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.
Повний текст джерелаL'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.
Sharad, Kumar. "Learning to de-anonymize social networks." Thesis, University of Cambridge, 2016. https://www.repository.cam.ac.uk/handle/1810/262750.
Повний текст джерелаRogers, Brian W. Palfrey Thomas R. "Learning and status in social networks /." Diss., Pasadena, Calif. : Caltech, 2006. http://resolver.caltech.edu/CaltechETD:etd-05262006-004112.
Повний текст джерелаMilán, Pau. "The Social economics of networks and learning." Doctoral thesis, Universitat Pompeu Fabra, 2016. http://hdl.handle.net/10803/393733.
Повний текст джерелаEsta 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.
Lobel, Ilan. "Social networks : rational learning and information aggregation." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/54232.
Повний текст джерелаThis 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.
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.
Повний текст джерелаde, Albuquerque Melo Cassio. "Scaffolding of self-regulated learning in social networks." Universidade Federal de Pernambuco, 2010. https://repositorio.ufpe.br/handle/123456789/2223.
Повний текст джерелаConselho 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
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.
Повний текст джерелаThis 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.
Laghos, Andrew. "Assessing the evolution of social networks in e-learning." Thesis, City University London, 2007. http://openaccess.city.ac.uk/8504/.
Повний текст джерелаHarris, 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.
Повний текст джерелаKhurshid, Imran, and Maciej Twardowski. "Interorganizational Networks as Emerging Learning Organizations." Thesis, Malmö universitet, Fakulteten för kultur och samhälle (KS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-22346.
Повний текст джерелаIsaacs, Lorraine Ann. "Social constructivism and collaborative learning in social networks: the case of an online masters programme in adult learning." Thesis, University of the Western Cape, 2013. http://hdl.handle.net/11394/5130.
Повний текст джерелаThis study investigates how students in an online Masters Programme in Adult Learning, although geographically dispersed used SNs to develop a supportive environment that enables collaborative learning to support and deepen their learning. Web 2.0 social software provided the tools for various forms of communication and information sharing amongst student within the social networks. This study shows how the use of Web 2.0 tools such as wikis, podcasts, blogs, chat rooms, social networking sites and email have the potential to expand the learning environment, increase participation and enrich the learning experience. Rapid technological developments transform our world into a global society which is ever changing and interconnected. The SNs as a learning environment in this technological driven global society is complex and not clearly defined; therefore it was not easy for me to understand the nature of the SNs as learning environment. The social nature of this study has therefore urged me to use social constructivism as a conceptual framework to gain insights into how students have used the social networks to develop a supportive environment that enables collaborative learning to support and deepen their learning. The utilisation of social constructivism as theoretical lens has helped to broaden my perceptions of the SNs as learning environment, to deepen my understanding of how learning occurs in the SNs and to comprehend learner behaviour within this pedagogical space. Social constructivists view learning as a social process in which people make sense of their world by interacting with other people (Doolittle & Camp, 1999). Social constructivists belief in the social nature of knowledge, and the belief that knowledge is the result of social interaction and language usage, and, thus, is a shared, rather than an individual, experience (Prawat & Floden, 1994). Furthermore, they believe that this social interaction always occurs within a socio-cultural context, resulting in knowledge that is bound to a specific time and place (Vygotsky, 1978).
Lam, Ho-Yu. "A learning approach to spam detection based on social networks /." View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?CSED%202007%20LAM.
Повний текст джерелаKent, Daniel N. "Essays on Machine Learning in International Conflict and Social Networks." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1594487914627131.
Повний текст джерелаArnell, Matilda, and Yuliya Bilinskaya. "Business opportunity creation through Social Networking Sites : A network perspective." Thesis, Uppsala universitet, Företagsekonomiska institutionen, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-179833.
Повний текст джерелаMina, Christakis. "Open Technological Standardization Processes Through Learning Networks." 京都大学 (Kyoto University), 2010. http://hdl.handle.net/2433/120839.
Повний текст джерелаSkyring, Carol A. "Learning in 140 characters : microblogging for professional learning." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/65854/1/Carol_Skyring_Thesis.pdf.
Повний текст джерелаEzzeddine, Diala. "A contribution to topological learning and its application in Social Networks." Thesis, Lyon 2, 2014. http://www.theses.fr/2014LYO22011/document.
Повний текст джерелаSupervised Learning is a popular field of Machine Learning that has made recent progress. In particular, many methods and procedures have been developed to solve the classification problem. Most classical methods in Supervised Learning use the density estimation of data to construct their classifiers.In this dissertation, we show that the topology of data can be a good alternative in constructing classifiers. We propose using topological graphs like Gabriel graphs (GG) and Relative Neighborhood Graphs (RNG) that can build the topology of data based on its neighborhood structure. To apply this concept, we create a new method called Random Neighborhood Classification (RNC).In this method, we use topological graphs to construct classifiers and then apply Ensemble Methods (EM) to get all relevant information from the data. EM is well known in Machine Learning, generates many classifiers from data and then aggregates these classifiers into one. Aggregate classifiers have been shown to be very efficient in many studies, because it leverages relevant and effective information from each generated classifier. We first compare RNC to other known classification methods using data from the UCI Irvine repository. We find that RNC works very well compared to very efficient methods such as Random Forests and Support Vector Machines. Most of the time, it ranks in the top three methods in efficiency. This result has encouraged us to study the efficiency of RNC on real data like tweets. Twitter, a microblogging Social Network, is especially useful to mine opinion on current affairs and topics that span the range of human interest, including politics. Mining political opinion from Twitter poses peculiar challenges such as the versatility of the authors when they express their political view, that motivate this study. We define a new attribute, called couple, that will be very helpful in the process to study the tweets opinion. A couple is an author that talk about a politician. We propose a new procedure that focuses on identifying the opinion on tweet using couples. We think that focusing on the couples's opinion expressed by several tweets can overcome the problems of analysing each single tweet. This approach can be useful to avoid the versatility, language ambiguity and many other artifacts that are easy to understand for a human being but not automatically for a machine.We use classical Machine Learning techniques like KNN, Random Forests (RF) and also our method RNC. We proceed in two steps : First, we build a reference set of classified couples using Naive Bayes. We also apply a second alternative method to Naive method, sampling plan procedure, to compare and evaluate the results of Naive method. Second, we evaluate the performance of this approach using proximity measures in order to use RNC, RF and KNN. The expirements used are based on real data of tweets from the French presidential election in 2012. The results show that this approach works well and that RNC performs very good in order to classify opinion in tweets.Topological Learning seems to be very intersting field to study, in particular to address the classification problem. Many concepts to get informations from topological graphs need to analyse like the ones described by Aupetit, M. in his work (2005). Our work show that Topological Learning can be an effective way to perform classification problem
Gulak, Denis. "The impact of information services and social networks on learning English." Thesis, Молодь у глобалізованому світі: академічні аспекти англомовних фахових досліджень (англ. мовою) / Укл., ред. А.І.Раду: збірник мат. конф. - Львів: ПП "Марусич", 2011. - 147 с, 2011. http://er.nau.edu.ua/handle/NAU/20771.
Повний текст джерелаPan, Zhengzheng. "Learning, Game Play, and Convergence of Behavior in Evolving Social Networks." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/27460.
Повний текст джерелаPh. D.
Idani, Arman. "Assessment of individual differences in online social networks using machine learning." Thesis, University of Cambridge, 2017. https://www.repository.cam.ac.uk/handle/1810/270109.
Повний текст джерелаJunuthula, Ruthwik Reddy. "Modeling, Evaluation and Analysis of Dynamic Networks for Social Network Analysis." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1544819215833249.
Повний текст джерелаRawashdeh, Ahmad. "Semantic Similarity of Node Profiles in Social Networks." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439279922.
Повний текст джерелаMagnusson, Jonathan. "Social Network Analysis Utilizing Big Data Technology." Thesis, Uppsala universitet, Avdelningen för datalogi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-170926.
Повний текст джерелаWang, Xi. "Learning Collective Behavior in Multi-relational Networks." Doctoral diss., University of Central Florida, 2014. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/6379.
Повний текст джерелаPh.D.
Doctorate
Electrical Engineering and Computer Science
Engineering and Computer Science
Electrical Engineering
Wang, Hao, and Yun Xie. "The use of Online Social Networks in Chinese Collaborative E-learning Education." Thesis, Örebro universitet, Handelshögskolan vid Örebro universitet, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-16037.
Повний текст джерелаWu, Pi-Chu. "Social networks, language learning and language school student sojourners : a qualitative study." Thesis, University of Warwick, 2008. http://wrap.warwick.ac.uk/2132/.
Повний текст джерелаSalehi, Rizi Fatemeh [Verfasser], and Michael [Akademischer Betreuer] Granitzer. "Graph Representation Learning for Social Networks / Fatemeh Salehi Rizi ; Betreuer: Michael Granitzer." Passau : Universität Passau, 2021. http://d-nb.info/1238343821/34.
Повний текст джерелаJayarathna, Lakmali. "Effective use of social media networks for collaborative learning in higher education." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/235152/13/Lakmali_Jayarathna_Thesis.pdf.
Повний текст джерелаCampana, Giuseppe. "Professional development and social networks in digital content industry micro businesses." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/71764/1/Giuseppe_Campana_Thesis.pdf.
Повний текст джерелаLippold, Tessa. "The significance of social support and close relationships for people with learning disabilities." Thesis, n.p, 2000. http://ethos.bl.uk/.
Повний текст джерелаRapanos, Theodoros. "Essays on the Economics of Networks Under Incomplete Information." Doctoral thesis, Stockholms universitet, Nationalekonomiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-132199.
Повний текст джерелаYang, Guoli. "Learning in adaptive networks : analytical and computational approaches." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/20956.
Повний текст джерелаShekfeh, Marwa. "MANILA: A Multi-Agent Framework for Emergent Associative Learning and Creativity in Social Networks." University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1511861383686974.
Повний текст джерелаAl-Janabi, Mohammed Fadhil Zamil. "Detection of suspicious URLs in online social networks using supervised machine learning algorithms." Thesis, Keele University, 2018. http://eprints.keele.ac.uk/5581/.
Повний текст джерелаEberlen, Julia. "Learning about Groups: The Self and Social Networks in the Emergence of Stereotypes." Doctoral thesis, Universite Libre de Bruxelles, 2020. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/305347.
Повний текст джерелаDoctorat en Sciences psychologiques et de l'éducation
info:eu-repo/semantics/nonPublished
Hoyle, Sally G. "Stability and change in social relations of children with and without learning disabilities : social status, social networks, perceived social competence, social cognition, behavior problems, and ecological factors /." The Ohio State University, 1986. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487322984315161.
Повний текст джерелаPérez-Solà, Cristina. "Towards understanding privacy risks in online social networks." Doctoral thesis, Universitat Autònoma de Barcelona, 2016. http://hdl.handle.net/10803/386415.
Повний текст джерелаOnline Social Networks (OSNs) are now one of the most popular services on the Internet. When these lines were written, there were four OSN sites in the Alexa's top ten global ranking and the most used OSNs were having hundreds of millions of daily active users. People use OSNs to share all kinds of contents: from personal attributes (like names, age, or gender), to location data, photos, or comments. Moreover, OSNs are characterized by allowing its users to explictly form relationships (e.g. friendship). Additionally, OSNs include not only information the users conscientiously post about themselves, but also information that is generated from the interaction of users in the platform. Both the number of users and the volume of data shared make privacy in OSNs critical. This thesis is focused on studying privacy related to OSNs in two different contexts: crawling and learning. First, we study the relation between OSN crawling and privacy, a topic that so far received limited attention. We find this scenario interesting because it is affordable for even a low-budget attacker. Second, we study how to extract information from the relationships OSN users form. We then expand our findings to other graph-modeled problems.
Ashton, Philippa. "The social context for design learning : an investigation of social networks in the undergraduate design school studio." Thesis, Staffordshire University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.247291.
Повний текст джерелаWilkie, Tara V. "A qualitative investigation into adolescents with learning disabilities : their perceptions and uses of social support." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0021/NQ55392.pdf.
Повний текст джерелаAbufouda, Mohammed [Verfasser], and Katharina [Akademischer Betreuer] Zweig. "Learning From Networked-data: Methods and Models for Understanding Online Social Networks Dynamics / Mohammed Abufouda ; Betreuer: Katharina Zweig." Kaiserslautern : Technische Universität Kaiserslautern, 2020. http://d-nb.info/1221599747/34.
Повний текст джерелаEller, Linda S. "Social media as avenue for personal learning for educators: Personal learning networks encourage application of knowledge and skills." PEPPERDINE UNIVERSITY, 2012. http://pqdtopen.proquest.com/#viewpdf?dispub=3498101.
Повний текст джерелаMalerba, Candilio Maria Luisa. "Social Networking in Second Language Learning." Doctoral thesis, Universitat Oberta de Catalunya, 2015. http://hdl.handle.net/10803/565551.
Повний текст джерелаEsta tesis está centrada en el aprendizaje informal de una segunda lengua en comunidades en línea como Livemocha y Busuu. Los objetivos son: (1) analizar el potencial de las comunidades en línea para lograr resultados de aprendizaje a largo plazo; (2) examinar las acciones de los estudiantes mientras construyen oportunidades de uso de la segunda lengua en estos entornos, y (3) explorar las potencialidades y las limitaciones de las herramientas de las comunidades en línea. Con la finalidad de alcanzar estos objetivos, el estudio, que se inscribe en el marco teórico de la perspectiva sociocultural y de la teoría de la actividad, ha utilizado una metodología de investigación principalmente cualitativa y centrada en el método etnográfico. La investigación concluye con una reflexión crítica sobre la importancia de la autonomía del estudiante. Se ha destacado que la autonomía del estudiante es un requisito importante para que la experiencia de aprendizaje informal en estos entornos sea eficaz. Además, este estudio traduce los resultados obtenidos en una serie de recomendaciones pedagógicas dirigidas a expertos de entornos de aprendizaje, a estudiantes y a profesores de idiomas, con el fin de fomentar una mejor experiencia de aprendizaje en las comunidades en línea tomando en consideración también su posible aplicación en un contexto de aprendizaje formal.
This thesis deals with informal second language learning in online communities such as Livemocha and Busuu. The thesis' objectives are: (1) analyse the potential effectiveness of these communities for long-term learning outcomes; (2) examine learners' construction of opportunities for L2 use in these environments; (3) explore affordances and constraints of online communities. To this end, a longitudinal multiple ethnographic case study approach was used under the theoretical framework of Socio-Cultural Theory and Activity Theory (AT). The research concludes with a critical reflection on the role of learner autonomy as a prerequisite for the creation of effective learning experiences in these environments, as this study clearly demonstrates. Moreover, the study translates its findings into a set of pedagogical recommendations for platform developers, learners and teachers to maximize the advantages of L2 learning in online communities as well as establish possible applications in formal learning settings.
Åsberg, Samira. "Social Networks in Education: A Facebook-Based Educational Platform." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-93649.
Повний текст джерелаPapadimitriou, Aristea. "The Future of Communication: Artificial Intelligence and Social Networks." Thesis, Malmö högskola, Fakulteten för kultur och samhälle (KS), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-21886.
Повний текст джерелаOlamijulo, Christianah. "An investigation into integrating social sites as a teaching and learning practice to create dialogue spaces in the language classroom." Thesis, Nelson Mandela Metropolitan University, 2012. http://hdl.handle.net/10948/d1020149.
Повний текст джерелаLögdlund, Ulrik. "Networks and Nodes : The Practices of Local Learning Centres." Doctoral thesis, Linköpings universitet, Avdelningen för sociologi (SOC), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-69601.
Повний текст джерелаDenna avhandling är en studie om lärcentra i Sverige. Syftet är att beskriva och öka kunskapen om de relationer och de aktörsnätverk som omger praktiken. Avhandlingen bygger på resultat från fyra olika delstudier. Fokus i två av dessa ligger på lärcentra som organisation. Hur ser relationen mellan lärcentra och omgivande aktörer ut i regionen och vilka strategier används för att skapa aktörsnätverk? De övriga två studierna handlar om videokonferens där fokus ligger på hur relationer skapas mellan miljö, teknik och människor. Särskilt studeras interaktion och kommunikation mellan dessa aktörer i en utpräglat socioteknisk lärandemiljö. Den teoretiska ramen för de olika delstudierna är aktörsnätverksteori som används tillsammans med begrepp som spatiala relationer. De fyra studierna använder sig i huvudsak av kvalitativa metoder som intervjuer och observationsstudier. Datainsamlingen berör en bred samling informanter som rektorer, lärare och studenter tillsammans med projektledare, politiker och företagare. Studiernas resultat visar att det finns skilda synsätt på utbildning och kompetens mellan olika grupper av aktörer. Trots involveringsstrategier av aktörer från omgivande aktörsnätverk lyckas man inte agera som en mäklare på en utbildningsmarknad. Resultaten visar vidare att miljö tillsammans med teknik har stort inflytande på hur studenter och lärare agerar i videokonferensklassrummen. Det är den materiella designen och den tekniska logiken som styr praktiken. Resultaten visar också på hur olika studerandestrategier utvecklas för att stå utanför interaktion i klassrummet tillsammans med hur lärares kommunikation utvecklas för att överbrygga avståndet till de studerande. Sammantaget visar de fyra studierna på hur olika aktörsnätverk inverkar på praktiken genom representationer.
Suter, Deitra L. "The Role of Religion in Predicting Recidivism: Considering Elements of Social Networking, Social Capital, and Social Learning Theories." Connect to this title online, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=bgsu1131134485.
Повний текст джерелаFang, Chunsheng. "Novel Frameworks for Mining Heterogeneous and Dynamic Networks." University of Cincinnati / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1321369978.
Повний текст джерелаWu, Hao, and 吴颢. "SNS use in teaching and learning in China." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hdl.handle.net/10722/198865.
Повний текст джерелаpublished_or_final_version
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