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Cognitive load and knowledge sharing in Learning Networks

Profile image of Ya Ping HsiaoYa Ping Hsiao

2013, Interactive Learning Environments

https://doi.org/10.1080/10494820.2010.548068
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18 pages

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Abstract

Learning Networks are online social networks designed to support non-formal learning; they are therefore particularly suitable for self-directed learners. In Learning Networks, learners need to acquire knowledge through knowledge sharing with other participants. However, without a support structure, learners have to organize knowledge sharing themselves and this could induce extraneous cognitive load. When working on complex learning tasks, this organizing process could have a detrimental effect on knowledge acquisition. To optimize cognitive load, we propose to use a particular type of collaborative learning, peer tutoring, as a support structure. Its mechanisms reduce, we argue, the extraneous load imposed by organizing knowledge sharing as well as induce germane load by directing the freed cognitive capacity to processes that contribute to knowledge acquisition.

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Optimizing knowledge sharing in learning networks through peer tutoring
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Effects of peer-tutor competences on learner cognitive load and learning performance during knowledge sharing
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In Learning Networks, learners need to share knowledge with others to build knowledge. In particular, when working on complex tasks, they often need to acquire extra cognitive resources from others to process a high task load. However, without support high task load and organizing knowledge sharing themselves might easily overload learners' limited cognitive capacities because learners first have to find relevant peer tutors (ie, those who provide help) and then maintain the social interaction.

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A cognitive load approach to collaborative learning: United brains for complex tasks
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Educational Psychology Review, 2009

This article presents a review of research comparing the effectiveness of individual learning environments with collaborative learning environments. In reviewing the literature, it was determined that there is no clear and unequivocal picture of how, when and why the effectiveness of these two approaches to learning differ, a result which may be due to differing complexities of the learning tasks used in the research and the concomitant load imposed on the learner's cognitive system. Based upon cognitive load theory, it is argued that learning by an individual becomes less effective and efficient than learning by a group of individuals as task complexity increases. Dividing the processing of information across individuals is useful when the cognitive load is high because it allows information to be divided across a larger reservoir of cognitive capacity. Although such division requires that information be recombined and that processing be coordinated, under high load conditions these costs are minimal compared to the gain achieved by this division of labor. In contrast, under low load conditions, an individual can adequately carry out the required processing activities, and the costs of recombination and coordination are relatively more substantial. Implications of these ideas for research and practice of collaborative learning are discussed.

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Understanding and Identifying Cognitive Load in Networked Learning
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Cognitive load theory (CLT) focuses on human cognition and the limitations of short-term memory. CLT seeks to appreciate the cognitive effort required to complete a learning task relative to the capacity of the short-term memory (Sweller 1988, 1994). It provides a framework for understanding practical implications for both the design of learning situations (sometimes called 'instructional design' or 'learning design') and the support and facilitation of learning (often called 'teaching'). As De Jong (2010) points out, CLT has supported the advancement of educational theory and practice by aiding in the explanation of a large set of experimental findings. The premise that underpins the application of CLT is as follows: by recognising and addressing (reducing or eliminating) instances of cognitive load in learning situations, educators can improve learners' ability to acquire and develop schema and, in doing so, support learning. This chapter considers CLT in networked learning (NL) and seeks to provide guidance in the identification and description of instances of cognitive load in NL so that they can be addressed through design and teaching practices that specifically aim to reduce cognitive load in NL situations. This chapter is guided by two broad questions: • How does cognitive load manifest in learning (in general)? • How does cognitive load manifest in in networked learning (in particular)?

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Making the black box of collaborative learning transparent: Combining process-oriented and cognitive load approaches
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Traditional research on collaborative learning employs a "black box" approach that makes it difficult to gain a deeper understanding of the differential effects of collaborative learning. To make the black box transparent, researchers have studied the process of collaboration, in order to establish which interaction features are likely to make learning more effective and efficient for group members. Although cognitive load theory has been developed in the context of individual learning situations, it may provide a promising new way of looking inside the black box, assuming that students working in groups have more processing capacity than students working individually. The aim of this article is to provide an overview of the process-oriented and cognitive-load approaches to conducting collaborative learning research, to highlight their respective advantages and disadvantages, and to suggest how they can be combined in order to address new research questions.

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Guided Self-organisation in Learning Networks as a Means to Optimize Cognitive Load and Transfer of Information Seeking Skills
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Learning Networks are designed to support the lifelong learner. In such settings, learning needs to be guided for self-organisational processes to emerge. Using a cognitive load approach it is argued that without guidance, learners have to allocate cognitive capacity to structuring the cooperative process of knowledge sharing, thus diminishing the capacity available for knowledge sharing and learning.

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The effects of peer instruction through social learning environment towards students’ cognitive load and learning performance
Bosede I Edwards

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The use of social media like Facebook as an educational tool has been reported to be able to promote learning and serve as a learning platform. However, Facebook also contributes to promote increased mental demand or Cognitive Load (CL). It is a challenge to learning due to the limitation of the working memory for information processing can have effects on learning performance. This situation has not been well emphasized by most instructors. Studies reported that Peer Instruction (PI) learning strategy has the potential to reduce CL while also improving learning performance but implementation of PI in social media is rather new. Thus, this study focuses on the development of a framework for best practices on the use of social media for education through enhancing learning performance and reducing CL using PI. A two-phase study was implemented for this purpose where Phase 1 involved a survey to 226 students at a Malaysian public university to identify students’ experience of CL durin...

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Informal and autonomous learning in networked learning environments
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In the digital age of social media, the act of learning has become transformed. In particular, the network offers unprecedented access to informal learning, to the point where the value of formal learning is being openly questioned. In the new environment, the value of learning takes on new meaning, as it can be construed as a function of the application of some tangible results, and otherwise as the acquisition of a credential, or more generally as a manifestation of human capital , defined as anything that has value to humans, including intangibles such as health or happiness. Informal learning can be understood as a multidimensional construct, and its various manifestations in networked environments carry multiple opportunities as well as very real hazards. Traditional definitions of learning and knowing are being challenged by the emergence of networked learning environments. In the recent history of technological developments, there has been a tendency to construct learning theories that take into account the evolving technological environments. For example, holonomic brain theory could not have emerged before the mathematical appearance of fractals and holograms. Computers themselves have been the basis of three powerful metaphors, namely computer-as-memory; computer-as-mind; and computer-as-society. Similarly, the skills and awareness of the learners must be examined in light of the features of the new learning environment. Informal learning can be intentional / or incidental; self-directed / or other-directed; conscious / or unconscious. Depending on each environment's characteristics, communities of networked learners are exposed to various degrees of peer-control, serendipitous encounters, personal initiative and, in some cases, manipulation from others. Networks themselves offer some features that set them apart from other forms of mediated learning. The scale-free property of networks most notably carries some implications regarding the structure and power distribution within them. This in turn will affect the quality and outcomes of interaction in networked learning environments. Finally, learners vary in the degree of development of their epistemic beliefs. It can be argued that networked learning has a double implication: on the one hand, it often compels learners to question their current views of knowledge. On the other hand, networked learning, in many instances, requires an already well-defined set of epistemic beliefs.

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References (47)

  1. Beckmann, J. F. (2010). Taming a beast of burden -On some issues with the conceptualisation and operationalisation of cognitive load. Learning and Instruction, 20(3), 250-264.
  2. Cole, M. (2009). Using Wiki technology to support student engagement: Lessons from the trenches. Computers & Education, 52(1), 141-146.
  3. Cress, U., & Hesse, F. (2006). The social complexity in establishing and sharing information resources. In J. Elen & R. E. Clark (Eds.), Handling complexity in learning environments: Theory and research (pp. 67-87). Oxford, UK: Elsevier Ltd.
  4. De Bakker, G., Sloep, P., & Jochems, W. (2008). Towards a system for allocated peer tutoring via instant messaging: a pilot study. Paper presented at the ALT-C conference 2008.
  5. De Jong, T. (2010). Cognitive load theory, educational research, and instructional design: some food for thought. Instructional Science, 38(2), 105-134.
  6. Dillenbourg, P., & Schneider, D. (1995). Collaborative learning and the Internet. Retrieved May 26, 2009, from http://tecfa.unige.ch/tecfa/research/CMC/colla/iccai95_1.html
  7. Fantuzzo, J. W., Riggio, R. W., Connelly, S., & Dimeff, L. (1989). Effects of Reciprocal Peer Tutoring on Academic Achievement and Psychological Adjustment: A Component Analysis. Journal of Educational Psychology, 81(2), 173-177.
  8. Fountain, R. (2005). Wiki pedagogy. Retrieved March 31th, 2010, from http://profetic.org/dossiers/article.php3?id_article=969.
  9. Greenwood, C. R., Delquadri, J. C., & Hall, R. V. (1989). Longitudinal Effects of Classwide Peer Tutoring. Journal of Educational Psychology, 81, 371-383.
  10. Gyanani, T. C., & Premlata, P. (1995). Effects of Peer Tutoring on Abilities and Achievement Contemporary Educational Psychology, 20, 469-475.
  11. Harasim, L., Hiltz, R., Teles, L., & Turoff, M. (1995). Learning Networks: A field guide to teaching and learning online. Cambridge: MIT Press.
  12. Hsiao, Y. P. (2009, September 18th, 2009). Interactief Chinees leren [Interactive Chinese learning].
  13. Retrieved November 15th, 2009, from http://www.surfspace.nl/nl/Praktijkvoorbeelden/Pages/INTERACTIEFCHINEESLER EN.aspx Kester, L., & Sloep, P. (2009). Knowledge dating and knowledge sharing in Ad-Hoc Transient Communities. In R. Koper (Ed.), Learning Network Services for Professional Development (pp. 43-56). Berlin and Heidelberg: Springer-Verlag.
  14. Kester, L., Sloep, P., Brouns, F., Van Rosmalen, P., De Vries, F., De Croock, M., et al. (2006a, 26-28 February). Enhancing social interaction and spreading tutor responsibilities in bottom-up organized learning networks. Paper presented at the Proceedings IADIS Conference Web Based Communities, San Sebastian, Spain.
  15. Kester, L., Sloep, P., Van Rosmalen, P., Brouns, F., Koné, M., & Koper, R. (2006b). Facilitating community building in Learning Networks through peer tutoring in ad hoc transient communities. International Journal of Web based Communities, 3(2), 198-205.
  16. Kester, L., Van Rosmalen, P., Sloep, P., Brouns, F., Koné, M., & Koper, R. (2007). Matchmaking in Learning Networks: Bringing Learners Together for Knowledge Sharing. Interactive Learning Environments, 15(2), 117-126.
  17. Kiesler, S., Siegel, J., & McGuire, T. W. (1988). Social psychological aspects of computer- mediated communication (Reprint). In I. Greif (Ed.), Computer-supported cooperative work: a book of readings (pp. 657-682). San Mateo: Morgan Kaufmann Publishers Inc.
  18. King, A. (1997). ASK to THINK-TEL WHY: A model of transactive peer tutoring for scaffolding higher level complex learning. Educational Psychologist, 32(4), 221-235.
  19. King, A. (1998). Transactive Peer Tutoring: Distributing Cognition and Metacognition. Educational Psychology Review, 10(1), 57-74.
  20. King, A. (2002). Structuring peer interaction to promote high-level cognitive processing. Theory into Practice, 41(1), 33-39.
  21. King, A., Staffieri, A., & Adelgais, A. (1998). Mutual peer tutoring: Effects of structuring tutorial interaction to scaffold peer learning. Journal of Educational Psychology, 90(1), 134-152.
  22. Kirschner, F., Paas, F., & Kirschner, P. A. (2008a). Individual versus group learning as a function of task complexity: An exploration into the measurement of group cognitive load. In J. Zumbach, N. Schwartz, T. Seufert & L. Kester (Eds.), Beyond knowledge: The legacy of competence (pp. 21-28): Springer.
  23. Kirschner, F., Paas, F., & Kirschner, P. A. (2009a). A cognitive-load approach to collaborative learning: United brains for complex tasks. Educational Psychology Review, 21, 31-42.
  24. Kirschner, F., Paas, F., & Kirschner, P. A. (2009b). Individual and group-based learning from complex cognitive tasks: Effects on retention and transfer efficiency. Computers in Human Behavior, 25(2), 306-314.
  25. Kirschner, P. A., Beers, P. J., Boshuizen, H. P. A., & Gijselaers, W. H. (2008b). Coercing shared knowledge in collaborative learning environments. Computers in Human Behavior, 24(2), 403-420.
  26. Koper, R., & Sloep, P. B. (2002). Learning Networks: connecting people, organizations, autonomous agents and learning resources to establish the emergence of effective lifelong learning: Education Technology Expertise Centre (OTEC), Open University of the Netherlands.
  27. Koper, R., & Tattersall, C. (2004). New directions for lifelong learning using network technologies. British Journal of Educational Technology, 35(6), 689-700.
  28. Livingstone, D. W. (1999). Exploring the Icebergs of Adult Learning: Findings of the First Canadian Survey of Informal Learning Practices. The Canadian Journal for the Study of Adult Education, 13, 49-72.
  29. Moust, J. H. C., & Schmidt, H. G. (1994). Facilitating small-group learning: a comparison of student and staff tutors' behavior. Instructional Science, 22, 287-301.
  30. Paas, F. (1992). Training Strategies for Attaining Transfer of Problem-Solving Skill in Statistics: A Cognitive-Load Approach. Journal of educational psychology, 84(4), 429-434.
  31. Paas, F., Renkl, A., & Sweller, J. (2003a). Cognitive Load Theory and Instructional Design: Recent Developments. Educational Psychologist, 38(1), 1-4.
  32. Paas, F., Renkl, A., & Sweller, J. (2004). Cognitive Load Theory: Instructional Implications of the Interaction between Information Structures and Cognitive Architecture. Instructional Science, 32, 1-9.
  33. Paas, F., Tuovinen, J., Tabbers, H., & Van Gerven, P. (2003b). Cognitive Load Measurement as a Means to advance Cognitive Load Theory. Educational Psychologist, 38(1), 63- 71.
  34. Paas, F., & Van Merriënboer, J. (1993). The Efficiency of Instructional Conditions: An Approach to Combine Mental Effort and Performance Measures. Human Factors, 35(4), 737-743.
  35. Sloep, P. (2009, June, 11-13, 2008). Fostering Sociability in Learning Networks through Ad- Hoc Transient Communities. Paper presented at the Computer-Mediated Social Networking, Dunedin, New Zealand.
  36. Sloep, P., Kester, L., Brouns, F., Van Rosmalen, P., De Vries, F., De Croock, M., et al. (2007, 14-16 March). Ad Hoc Transient Communities to Enhance Social Interaction and Spread Tutor Responsibilities. Paper presented at the The Sixth IASTED International conference Web-Based Education, Chamonix, France.
  37. Sweller, J. (2006). How the human cognitive system deals with complexity. In J. Elen & R. E. Clark (Eds.), Handling complexity in learning environments: Theory and research (pp. 13-25). Oxford, UK: Elsevier.
  38. Sweller, J., Van Merriënboer, J., & Paas, F. (1998). Cognitive Architecture and Instructional Design. Educational Psychology Review, 10(3), 251-296.
  39. Topping, K. J. (1996). The effectiveness of peer tutoring in further and higher education: A typology and review of the literature. Higher Education, 32(3), 321-345.
  40. Van Gog, T., & Paas, F. (2008). Instructional Efficiency: Revisiting the Original Construct in Educational Research. Educational Psychologist, 43(1), 16-26.
  41. Van Merriënboer, J., Kirschner, P., Paas, F., Sloep, P., & Caniëls, M. (2009). Towards an integrated approach for research on lifelong learning. Educational Technology Magazine, 49(3), 3-15.
  42. Van Merriënboer, J., & Sweller, J. (2005). Cognitive Load Theory and Complex Learning: Recent Developments and Future Directions. Educational Psychology Review, 17(2), 147-199.
  43. Van Rosmalen, P. (2008). Supporting the tutor in the design and support of adaptive e- learning. Open University, Heerlen.
  44. Van Rosmalen, P., Sloep, P., Brouns, F., Kester, L., Berlanga, A., Bitter, M., et al. (2008a). A model for online learner support based on selecting appropriate peer tutors. Journal of Computer Assisted Learning, 24(6), 483-493.
  45. Van Rosmalen, P., Sloep, P., Brouns, F., Kester, L., Koné, M., & Koper, R. (2006). Knowledge matchmaking in Learning Networks: Alleviating the tutor load by mutually connecting Learning Network users. British Journal of Educational Technology, 37(6), 881-895.
  46. Van Rosmalen, P., Sloep, P., Kester, L., Brouns, F., De Croock, M., Pannekeet, K., et al. (2008b). A learner support model based on peer tutor selection. Journal of Computer Assisted Learning, 24(1), 74-86.
  47. Westera, W., De Bakker, G., & Wagemans, L. (2009). Self-arrangement of fleeting student pairs: a Web 2.0 approach for peer tutoring. Interactive Learning Environments, 17(4), 341-349.

Related papers

Reducing extraneous load of self-organized knowledge sharing by applying peer tutoring in Learning Networks
Ya Ping (Amy) Hsiao

2010

In Learning Networks, learners need to acquire knowledge through knowledge sharing with other participants. However, without support, learners have to self-organize knowledge sharing by finding a relevant knowledge sharer, structuring the interaction and maintaining the communication process. According to cognitive load theory, these activities could induce extraneous load because they are not directly relevant to learning itself but to the learning environment. When working on complex tasks, extraneous load becomes detrimental to learning effectiveness and efficiency because learners have to simultaneously deal with the high intrinsic load of the complex tasks and the extraneous load of knowledge sharing activities. For such tasks, it is considered imperative to reduce extraneous load and we used a peer tutoring system to support knowledge sharing by matching learners together, providing role specifications and an interactional tool of wiki. This study investigated the effect of using this peer tutoring system to support knowledge sharing on different levels of task complexity in the Learning Network of Internet Basics. Based upon cognitive load theory, an interaction effect was expected that peer tutoring would reduce extraneous load and result in better learning effectiveness and efficiency only on complex tasks. In addition, we expected that using peer tutoring would result in better knowledge sharing on complex tasks. However, these hypotheses were not confirmed because of the limited number of knowledge sharing inquiries: this indicated that the peer tutoring support was not used sufficiently to have effects on cognitive load, learning effectiveness and efficiency. The major contributions of this study were i) we explored the effect of using different supports of knowledge sharing on humans’ cognitive system in a non-formal Learning Network by applying cognitive load theory, ii) we showed the challenges of data collection, especially for measuring cognitive load in such learning environments.

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Effects of using a peer support system to optimise knowledge sharing in learning networks: a cognitive load perspective
Ya Ping Hsiao

International Journal of Continuing Engineering Education and Life-Long Learning, 2016

In a non-formal learning network, knowledge sharing is often desirable when working on complex tasks. However, without support, learners need to first find a tutor and then maintain social interaction, which, according to cognitive load theory, may hamper learning. After all, the extraneous load imposed by these two activities and the intrinsic load imposed by the task itself might easily overload learners' cognitive capacity. We compared the effects of using a peer support system with an automatic tutor assignment and an interaction tool (wiki) to a forum and control group (without any support) on learners' cognitive load and learning efficiency for simple and complex tasks. The results did not significantly show that this peer support system was instrumental in reducing cognitive load and improving learning efficiency. However, the study did shed an illuminating light on how to apply instructional guidelines of cognitive load theory to non-formal learning networks.

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Effect of using peer tutoring to support knowledge sharing in Learning Networks: A cognitive load perspective
Ya Ping (Amy) Hsiao

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In Learning Networks, learners need to acquire knowledge through knowledge sharing with other participants. However, without support, learners have to self-organize knowledge sharing by finding a relevant knowledge sharer, structuring the interaction and maintaining the communication process. According to cognitive load theory, these activities could induce extraneous load because they are not directly relevant to learning itself but to the learning environment. When working on complex tasks, extraneous load becomes detrimental to learning effectiveness and efficiency because learners have to simultaneously deal with the high intrinsic load of the complex tasks and the extraneous load of knowledge sharing activities. For such tasks, it is considered imperative to reduce extraneous load and we used a peer tutoring system to support knowledge sharing by matching learners together, providing role specifications and an interactional tool of wiki. This study investigated the effect of using this peer tutoring system to support knowledge sharing on different levels of task complexity in the Learning Network of Internet Basics. Based upon cognitive load theory, an interaction effect was expected that peer tutoring would reduce extraneous load and result in better learning effectiveness and efficiency only on complex tasks. In addition, we expected that using peer tutoring would result in better knowledge sharing on complex tasks. However, these hypotheses were not confirmed because of the limited number of knowledge sharing inquiries: this indicated that the peer tutoring support was not used sufficiently to have effects on cognitive load, learning effectiveness and efficiency. The major contributions of this study were i) we explored the effect of using different supports of knowledge sharing on humans’ cognitive system in a non-formal Learning Network by applying cognitive load theory, ii) we showed the challenges of data collection, especially for measuring cognitive load in such learning environments.

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Designing optimal peer support to alleviate learner cognitive load in Learning Networks
Francis Brouns

2012

In Learning Networks, learners have to engage in social interactions for sharing knowledge to achieve their personalized learning goals. When working on complex tasks, self-organized knowledge sharing imposes too much cognitive load and this is detrimental to learning. According to pedagogical guidelines of cognitive load theory, learning environments should not only avoid activities that distract learner attention but also focus learner attention on relevant activities that contribute to learning.

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Using technology-enhanced peer tutoring to support knowledge sharing in Learning Networks
Ya Ping (Amy) Hsiao

2010

In Learning Networks, learners need to acquire knowledge through knowledge sharing with other participants. However, without support, learners have to self-organize knowledge sharing and this could induce extraneous load. According to cognitive load theory, this self-organized knowledge sharing becomes detrimental to learning effectiveness and efficiency when working on complex tasks. This study investigated the effect of using peer tutoring to support knowledge sharing on different levels of task complexity in the Learning Network of Internet Basics. Based upon cognitive load theory, an interaction effect was expected that peer tutoring would reduce extraneous load and result in better learning effectiveness and efficiency only on complex tasks. In addition, we expected that using peer tutoring would result in better knowledge sharing on complex tasks. However, these hypotheses were not confirmed because of the limited number of knowledge sharing inquiries and this indicated that the peer tutoring support was not used sufficiently to have effects on cognitive load, learning effectiveness and efficiency. The exploration of applying cognitive load theory in a non-formal Learning network showed the challenges of data collection, especially for measuring cognitive load in such learning environments.

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  • Computer Science
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