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Journal articles on the topic 'Collective learning'

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Published: 4 June 2021
Last updated: 25 January 2023

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1

Backström, Tomas. "Collective learning." Learning Organization 11, no. 6 (December 2004): 466–77. http://dx.doi.org/10.1108/09696470410548827.

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2

Fu, Wai‐Ki, Hing‐Po Lo, and Derek S. Drew. "Collective learning, collective knowledge and learning networks in construction." Construction Management and Economics 24, no. 10 (October 2006): 1019–28. http://dx.doi.org/10.1080/01446190500228258.

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3

Vengerov, Alexander. "Collective Learning and Collective Intelligence Working Together." International Journal of Learning: Annual Review 18, no. 2 (2011): 45–56. http://dx.doi.org/10.18848/1447-9494/cgp/v18i02/47500.

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4

Zhang, Hanwang, Xindi Shang, Huanbo Luan, Meng Wang, and Tat-Seng Chua. "Learning from Collective Intelligence." ACM Transactions on Multimedia Computing, Communications, and Applications 13, no. 1 (January 17, 2017): 1–23. http://dx.doi.org/10.1145/2978656.

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5

Forsyth, Lachlan, and Lynette Schaverien. "Re-presenting collective learning." ACM SIGGROUP Bulletin 24, no. 3 (December 2003): 25–31. http://dx.doi.org/10.1145/1052829.1052836.

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6

Novikov, Dmitry A. "COLLECTIVE LEARNING-BY-DOING." IFAC Proceedings Volumes 45, no. 11 (2012): 408–12. http://dx.doi.org/10.3182/20120619-3-ru-2024.00002.

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7

Sitkin, Sim B. "SHAPING COLLECTIVE COGNITION AND BEHAVIOR THROUGH COLLECTIVE LEARNING." Academy of Management Proceedings 2000, no. 1 (August 2000): B1—B6. http://dx.doi.org/10.5465/apbpp.2000.5535126.

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8

Fadul, Jose A. "Collective Learning: Applying Distributed Cognition for Collective Intelligence." International Journal of Learning: Annual Review 16, no. 4 (2009): 211–20. http://dx.doi.org/10.18848/1447-9494/cgp/v16i04/46223.

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9

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, no. 11 (November 2011): 2415–59. http://dx.doi.org/10.1177/016146811111301107.

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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, and Dora Biro. "Collective learning in route navigation." Communicative & Integrative Biology 6, no. 6 (November 9, 2013): e26521. http://dx.doi.org/10.4161/cib.26521.

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11

Tang, Lei, Xufei Wang, and Huan Liu. "Scalable Learning of Collective Behavior." IEEE Transactions on Knowledge and Data Engineering 24, no. 6 (June 2012): 1080–91. http://dx.doi.org/10.1109/tkde.2011.38.

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12

WU, ZHICHAO, and ALEX H. B. DUFFY. "Modeling collective learning in design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 18, no. 4 (November 2004): 289–313. http://dx.doi.org/10.1017/s0890060404040193.

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In this paper, a model of collective learning in design is developed in the context of team design. It explains that a team design activity uses input knowledge, environmental information, and design goals to produce output knowledge. A collective learning activity uses input knowledge from different agents and produces learned knowledge with the process of knowledge acquisition and transformation between different agents, which may be triggered by learning goals and rationale triggers. Different forms of collective learning were observed with respect to agent interactions, goal(s) of learning, and involvement of an agent. Three types of links between team design and collective learning were identified, namely teleological, rationale, and epistemic. Hypotheses of collective learning are made based upon existing theories and models in design and learning, which were tested using a protocol analysis approach. The model of collective learning in design is derived from the test results. The proposed model can be used as a basis to develop agent-based learning systems in design. In the future, collective learning between design teams, the links between collective learning and creativity, and computational support for collective learning can be investigated.
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13

Kim, Jeong Seop. "Community Business and Collective Learning." Journal of Agricultural Extension & Community Development 20, no. 3 (September 30, 2013): 603–42. http://dx.doi.org/10.12653/jecd.2013.20.3.0603.

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14

Enn, Rosa. "Indigenous empowerment through collective learning." Multicultural Education & Technology Journal 6, no. 3 (August 17, 2012): 149–61. http://dx.doi.org/10.1108/17504971211253994.

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15

Osherson, D. N., M. Stob, and S. Weinstein. "Social learning and collective choice." Synthese 70, no. 3 (March 1987): 319–47. http://dx.doi.org/10.1007/bf00414154.

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16

Krafft, P. M., Erez Shmueli, Thomas L. Griffiths, Joshua B. Tenenbaum, and Alex “Sandy” Pentland. "Bayesian collective learning emerges from heuristic social learning." Cognition 212 (July 2021): 104469. http://dx.doi.org/10.1016/j.cognition.2020.104469.

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17

Peters, Linda D., Wesley J. Johnston, Andrew D. Pressey, and Terry Kendrick. "Collaboration and collective learning: networks as learning organisations." Journal of Business & Industrial Marketing 25, no. 6 (August 3, 2010): 478–84. http://dx.doi.org/10.1108/08858621011066062.

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18

Wærn, Yvonne. "Collective learning and collective memory for coping with dynamic complexity." ACM SIGCHI Bulletin 28, no. 3 (July 1996): 34–41. http://dx.doi.org/10.1145/231132.231141.

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Li, Mo, Hu Haoyi, and Sun Wenbin. "Learning levels of collective learning mechanism in industrial cluster." International Journal of Human Resources Development and Management 8, no. 1/2 (2008): 43. http://dx.doi.org/10.1504/ijhrdm.2008.018201.

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20

Curnow, Joe, and A. Susan Jurow. "Learning in and for collective action." Journal of the Learning Sciences 30, no. 1 (January 1, 2021): 14–26. http://dx.doi.org/10.1080/10508406.2021.1880189.

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21

Titus, Mathew, George Hagstrom, and James R. Watson. "Unsupervised manifold learning of collective behavior." PLOS Computational Biology 17, no. 2 (February 12, 2021): e1007811. http://dx.doi.org/10.1371/journal.pcbi.1007811.

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Collective behavior is an emergent property of numerous complex systems, from financial markets to cancer cells to predator-prey ecological systems. Characterizing modes of collective behavior is often done through human observation, training generative models, or other supervised learning techniques. Each of these cases requires knowledge of and a method for characterizing the macro-state(s) of the system. This presents a challenge for studying novel systems where there may be little prior knowledge. Here, we present a new unsupervised method of detecting emergent behavior in complex systems, and discerning between distinct collective behaviors. We require only metrics, d(1), d(2), defined on the set of agents, X, which measure agents’ nearness in variables of interest. We apply the method of diffusion maps to the systems (X, d(i)) to recover efficient embeddings of their interaction networks. Comparing these geometries, we formulate a measure of similarity between two networks, called the map alignment statistic (MAS). A large MAS is evidence that the two networks are codetermined in some fashion, indicating an emergent relationship between the metrics d(1) and d(2). Additionally, the form of the macro-scale organization is encoded in the covariances among the two sets of diffusion map components. Using these covariances we discern between different modes of collective behavior in a data-driven, unsupervised manner. This method is demonstrated on a synthetic flocking model as well as empirical fish schooling data. We show that our state classification subdivides the known behaviors of the school in a meaningful manner, leading to a finer description of the system’s behavior.
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22

Macintyre, Ronald. "Open Educational Partnerships and Collective Learning." Journal of Interactive Media in Education 2013, no. 3 (August 2, 2013): 20. http://dx.doi.org/10.5334/2013-20.

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23

Tompkins, Ten C. "ROLE OF DIFFUSION IN COLLECTIVE LEARNING." International Journal of Organizational Analysis 3, no. 1 (January 1995): 69–85. http://dx.doi.org/10.1108/eb028824.

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24

Cook, Chelsea N., Natalie J. Lemanski, Thiago Mosqueiro, Cahit Ozturk, Jürgen Gadau, Noa Pinter-Wollman, and Brian H. Smith. "Individual learning phenotypes drive collective behavior." Proceedings of the National Academy of Sciences 117, no. 30 (July 15, 2020): 17949–56. http://dx.doi.org/10.1073/pnas.1920554117.

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Individual differences in learning can influence how animals respond to and communicate about their environment, which may nonlinearly shape how a social group accomplishes a collective task. There are few empirical examples of how differences in collective dynamics emerge from variation among individuals in cognition. Here, we use a naturally variable and heritable learning behavior called latent inhibition (LI) to show that interactions among individuals that differ in this cognitive ability drive collective foraging behavior in honey bee colonies. We artificially selected two distinct phenotypes: high-LI bees that ignore previously familiar stimuli in favor of novel ones and low-LI bees that learn familiar and novel stimuli equally well. We then provided colonies differentially composed of different ratios of these phenotypes with a choice between familiar and novel feeders. Colonies of predominantly high-LI individuals preferred to visit familiar food locations, while low-LI colonies visited novel and familiar food locations equally. Interestingly, in colonies of mixed learning phenotypes, the low-LI individuals showed a preference to visiting familiar feeders, which contrasts with their behavior when in a uniform low-LI group. We show that the shift in feeder preference of low-LI bees is driven by foragers of the high-LI phenotype dancing more intensely and attracting more followers. Our results reveal that cognitive abilities of individuals and their social interactions, which we argue relate to differences in attention, drive emergent collective outcomes.
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25

Boom, Jan. "Collective Development and the Learning Paradox." Human Development 34, no. 5 (1991): 273–87. http://dx.doi.org/10.1159/000277061.

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26

Burini, D. "Collective learning dynamics in behavioral crowds." Physics of Life Reviews 18 (September 2016): 25–26. http://dx.doi.org/10.1016/j.plrev.2016.07.002.

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27

Ahmed, Mohiuddin, and Al Sakib Khan Pathan. "Deep learning for collective anomaly detection." International Journal of Computational Science and Engineering 21, no. 1 (2020): 137. http://dx.doi.org/10.1504/ijcse.2020.10026872.

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Ahmed, Mohiuddin, and Al Sakib Khan Pathan. "Deep learning for collective anomaly detection." International Journal of Computational Science and Engineering 21, no. 1 (2020): 137. http://dx.doi.org/10.1504/ijcse.2020.105220.

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29

Saxe, Geoffrey B., and Steven R. Guberman. "Studying mathematics learning in collective activity." Learning and Instruction 8, no. 6 (December 1998): 489–501. http://dx.doi.org/10.1016/s0959-4752(98)00037-1.

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30

Chen, Jinyin, Keke Hu, Yitao Yang, Yi Liu, and Qi Xuan. "Collective transfer learning for defect prediction." Neurocomputing 416 (November 2020): 103–16. http://dx.doi.org/10.1016/j.neucom.2018.12.091.

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31

Luan, Kun, Ramón Rico, Xiao-Yun Xie, and Qian Zhang. "Collective Team Identification and External Learning." Small Group Research 47, no. 4 (June 8, 2016): 384–405. http://dx.doi.org/10.1177/1046496416653664.

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32

Schechter, Chen. "Toward Collective Learning in Schools: Exploring U.S. and Israeli Teachers’ Perceptions of Collective Learning from Success." International Journal of Educational Reform 24, no. 2 (April 2015): 160–84. http://dx.doi.org/10.1177/105678791502400206.

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33

Schechter, Chen. "Developing teachers’ collective learning: Collective learning from success as perceived by three echelons in the school system." International Journal of Educational Research 56 (January 2012): 60–74. http://dx.doi.org/10.1016/j.ijer.2012.06.005.

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34

Béliveau, Julie, and Anne-Marie Corriveau. "The Learning History Methodology: An Infrastructure for Collective Reflection to Support Organizational Change and Learning." Electronic Journal of Business Research Methods 19, no. 2 (December 3, 2021): pp71–83. http://dx.doi.org/10.34190/ejbrm.19.2.2510.

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Organization members often complain about insufficient time to reflect collectively as they grapple with constant significant changes. The Learning History methodology can support this collective reflection. Given the scant empirical studies of this action research approach, the present paper fills this gap by giving an overview of this methodology and by presenting a qualitative study that answers the following research question: How does the Learning History methodology contribute to collective reflection among organization members during major organizational change? To answer this question, an empirical research project was led within five healthcare organizations in Canada during their implementation of the Planetree person-centered approach to management, care, and services. The data set includes 150 semi-structured interviews, 20 focus groups and 10 feedback meetings involving organization members representing all hierarchical levels in the five participating institutions. The results highlight the five types of contributions of the Learning History methodology to collective reflection within the five institutions that participated in the study: 1) a process of expression, dialogue, and reflection among organization members; 2) a portrait of the change underway; 3) a support tool for the change process; 4) a vector for mobilizing stakeholders; and 5) a source of organizational learning. The results also show how organization members’ collective reflection is built through the various stages of the Learning History methodology. By demonstrating that this collective reflection leads to true organizational learning, the findings position the Learning History as a research-action method useful both from a research standpoint and as an organizational development tool. In the conclusion, lessons learned using the LH approach are shared from a researcher’s perspective. This paper should interest researchers and practitioners who seek research methodologies that can offer an infrastructure for collective reflection to support organizational change and learning.
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35

KILGORE, DEBORAH W. "Understanding learning in social movements: a theory of collective learning." International Journal of Lifelong Education 18, no. 3 (May 1999): 191–202. http://dx.doi.org/10.1080/026013799293784.

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36

Mavunga, George. "The Influence of Collective Agency on the Culture of Employee Learning:." International Journal of African Higher Education 8, no. 1 (April 18, 2021): 43–69. http://dx.doi.org/10.6017/ijahe.v8i1.13369.

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This study investigated the influence of collective agency on the culture of employee learning among administrative assistants at a comprehensive South African university. Data were gathered through semi-structured interviews with 15 participants; the senior human resource training and development coordinator and a trade union leader. The study found that there is positive intentionality in the official domain of employee learning at the institution, resulting from the need for agents to collectively re-contextualise the knowledge constituting the employee learning curriculum. Similarly, there was evidence of the positive influence of collect- -ive agency in the administrative assistants’ responses to institutional employee learning initiatives. However, some tensions were noted between senior management’s and administrative assistants’ exercise of collective agency in the practice of employee learning. This hampered alignment between the institution’s strategic intentions and the administrative assistants’ collective employee learning goals. Based on these findings, it is recommended that efforts can be made to promote congruence between management’s exercise of collective agency in the official re-contextualisation of the employee learning curriculum and the administrative assist- -tants’ collective responses to employee learning practices. Key words: employee learning, administrative assistants, collective agency
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37

Mena, Francisco, Ricardo Ñanculef, and Carlos Valle. "Collective annotation patterns in learning from crowds." Intelligent Data Analysis 24 (December 4, 2020): 63–86. http://dx.doi.org/10.3233/ida-200009.

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The lack of annotated data is one of the major barriers facing machine learning applications today. Learning from crowds, i.e. collecting ground-truth data from multiple inexpensive annotators, has become a common method to cope with this issue. It has been recently shown that modeling the varying quality of the annotations obtained in this way, is fundamental to obtain satisfactory performance in tasks where inexpert annotators may represent the majority but not the most trusted group. Unfortunately, existing techniques represent annotation patterns for each annotator individually, making the models difficult to estimate in large-scale scenarios. In this paper, we present two models to address these problems. Both methods are based on the hypothesis that it is possible to learn collective annotation patterns by introducing confusion matrices that involve groups of data point annotations or annotators. The first approach clusters data points with a common annotation pattern, regardless the annotators from which the labels have been obtained. Implicitly, this method attributes annotation mistakes to the complexity of the data itself and not to the variable behavior of the annotators. The second approach explicitly maps annotators to latent groups that are collectively parametrized to learn a common annotation pattern. Our experimental results show that, compared with other methods for learning from crowds, both methods have advantages in scenarios with a large number of annotators and a small number of annotations per annotator.
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38

Huang, Yen-Chih, and Yang-Chieh Chin. "Transforming collective knowledge into team intelligence: the role of collective teaching." Journal of Knowledge Management 22, no. 6 (August 13, 2018): 1243–63. http://dx.doi.org/10.1108/jkm-03-2017-0106.

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Purpose The purpose of this study is to explore the pivotal role that collective teaching plays in knowledge transfer between new product development teams. This study develops a theoretical model of collective teaching, where team intelligence is its consequence and learning orientation cognitive skills are moderators. Design/methodology/approach Based on a questionnaire survey of 156 pairs of new product development project teams of information technology firms, the authors used partial least squares to test the study’s hypotheses. Findings The findings reveal that the use of collective teaching is positively related to team intelligence of recipient teams. In addition, T-shaped skills of source teams exert positive moderating influence on this relationship and so does a learning orientation of recipient teams. Research limitations/implications First, the sample firms used in the study are from the IT industry, which is characterized by extremely short product life cycles, thereby limiting the generalizability of the study’s findings. Second, the authors did not examine whether the effect of T-shaped skills is different at various NPD stages; the contributions of each functional expertise may vary depending on the NPD stage (e.g. the idea generation or pre-launch stage). Third, the use of cross-sectional design precludes a causal inference. The role of focal constructs and moderators and their consequent effects would benefit from more stringent, longitudinal research. Finally, the authors controlled for only a limited set of factors of team intelligence because other potential antecedents of this variable still await identification by future studies. Practical implications This study suggests that the implementation of collective teaching can enhance the capacity of a project team as a whole to manage and innovate information, namely, team intelligence. The study’s findings also suggest that the management must recognize the significance of teams’ learning orientation and thereby proactively develop teams’ learning culture by redesigning work, reward systems or performance evaluation to promote learning. Additionally, it is prudent for managers to reconsider their recruitment criteria to incorporate T-shaped skills. Originality/value This study represents the first step in developing an empirically grounded framework linking collective teaching with team intelligence. Additionally, the authors confirm that team intelligence is a four-dimensional construct.
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39

Garavan, Thomas N., and Alma McCarthy. "Collective Learning Processes and Human Resource Development." Advances in Developing Human Resources 10, no. 4 (June 5, 2008): 451–71. http://dx.doi.org/10.1177/1523422308320473.

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40

Rouchier, Juliette, and Emily Tanimura. "When overconfident agents slow down collective learning." SIMULATION 88, no. 1 (November 30, 2011): 33–49. http://dx.doi.org/10.1177/0037549711428948.

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41

Allen, David. "Reconstructing professional learning community as collective creation." Improving Schools 16, no. 3 (October 29, 2013): 191–208. http://dx.doi.org/10.1177/1365480213501056.

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42

Kwifi, Osama Sam Al. "Implementing strategic renewal by collective organisational learning." J. for International Business and Entrepreneurship Development 6, no. 2 (2012): 125. http://dx.doi.org/10.1504/jibed.2012.048570.

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43

Staber, Udo. "Collective learning in clusters: Mechanisms and biases." Entrepreneurship & Regional Development 21, no. 5-6 (September 2009): 553–73. http://dx.doi.org/10.1080/08985620802529526.

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Herbert-Read, James. "Collective Behaviour: Leadership and Learning in Flocks." Current Biology 25, no. 23 (December 2015): R1127—R1129. http://dx.doi.org/10.1016/j.cub.2015.10.031.

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45

Towers, Jo, Lyndon C. Martin, and Brenda Heater. "Teaching and learning mathematics in the collective." Journal of Mathematical Behavior 32, no. 3 (September 2013): 424–33. http://dx.doi.org/10.1016/j.jmathb.2013.04.005.

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46

Gear, Tony, Russ Vince, Martin Read, and A. Leonard Minkes. "Group enquiry for collective learning in organisations." Journal of Management Development 22, no. 2 (March 2003): 88–102. http://dx.doi.org/10.1108/02621710310459676.

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47

Gustavsson, Bengt, and Harald S. Harung. "Organizational Learning Based on Transforming Collective Consciousness." Learning Organization 1, no. 1 (April 1994): 33–40. http://dx.doi.org/10.1108/09696479410053421.

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48

De Lillo, S., and N. Bellomo. "On the modeling of collective learning dynamics." Applied Mathematics Letters 24, no. 11 (November 2011): 1861–66. http://dx.doi.org/10.1016/j.aml.2011.05.007.

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49

Jones, Matthew. "Organisational learning: Collective mind or cognitivist metaphor?" Accounting, Management and Information Technologies 5, no. 1 (January 1995): 61–77. http://dx.doi.org/10.1016/0959-8022(95)90014-4.

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50

Ferreira, Sebastião. "Collective learning for transformational change: a guide to collective learning, by Valerie A. Brown and Judith A. Lambert." Knowledge Management for Development Journal 8, no. 2-3 (September 2012): 219–24. http://dx.doi.org/10.1080/19474199.2012.717751.

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