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"Learning -- Methodology"
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Social learning
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
eBook
Redefining post-traditional learning : emerging research and opportunities
\"\"This book explores changing student demographics and offers recommendations to current teaching methodologies through the lens of andragogy\"--Provided by publisher\"-- Provided by publisher.
Book
The Johnstone triangle : the key to understanding chemistry
Chemistry is often seen as a difficult subject to understand. This book focusses on the triangle model that Alex H. Johnstone developed in the early 1980s. The model has been applied in almost every area of education in chemistry at all stages of learning.
eBook
Interpretation of intelligence in CNN-pooling processes: a methodological survey
The convolutional neural network architecture has different components like convolution and pooling. The pooling is crucial component placed after the convolution layer. It plays a vital role in visual recognition, detection and segmentation course to overcome the concerns like overfitting, computation time and recognition accuracy. The elementary pooling process involves down sampling of feature map by piercing into subregions. This piercing and down sampling is defined by the pooling hyperparameters, viz. stride and filter size. This down sampling process discards the irrelevant information and picks the defined global feature. The generally used global feature selection methods are average and max pooling. These methods decline, when the main element has higher or lesser intensity than the nonsignificant element. It also suffers with locus and order of nominated global feature, hence not suitable for every situation. The pooling variants are proposed by numerous researchers to overcome concern. This article presents the state of the art on selection of global feature for pooling process mainly based on four categories such as value, probability, rank and transformed domain. The value and probability-based methods use the criteria such as the way of down sampling, size of kernel, input output feature map, location of pooling, number stages and random selection based on probability value. The rank-based methods assign the rank and weight to activation; the feature is selected based on the defined criteria. The transformed domain pooling methods transform the image to other domains such as wavelet, frequency for pooling the feature.
Journal Article
Student-centered learning environments in higher education classrooms
This book aims to develop a situative educational model to guide the design and implementation of powerful student-centered learning environments in higher education classrooms. Rooted in educational science, Hoidn contributes knowledge in the fields of general pedagogy, and more specifically, higher education learning and instruction.
Book
Application of RBF neural network optimal segmentation algorithm in credit rating
Credit rating is an important part of bank credit risk management. Since the traditional radial basis function network model is more susceptible to outliers and cannot effectively process the classification data, it is very sensitive in terms of the initial center and class width of the selected model. This paper mainly studies the application of the radial basis function neural network model combined with the optimal segmentation algorithm in the personal loan credit rating model of banks or other financial institutions. The optimal segmentation algorithm is improved and applied to the training of RBF neural network parameters in this paper to increase the center and width of the class, and the center and width of the RBF network model are further improved. Finally, the adaptive selection of the number of hidden nodes is realized by using the differential objective function of the class to adjust dynamically the structure of the radial basis function network model, which is used to establish the credit rating model. The experimental results show that the improved model has higher precision when dealing with non-numeric data, and the robustness of the improved model has been improved.
Journal Article
Flipped Classroom as an Active Learning Methodology in Sustainable Development Curricula
Goal 4 of the Agenda 2030 sustainable development goals (SDGs) is aimed at working towards quality in education. Universities have an important role in teaching sustainability principles. Yet, which methods are effective for engaging students in understanding the importance of sustainable development and introducing them to new perspectives to make changes? The methodology of the flipped classroom is a possible alternative for the pedagogic renovation. This is known as an information-based environment in which teachers provide a variety of learning resources so that students can complete the knowledge transfer process before the class. Once inside classroom, teachers and students can complete the internalization of knowledge by answering questions, and through collaborative consultations and interactive exchanges, among others. A survey of 154 students taught by flipped classroom methodology was conducted in order to analyze whether this helps with learning about sustainable development. The results show the active and reflexive learning from flipped classroom methodology makes students more committed to sustainable development. This research would be useful to anyone interested in applying the flip the class teaching methodology as an integrated form of thinking and training in the curriculum of sustainable development for higher education students.
Journal Article