Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
39,739
result(s) for
"Active learning"
Sort by:
Active learning for data streams: a survey
Online active learning is a paradigm in machine learning that aims to select the most informative data points to label from a data stream. The problem of minimizing the cost associated with collecting labeled observations has gained a lot of attention in recent years, particularly in real-world applications where data is only available in an unlabeled form. Annotating each observation can be time-consuming and costly, making it difficult to obtain large amounts of labeled data. To overcome this issue, many active learning strategies have been proposed in the last decades, aiming to select the most informative observations for labeling in order to improve the performance of machine learning models. These approaches can be broadly divided into two categories: static pool-based and stream-based active learning. Pool-based active learning involves selecting a subset of observations from a closed pool of unlabeled data, and it has been the focus of many surveys and literature reviews. However, the growing availability of data streams has led to an increase in the number of approaches that focus on online active learning, which involves continuously selecting and labeling observations as they arrive in a stream. This work aims to provide an overview of the most recently proposed approaches for selecting the most informative observations from data streams in real time. We review the various techniques that have been proposed and discuss their strengths and limitations, as well as the challenges and opportunities that exist in this area of research.
Journal Article
Foxfire approach : inspiration for classrooms and beyond
\"This collection of essays by Foxfire practitioners represents the wide range of adaptations by educators of the pedagogical orientation of the Foxfire Magazine and Foxfire Programs for Teachers\"--Back cover.
Book
Active Learning Query Strategies for Classification, Regression, and Clustering: A Survey
Generally, data is available abundantly in unlabeled form, and its annotation requires some cost. The labeling, as well as learning cost, can be minimized by learning with the minimum labeled data instances. Active learning (AL), learns from a few labeled data instances with the additional facility of querying the labels of instances from an expert annotator or oracle. The active learner uses an instance selection strategy for selecting those critical query instances, which reduce the generalization error as fast as possible. This process results in a refined training dataset, which helps in minimizing the overall cost. The key to the success of AL is query strategies that select the candidate query instances and help the learner in learning a valid hypothesis. This survey reviews AL query strategies for classification, regression, and clustering under the pool-based AL scenario. The query strategies under classification are further divided into: informative-based, representative-based, informative- and representative-based, and others. Also, more advanced query strategies based on reinforcement learning and deep learning, along with query strategies under the realistic environment setting, are presented. After a rigorous mathematical analysis of AL strategies, this work presents a comparative analysis of these strategies. Finally, implementation guide, applications, and challenges of AL are discussed.
Journal Article
Learning as a generative activity : eight learning strategies that promote understanding
This book presents eight evidence-based strategies that promote generative learning, which enables learners to apply their knowledge to new problems.
Book
Effect of active learning versus traditional lecturing on the learning achievement of college students in humanities and social sciences: a meta-analysis
A previous meta-analysis found that active learning has a positive impact on learning achievements for college students in STEM fields of study. However, no similar meta-analyses have been conducted in the humanities and social sciences. Because major dissimilarities may exist between different fields or domain of knowledge, there can be issues with transferring research findings or knowledge across fields. We therefore meta-analyzed 104 studies that used assessment scores to compare the learning achieved by college students in humanities and social science programs under active instruction versus traditional lecturing. Student performance on assessment scores was found to be higher by 0.489 standard deviations under active instruction (Z = 6.521, p < 0.001, k = 111, N = 15,896). The relative beneficial effect of active instruction was found to be higher for some course subject matters (i.e., Sociology, Psychology, Language, Education, and Economics), for smaller (≤ 20 students) rather than larger class or group sizes, and for upper level rather than introductory courses. Analyses further suggest that these findings are not affected by publication bias.
Journal Article
Actions of their own to learn : studies in knowing, acting, and being
\"What does it mean to take actions of one's own to learn? How do human beings create meaning for themselves and with others? How can learners' active efforts to build knowledge be encouraged and supported? In this edited compilation, scholars from a diverse range of academic and professional backgrounds address these questions, grounded in the conviction that the ability to take effective action of one's own to learn is itself an essential form. In an era of dramatic social, environmental and political change, the need to access vast amounts of information to make decisions demands that learners become active agents in their own knowledge development. Educators are transforming ideas about their role(s) as they strive to provide guidance to help learners take the lead in their own learning. Learners are building new ideas about their capacities to gather and organize information while working with others. No longer simply consumers of information, they are beginning to see themselves as capable and effective researchers. Researchers are also expanding ideas about their knowledge-gathering work and identities. No longer simply reporters of information, researchers are seeing themselves as learners, as they engage in deeper, more collaborative ways with participants in their research. Chapter authors describe their dedicated, and often career long journeys to show the vital connections between knowledge, acting to learn, identity and being. To engage in this work means disrupting traditional ideas about how knowledge is most effectively acquired. This book will inspire researchers, educators and educational planners as they build the kinds of new participative structures needed to support individual and collective actions to learn\"-- Provided by publisher.
Book
A Framework of Implementing Strategies for Active Student Engagement in Remote/Online Teaching and Learning during the COVID-19 Pandemic
The COVID-19 pandemic has caused a drastic shift of face-to-face teaching and learning to remote/online teaching and learning at all levels of education worldwide. Active student engagement is always a challenging task for educators regardless of the teaching modalities. The degree of challenge for active student engagement increases significantly in remote/online teaching and learning. This paper presents a framework that implements activities/strategies to ensure active student engagement in remote/online teaching and learning during this COVID-19 pandemic. The structure of the developed framework combines the balanced use of adjusted teaching pedagogy, educational technologies, and an e-learning management system. Teaching pedagogy involves various active learning techniques, synchronous teaching, asynchronous teaching, and segmentation. The educational technologies, such as Google Meet, Jamboard, Google Chat, Breakout room, Mentimeter, Moodle, electronic writing devices, etc., enable the developed framework for active student engagement. An e-learning management system, Moodle, is used for course management purposes. Over the last three semesters (Fall 2020, Spring 2021, and Summer 2021), the framework is tested for three different engineering courses. A questionnaire draws out student perception on the developed framework in terms of active student engagement that ensures student–student interactions, student–instructor interactions, social presence, reinforces learning and deepens understanding of the materials in remote teaching. The feedback also indicates that combining the utilized technologies, synchronous teaching, and active learning activities in the developed framework is effective for interactive learning; hence a practical approach for active student engagement in remote/online teaching and learning. The article focuses on contributing to present research and infusing future research direction about technology-enhanced active student engagement in Engineering Education.
Journal Article
A Novel Active Learning Method Using SVM for Text Classification
Support vector machines (SVMs) are a popular class of supervised learning algorithms, and are particularly applicable to large and high-dimensional classification problems. Like most machine learning methods for data classification and information retrieval, they require manually labeled data samples in the training stage. However, manual labeling is a time consuming and errorprone task. One possible solution to this issue is to exploit the large number of unlabeled samples that are easily accessible via the internet. This paper presents a novel active learning method for text categorization. The main objective of active learning is to reduce the labeling effort, without compromising the accuracy of classification, by intelligently selecting which samples should be labeled. The proposed method selects a batch of informative samples using the posterior probabilities provided by a set of multi-class SVM classifiers, and these samples are then manually labeled by an expert. Experimental results indicate that the proposed active learning method significantly reduces the labeling effort, while simultaneously enhancing the classification accuracy.
Journal Article