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Опубліковано: 10 грудня 2022
Оновлено: 28 січня 2023

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Статті в журналах з теми "Social Learning Networks"

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Board, Simon, and Moritz Meyer-ter-Vehn. "Learning Dynamics in Social Networks." Econometrica 89, no. 6 (2021): 2601–35. http://dx.doi.org/10.3982/ecta18659.

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This paper proposes a tractable model of Bayesian learning on large random networks where agents choose whether to adopt an innovation. We study the impact of the network structure on learning dynamics and product diffusion. In directed networks, all direct and indirect links contribute to agents' learning. In comparison, learning and welfare are lower in undirected networks and networks with cliques. In a rich class of networks, behavior is described by a small number of differential equations, making the model useful for empirical work.
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Sloep, Peter, and Adriana Berlanga. "Learning Networks, Networked Learning." Comunicar 19, no. 37 (October 1, 2011): 55–64. http://dx.doi.org/10.3916/c37-2011-02-05.

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Learning Networks are on-line social networks through which users share knowledge with each other and jointly develop new knowledge. This way, Learning Networks may enrich the experience of formal, school-based learning and form a viable setting for professional development. Although networked learning enjoys an increasing interest, many questions remain on how exactly learning in such networked contexts can contribute to successful education and training. Put differently, how should networked learning be designed best to facilitate education and training? Taking this as its point of departure, the chapter addresses such issues as the dynamic evolution of Learning Networks, trust formation and profiling in Learning Networks, and peer-support among Learning Network participants. This discussion will be interspersed with implementation guidelines for Learning Networks and with a discussion of the more extended case of a Learning Network for Higher Education. Taking into consideration research currently carried out at our own centre and elsewhere, the chapter will close off with a look into the future of Learning Networks.Las redes de aprendizaje (Learning Networks) son redes sociales en línea mediante las cuales los participantes comparten información y colaboran para crear conocimiento. De esta manera, estas redes enriquecen la experiencia de aprendizaje en cualquier contexto de aprendizaje, ya sea de educación formal (en escuelas o universidades) o educación no-formal (formación profesional). Aunque el concepto de aprendizaje en red suscita el interés de diferentes actores del ámbito educativo, aún existen muchos interrogantes sobre cómo debe diseñarse el aprendizaje en red para facilitar adecuadamente la educación y la formación. El artículo toma este interrogante como punto de partida, y posteriormente aborda cuestiones como la dinámica de la evolución de las redes de aprendizaje, la importancia de fomentar la confianza entre los participantes y el papel central que desempeña el perfil de usuario en la construcción de la confianza, así como el apoyo entre compañeros. Además, se elabora el proceso de diseño de una red de aprendizaje, y se describe un ejemplo en el contexto universitario. Basándonos en la investigación que actualmente se lleva a cabo en nuestro propio centro y en otros lugares, el capítulo concluye con una visión del futuro de las redes de aprendizaje.
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Dasaratha, Krishna, and Kevin He. "Network structure and social learning." ACM SIGecom Exchanges 19, no. 2 (November 2021): 62–67. http://dx.doi.org/10.1145/3505156.3505163.

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We describe results from Dasaratha and He [DH21a] and Dasaratha and He [DH20] about how network structure influences social learning outcomes. These papers share a tractable sequential model that lets us compare learning dynamics across networks. With Bayesian agents, incomplete networks can generate informational confounding that makes learning arbitrarily inefficient. With naive agents, related forces can lead to mislearning.
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4

Levin, Ilya, Mark Korenblit, and Vadim Talis. "STUDY OF SOCIAL NETWORKS’ DYNAMICS BY SIMULATION WITHIN THE NODEXL-EXCEL ENVIRONMENT." Problems of Education in the 21st Century 54, no. 1 (June 20, 2013): 125–37. http://dx.doi.org/10.33225/pec/13.54.125.

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The present study is an analysis of the learning activity, which constitutes simulation of networks and studying their functioning and dynamics. The study is based on using network-like learning environments. Such environments allow building computer models of the network graphs. According to the suggested approach, the students construct dynamic computer models of the networks' graphs, thus implementing various algorithms of such networks’ dynamics. The suggested tool for building the models is the software environment comprising network analysis software NodeXL and a standard spreadsheet Excel. The proposed approach enables the students to visualize the network's dynamics. The paper presents specific examples of network models and various algorithms of the network's dynamics, which were developed based on the proposed approach. Key words: learning environments, modelling, social networks.
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Derakhshan, Ali, and Samareh Hasanabbasi. "Social Networks for Language Learning." Theory and Practice in Language Studies 5, no. 5 (May 17, 2015): 1090. http://dx.doi.org/10.17507/tpls.0505.25.

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Acemoglu, D., M. A. Dahleh, I. Lobel, and A. Ozdaglar. "Bayesian Learning in Social Networks." Review of Economic Studies 78, no. 4 (March 7, 2011): 1201–36. http://dx.doi.org/10.1093/restud/rdr004.

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Gale, Douglas, and Shachar Kariv. "Bayesian learning in social networks." Games and Economic Behavior 45, no. 2 (November 2003): 329–46. http://dx.doi.org/10.1016/s0899-8256(03)00144-1.

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Zhang, Zhenliang, Edwin K. P. Chong, Ali Pezeshki, William Moran, and Stephen D. Howard. "Learning in Hierarchical Social Networks." IEEE Journal of Selected Topics in Signal Processing 7, no. 2 (April 2013): 305–17. http://dx.doi.org/10.1109/jstsp.2013.2245859.

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Nie, Liqiang, Xuemeng Song, and Tat-Seng Chua. "Learning from Multiple Social Networks." Synthesis Lectures on Information Concepts, Retrieval, and Services 8, no. 2 (April 21, 2016): 1–118. http://dx.doi.org/10.2200/s00714ed1v01y201603icr048.

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Greenhow, Christine. "Online social networks and learning." On the Horizon 19, no. 1 (February 2011): 4–12. http://dx.doi.org/10.1108/10748121111107663.

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Дисертації з теми "Social Learning Networks"

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Bordianu, Gheorghita. "Learning influence probabilities in social networks." Thesis, McGill University, 2013. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=114597.

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Social network analysis is an important cross-disciplinary area of research, with applications in fields such as biology, epidemiology, marketing and even politics. Influence maximization is the problem of finding the set of seed nodes in an information diffusion process that guarantees maximum spread of influence in a social network, given its structure. Most approaches to this problem make two assumptions. First, the global structure of the network is known. Second, influence probabilities between any two nodes are known beforehand, which is rarely the case in practical settings. In this thesis we propose a different approach to the problem of learning those influence probabilities from past data, using only the local structure of the social network. The method is grounded in unsupervised machine learning techniques and is based on a form of hierarchical clustering, allowing us to distinguish between influential and the influenceable nodes. Finally, we provide empirical results using real data extracted from Facebook.
L'analyse des réseaux sociaux est un domaine d'études interdisciplinaires qui comprend des applications en biologie, épidémiologie, marketing et même politique. La maximisation de l'influence représente un problème où l'on doit trouver l'ensemble des noeuds de semence dans un processus de diffusion de l'information qui en même temps garantit le maximum de propagation de son influence dans un réseau social avec une structure connue. La plupart des approches à ce genre de problème font appel à deux hypothèses. Premièrement, la structure générale du réseau social est connue. Deuxièmement, les probabilités des influences entre deux noeuds sont connues à l'avance, fait qui n'est d'ailleurs pas valide dans des circonstances pratiques. Dans cette thèse, on propose un procédé différent visant la problème de l'apprentissage de ces probabilités d'influence à partir des données passées, en utilisant seulement la structure locale du réseau social. Le procédé se base sur l'apprentissage automatique sans surveillance et il est relié à une forme de regroupement hiérarchique, ce qui nous permet de faire la distinction entre les noeuds influenceurs et les noeuds influencés. Finalement, on fournit des résultats empiriques en utilisant des données réelles extraites du réseau social Facebook.
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Sharad, Kumar. "Learning to de-anonymize social networks." Thesis, University of Cambridge, 2016. https://www.repository.cam.ac.uk/handle/1810/262750.

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Releasing anonymized social network data for analysis has been a popular idea among data providers. Despite evidence to the contrary the belief that anonymization will solve the privacy problem in practice refuses to die. This dissertation contributes to the field of social graph de-anonymization by demonstrating that even automated models can be quite successful in breaching the privacy of such datasets. We propose novel machine-learning based techniques to learn the identities of nodes in social graphs, thereby automating manual, heuristic-based attacks. Our work extends the vast literature of social graph de-anonymization attacks by systematizing them. We present a random-forests based classifier which uses structural node features based on neighborhood degree distribution to predict their similarity. Using these simple and efficient features we design versatile and expressive learning models which can learn the de-anonymization task just from a few examples. Our evaluation establishes their efficacy in transforming de-anonymization to a learning problem. The learning is transferable in that the model can be trained to attack one graph when trained on another. Moving on, we demonstrate the versatility and greater applicability of the proposed model by using it to solve the long-standing problem of benchmarking social graph anonymization schemes. Our framework bridges a fundamental research gap by making cheap, quick and automated analysis of anonymization schemes possible, without even requiring their full description. The benchmark is based on comparison of structural information leakage vs. utility preservation. We study the trade-off of anonymity vs. utility for six popular anonymization schemes including those promising k-anonymity. Our analysis shows that none of the schemes are fit for the purpose. Finally, we present an end-to-end social graph de-anonymization attack which uses the proposed machine learning techniques to recover node mappings across intersecting graphs. Our attack enhances the state of art in graph de-anonymization by demonstrating better performance than all the other attacks including those that use seed knowledge. The attack is seedless and heuristic free, which demonstrates the superiority of machine learning techniques as compared to hand-selected parametric attacks.
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Rogers, Brian W. Palfrey Thomas R. "Learning and status in social networks /." Diss., Pasadena, Calif. : Caltech, 2006. http://resolver.caltech.edu/CaltechETD:etd-05262006-004112.

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Milán, Pau. "The Social economics of networks and learning." Doctoral thesis, Universitat Pompeu Fabra, 2016. http://hdl.handle.net/10803/393733.

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This thesis explores various economic environments where the structure of social interactions across individuals determines outcomes. In the first chapter, I study mutual insurance arrangements restricted on a social network. I test the network-based sharing rules on data from Bolivian communities, and I argue that this framework provides a reinterpretation of the standard risk sharing results, predicting household heterogeneity in response to income shocks. In the second paper, I study individual and collective behavior in coordination games where information is dispersed through a network. I show how changes in the distribution of connectivities in the population affect the types of coordination in equilibrium as well as the probability of success. In the third chapter, I explore a framework of learning and turnover in the labor market. I show that positive assortative matching (PAM) extends beyond the stable environment of Eeckhout & Weng (2010) to a situation of residual uncertainty that exhibits periods of unlearning. I also extend this setting to allow for career concerns and I show that PAM can only be sustained under strong assumptions.
Esta tesis explora diversos entornos económicos en los que la estructura de las interacciones sociales entre los individuos determina los distintos resultados. En el primer capítulo, se estudia acuerdos de seguro mutuo restringidos en una red social. Utilizo datos de comunidades bolivianas para medir las predicciones teóricas y encuentro que los intercambios observados entre los hogares coinciden con la regla de reparto basada en la red obtenida por la teoría. Sostengo que este marco ofrece una reinterpretación de los resultados estándar de distribución de riesgos, prediciendo heterogeneidad entre los hogares en respuesta a los shocks de ingresos. En el segundo artículo, estudio el comportamiento individual y colectivo en juegos de coordinación, donde la información se dispersa a través de una red. Demuestro cómo los cambios en la distribución de las conectividades de la población afectan a los tipos de coordinación en equilibrio, así como la probabilidad de éxito. En el tercer capítulo, analizo un marco de aprendizaje y cambio de personal en el mercado de trabajo. Muestro que emparejamiento selectivo positivo (PAM) se extiende más allá del entorno estable de Eeckhout y Weng (2010) a una situación de incertidumbre residual que exhibe períodos de des-aprendizaje. También extiendo esta configuración para permitir elementos de career concerns y muestro que el equilibrio de PAM sólo puede sostenerse bajo fuertes supuestos.
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Lobel, Ilan. "Social networks : rational learning and information aggregation." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/54232.

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Анотація:
Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2009.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (p. 137-140).
This thesis studies the learning problem of a set of agents connected via a general social network. We address the question of how dispersed information spreads in social networks and whether the information is efficiently aggregated in large societies. The models developed in this thesis allow us to study the learning behavior of rational agents embedded in complex networks. We analyze the perfect Bayesian equilibrium of a dynamic game where each agent sequentially receives a signal about an underlying state of the world, observes the past actions of a stochastically-generated neighborhood of individuals, and chooses one of two possible actions. The stochastic process generating the neighborhoods defines the network topology (social network). We characterize equilibria for arbitrary stochastic and deterministic social networks and characterize the conditions under which there will be asymptotic learning -- that is, the conditions under which, as the social network becomes large, the decisions of the individuals converge (in probability) to the right action. We show that when private beliefs are unbounded (meaning that the implied likelihood ratios are unbounded), there will be asymptotic learning as long as there is some minimal amount of expansion in observations. This result therefore establishes that, with unbounded private beliefs, there will be asymptotic learning in almost all reasonable social networks. Furthermore, we provide bounds on the speed of learning for some common network topologies. We also analyze when learning occurs when the private beliefs are bounded.
(cont.) We show that asymptotic learning does not occur in many classes of network topologies, but, surprisingly, it happens in a family of stochastic networks that has infinitely many agents observing the actions of neighbors that are not sufficiently persuasive. Finally, we characterize equilibria in a generalized environment with heterogeneity of preferences and show that, contrary to a nave intuition, greater diversity (heterogeneity) 3 facilitates asymptotic learning when agents observe the full history of past actions. In contrast, we show that heterogeneity of preferences hinders information aggregation when each agent observes only the action of a single neighbor.
by Ilan Lobel.
Ph.D.
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Oddone, Kay. "Teachers' experience of professional learning through personal learning networks." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/127928/1/Kay_Oddone_Thesis.pdf.

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There is an urgent need to improve continuing professional learning for teachers as education becomes increasingly complex. Traditional models of professional development are often fragmented, discrete events, disconnected from teachers' practice and perceived as empty measures of compliance. There is limited research that investigates alternative professional learning approaches that leverage online social technologies and involve teacher agency, collaboration and active participation. Therefore, this research explores teachers' experience of professional learning through personal learning networks (PLNs). The findings have supported the development of a new model of learning as a connected professional, which makes a significant contribution to theory and practice in the emerging field of professional networks and learning, enabled through the affordances of social technologies.
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de, Albuquerque Melo Cassio. "Scaffolding of self-regulated learning in social networks." Universidade Federal de Pernambuco, 2010. https://repositorio.ufpe.br/handle/123456789/2223.

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Анотація:
Made available in DSpace on 2014-06-12T15:55:34Z (GMT). No. of bitstreams: 2 arquivo2267_1.pdf: 3921351 bytes, checksum: e41bb7565ab8ea4825759082c478c58b (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2010
Conselho Nacional de Desenvolvimento Científico e Tecnológico
Scaffoldings são apoios a aprendizes novatos através de uma simplificação do contexto de aprendizagem. Estes apoios são gradualmente removidos à medida que os alunos desenvolvem estratégias autônomas de aprendizagem (processo conhecido como fading ). Em ambientes de aprendizagem online, os scaffoldings podem ser implementados através de um conjunto de funcionalidades que promovam o planejamento de objetivos, auto-monitoramento, auto-avaliação, estratégias de aprendizado, procura de ajuda, e planejamento e gerenciamento do tempo. Enquanto scaffoldings do Aprendizado Auto- Regulado (AAR) têm sido discutidos em ambientes tradicionais de aprendizagem, as redes sociais online têm pouca ou nenhuma atenção neste domínio. O presente estudo é focado em scaffoldings do AAR em redes sociais, pois acreditamos que as redes sociais têm estilos de interação que influenciam mais notadamente as habilidades individuais e coletivas do AAR. Nós coletamos itens do AAR no estado-da-arte sobre metacognição e aprendizagem, definimos suas metas e sugerimos scaffoldings para o AAR em redes sociais. Cada item foi extraído a partir de vários estudos na literatura sobre Computer-Supported Collaborative Learning (CSCL) e o AAR; dados quantitativos e qualitativos a partir de relatórios; estudos de caso; questionários AAR e outros recursos mencionados ao longo deste trabalho. Nós implementamos os mecanismos de scaffoldings na rede social Rede Social Educacional (Redu). Redu oferece um espaço de trabalho compartilhado, onde os alunos são incentivados a publicar os seus documentos e notas de aula, enquanto o professor fornece documentos e faz comentários para a classe. Os mecanismos de scaffoldings sugeridos incluem: 1) Blogs, comentários e fórum; 2) Instruções sobre tarefas, 3) Ajuda contextual e políticas de uso; 4) Perguntas para reflexão; 5) Fluxo de atividades; 6) Criação e compartilhamento de recursos; 7) Perfil de aprendizagem, 8) Notas de aula; 9) Discussões e assitência par-a-par; 10) Exames formativos; 11) Feedback de desempenho e orientação; 12) Mecanismos de recompensa e; 13) Visualização de informação. Em resumo, este trabalho sugere que uma rede social de aprendizagem pode ser concebida para melhorar o aprendizado auto-regulado através de mecanismos de scaffoldings apropriados
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Fidalgo, Patrícia Seferlis Pereira. "Learning networks and moodle use in online courses: a social network analysis study." Doctoral thesis, Faculdade de Ciências e Tecnologia, 2012. http://hdl.handle.net/10362/8862.

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Dissertação para obtenção do Grau de Doutor em Ciências da Educação Especialidade em Tecnologias, Redes e Multimédia na Educação e Formação
This research presents a case study on the interactions between the participants of the forums of four online undergraduate courses from the perspective of social network analysis (SNA). Due to lack of studies on social networks in online learning environments in higher education in Portugal we have choose a qualitative structural analysis to address this phenomenon. The context of this work was given by the new experiences in distance education (DE) that many institutions have been making. Those experiences are a function of the changes in educational paradigms and due to a wider adoption of Information and Communication Technologies (ICT) from schools as well as to the competitive market. Among the technologies adopted by universities are the Learning Management Systems (LMSs) that allow recording, storing and using large amounts of relational data about their users and that can be accessed through Webtracking. We have used this information to construct matrices that allowed the SNA. In order to deepen knowledge about the four online courses we were studying we have also collect data with questionnaires and interviews and we did a content analysis to the participations in the forums. The three main sources of data collection led us to three types of analysis: SNA, statistical analysis and content analysis. These types of analysis allowed, in turn, a three-dimensional study on the use of the LMS: 1) the relational dimension through the study of forums networks and patterns of interaction among participants in those networks, 2) the dimension relative to the process of teaching and learning through content analysis of the interviews; 3) and finally the dimension related to the participants' perceptions about the use of LMS for educational purposes and as a platform for creating social networks through the analysis of questionnaires.With the results obtained we carried out a comparative study between the four courses and tried to present a reflection on the Online Project of the University as well as possible causes that led to what was observed. We have finished with a proposal of a framework for studying the relational aspects of online learning networks aimed at possible future research in this area.
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Laghos, Andrew. "Assessing the evolution of social networks in e-learning." Thesis, City University London, 2007. http://openaccess.city.ac.uk/8504/.

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This research provides a new approach to analysing the evolutionary nature of social networks that are formed around computer-mediated-communication (CMC) in e-Learning courses. Aspects that have been studied include Online Communities and student communication e-Learning environments. The literature review performed identified weaknesses in the current methods of analyzing CMC activity. A proposed unfied analysis framework (FESNeL) was developed which enables us to explore students' interactions and to test a number of hypotheses. The creation of the framework is discussed in detail along with its major components (e.g. Social Network Analysis and Human Computer Interaction techniques). Furthermore this framework was tested on a case study of an online Language Learning Course. The novelty of this study lies in the investigation of the evolution of online social networks, filling a gap in current research which focuses on specific time stamps (usually the end of the course) when analysing CMC. In addition, the framework uses both qualitative and quantitative methods allowing for a complete assessment of such social networks. Results indicate that FESNeL is a useful methodological framework that can be used to assess student communication and interaction in web-based courses. In addition, through the use of this framework, several characteristic hypotheses were tested which provided useful insights about the nature of learning and communicating online.
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Harris, Lisa, and Lisa Harris@rmit edu au. "Electronic Classroom, Electronic Community: Virtual Social Networks and Student Learning." RMIT University. Global Studies, Social Science and Planning, 2008. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080717.144715.

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The capacity for online learning environments to provide quality learning experiences for students has been the focus of much speculation and debate in the higher education sector from the late 1990s to the present day. In this area, 'quality' has become synonymous with engaging students in a learning community. This study reports on a qualitative research project designed to explore the significance of community for students when they study in online learning environments. This project used three case studies to explore tertiary students' thoughts and expectations about community in the online environment. The research was constructed iteratively. Data from the initial case suggested the need to explore the relationship between the constructed online learning environment and the development of learning communities or what I have termed Social Learning Support Networks (SLSN). To explore this issue further, the project was expanded and subsequent cases were chosen that included fundamentally different types of online learning environments. The project had two significant results. Firstly, students not only confirmed popular educational theories on the value of learning communities, but also described how this form of social connection might practically benefit their learning. Secondly, the project found that certain forms of synchronous online environments provided enhanced opportunities for students to form social connections that supported their learning. This project provides new evidence of the benefit of community for students studying online and argues that future online learning environments should be shaped by five key principles designed to foster a sense of social connection between students.
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Книги з теми "Social Learning Networks"

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Nie, Liqiang, Xuemeng Song, and Tat-Seng Chua. Learning from Multiple Social Networks. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-031-02300-2.

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Aggarwal, Manasvi, and M. N. Murty. Machine Learning in Social Networks. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4022-0.

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Rezvanian, Alireza, Behnaz Moradabadi, Mina Ghavipour, Mohammad Mehdi Daliri Khomami, and Mohammad Reza Meybodi. Learning Automata Approach for Social Networks. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10767-3.

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Özyer, Tansel, and Reda Alhajj, eds. Machine Learning Techniques for Online Social Networks. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89932-9.

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1973-, Lytras Miltiadis D., Tennyson Robert D, and Pablos Patricia Ordonez de, eds. Knowledge networks: The social software perspective. Hershey, PA: Information Science Reference, 2009.

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6

Socializing the classroom: Social networks and online learning. Lanham: Lexington Books, 2012.

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7

Digital literacies: Social learning and classroom practices. Los Angeles: SAGE Publications, 2009.

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Dennen, Vanessa L., and Jennifer B. Myers. Virtual professional development and informal learning via social networks. Hershey PA: Information Science Reference, 2012.

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9

Kurata, Naomi. Foreign language learning and use: Interaction in informal social networks. New York, NY: Continuum International Pub. Group, 2010.

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10

Networked learning: An educational paradigm for the age of digital networks. Cham, Switzerland: Springer, 2015.

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Частини книг з теми "Social Learning Networks"

1

Haythornthwaite, Caroline. "Learning Networks." In Encyclopedia of Social Network Analysis and Mining, 1–8. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4614-7163-9_67-1.

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Haythornthwaite, Caroline. "Learning Networks." In Encyclopedia of Social Network Analysis and Mining, 785–93. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-6170-8_67.

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Haythornthwaite, Caroline. "Learning Networks." In Encyclopedia of Social Network Analysis and Mining, 1165–73. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7131-2_67.

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4

Aggarwal, Manasvi, and M. N. Murty. "Deep Learning." In Machine Learning in Social Networks, 35–66. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-33-4022-0_3.

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Aggarwal, Manasvi, and M. N. Murty. "Representations of Networks." In Machine Learning in Social Networks, 7–33. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-33-4022-0_2.

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6

Ha, Quang-Vinh, Bao-Dai Nguyen-Hoang, and Minh-Quoc Nghiem. "Lifelong Learning for Cross-Domain Vietnamese Sentiment Classification." In Computational Social Networks, 298–308. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42345-6_26.

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Clanon, Jeff. "The Relevance of Organizational Learning for High Performing Social Networks." In Dynamic Learning Networks, 43–56. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0251-1_3.

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Utz, Sonja, and Ana Levordashka. "Knowledge Networks in Social Media." In The Psychology of Digital Learning, 171–86. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49077-9_9.

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Jones, Karen, Rhian Pole, Stephen Hole, and James Williams. "Social Networks for Learning: Breaking Through the Walled Garden of the VLE." In Computational Social Networks, 417–44. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4048-1_17.

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Zinke, Christian, Kyrill Meyer, Julia Friedrich, and Leopold Reif. "Digital Social Learning – Collaboration and Learning in Enterprise Social Networks." In Advances in Intelligent Systems and Computing, 3–11. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60018-5_1.

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Тези доповідей конференцій з теми "Social Learning Networks"

1

Huang, Yin-Fu, Jung-Sheng Liu, and Po-Hong Chen. "Social Content Mining in Social Networks." In 2019 International Conference on Machine Learning and Data Engineering (iCMLDE). IEEE, 2019. http://dx.doi.org/10.1109/icmlde49015.2019.00021.

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Poquet, Oleksandra, Liubov Tupikina, and Marc Santolini. "Are forum networks social networks?" In LAK '20: 10th International Conference on Learning Analytics and Knowledge. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3375462.3375531.

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3

"Part I: Social networks and social learning." In 2017 IEEE International Conference on Agents (ICA). IEEE, 2017. http://dx.doi.org/10.1109/agents.2017.8015290.

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"Part VI: Social Networks and Social Learning." In 2018 IEEE International Conference on Agents (ICA). IEEE, 2018. http://dx.doi.org/10.1109/agents.2018.8460077.

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Xu, Hao, and YuTao Bie. "Social JLU: Towards Building Social Learning Networks." In 2013 the International Conference on Education Technology and Information Systems (ICETIS 2013). Paris, France: Atlantis Press, 2013. http://dx.doi.org/10.2991/icetis-13.2013.211.

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Vegliante, Rosa, Sergio Miranda, and Marta De Angelis. "SOCIAL NETWORKS IN LEARNING PROCESSES." In 11th annual International Conference of Education, Research and Innovation. IATED, 2018. http://dx.doi.org/10.21125/iceri.2018.0488.

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Pereira, Fabiola S. F., Gina M. B. Oliveira, and João Gama. "User Preference Dynamics on Evolving Social Networks - Learning, Modeling and Prediction." In XXV Simpósio Brasileiro de Sistemas Multimídia e Web. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/webmedia_estendido.2019.8129.

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Анотація:
The preferences adopted by individuals are constantly modified as these are driven by new experiences, natural life evolution and, mainly, influence from friends. Studying these temporal dynamics of user preferences has become increasingly important for personalization tasks. Online social networks contain rich information about social interactions and relations, becoming essential source of knowledge for the understanding of user preferences evolution. In this thesis, we investigate the interplay between user preferences and social networks over time. We use temporal networks to analyze the evolution of social relationships and propose strategies to detect changes in the network structure based on node centrality. Our findings show that we can predict user preference changes by just observing how her social network structure evolves over time.
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Derawi, Mohammad. "Distance Learning via Social Media." In 9th International Conference on Networks & Communications. Academy & Industry Research Collaboration Center (AIRCC), 2017. http://dx.doi.org/10.5121/csit.2017.71505.

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Abelairas-Etxebarria, Patricia, and Jon Mentxaka. "SOCIAL NETWORKS APPLIED TO UNIVERSITY." In International Conference on Education and New Learning Technologies. IATED, 2017. http://dx.doi.org/10.21125/edulearn.2017.0275.

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Caramutti, Rosalía, and Claudia Villaseca. "GENDER VIOLENCE IN SOCIAL NETWORKS." In 12th International Conference on Education and New Learning Technologies. IATED, 2020. http://dx.doi.org/10.21125/edulearn.2020.1056.

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Звіти організацій з теми "Social Learning Networks"

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Acemoglu, Daron, Munther Dahleh, Ilan Lobel, and Asuman Ozdaglar. Bayesian Learning in Social Networks. Cambridge, MA: National Bureau of Economic Research, May 2008. http://dx.doi.org/10.3386/w14040.

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Tian, Yuan, Maria Esther Caballero, and Brian Kovak. Social Learning along International Migrant Networks. Cambridge, MA: National Bureau of Economic Research, August 2020. http://dx.doi.org/10.3386/w27679.

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Pal, Chris, Xuerui Wang, and Andrew McCallum. Transfer Learning for Enhancing Information Flow in Organizations and Social Networks. Fort Belvoir, VA: Defense Technical Information Center, September 2007. http://dx.doi.org/10.21236/ada534353.

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4

Liebeskind, Julia Porter, Amalya Lumerman Oliver, Lynne Zucker, and Marilynn Brewer. Social Networks, Learning, and Flexibility: Sourcing Scientific Knowledge in New Biotechnology Firms. Cambridge, MA: National Bureau of Economic Research, October 1995. http://dx.doi.org/10.3386/w5320.

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Ornetzeder, Michael, ed. Habilitation Thesis: Sustainable Technology - Studies on User Innovation, Social Learning and Innovation Networks. Vienna: self, 2016. http://dx.doi.org/10.1553/ita-pa-mo-10-1.

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6

Chandrasekhar, Arun, Horacio Larreguy, and Juan Pablo Xandri. Testing Models of Social Learning on Networks: Evidence from a Lab Experiment in the Field. Cambridge, MA: National Bureau of Economic Research, August 2015. http://dx.doi.org/10.3386/w21468.

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7

Pastorelli1, Gianluca, Anastasia Costantini, and Samuel Barco Serrano. Social and green economies in the Mena region. Liège: CIRIEC, 2022. http://dx.doi.org/10.25518/ciriec.wp202203.

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Анотація:
This working paper is based both on literature review and interviews to key informants and stakeholders from or active in the region conducted in the framework of various initiatives: research projects, peer-learning activities, support to networks, policy makers and entrepreneurs. These initiatives have been leading us to connect with the SSE ecosystems in the area called “Southern Neighbourhood” in a European (centric?) perspective. The rationale behind this exercise is an attempt to share a light on the state of play of the public policies and international initiatives bound to support the social and green economies showcasing some examples we consider particularly relevant.
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Fafchamps, Marcel, Mans Soderbom, and Monique vanden Boogaart. Adoption with Social Learning and Network Externalities. Cambridge, MA: National Bureau of Economic Research, May 2016. http://dx.doi.org/10.3386/w22282.

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9

Pererva, Victoria V., Olena O. Lavrentieva, Olena I. Lakomova, Olena S. Zavalniuk, and Stanislav T. Tolmachev. The technique of the use of Virtual Learning Environment in the process of organizing the future teachers' terminological work by specialty. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3868.

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This paper studies the concept related to E-learning and the Virtual Learning Environment (VLE) and their role in organizing future teachers’ terminological work by specialty. It is shown the creation and use of the VLE is a promising approach in qualitative restructuring of future specialists’ vocation training, a suitable complement rather than a complete replacement of traditional learning. The concept of VLE has been disclosed; its structure has been presented as a set of components, such as: the Data-based component, the Communication-based, the Management-and-Guiding ones, and the virtual environments. Some VLE’s potential contributions to the organization of terminological work of future biology teachers’ throughout a traditional classroom teaching, an independent work, and during the field practices has been considered. The content of professionally oriented e-courses “Botany with Basis of Geobotany” and “Latin. Botany Terminology” has been revealed; the ways of working with online definer (guide), with UkrBIN National Biodiversity Information Network, with mobile apps for determining the plant species, with digital virtual herbarium, with free software have been shown. The content of students’ activity in virtual biological laboratories and during virtual tours into natural environment has been demonstrated. The explanations about the potential of biological societies in social networks in view of students’ terminology work have been given. According to the results of empirical research, the expediency of using VLEs in the study of professional terminology by future biology teachers has been confirmed.
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Cuesta-Valiño, Pedro. Happiness Management. A Social Well-being multiplier. Social Marketing and Organizational Communication. Edited by Rafael Ravina-Ripoll. Editorial Universidad de Sevilla, 2022. http://dx.doi.org/10.12795/2022.happiness-management.

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Анотація:
On behalf of the Happiness University Network, we are pleased to present here an extract of the information concerning the universities working to generate the diffusion of this network. Specifically, with the support of the University of Salamanca and the Pontifical University of Salamanca the aim is to create a friendly and working environment for the dissemination and discussion of the latest scientific and practical developments in the fields of happiness economics, corporate wellbeing, happiness management and organisational communication. It also offers an opportunity for productive encounters, the promotion of collaborative projects and the encouragement of international networking. Below you will find papers related to: Economics of happiness, happiness management, organisational communication, welfare state economics, consumer happiness, leadership, social marketing, happiness management and SDGs, happiness management in human resource strategies, learning and competencies in happiness management, learning and competencies in social well-being, measurement and indicators of happiness and well-being and history of welfare economics.
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