in learning from imbalanced data. Our focus is to provide a critical review of the nature of the problem, the state-of-the-art technologies, and the current assessment metrics used to evaluate learning performance under the imbalanced learning scenario. Furthermore, in order to stimulate future research in this field, we also highlight the major opportunities and challenges, as well as potential important research directions for learning from imbalanced data.
01 Jul 2004-Journal of Engineering Education
Abstract: This study examines the evidence for the effectiveness of active learning. It defines the common forms of active learning most relevant for engineering faculty and critically examines the core element of each method. It is found that there is broad but uneven support for the core elements of active, collaborative, cooperative and problem-based learning.