Bibliografie tematiche / Collective learning

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Letteratura scientifica selezionata sul tema "Collective learning"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 25 gennaio 2023

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Articoli di riviste sul tema "Collective learning"

Backström, Tomas. "Collective learning". Learning Organization 11, n. 6 (dicembre 2004): 466–77. http://dx.doi.org/10.1108/09696470410548827.

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Fu, Wai‐Ki, Hing‐Po Lo e Derek S. Drew. "Collective learning, collective knowledge and learning networks in construction". Construction Management and Economics 24, n. 10 (ottobre 2006): 1019–28. http://dx.doi.org/10.1080/01446190500228258.

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Vengerov, Alexander. "Collective Learning and Collective Intelligence Working Together". International Journal of Learning: Annual Review 18, n. 2 (2011): 45–56. http://dx.doi.org/10.18848/1447-9494/cgp/v18i02/47500.

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Zhang, Hanwang, Xindi Shang, Huanbo Luan, Meng Wang e Tat-Seng Chua. "Learning from Collective Intelligence". ACM Transactions on Multimedia Computing, Communications, and Applications 13, n. 1 (17 gennaio 2017): 1–23. http://dx.doi.org/10.1145/2978656.

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Forsyth, Lachlan, e Lynette Schaverien. "Re-presenting collective learning". ACM SIGGROUP Bulletin 24, n. 3 (dicembre 2003): 25–31. http://dx.doi.org/10.1145/1052829.1052836.

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Novikov, Dmitry A. "COLLECTIVE LEARNING-BY-DOING". IFAC Proceedings Volumes 45, n. 11 (2012): 408–12. http://dx.doi.org/10.3182/20120619-3-ru-2024.00002.

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Sitkin, Sim B. "SHAPING COLLECTIVE COGNITION AND BEHAVIOR THROUGH COLLECTIVE LEARNING." Academy of Management Proceedings 2000, n. 1 (agosto 2000): B1—B6. http://dx.doi.org/10.5465/apbpp.2000.5535126.

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Fadul, Jose A. "Collective Learning: Applying Distributed Cognition for Collective Intelligence". International Journal of Learning: Annual Review 16, n. 4 (2009): 211–20. http://dx.doi.org/10.18848/1447-9494/cgp/v16i04/46223.

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Schechter, Chen. "Toward Communal Negotiation of Meaning in Schools: Principals’ Perceptions of Collective Learning from Success". Teachers College Record: The Voice of Scholarship in Education 113, n. 11 (novembre 2011): 2415–59. http://dx.doi.org/10.1177/016146811111301107.

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Abstract (sommario):

Background In light of the growing complexity of schoolwork, it is important that faculty members move away from isolated learning toward a more collective type of thinking regarding teaching and learning issues. Purpose Whereas collective learning has mostly been approached from a deficit-based orientation (finding/solving problems and overcoming failures), this study examines principals’ perceptions (mindscapes) about the notion and strategy of collective learning from faculty members’ successful practices. Research Design The study employed a qualitative topic-oriented methodology to explore principals’ mindscapes concerning collective learning from success in schools. Data Collection and Analysis Data were collected via face-to-face interviews with 65 elementary, middle, and high school principals. The analysis process involved identifying common themes, contrasting patterns, and elucidating the differences among principals’ voices. Findings Principals argued that in contrast to collective learning processes to evaluate failures and problems, collective learning from successful practices requires a deliberate and conscious shift in mindset with regard to collaborative learning in schools. Principals perceived the competitive culture and the comparison of professional abilities among faculty members as major determinants of a productive collective learning from success. Principals envisioned their role in this interactive process as promoting a learning culture of inquiry, openness, and trust. Recommendations As a leadership strategy to foster collective learning in schools, both practitioners and researchers need to evaluate whether a learning community can be developed when staff members are encouraged to collectively analyze their successful practices and receive affirmation for doing so. It is important to further inquire how different stakeholders perceive this collaborative learning from successful practices.

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Flack, Andrea, e Dora Biro. "Collective learning in route navigation". Communicative & Integrative Biology 6, n. 6 (9 novembre 2013): e26521. http://dx.doi.org/10.4161/cib.26521.

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Tesi sul tema "Collective learning"

Holmquist, Mats. "Collective Learning in Innovative Networks". Högskolan i Halmstad, Regionalt lärande och ledarskap (RELL), 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-21771.

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There are different forms for boundary-crossing learning in and between organizations. One growing form is network. This paper will present a study of collective learning in two innovative networks at the west coast of Sweden. One network is about regional health and growth, the other about leadership development among general directors. Representatives from different organizations meet to learn from each other’s practice. How does this learning take place? What supports and what prevents it? Does it have any effects in their own organizations? Data has been collected through questionnaires, observations, systematic reflections and focus interviews. Results show that the participants express great satisfaction with their outcomes on a personal base. It gives them inspiration, ideas, knowledge and contacts. It also gives them support, strength and self-confidence. One important factor in this context is differences. The participants are coming from different organizations, have different perspectives and experiences. The findings show interesting variances between the two networks that has effects on the type of learning. A problem is the transformation of learning from the network, by the participants into their organizations. There seems to be some important learning barriers. This will be further discussed in the paper.

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Xiong, Liang. "On Learning from Collective Data". Research Showcase @ CMU, 2013. http://repository.cmu.edu/dissertations/560.

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In many machine learning problems and application domains, the data are naturally organized by groups. For example, a video sequence is a group of images, an image is a group of patches, a document is a group of paragraphs/words, and a community is a group of people. We call them the collective data. In this thesis, we study how and what we can learn from collective data. Usually, machine learning focuses on individual objects, each of which is described by a feature vector and studied as a point in some metric space. When approaching collective data, researchers often reduce the groups into vectors to which traditional methods can be applied. We, on the other hand, will try to develop machine learning methods that respect the collective nature of data and learn from them directly. Several different approaches were taken to address this learning problem. When the groups consist of unordered discrete data points, it can naturally be characterized by its sufficient statistics – the histogram. For this case we develop efficient methods to address the outliers and temporal effects in the data based on matrix and tensor factorization methods. To learn from groups that contain multi-dimensional real-valued vectors, we develop both generative methods based on hierarchical probabilistic models and discriminative methods using group kernels based on new divergence estimators. With these tools, we can accomplish various tasks such as classification, regression, clustering, anomaly detection, and dimensionality reduction on collective data. We further consider the practical side of the divergence based algorithms. To reduce their time and space requirements, we evaluate and find methods that can effectively reduce the size of the groups with little impact on the accuracy. We also proposed the conditional divergence along with an efficient estimator in order to correct the sampling biases that might be present in the data. Finally, we develop methods to learn in cases where some divergences are missing, caused by either insufficient computational resources or extreme sampling biases. In addition to designing new learning methods, we will use them to help the scientific discovery process. In our collaboration with astronomers and physicists, we see that the new techniques can indeed help scientists make the best of data.

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Wu, Zhichao. "Modelling collective learning in conceptual design". Thesis, University of Strathclyde, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.405494.

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Wang, 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.

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With the rapid expansion of the Internet and WWW, the problem of analyzing social media data has received an increasing amount of attention in the past decade. The boom in social media platforms offers many possibilities to study human collective behavior and interactions on an unprecedented scale. In the past, much work has been done on the problem of learning from networked data with homogeneous topologies, where instances are explicitly or implicitly inter-connected by a single type of relationship. In contrast to traditional content-only classification methods, relational learning succeeds in improving classification performance by leveraging the correlation of the labels between linked instances. However, networked data extracted from social media, web pages, and bibliographic databases can contain entities of multiple classes and linked by various causal reasons, hence treating all links in a homogeneous way can limit the performance of relational classifiers. Learning the collective behavior and interactions in heterogeneous networks becomes much more complex. The contribution of this dissertation include 1) two classification frameworks for identifying human collective behavior in multi-relational social networks; 2) unsupervised and supervised learning models for relationship prediction in multi-relational collaborative networks. Our methods improve the performance of homogeneous predictive models by differentiating heterogeneous relations and capturing the prominent interaction patterns underlying the network structure. The work has been evaluated in various real-world social networks. We believe that this study will be useful for analyzing human collective behavior and interactions specifically in the scenario when the heterogeneous relationships in the network arise from various causal reasons.
Ph.D.
Doctorate
Electrical Engineering and Computer Science
Engineering and Computer Science
Electrical Engineering

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Kim, Juho Ph D. Massachusetts Institute of Technology. "Learnersourcing : improving learning with collective learner activity". Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/101464.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
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 (pages [199]-213).
Millions of learners today are watching videos on online platforms, such as Khan Academy, YouTube, Coursera, and edX, to take courses and master new skills. But existing video interfaces are not designed to support learning, with limited interactivity and lack of information about learners' engagement and content. Making these improvements requires deep semantic information about video that even state-of-the-art AI techniques cannot fully extract. I take a data-driven approach to address this challenge, using large-scale learning interaction data to dynamically improve video content and interfaces. Specifically, this thesis introduces learnersourcing, a form of crowdsourcing in which learners collectively contribute novel content for future learners while engaging in a meaningful learning experience themselves. I present learnersourcing applications designed for massive open online course videos and how-to tutorial videos, where learners' collective activities 1) highlight points of confusion or importance in a video, 2) extract a solution structure from a tutorial, and 3) improve the navigation experience for future learners. This thesis demonstrates how learnersourcing can enable more interactive, collaborative, and data-driven learning.
by Juho Kim.
Ph. D.

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Tödtling, Franz, Patrick Lehner e Michaela Trippl. "Knowledge intensive industries, networks, and collective learning". Institut für Wirtschaftsgeographie, Abt. Stadt- und Regionalentwicklung, WU Vienna University of Economics and Business, 2004. http://epub.wu.ac.at/636/1/document.pdf.

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Knowledge has become a key source of competitiveness for advanced regions and nations, indicating a transformation of advanced capitalist economies towards "knowledge-based economies". Knowledge intensive sectors in production and in services have a lead in this respect, they can be considered as role models for the future. The innovation process, the mechanisms of knowledge exchange and the respective linkages in those industries differ quite markedly from those in other sectors. Clustering and local knowledge spillovers are frequently stated phenomena, although it is still unclear to what extent regional networks and collective learning are indeed relevant and what the mechanisms of knowledge flows are. The aim of the paper is to examine in a differentiated way the character of the innovation process and the type of interactions in those industries, in order to find out how strongly they are related to regional, national and international innovation systems. We will analyse the relevant types of actors, the respective mechanisms of knowledge exchange and the importance of collective learning and innovation. The paper will discuss relevant theoretical concepts and available evidence and it will be based on an empirical analysis for Austria. The data base is a recent firm survey which was carried out in the year 2003. From this analysis conclusions regarding the role of regional and other innovation systems for the development of knowledge-based industries will be drawn. (authors' abstract)
Series: SRE - Discussion Papers

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Richardson, Matthew. "Learning and inference in collective knowledge bases /". Thesis, Connect to this title online; UW restricted, 2004. http://hdl.handle.net/1773/6926.

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Rhamachan, Molly. "Social movement learning: Collective,participatory learning within the jyoti jivanam movement of south Africa". University of the Western Cape, 2014. http://hdl.handle.net/11394/4401.

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Magister Educationis (Adult Learning and Global Change) - MEd(AL)
The purpose of this research paper is to explore and examine the nature of learning within the context of and situated within a social movement. Based on an exploratory qualitative study of learning within the Jyoti Jivanam Movement of South Africa, this research explores the nature and purpose/s of learning within a social movement. Accordingly, this study is guided by the research questions: How and why do adults learn as they collectively participate in social movements; and what factors facilitate, contribute, hinder and influence learning within social movement? This study confirms that social movements are important sites for. Collective learning and knowledge construction. For this reason, social movements need to be acknowledged as pedagogical sites that afford adults worthwhile learning opportunities. Furthermore, social movements, as pedagogical sites, not only contribute to conceptions of what constitute legitimate knowledge(s), social movements also contribute to the creation of transformative knowledge(s).

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Gulcehre, Caglar. "Two Approaches For Collective Learning With Language Games". Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613109/index.pdf.

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Recent studies in cognitive science indicate that language has an important social function. The structure and knowledge of language emerges from the processes of human communication together with the domain-general cognitive processes. Each individual of a community interacts socially with a limited number of peers. Nevertheless societies are characterized by their stunning global regularities. By dealing with the language as a complex adaptive system, we are able to analyze how languages change and evolve over time. Multi-agent computational simulations assist scientists from different disciplines to build several language emergence scenarios. In this thesis several simulations are implemented and tested in order to categorize examples in a test data set efficiently and accurately by using a population of agents interacting by playing categorization games inspired by L. Steels'
s naming game. The emergence of categories throughout interactions between a population of agents in the categorization games are analyzed. The test results of categorization games as a model combination algorithm with various machine learning algorithms on different data sets have shown that categorization games can have a comparable performance with fast convergence.

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Verri, Filipe Alves Neto. "Collective dynamics in complex networks for machine learning". Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-18102018-113054/.

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Machine learning enables machines to learn automatically from data. In literature, graph-based methods have received increasing attention due to their ability to learn from both local and global information. In these methods, each data instance is represented by a vertex and is linked to other vertices according to a predefined affinity rule. However, they usually have unfeasible time cost for large problems. To overcome this problem, techniques can employ a heuristic to find suboptimal solutions in a feasible time. Early heuristic optimization methods exploit nature-inspired collective processes, such as ants looking for food sources and swarms of bees. Nowadays, advances in the field of complex systems provide powerful tools to assess and to understand dynamical systems. Complex networks, which are graphs with nontrivial topology, are among these theoretical tools capable of describing the interplay of topology, structure, and dynamics of complex systems. Therefore, machine learning methods based on complex networks and collective dynamics have been proposed. They encompass three steps. First, a complex network is constructed from the input data. Then, the simulation of a distributed collective system in the network generates rich information. Finally, the collected information is used to solve the learning problem. The coordination of the individuals in the system permit to achieve dynamics that is far more complex than the behavior of single individuals. In this research, I have explored collective dynamics in machine learning tasks, both in unsupervised and semi-supervised scenarios. Specifically, I have proposed a new collective system of competing particles that shifts the traditional vertex-centric dynamics to a more informative edge-centric one. Moreover, it is the first particle competition system applied in machine learning task that has deterministic behavior. Results show several advantages of the edge-centric model, including the ability to acquire more information about overlapping areas, a better exploration behavior, and a faster convergence time. Also, I have proposed a new network formation technique that is not based on similarity and has low computational cost. Since addition and removal of samples in the network is cheap, it can be used in real-time application. Finally, I have conducted analytical investigations of a flocking-like system that was needed to guarantee the expected behavior in community detection tasks. In conclusion, the result of the research contributes to many areas of machine learning and complex systems.
Aprendizado de máquina permite que computadores aprendam automaticamente dos dados. Na literatura, métodos baseados em grafos recebem crescente atenção por serem capazes de aprender através de informações locais e globais. Nestes métodos, cada item de dado é um vértice e as conexões são dadas uma regra de afinidade. Todavia, tais técnicas possuem custo de tempo impraticável para grandes grafos. O uso de heurísticas supera este problema, encontrando soluções subótimas em tempo factível. No início, alguns métodos de otimização inspiraram suas heurísticas em processos naturais coletivos, como formigas procurando por comida e enxames de abelhas. Atualmente, os avanços na área de sistemas complexos provêm ferramentas para medir e entender estes sistemas. Redes complexas, as quais são grafos com topologia não trivial, são uma das ferramentas. Elas são capazes de descrever as relações entre topologia, estrutura e dinâmica de sistemas complexos. Deste modo, novos métodos de aprendizado baseados em redes complexas e dinâmica coletiva vêm surgindo. Eles atuam em três passos. Primeiro, uma rede complexa é construída da entrada. Então, simula-se um sistema coletivo distribuído na rede para obter informações. Enfim, a informação coletada é utilizada para resolver o problema. A interação entre indivíduos no sistema permite alcançar uma dinâmica muito mais complexa do que o comportamento individual. Nesta pesquisa, estudei o uso de dinâmica coletiva em problemas de aprendizado de máquina, tanto em casos não supervisionados como semissupervisionados. Especificamente, propus um novo sistema de competição de partículas cuja competição ocorre em arestas ao invés de vértices, aumentando a informação do sistema. Ainda, o sistema proposto é o primeiro modelo de competição de partículas aplicado em aprendizado de máquina com comportamento determinístico. Resultados comprovam várias vantagens do modelo em arestas, includindo detecção de áreas sobrepostas, melhor exploração do espaço e convergência mais rápida. Além disso, apresento uma nova técnica de formação de redes que não é baseada na similaridade dos dados e possui baixa complexidade computational. Uma vez que o custo de inserção e remoção de exemplos na rede é barato, o método pode ser aplicado em aplicações de tempo real. Finalmente, conduzi um estudo analítico em um sistema de alinhamento de partículas. O estudo foi necessário para garantir o comportamento esperado na aplicação do sistema em problemas de detecção de comunidades. Em suma, os resultados da pesquisa contribuíram para várias áreas de aprendizado de máquina e sistemas complexos.

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Libri sul tema "Collective learning"

Kassam, Tamiza A. Collective learning within nursing clinical groups. St. Catharines, Ont: Brock University, Faculty of Education, 2002.

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Steiner, Roy, e Duncan Hanks, a cura di. Harnessing the Power of Collective Learning. and Duncan Hanks.Description: New York, NY : Routledge, 2016.: Routledge, 2016. http://dx.doi.org/10.4324/9781315651248.

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Sanders, William R. Collective staff training in a virtual learning environment. Alexandria, Va: U.S. Army Research Institute for the Behavioral and Social Sciences, 2002.

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National Joint Committee on Learning Disabilities (U.S.), a cura di. Collective perspectives on issues affecting learning disabilities: Position papers and statements. Austin, Tex: Pro-Ed, 1994.

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Datta, Rukmini. Behind the scenes action: Learning from a collective process. New Delhi: Programme on Women's Economic Social and Cultural Rights, 2010.

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David, Keeble, e Wilkinson Frank, a cura di. High-technology clusters, networking, and collective learning in Europe. Aldershot , Hampshire, England: Ashgate, 2000.

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Rational herds: Economic models of social learning. Cambridge: Cambridge University Press, 2004.

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A, Lambert Judith, a cura di. Collective learning for transformational change: A guide to collaborative action. New York: Routledge, 2013.

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The emergence of artificial cognition: An introduction to collective learning. Singapore: World Scientific, 1993.

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Kafker, Frank A. The encyclopedists as a group: A collective biography of the authors of the Encyclopédie. Oxford: Voltaire Foundation, 1996.

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Capitoli di libri sul tema "Collective learning"

Hu, Angang. "Collective Learning". In China’s Collective Presidency, 73–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55279-3_5.

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Garavan, Thomas N., e Ronan Carbery. "Collective Learning". In Encyclopedia of the Sciences of Learning, 646–49. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_136.

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Shultz, Thomas R., Scott E. Fahlman, Susan Craw, Periklis Andritsos, Panayiotis Tsaparas, Ricardo Silva, Chris Drummond et al. "Collective Classification". In Encyclopedia of Machine Learning, 189–93. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_140.

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Hager, Paul, e Mary C. Johnsson. "Collective Learning Practice". In Professional and Practice-based Learning, 249–65. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4774-6_16.

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Hayes, John. "Facilitating collective learning". In The Theory and Practice of Change Management, 501–14. London: Macmillan Education UK, 2018. http://dx.doi.org/10.1057/978-1-352-00132-7_33.

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Boreham, Nick. "Competence as Collective Process". In Vocational Learning, 77–91. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1539-4_5.

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Namata, Galileo, Prithviraj Sen, Mustafa Bilgic e Lise Getoor. "Collective Classification". In Encyclopedia of Machine Learning and Data Mining, 1–7. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-1-4899-7502-7_44-1.

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Namata, Galileo, Prithviraj Sen, Mustafa Bilgic e Lise Getoor. "Collective Classification". In Encyclopedia of Machine Learning and Data Mining, 238–42. Boston, MA: Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_44.

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Xynogala, Lydia. "Learning from Loutraki". In Architecture and Collective Life, 182–94. London: Routledge, 2021. http://dx.doi.org/10.4324/9781003118985-19.

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Hayes, John. "Individual and collective learning". In The Theory and Practice of Change Management, 487–511. London: Macmillan Education UK, 2014. http://dx.doi.org/10.1007/978-1-137-28902-5_29.

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Atti di convegni sul tema "Collective learning"

Mittrick, Mark R., John Richardson, Mark Dennison, Theron Trout, Eric Heilman e Timothy Hanratty. "Investigating immersive collective intelligence". In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, a cura di Tien Pham. SPIE, 2019. http://dx.doi.org/10.1117/12.2519364.

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Chan, Rosanna Y. Y., Morris S. Y. Jong, Eric T. H. Luk e Xueqi Zhang. "Dynamic Collective Mobile Gaming". In 2013 IEEE 13th International Conference on Advanced Learning Technologies (ICALT). IEEE, 2013. http://dx.doi.org/10.1109/icalt.2013.145.

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Singh, Ajit P., e Geoffrey J. Gordon. "Relational learning via collective matrix factorization". In the 14th ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1401890.1401969.

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Choi, Wongun, Khuram Shahid e Silvio Savarese. "Learning context for collective activity recognition". In 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2011. http://dx.doi.org/10.1109/cvpr.2011.5995707.

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Liu, Zixuan, Michael Crosscombe e Jonathan Lawry. "Imprecise Fusion Operators for Collective Learning". In The 2021 Conference on Artificial Life. Cambridge, MA: MIT Press, 2021. http://dx.doi.org/10.1162/isal_a_00407.

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Gea, Miguel, Rosana Montes Soldado e Vanesa Gamiz. "Collective intelligence and online learning communities". In 2011 International Conference on Information Society (i-Society). IEEE, 2011. http://dx.doi.org/10.1109/i-society18435.2011.5978461.

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Dongwon Lee, Jaejeung Kim e Howon Lee. "Collective intelligence based Collaborative Learning platform". In 2010 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2010. http://dx.doi.org/10.1109/ictc.2010.5674754.

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Chen, Xiaoyu, Jiangchao Yao, Yanfeng Wang e Ya Zhang. "Online Learning Algorithm for Collective LDA". In 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA). IEEE, 2015. http://dx.doi.org/10.1109/icmla.2015.177.

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Meza, Jaime, Oswaldo Ortiz, Ester Simó e Joseph M. Monguet. "MEASURING THE COLLECTIVE INTELLIGENCE EDUCATION INDEX". In International Conference on Education and New Learning Technologies. IATED, 2017. http://dx.doi.org/10.21125/edulearn.2017.1647.

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Brennan, Riordan, e Debbie Perouli. "Generating and Evaluating Collective Concept Maps". In LAK22: 12th International Learning Analytics and Knowledge Conference. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3506860.3506918.

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Rapporti di organizzazioni sul tema "Collective learning"

Singh, Ajit P., e Geoffrey J. Gordon. Relational Learning via Collective Matrix Factorization. Fort Belvoir, VA: Defense Technical Information Center, giugno 2008. http://dx.doi.org/10.21236/ada486804.

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Sanders, William R. Collective Staff Training in a Virtual Learning Environment. Fort Belvoir, VA: Defense Technical Information Center, marzo 2002. http://dx.doi.org/10.21236/ada400495.

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McGilvray, David H., Bruce C. Leibrecht e Karen J. Lockaby. Measuring Learning and Performance in Collective Training Exercises. Fort Belvoir, VA: Defense Technical Information Center, marzo 2008. http://dx.doi.org/10.21236/ada479614.

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Elliott, Kerry, Hilary Hollingsworth, Aiden Thornton, Liz Gillies e Katherine Henderson. School leadership that cultivates collective efficacy: Emerging insights 2022. Australian Council for Educational Research, novembre 2022. http://dx.doi.org/10.37517/978-1-74286-694-9.

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The work of the Menzies School Leadership Incubator suggests we need a new approach to leadership that supports school leaders to better manage transformational change and deepen collaborative capacity necessary to cultivate collective efficacy to improve student learning outcomes. This paper provides a description of the work of the Menzies School Leadership Incubator (“the Incubator’) and insights generated so far. The Incubator has identified five leadership domains which underpin the leadership of Collective Efficacy: Understanding Collective Efficacy; Systems Leadership; Change Leadership; Team Leadership; and Collaborative Capacity.

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Howard, Jo, Evert-jan Quak e Jim Woodhill. A Practical Approach for Supporting Learning in Development Organisations. Institute of Development Studies, settembre 2022. http://dx.doi.org/10.19088/k4d.2022.120.

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The Knowledge, Evidence and Learning for Development (K4D) Programme, which started in 2016, comes to an end in September 2022. K4D is a programme funded by and for the UK Government’s Foreign, Commonwealth and Development Office (initially with the Department for International Development, DFID, which was merged with the FCO in 2020). To reflect on the processes and approaches of learning that K4D enabled over the years, a special Working Paper series will be published. One important pillar of the programme was to facilitate learning processes through learning journeys. A total of 33 learning journeys took place during K4D. This summary looks back at the K4D concept, the learning journeys, the learning processes it supported, and the outcomes enabled. The paper finds that there is evidence that K4D learning journeys have helped enable sound, informed decision-making through collective understanding of issues and options, and through internal consensus on directions. Effective learning spaces were created and the methods used (including online tools for participation) were able to capture and share internal learning, foster internal connections, present external evidence and bring in other perspectives. However, success in enabling external alliances for decisions and change was more constrained, since most learning journeys engaged only in limited ways with external organisations. Further challenges were encountered when staff were redeployed to respond to political (Brexit) or international development (COVID-19, Ukraine) priorities.

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Young, Stephen, Jessica Diaz, Bert De Coutere e Holly Downs. Leadership Development in the Flow of Work: Leveraging Technology to Accelerate Learning. Center for Creative Leadership, 2022. http://dx.doi.org/10.35613/ccl.2022.2047.

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"A recent industry trend survey of CEOs found that only 11% of organizations report having a strong enough bench to fill leadership roles (Rhyne, 2021). As such, effective leadership development is an imperative for any high-performing organization. Rather than focusing time, money, and energy on only a small subset of “high-potential” employees, organizations can realize the full potential of their entire workforce by providing tech-enabled leadership development to leaders at all levels. This paper shares the following insights for Chief Learning Officers interested in leveraging evidence-based practices to accelerate leader development at scale and unlock the collective potential of their workforce: • A brief overview of why we need new ways to develop leaders and a high-level description of the new digital assessment and development tools that meet individuals where they are – offering a highly personalized approach to development in-the-flow of work. • A review of eight research-based learning practices that provide a foundation for leveraging technology to make in-the-flow leadership development better, faster, and more accessible to leaders at all levels. For every learning practice, we provide implementation tips and discuss illustrative example tools. • We conclude with a discussion around the strategic use of the eight learning practices for enabling better organization-wide development outcomes. "

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Shaw, Jackie, Masa Amir, Tessa Lewin, Jean Kemitare, Awa Diop, Olga Kithumbu, Danai Mupotsa e Stella Odiase. Contextualising Healing Justice as a Feminist Organising Framework in Africa. Institute of Development Studies, agosto 2022. http://dx.doi.org/10.19088/ids.2022.063.

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Healing justice is a political organising framework that aims to address the systemic causes of injustice experienced by marginalised peoples due to the harmful impacts of oppressive histories, intergenerational trauma, and structural violence. It recognises that these damaging factors generate collective trauma, which manifests in negative physical, mental–emotional, and spiritual effects in activists and in the functioning of their movements. Healing justice integrates collective healing in political organising processes, and is contextualised as appropriate to situational needs. This provided the rationale for a research study to explore the potential of healing justice for feminist activists in Africa, and how pathways to collective healing could be supported in specific contexts. Research teams in DRC, Senegal, and South Africa conducted interviews with feminist activists and healers, in addition to supplementary interviews across sub-regions of Africa and two learning events with wider stakeholders.

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Eblie Trudel, Lesley. Leveraging Collective Efficacy in The Dangerous Space Between Good Intentions and Meaningful Interventions: A Study on the Use of School Suspensions in Manitoba; A Review of Literature for Manitoba Education and Early Childhood Learning. University of Winnipeg, dicembre 2022. http://dx.doi.org/10.36939/ir.202212191117.

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Tumer, Kagan. Coordinating Learning Agents for Active Information Collection. Fort Belvoir, VA: Defense Technical Information Center, giugno 2011. http://dx.doi.org/10.21236/ada563864.

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Lavrentieva, Olena O., Lina M. Rybalko, Oleh O. Tsys e Aleksandr D. Uchitel. Theoretical and methodical aspects of the organization of students’ independent study activities together with the use of ICT and tools. [б. в.], settembre 2019. http://dx.doi.org/10.31812/123456789/3244.

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In the article the possibilities and classification of ICTs and tools that can be used in organizing students’ independent study activities of higher education institutions has been explored. It is determined the students’ independent study activities is individual, group, collective activity and is implemented within the process of education under the condition of no pedagogy’s direct involvement. It complies with the requirements of the curriculum and syllabus and is aimed at students’ acquisition of some social experiences in line with the learning objectives of vocational training. The analysis of the latest information and technological approaches to the organization of students’ independent study activities made it possible to determine the means of realization of the leading forms of organization for this activity (independent and research work, lectures, consultations and non-formal education), to characterize and classify the ICTs and tools that support presentation of teaching materials, electronic communication, mastering of learning material, monitoring of students’ learning and cognitive activity, such as ones that serve for the sake of development and support of automated training courses, systems of remote virtual education with elements of artificial intelligence, which implement the principle of adaptive management of learning and the organization of students’ independent study activities. The paper provides the insight into the essence of the conducted investigation on the assesses of the effectiveness of ICTs and tools in the process of organizing students’ independent study activities.

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