About: Active learning is a research topic. Over the lifetime, 42301 publications have been published within this topic receiving 1120603 citations.
Papers published on a yearly basis
01 Jan 1999
TL;DR: New developments in the science of learning as mentioned in this paper overview mind and brain how experts differ from novices how children learn learning and transfer the learning environment curriculum, instruction and commnity effective teaching.
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.
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.
03 Sep 1991
TL;DR: In this paper, the authors discuss the dynamics of learning and make meaning through reflection, making meaning through reflection, and perspective transformation, how learning leads to change, and how to foster transformative adult learning.
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: The analysis supports theory claiming that calls to increase the number of students receiving STEM degrees could be answered, at least in part, by abandoning traditional lecturing in favor of active learning and supports active learning as the preferred, empirically validated teaching practice in regular classrooms.
Abstract: creased by 0.47 SDs under active learning (n = 158 studies), and that the odds ratio for failing was 1.95 under traditional lecturing (n = 67 studies). These results indicate that average examination scores improved by about 6% in active learning sections, and that students in classes with traditional lecturing were 1.5 times more likely to fail than were students in classes with active learning. Heterogeneity analyses indicated that both results hold across the STEM disciplines, that active learning increases scores on concept inventories more than on course examinations, and that active learning appears effective across all class sizes—although the greatest effects are in small (n ≤ 50) classes. Trim and fill analyses and fail-safe n calculations suggest that the results are not due to publication bias. The results also appear robust to variation in the methodological rigor of the included studies, based on the quality of controls over student quality and instructor identity. This is the largest and most comprehensive metaanalysis of undergraduate STEM education published to date. The results raise questions about the continued use of traditional lecturing as a control in research studies, and support active learning as the preferred, empirically validated teaching practice in regular classrooms.
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