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Journal articles on the topic 'Social Learning Networks'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

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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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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7

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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8

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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9

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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10

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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11

Knoepfel, Peter, and Ingrid Kissling-Näf. "Social Learning in Policy Networks." Policy & Politics 26, no. 3 (July 1, 1998): 343–67. http://dx.doi.org/10.1332/030557398782213638.

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12

Ko, Pei-Chun. "INVESTIGATING SOCIAL NETWORKS OF OLDER SINGAPOREAN LEARNERS: THE MIXED-METHODS SOCIAL NETWORK APPROACH." Innovation in Aging 3, Supplement_1 (November 2019): S754. http://dx.doi.org/10.1093/geroni/igz038.2768.

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Abstract Lifelong learning has been regarded as an important factor of promoting active engagement in later life for researchers and policy makers. Most of the studies tend to illustrate old learners as a homogeneous and self-resilient group of people to engage in lifelong learning. Few studies address older learners’ social capital in affecting their decision of engagement and in sustaining their motivation. The study documented the existing social networks of older Singaporeans in lifelong learning programs and illustrated how social networks contributed their participation in learning. The mixed methods consist of in-depth interviews and two network instruments (Name Generator and Position Generator) based on 30 older Singaporeans (between 50 and 79 years old) who attended lifelong learning courses between 2016 and 2018. Interviews are transcribed and analyzed. The network instruments of are quantified and visualized. The findings show that older learners’ networks included a mixture of social ties from family and friends. Learners’ closeness with network members and their living arrangement with them influenced learners’ involvement in learning and future planning. Single respondents who had more non-kin members in the networks reported to be more active due to their weak ties. Overlapping networks among couple learners increase the spousal support for learning. Learners who had wider ranges of social resources are associated with their interest in learning activities. The study suggests that advocating lifelong learning needs to take older adults’ networks into considerations as networks represent the social forces that influence their decisions and motivations.

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Haythornthwaite, Caroline. "Learning, connectivity and networks." Information and Learning Sciences 120, no. 1/2 (January 14, 2019): 19–38. http://dx.doi.org/10.1108/ils-06-2018-0052.

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PurposeThis is paper is concerned with the learning outcomes associated with connectivity through online networks, open online exchange and wider changes associated with contemporary information practices. The theme of connectivity is used here to capture both the detailed specificity of relations that define networks of learners and the ambient effect of wide accessibility to resources and people through open, online forums.Design/methodology/approachThe paper follows the idea of a network from the ground up, outlining the social network perspective as a way to consider the foundational bases of learning and networks, as well as the effect of ambient influence. The paper addresses the ways learning may be viewed as a social network relation, an interpersonal relationship and an outcome of interaction and connectivity, and how network connectivity can be used as input for design for learning.FindingsThe paper presents a range of perspectives and studies that view learning from a social network and connectivity perspective, emphasizing both the person-to-person connectivity of a learning tie and the impact of contemporary data and information sharing through the dynamics of open contributory practice.Practical implicationsThe outcome of connectivity in the service of learning is bound up with digital information practices, including individual practices of search, retrieval, participation, knowledge dissemination, knowledge construction and more. This paper provides a network perspective on learning relations that accommodates analysis in online and offline environments, but incorporates attention to the open, online retrieval and contributory practices that now influence learning practices and which may support design of new learning environments.Originality/valueThis paper offers insight into the way social networks and connectivity combine to show network relations, relationships, outcomes and design input at the actor, network and societal levels.

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Tamura, Kohei, Yutaka Kobayashi, and Yasuo Ihara. "Evolution of individual versus social learning on social networks." Journal of The Royal Society Interface 12, no. 104 (March 2015): 20141285. http://dx.doi.org/10.1098/rsif.2014.1285.

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A number of studies have investigated the roles played by individual and social learning in cultural phenomena and the relative advantages of the two learning strategies in variable environments. Because social learning involves the acquisition of behaviours from others, its utility depends on the availability of ‘cultural models’ exhibiting adaptive behaviours. This indicates that social networks play an essential role in the evolution of learning. However, possible effects of social structure on the evolution of learning have not been fully explored. Here, we develop a mathematical model to explore the evolutionary dynamics of learning strategies on social networks. We first derive the condition under which social learners (SLs) are selectively favoured over individual learners in a broad range of social network. We then obtain an analytical approximation of the long-term average frequency of SLs in homogeneous networks, from which we specify the condition, in terms of three relatedness measures, for social structure to facilitate the long-term evolution of social learning. Finally, we evaluate our approximation by Monte Carlo simulations in complete graphs, regular random graphs and scale-free networks. We formally show that whether social structure favours the evolution of social learning is determined by the relative magnitudes of two effects of social structure: localization in competition, by which competition between learning strategies is evaded, and localization in cultural transmission, which slows down the spread of adaptive traits. In addition, our estimates of the relatedness measures suggest that social structure disfavours the evolution of social learning when selection is weak.

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Bektaş, Çetin, and Rima Fayad. "Learning framework using social media networks." Global Journal of Information Technology: Emerging Technologies 7, no. 1 (June 27, 2017): 8–13. http://dx.doi.org/10.18844/gjit.v7i1.1933.

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Abstract Social media networks are being used heavily by people from different social, economic, and educational backgrounds all over the world. University, college, and high-school students constitute a main faction of social media network users. In this study, a framework for student learning using social media network environment is developed. The framework is founded in the self-determination theory (SDT). The self-determination theory is one of the important theories of motivation and personality. Its focus is geared towards both intrinsic and extrinsic motivation issues. It addresses three universal innate and psychological needs: competence, autonomy, and psychological relatedness. A person’s social environment necessitates caring for these three needs in order for the person to actualize their potential, function and grow optimally. In addition to creating the social environment that caters for students’ psychological needs, for a new framework of learning using social media to be successfully adopted by students it needs to address their cognitive, emotional and contextual interests. Towards this end, this study explores and founds the conceptual grounds of a social media learning framework. Keywords: Social media network, learning, self-determination theory, motivation, competence, autonomy, relatedness.

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Janicik, Gregory A., and Richard P. Larrick. "Social Network Schemas and the Learning of Incomplete Networks." Journal of Personality and Social Psychology 88, no. 2 (2005): 348–64. http://dx.doi.org/10.1037/0022-3514.88.2.348.

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Lemay, David, Tenzin Doleck, and Christopher Brinton. "SLOAN: Social Learning Optimization Analysis of Networks." International Review of Research in Open and Distributed Learning 23, no. 4 (November 1, 2022): 93–122. http://dx.doi.org/10.19173/irrodl.v23i4.6484.

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Online discussion research has mainly been conducted using case methods. This article proposes a method for comparative analysis based on network metrics such as information entropy and global network efficiency as more holistic measures characterizing social learning group dynamics. We applied social learning optimization analysis of networks (SLOAN) to a data set consisting of Coursera courses from a range of disciplines. We examined the relationship of discussion forum uses and measures of network efficiency, characterized by the information flow through the network. Discussion forums vary greatly in size and in use. Courses with a greater prevalence of subject-related versus procedural talk differed significantly in seeking but not disseminating behaviors in massive open online course discussion forums. Subject-related talk was related to higher network efficiency and had higher seeking and disseminating scores overall. We discuss the value of SLOAN for social learning and argue for the experimental study of online discussion optimization using a discussion post recommendation system for maximizing social learning.

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18

Shepherd, Patrick, and Judy Goldsmith. "A Reinforcement Learning Approach to Strategic Belief Revelation with Social Influence." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (April 3, 2020): 13734–35. http://dx.doi.org/10.1609/aaai.v34i10.7139.

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The study of social networks has increased rapidly in the past few decades. Of recent interest are the dynamics of changing opinions over a network. Some research has investigated how interpersonal influence can affect opinion change, how to maximize/minimize the spread of opinion change over a network, and recently, if/how agents can act strategically to effect some outcome in the network's opinion distribution. This latter problem can be modeled and addressed as a reinforcement learning problem; we introduce an approach to help network agents find strategies that outperform hand-crafted policies. Our preliminary results show that our approach is promising in networks with dynamic topologies.

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Sense, Andrew, and Matthew Pepper. "Social Networks, Social Learning and Service Systems Improvement." Asia Pacific Journal of Public Administration 34, no. 1 (June 2012): 95–111. http://dx.doi.org/10.1080/23276665.2012.10779389.

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N. Fardeeva, Irina. "Social Networks as a Learning Tool." International Journal of Psychosocial Rehabilitation 23, no. 1 (March 30, 2019): 137–41. http://dx.doi.org/10.37200/ijpr/v23i1/pr190221.

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21

Chang, Bo. "Social Networks in the Learning Community." International Journal of Virtual and Personal Learning Environments 12, no. 1 (January 2022): 1–16. http://dx.doi.org/10.4018/ijvple.295308.

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The purpose of this study is to explore how social networks impact learners’ knowledge sharing in the context of a learning community. The findings indicate that the various networks collaboratively support learners’ knowledge sharing in a local community. A better social position gives rise to more networks and correspondingly more knowledge accessible to learners. The between-group ties such as residential committees are crucial for knowledge exchange through connecting various social networks. Social isolation impacts learners’ interactions with others. However, more networks do not necessarily lead to more advanced knowledge acquisition. Accessing social networks cannot guarantee that learners will successfully access substantial knowledge in other fields. Social networks targeting learners’ need influence learners’ interest in social networks.

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Tian, Yuan, Maria Esther Caballero, and Brian K. Kovak. "Social learning along international migrant networks." Journal of Economic Behavior & Organization 195 (March 2022): 103–21. http://dx.doi.org/10.1016/j.jebo.2021.12.028.

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23

Ammar, Brika, and Nader Fahima. "Digital Social Networks Dedicated to Learning." International Journal for Infonomics 5, no. 3/4 (September 1, 2012): 640–45. http://dx.doi.org/10.20533/iji.1742.4712.2012.0073.

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González-Avella, Juan Carlos, Victor M. Eguíluz, Matteo Marsili, Fernado Vega-Redondo, and Maxi San Miguel. "Threshold Learning Dynamics in Social Networks." PLoS ONE 6, no. 5 (May 27, 2011): e20207. http://dx.doi.org/10.1371/journal.pone.0020207.

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Molavi, Pooya, Ceyhun Eksin, Alejandro Ribeiro, and Ali Jadbabaie. "Learning to Coordinate in Social Networks." Operations Research 64, no. 3 (June 2016): 605–21. http://dx.doi.org/10.1287/opre.2015.1381.

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Sun, Jun, Steffen Staab, and Jerome Kunegis. "Understanding Social Networks Using Transfer Learning." Computer 51, no. 6 (June 2018): 52–60. http://dx.doi.org/10.1109/mc.2018.2701640.

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Mossel, Elchanan, Allan Sly, and Omer Tamuz. "Asymptotic learning on Bayesian social networks." Probability Theory and Related Fields 158, no. 1-2 (February 13, 2013): 127–57. http://dx.doi.org/10.1007/s00440-013-0479-y.

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Liu, Lu, Jie Tang, Jiawei Han, and Shiqiang Yang. "Learning influence from heterogeneous social networks." Data Mining and Knowledge Discovery 25, no. 3 (March 3, 2012): 511–44. http://dx.doi.org/10.1007/s10618-012-0252-3.

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Deng, Liqiong, and Marshall Scott Poole. "Learning through ICT-enabled social networks." International Journal of Information Technology and Management 7, no. 4 (2008): 374. http://dx.doi.org/10.1504/ijitm.2008.018655.

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Voitko, Oksana. "LEARNING FOREIGN LANGUAGES USING SOCIAL NETWORKS." Ukrainian Educational Journal, no. 1 (2019): 57–68. http://dx.doi.org/10.32405/2411-1317-2019-1-57-68.

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Bartlett‐Bragg, Anne. "Reframing practice: creating social learning networks." Development and Learning in Organizations: An International Journal 23, no. 4 (June 26, 2009): 16–20. http://dx.doi.org/10.1108/14777280910970747.

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32

Yang, Qiang, Zhi-Hua Zhou, Wenji Mao, Wei Li, and Nathan Nan Liu. "Social Learning." IEEE Intelligent Systems 25, no. 4 (July 2010): 9–11. http://dx.doi.org/10.1109/mis.2010.103.

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Carchiolo, Vincenza, Christian Cavallo, Marco Grassia, Michele Malgeri, and Giuseppe Mangioni. "Link Prediction in Time Varying Social Networks." Information 13, no. 3 (March 1, 2022): 123. http://dx.doi.org/10.3390/info13030123.

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Predicting new links in complex networks can have a large societal impact. In fact, many complex systems can be modeled through networks, and the meaning of the links depend on the system itself. For instance, in social networks, where the nodes are users, links represent relationships (such as acquaintance, friendship, etc.), whereas in information spreading networks, nodes are users and content and links represent interactions, diffusion, etc. However, while many approaches involve machine learning-based algorithms, just the most recent ones account for the topology of the network, e.g., geometric deep learning techniques to learn on graphs, and most of them do not account for the temporal dynamics in the network but train on snapshots of the system at a given time. In this paper, we aim to explore Temporal Graph Networks (TGN), a Graph Representation Learning-based approach that natively supports dynamic graphs and assigns to each event (link) a timestamp. In particular, we investigate how the TGN behaves when trained under different temporal granularity or with various event aggregation techniques when learning the inductive and transductive link prediction problem on real social networks such as Twitter, Wikipedia, Yelp, and Reddit. We find that initial setup affects the temporal granularity of the data, but the impact depends on the specific social network. For instance, we note that the train batch size has a strong impact on Twitter, Wikipedia, and Yelp, while it does not matter on Reddit.

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Li, Chaozhuo, Senzhang Wang, Yukun Wang, Philip Yu, Yanbo Liang, Yun Liu, and Zhoujun Li. "Adversarial Learning for Weakly-Supervised Social Network Alignment." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 996–1003. http://dx.doi.org/10.1609/aaai.v33i01.3301996.

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Nowadays, it is common for one natural person to join multiple social networks to enjoy different kinds of services. Linking identical users across multiple social networks, also known as social network alignment, is an important problem of great research challenges. Existing methods usually link social identities on the pairwise sample level, which may lead to undesirable performance when the number of available annotations is limited. Motivated by the isomorphism information, in this paper we consider all the identities in a social network as a whole and perform social network alignment from the distribution level. The insight is that we aim to learn a projection function to not only minimize the distance between the distributions of user identities in two social networks, but also incorporate the available annotations as the learning guidance. We propose three models SNNAu, SNNAb and SNNAo to learn the projection function under the weakly-supervised adversarial learning framework. Empirically, we evaluate the proposed models over multiple datasets, and the results demonstrate the superiority of our proposals.

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Cankaya, Serkan, and Eyup Yunkul. "Learner Views about Cooperative Learning in Social Learning Networks." International Education Studies 11, no. 1 (December 22, 2017): 52. http://dx.doi.org/10.5539/ies.v11n1p52.

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The purpose of this study was to reveal the attitudes and views of university students about the use of Edmodo as a cooperative learning environment. In the research process, the students were divided into groups of 4 or 5 within the scope of a course given in the department of Computer Education and Instructional Technology. For each group, Edmodo small groups were formed, and the students used these Edmodo small groups to share and communicate with their group friends in relation to the group tasks assigned to them within the scope of the study. This process lasted one academic term. As the data collection tool, an online cooperative learning attitude scale and a semi-structured interview form were used. At the end of the academic term, 15 students were interviewed about their cooperative learning experiences within the scope of the course as well as about how they made use of Edmodo in the process. The results demonstrated that the students had positive attitudes towards online cooperative learning. The findings obtained via the qualitative data analysis were examined under the headings of “social networks used”, “preferences of forming groups”, “communication within group” and “views about the courses executed via Edmodo”.

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Dlouhá, Jana, Andrew Barton, Svatava Janoušková, and Jiří Dlouhý. "Social learning indicators in sustainability-oriented regional learning networks." Journal of Cleaner Production 49 (June 2013): 64–73. http://dx.doi.org/10.1016/j.jclepro.2012.07.023.

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Zhou, Nan, Junping Du, Zhe Xue, Chong Liu, and Jinxuan Li. "Cross-Modal Search for Social Networks via Adversarial Learning." Computational Intelligence and Neuroscience 2020 (July 11, 2020): 1–12. http://dx.doi.org/10.1155/2020/7834953.

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Cross-modal search has become a research hotspot in the recent years. In contrast to traditional cross-modal search, social network cross-modal information search is restricted by data quality for arbitrary text and low-resolution visual features. In addition, the semantic sparseness of cross-modal data from social networks results in the text and visual modalities misleading each other. In this paper, we propose a cross-modal search method for social network data that capitalizes on adversarial learning (cross-modal search with adversarial learning: CMSAL). We adopt self-attention-based neural networks to generate modality-oriented representations for further intermodal correlation learning. A search module is implemented based on adversarial learning, through which the discriminator is designed to measure the distribution of generated features from intramodal and intramodal perspectives. Experiments on real-word datasets from Sina Weibo and Wikipedia, which have similar properties to social networks, show that the proposed method outperforms the state-of-the-art cross-modal search methods.

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Zhang, Xiaoxian, Jianpei Zhang, and Jing Yang. "Large-scale dynamic social data representation for structure feature learning." Journal of Intelligent & Fuzzy Systems 39, no. 4 (October 21, 2020): 5253–62. http://dx.doi.org/10.3233/jifs-189010.

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The problems caused by network dimension disasters and computational complexity have become an important issue to be solved in the field of social network research. The existing methods for network feature learning are mostly based on static and small-scale assumptions, and there is no modified learning for the unique attributes of social networks. Therefore, existing learning methods cannot adapt to the dynamic and large-scale of current social networks. Even super large scale and other features. This paper mainly studies the feature representation learning of large-scale dynamic social network structure. In this paper, the positive and negative damping sampling of network nodes in different classes is carried out, and the dynamic feature learning method for newly added nodes is constructed, which makes the model feasible for the extraction of structural features of large-scale social networks in the process of dynamic change. The obtained node feature representation has better dynamic robustness. By selecting the real datasets of three large-scale dynamic social networks and the experiments of dynamic link prediction in social networks, it is found that DNPS has achieved a large performance improvement over the benchmark model in terms of prediction accuracy and time efficiency. When the α value is around 0.7, the model effect is optimal.

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Mueller-Frank, Manuel, and Claudia Neri. "A general analysis of boundedly rational learning in social networks." Theoretical Economics 16, no. 1 (2021): 317–57. http://dx.doi.org/10.3982/te2974.

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We analyze boundedly rational learning in social networks within binary action environments. We establish how learning outcomes depend on the environment (i.e., informational structure, utility function), the axioms imposed on the updating behavior, and the network structure. In particular, we provide a normative foundation for quasi‐Bayesian updating, where a quasi‐Bayesian agent treats others' actions as if they were based only on their private signal. Quasi‐Bayesian updating induces learning (i.e., convergence to the optimal action for every agent in every connected network) only in highly asymmetric environments. In all other environments, learning fails in networks with a diameter larger than 4. Finally, we consider a richer class of updating behavior that allows for nonstationarity and differential treatment of neighbors' actions depending on their position in the network. We show that within this class there exist updating systems that induce learning for most networks.

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Tosun, Nilgün. "Social Networks as a Learning and Teaching Environment and Security in Social Networks." Journal of Education and Training Studies 6, no. 11a (November 29, 2018): 194. http://dx.doi.org/10.11114/jets.v6i11a.3817.

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Technology is in a constantly evolving and changing structure since the existence of mankind. Because of this dynamic structure, technology fulfills a number of functions such as facilitating people's lives, time, profit from work, profit from cost, making life more enjoyable. At the same time, technology is used in all areas of life, and it also causes changes and transformations in these areas. Education is one of these areas, perhaps the most important, that technology affects. The hunter society, written with nails, made an important step with the paper's invention, and the written documents were moved from the stones to the books. The invention of computers and the internet has also opened an important milestone in human history and education. In the beginning, the course contents loaded on storage units such as floppy disks, CDs, DVDs were used by the students and teachers, computers were included in the education systems. During periods when we have not yet met with the internet, computer-assisted education has found a large place in many educational institutions and in the curriculum of education level. The development of information Technologies led to widespread use of the internet over time, and shortly thereafter examples of use in education began to increase. Computer-assisted education has also led to the rapid transition of education through internet-supported education, along with the different demands of the network society's individuals. Users are not satisfied with the internet environments where only reading authority is available, and more and more active and interacting requests have come to the agenda. Beyond reading, social networks that make it possible to comment, create content, upload/share/view images, upload video/audio files, and make video, text and voice calls have become popular for users. Social networking platforms where users interact with the environment or with other users in the environment have been attracted by the diversity of user profiles, the usage rates and durations, and the easy and versatility of accessibility. Because of these features, studies on the use of social networks in the field of education to support learning and teaching have also been accelerated and diversified. Social networks can also contain some security issues because they are huge platforms where billions of users are together. Having information about security issues as little as possible, what to do when they are encountered is important for the continuity of learning and teaching. The aim of this study is to demonstrate the importance of social networks, education, learning and teaching influences, possible security threats to be encountered in social networks, and measures to be taken. It is hoped that working in this context will shed light on the work of learners, teachers and decision makers on the subject.

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Banks Pidduck, Anne. "18. Electronic Social Networks, Teaching, and Learning." Collected Essays on Learning and Teaching 3 (June 13, 2011): 106. http://dx.doi.org/10.22329/celt.v3i0.3248.

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This paper explores the relationship between electronic social networks, teaching, and learning. Previous studies have shown a strong positive correlation between student engagement and learning. By extending this work to engage instructors and add an electronic component, our study shows possible teaching improvement as well. In particular, enthusiastic teachers and learners have a more positive attitude toward their work and studies.

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Kohler, Hans-Peter. "Learning in Social Networks and Contraceptive Choice." Demography 34, no. 3 (August 1997): 369. http://dx.doi.org/10.2307/3038290.

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Acemoglu, Daron, Munther Dahleh, Ilan Lobel, and Asuman Ozdaglar. "Learning Over Complex Social Networks [Extended Abstract]." IFAC Proceedings Volumes 42, no. 10 (2009): 770–73. http://dx.doi.org/10.3182/20090706-3-fr-2004.00128.

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Gulbis, Rūdolfs. "Social Networks in Regulation Learning Process Quality." SOCIETY, INTEGRATION, EDUCATION. Proceedings of the International Scientific Conference 1 (May 9, 2015): 271. http://dx.doi.org/10.17770/sie2012vol1.47.

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<p>Successful web-based learning depends on several key factors. These include the quality of the learning tools, the motivation of users, and the credibility of the learning materials as well as their significance in the eyes of users. It is possible to find numerous examples of web-based lifelong learning approaches that can be viewed on social networks and discussion forums. The research for this study included designing several blog-based discussion forums where user activities were logged and the results were compared to usercompleted questionnaires for similar activities. Several user behaviour identification models were designed based on algorithmically computed tallies of user behaviours. These models are currently being applied for further study to measure user behaviours in virtual learning environments.</p>

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Ravazzi, Chiara, Roberto Tempo, and Fabrizio Dabbene. "Learning Influence Structure in Sparse Social Networks." IEEE Transactions on Control of Network Systems 5, no. 4 (December 2018): 1976–86. http://dx.doi.org/10.1109/tcns.2017.2781367.

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Kolokytha, Eleftheria, Sofia Loutrouki, Stavros Valsamidis, and Giannoula Florou. "Social Media Networks as a Learning Tool." Procedia Economics and Finance 19 (2015): 287–95. http://dx.doi.org/10.1016/s2212-5671(15)00029-5.

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Lobel, Ilan, and Evan Sadler. "Information diffusion in networks through social learning." Theoretical Economics 10, no. 3 (September 2015): 807–51. http://dx.doi.org/10.3982/te1549.

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Tissington, Patrick, and Carl Senior. "Social networks: a learning tool for teams?" British Journal of Educational Technology 42, no. 5 (September 10, 2010): E89—E90. http://dx.doi.org/10.1111/j.1467-8535.2010.01129.x.

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Acemoglu, Daron, and Asuman Ozdaglar. "Opinion Dynamics and Learning in Social Networks." Dynamic Games and Applications 1, no. 1 (October 1, 2010): 3–49. http://dx.doi.org/10.1007/s13235-010-0004-1.

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O’Donnell, Joseph. "Learning Communities, Social Networks, and Dark Matter." Journal of Cancer Education 26, no. 4 (October 30, 2011): 595–96. http://dx.doi.org/10.1007/s13187-011-0281-4.

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