Social learning Hoppitt, William; Laland, Kevin N
2013., 20130721, 2013, 2013-07-21
eBook
Many animals, including humans, acquire valuable skills and knowledge by copying others. Scientists refer to this as social learning. It is one of the most exciting and rapidly developing areas of ...behavioral research and sits at the interface of many academic disciplines, including biology, experimental psychology, economics, and cognitive neuroscience.Social Learningprovides a comprehensive, practical guide to the research methods of this important emerging field. William Hoppitt and Kevin Laland define the mechanisms thought to underlie social learning and demonstrate how to distinguish them experimentally in the laboratory. They present techniques for detecting and quantifying social learning in nature, including statistical modeling of the spatial distribution of behavior traits. They also describe the latest theory and empirical findings on social learning strategies, and introduce readers to mathematical methods and models used in the study of cultural evolution. This book is an indispensable tool for researchers and an essential primer for students.
Provides a comprehensive, practical guide to social learning researchCombines theoretical and empirical approachesDescribes techniques for the laboratory and the fieldCovers social learning mechanisms and strategies, statistical modeling techniques for field data, mathematical modeling of cultural evolution, and more
In this engaging volume, Jon Dron views education, learning, and teaching through a technological lens that focuses on the parts we play in technologies, from language and pedagogies to computers and ...regulations. He proposes a new theory of education whereby individuals are not just users but co-participants in technologies—technologies that are intrinsic parts of our cognition, of which we form intrinsic parts, through which we are entangled with one another and the world around us. Dron reframes popular families of educational theory (objectivist, subjectivist, and complexivist) and explains a variety of educational phenomena, including the failure of learning style theories, the nature of literacies, systemic weaknesses in learning management systems, the prevalence of cheating in educational institutions, and the fundamental differences between online and in-person learning. Ultimately, How Education Works articulates how practitioners in education can usefully understand technology, education, and their relationship to improve teaching practice.
Machine learning (ML) models are nowadays used in complex applications in various domains, such as medicine, bioinformatics, and other sciences. Due to their black box nature, however, it may ...sometimes be hard to understand and trust the results they provide. This has increased the demand for reliable visualization tools related to enhancing trust in ML models, which has become a prominent topic of research in the visualization community over the past decades. To provide an overview and present the frontiers of current research on the topic, we present a State‐of‐the‐Art Report (STAR) on enhancing trust in ML models with the use of interactive visualization. We define and describe the background of the topic, introduce a categorization for visualization techniques that aim to accomplish this goal, and discuss insights and opportunities for future research directions. Among our contributions is a categorization of trust against different facets of interactive ML, expanded and improved from previous research. Our results are investigated from different analytical perspectives: (a) providing a statistical overview, (b) summarizing key findings, (c) performing topic analyses, and (d) exploring the data sets used in the individual papers, all with the support of an interactive web‐based survey browser. We intend this survey to be beneficial for visualization researchers whose interests involve making ML models more trustworthy, as well as researchers and practitioners from other disciplines in their search for effective visualization techniques suitable for solving their tasks with confidence and conveying meaning to their data.
This open access book explores new developments in various aspects of peer learning processes and outcomes. It brings together research studies examining how peer feedback, peer assessment, and small ...group learning activities can be designed to maximize learning outcomes in higher, but also secondary, education. Conceptual models and methodological frameworks are presented to guide teachers and educational designers for successful implementation of peer learning activities with the hope of maximizing the effectiveness of peer learning in real educational classrooms. There is a strong emphasis on how technology-enhanced tools can advance peer learning, both with respect to designing and implementing learning activities, as well as analyzing learning processes and outcomes. By providing empirical studies from different peer learning initiatives, both teachers and students in academic and professional contexts are informed about the state of the art developments of peer learning. This book contributes to the understanding of peer learning challenges and solutions in all level of education and provide avenues for future research. It includes theoretical, methodological, and empirical chapters which makes it a useful tool for both teaching and research.
This book is a reprint of papers in the Special Issue published in Education Sciences under the title "Online and Distance Learning during Lockdown Times: COVID-19 Stories". It includes papers ...covering Higher Education (post-secondary) sector representing international experience of teaching and learning from the start of the first episode of lockdown due to the Covid-19 pandemic.
This book is a reprint of papers in the Special Issue published in Education Sciences under the title "Online and Distance Learning during Lockdown Times: COVID-19 Stories". It includes papers ...covering K-12 educational sector representing international experience of teaching and learning from the start of the first episode of lockdown due to the Covid-19 pandemic.
With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and ...complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.
With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and ...complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.
In this study, an augmented reality-based mobile learning system is proposed for conducting inquiry-based learning activities. An experiment has been conducted to examine the effectiveness of the ...proposed approach in terms of learning achievements and motivations. The subjects were 57 fourth graders from two classes taught by the same teacher in an elementary school in northern Taiwan. The experimental results showed that the proposed approach is able to improve the students' learning achievements. Moreover, it was found that the students who learned with the augmented reality-based mobile learning approach showed significantly higher motivations in the attention, confidence, and relevance dimensions than those who learned with the conventional inquiry-based mobile learning approach.
