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About 10,000 papers for “Group learning”

Group Learning

Hoidn, Sabine · Alexandria (UniSG) (University of St.Gallen) · 2011

Cited by 0

Learning group and mature group.

Chin-Sheng Hung (3181788), Sheng-Wei Chang (622664), Li-Min Liao (4096774) et al. · Figshare · 2017

<p>Total 203 patients received an endoscopic mastectomy and 134 unilateral total mastectomy patients were divided into learning group and mature group.</p>

Cited by 0Open access

Individualized Group Learning

Chencheng Cai, Rong Chen, Minge Xie · arXiv (Cornell University) · 2019

Many massive data are assembled through collections of information of a large number of individuals in a population. The analysis of such data, especially in the aspect of individualized inferences and solutions, has the potential to create significant value for practical applications. Traditionally, inference for an individual in the data set is either solely relying on the information of the individual or from summarizing the information about the whole population. However, with the availability of big data, we have the opportunity, as well as a unique challenge, to make a more effective individualized inference that takes into consideration of both the population information and the individual discrepancy. To deal with the possible heterogeneity within the population while providing effective and credible inferences for individuals in a data set, this article develops a new approach called the individualized group learning (iGroup). The iGroup approach uses local nonparametric techniques to generate an individualized group by pooling other entities in the population which share similar characteristics with the target individual, even when individual estimates are biased due to limited number of observations. Three general cases of iGroup are discussed, and their asymptotic performances are investigated. Both theoretical results and empirical simulations reveal that, by applying iGroup, the performance of statistical inference on the individual level are ensured and can be substantially improved from inference based on either solely individual information or entire population information. The method has a broad range of applications. Two examples in financial statistics and maritime anomaly detection are presented.

Cited by 1Open access

Individualized Group Learning

Chencheng Cai, Rong Chen, Minge Xie · Journal of the American Statistical Association · 2021

Many massive data sets are assembled through collections of information of a large number of individuals in a population. The analysis of such data, especially in the aspect of individualized inferences and solutions, has the potential to create significant value for practical applications. Traditionally, inference for an individual in the dataset is either solely relying on the information of the individual or from summarizing the information about the whole population. However, with the availability of big data, we have the opportunity, as well as a unique challenge, to make a more effective individualized inference that takes into consideration of both the population information and the individual discrepancy. To deal with the possible heterogeneity within the population while providing effective and credible inferences for individuals in a dataset, this article develops a new approach called the individualized group learning (iGroup). The iGroup approach uses local nonparametric techniques to generate an individualized group by pooling other entities in the population which share similar characteristics with the target individual, even when individual estimates are biased due to limited number of observations. Three general cases of iGroup are discussed, and their asymptotic performances are investigated. Both theoretical results and empirical simulations reveal that, by applying iGroup, the performance of statistical inference on the individual level are ensured and can be substantially improved from inference based on either solely individual information or entire population information. The method has a broad range of applications. An example in financial statistics is presented.

Cited by 6

Group‐Based Learning

Russell Y. Garth · New Directions for Teaching and Learning · 1999

Abstract Issue 14, devoted to group learning, has become a classic. Despite the fact that some of the same concerns remain, the popularity of the method has expanded tremendously.

Cited by 2

Collective group learning

Brooks, Ann K. · New Prairie Press (Kansas State University) · 1993

This qualitative case study of four teams sought to understand the process of collective group learning. The study resulted in identification of a general process of group learning. However, this process was found to be profoundly affected by national ideology as manifested in authority structures and policies within the teams' institutional environment.

Cited by 0Open access

Individualized Group Learning

Cai, Chencheng, Chen, Rong, Xie, Min-ge · Figshare · 2021

Many massive data sets are assembled through collections of information of a large number of individuals in a population. The analysis of such data, especially in the aspect of individualized inferences and solutions, has the potential to create significant value for practical applications. Traditionally, inference for an individual in the dataset is either solely relying on the information of the individual or from summarizing the information about the whole population. However, with the availability of big data, we have the opportunity, as well as a unique challenge, to make a more effective individualized inference that takes into consideration of both the population information and the individual discrepancy. To deal with the possible heterogeneity within the population while providing effective and credible inferences for individuals in a dataset, this article develops a new approach called the individualized group learning (iGroup). The iGroup approach uses local nonparametric techniques to generate an individualized group by pooling other entities in the population which share similar characteristics with the target individual, even when individual estimates are biased due to limited number of observations. Three general cases of iGroup are discussed, and their asymptotic performances are investigated. Both theoretical results and empirical simulations reveal that, by applying iGroup, the performance of statistical inference on the individual level are ensured and can be substantially improved from inference based on either solely individual information or entire population information. The method has a broad range of applications. An example in financial statistics is presented.

Cited by 0Open access

Measuring Group Learning

Paul S. Goodman, Laura A. Dabbish · 2016

This article examines current empirical approaches to group learning. It focu ses on two central questions: Are these approaches really measuring group learning, and what critical issues should be resolved in future grouplearning research? The rationale for this examination is threefold. First, the last 10 years have seen a substantial increase in research on group learning. As this litera ture accumulates, it is useful to do a critical review of the connection between the concept of group learning and its operational form. Second, there is a lack of clarity in how learning is operationalized—how researchers capture whether learning has occurred. The third rationale is to make explicit some of the features of group learning to serve as a guide for future research.

Cited by 0

Small Group Learning

Lisa Dawley · IGI Global eBooks · 2011

The strengths and weaknesses of each tool are discussed in detail, and educators are taught to match the strengths of the specific tool to the learning objectives they seek to achieve in their courses. Multiple examples of objectives and online learning activities are provided to help instill a deep understanding of the power of each online tool. This leaves you with a long-term ability to adapt your own curriculum over time, because you have learned options for using a variety of online tools to achieve specific learning objectives. Finally, each chapter includes a sample lesson plan that demonstrates how to integrate the tool into the learning experience. These ideas and activities provide tried and true suggestions for creating an online environment that engages and empowers learners for success. And when your students are successful in learning, you are successful in teaching.Request access from your librarian to read this chapter's full text.

Cited by 0

Alpine Learning Group

Bridget A. Taylor, Carrie E. Hughes, Erin Richard et al. · 2001

Three teenagers with autism were taught to respond to a vibrating pager to seek assistance in community settings when physically separated from their parents or teachers. A mul-tiple baseline probe design across participants demonstrated that, upon being paged, participants successfully handed a communication card to a community member indi-cating that they were lost. Generalization was assessed in nontraining community sites and on outings with the participants ’ parents. DESCRIPTORS: autism, safety skills, vibrating pager, getting lost, seeking assistance Independent participation in community activities is an important goal for learners with autism, their families, and their teach-ers. Concerns about safety, however, may impede efforts to foster community inde-pendence. Although several studies have fo-cused on teaching safety skills to persons with disabilities (Poche, Brouwer, & Swear-ingen, 1981; Risley & Cuvo, 1980), no pub-lished studies have investigated strategies to teach individuals with autism to seek assis-tance when lost in community settings. The purpose of this study was to determine if individuals with autism could be taught to respond to a vibrating pager to seek assis-tance when separated from a teacher or par-We thank Jane, Lisa, Craig, and their families for participation in this study. Thanks also to Alpine Learning Group staff for assistance in implementing training sessions and to Linda Meyer for her helpful comments. Requests for reprints should be sent to Bridget A.

Cited by 0

Work Group Learning

Burke, C. Shawn, Salas, Eduardo, Diaz, Deborah · Psychology Press eBooks · 2007

Preface. Part 1. How Groups Learn and What They Learn. V.I. Sessa, M. London, Group Learning: An Introduction. S.W.J. Kozlowski, B.S. Bell, Team Learning, Development, and Adaptation. H. Arrow, J. Cook, Configuring and Reconfiguring Groups as Complex Learning Systems. R.S. Tindale, S. Stawiski, E. Jacobs, Shared Cognition and Group Learning. A.P.J. Ellis, C.O.L.H. Porter, S.A. Wolverton, Learning to Work Together: An Examination of Transactive Memory System Development in Teams. Part 2. Member, Group, and Organizational Factors Influencing Group Learning. K.A. Jehn, J. Rupert, Group Faultlines and Team Learning: How to Benefit from Different Perspectives. C.O.L.H. Porter, A Multilevel, Multiconceptualization Perspective of Goal Orientation in Teams. N. Zakaria, A. Amelinckx, D. Wilemon, Navigating Across Culture and Distance: Understanding Determinants of Global Virtual Team Performance. S.J. Zaccaro, K. Ely, M. Shuffler, The Leader's Role in Group Learning. Part 3. Learning Interventions. C.S. Burke, E. Salas, D. Diaz, The Role of Team Learning in Facilitating Team Adaptation Within Complex Environments: Tools and Strategies. E.A.J.A. Rouwette, J.A.M. Vennix, Team Learning on Messy Problems. D.P. Brandon, A.B. Hollingshead, Collaborative Knowledge and Training in Online Groups. J.A. Cannon-Bowers, C.A. Bowers, A. Sanchez, Using Synthetic Learning Environments to Train Teams. J. Silberstang, T. Diamante, Phased and Targeted Interventions: Improving Team Learning and Performance. Part 4. Assessment of Group Learning. K.C. Stagl, E. Salas, D.V. Day, Assessing Team Learning Outcomes: Improving Team Learning and Performance. T.L. Gessner, K.L. Langkamer, R.J. Klimoski, Research Designs for Assessing Group Learning.

Cited by 47Open access

Optimizing group learning

Nancy Yorra · 2012

Group learning is widely considered throughout literature to be an effective method of learning; however, there is also evidence for the claim that there is often a significant gap between theory and practice. The current highly competitive climate in higher education, combined with skyrocketing tuitions and increasingly vocal accountability and consumerism movements, makes this an ideal time for colleges and universities to examine how their students perceive group learning activities and take steps to reduce or eliminate this gap. This phenomenological study conducted in-depth one-on-one interviews with six senior undergraduate business students at a major research university in the northeast United States (referred to throughout as"Research University ") to gain an understanding of how they perceived their group learning experiences. The two research questions guiding this study were: 1) What group learning experiences have proven effective in meeting the learning needs of each member of the group? and 2) What factors either enhance or diminish the overall utility for each individual member of the group? Attentively listening to the respondents and employing three underpinning components of phenomenological inquiry 1) bracketing 2) horizonalization and 3) iterative analysis, allowed this researcher to go beyond just identifying factors that enhanced or diminished the overall utility for each individual member of the group and discern an all-encompassing commonality, or the essence of group learning experiences. Although the students acknowledged several benefits of group learning, they were significantly more vocal regarding the aspects that negatively affected them. The degree to which the students expressed a fear of the unpredictable that arises from group work was the most prevalent finding of this study.

Cited by 0Open access

Collaborative Group Learning

Shaoxiong Feng, Hongshen Chen, Xuancheng Ren et al. · Proceedings of the AAAI Conference on Artificial Intelligence · 2021

Collaborative learning has successfully applied knowledge transfer to guide a pool of small student networks towards robust local minima. However, previous approaches typically struggle with drastically aggravated student homogenization when the number of students rises. In this paper, we propose Collaborative Group Learning, an efficient framework that aims to diversify the feature representation and conduct an effective regularization. Intuitively, similar to the human group study mechanism, we induce students to learn and exchange different parts of course knowledge as collaborative groups. First, each student is established by randomly routing on a modular neural network, which facilitates flexible knowledge communication between students due to random levels of representation sharing and branching. Second, to resist the student homogenization, students first compose diverse feature sets by exploiting the inductive bias from sub-sets of training data, and then aggregate and distill different complementary knowledge by imitating a random sub-group of students at each time step. Overall, the above mechanisms are beneficial for maximizing the student population to further improve the model generalization without sacrificing computational efficiency. Empirical evaluations on both image and text tasks indicate that our method significantly outperforms various state-of-the-art collaborative approaches whilst enhancing computational efficiency.

Cited by 9Open access

Collaborative Group Learning

Shaoxiong Feng, Hongshen Chen, Xuancheng Ren et al. · arXiv (Cornell University) · 2020

Collaborative learning has successfully applied knowledge transfer to guide a pool of small student networks towards robust local minima. However, previous approaches typically struggle with drastically aggravated student homogenization when the number of students rises. In this paper, we propose Collaborative Group Learning, an efficient framework that aims to diversify the feature representation and conduct an effective regularization. Intuitively, similar to the human group study mechanism, we induce students to learn and exchange different parts of course knowledge as collaborative groups. First, each student is established by randomly routing on a modular neural network, which facilitates flexible knowledge communication between students due to random levels of representation sharing and branching. Second, to resist the student homogenization, students first compose diverse feature sets by exploiting the inductive bias from sub-sets of training data, and then aggregate and distill different complementary knowledge by imitating a random sub-group of students at each time step. Overall, the above mechanisms are beneficial for maximizing the student population to further improve the model generalization without sacrificing computational efficiency. Empirical evaluations on both image and text tasks indicate that our method significantly outperforms various state-of-the-art collaborative approaches whilst enhancing computational efficiency.

Cited by 0Open access