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Crowdsourcing: language learning and commercial imperatives in web 2.0 language learning communities

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Presentation at the workshop "workflows for crowdsourcing
of language learning materials" organised by Elena Volodina and Iztok Kosem, EnetCollect network (European Network for Combining Language Learning with Crowdsourcing Techniques http://enetcollect.eurac.edu ), Iasi, March 14, 2018

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Crowdsourcing: language learning and commercial imperatives in web 2.0 language learning communities

  1. 1. Crowdsourcing: language learning and commercial imperatives in web 2.0 language learning communities Katerina Zourou, Ph.D., Web2Learn, Greece EnetCollect workshop, Iasi, Romania, March 14, 2018 Funded by the Horizon 2020 Framework Programme of the European Union EnetCollect: European Network for Combining Language Learning with Crowdsourcing Techniques
  2. 2. Workshop’s topic: how crowdsourcing techniques are used for generating language learning materials? This presentation: What kind of mechanisms sustain crowdsourced language learning? • mechanics sustaining language learning and language tutoring. • Language tutoring: creation of language learning materials in one’s L1 as part of “duties” of an L2 learner • Emphasis on crowdsourcing dynamics enabled by social networking and gamification elements. • Crowdsourcing happening without remuneration (e.g. Mechanical Turk), on a “voluntary” basis. Scope Katerina Zourou, EnetCollect workshop, Iasi, March 14, 2018
  3. 3. An attempt to connect recent studies on gamification, social networks, crowdsourcing and computer assisted language learning (CALL). Contribute to an understanding of the nuances between implicit and explicit crowdsourcing, by looking at the game and social network dynamics and the way they are instrumentalized to serve crowdsourcing purposes, often for a value making process. Background Katerina Zourou, EnetCollect workshop, Iasi, March 14, 2018
  4. 4. « Explicit » crowdsourced production of language learning materials Example: Duolingo’s Incubator Note: just an indicative example. Developments are in progress. Katerina Zourou, EnetCollect workshop, Iasi, March 14, 2018
  5. 5. Katerina Zourou, EnetCollect workshop, Iasi, March 14, 2018
  6. 6. Katerina Zourou, EnetCollect workshop, Iasi, March 14, 2018
  7. 7. « Implicit » crowdsourced production of language learning materials • Field analysis: web 2.0 language learning communities (2012 typology by Loiseau, Potolia & Zourou) A. Structured web 2.0 language learning communities B. Language exchange sites C. Market places D. Language correction sites • Language tutoring: creation of language learning materials in one’s L1 as part of “duties” of an L2 learner • An incentivized process (gamification; incentivized learning) enhanced by social networking and gamification elements. Katerina Zourou, EnetCollect workshop, Iasi, March 14, 2018
  8. 8. Social networking and gamification elements From Zourou, K., & Lamy, M. (2013). Social networked game dynamics in web 2.0 language learning communities. Alsic, 16. Retrieved from http://alsic.revues.org/2642 Reminder: GM: Game mechanics Type of community: A.Structured web 2.0 lang. learning communities B.Language exchange sites C.Market places D.Language correction sites
  9. 9. Social networking and gamification elements In red box: tutor specific incentives In blue box: learner specific incentives
  10. 10. Gamification elements enabling crowdsourced language tutoring (and some content generation) Game mechanism 1 : teacher score (in points) 1. Through correction of learner input 2. By being the first person to correct a learner’s production 3. Through translation of materials into one’s native language 4. By creating flashcards 5. By contributing to ”Featured tips” 6. By giving quality feedback (by earning positive ratings) Game mechanism 2: badges 1. By commenting on learners’ posts 2. By creating and/or being leader in groups (Busuu only) 3. For being the top 10 Teacher or the top 100 teacher of the week/the month 4. By Translating content 5. By being rated as a (very) helpful corrector Game mechanism 4 rating systems 1. 1.Rating of learner input stars) 2. Rating of learner input followed by feedback stars) 3. Rating of tutor feedback (stars) 4. Intra-native speaker feedback (points or “nods”) Katerina Zourou, EnetCollect workshop, Iasi, March 14, 2018
  11. 11. Conclusion Katerina Zourou, EnetCollect workshop, Iasi, March 14, 2018 • Gaming rewards and reputation management as triggers to more user activity • "crowdsourcing" : Engagement of individuals who voluntarily offer their knowledge to a knowledge seeker (an organisation, a company, etc.) Howe (2006). Depending on the context • it can be seen not only as a movement towards massive user engagement in an unrestricted and collaborative manner, but also as a means by which companies exploit users' collective efforts of knowledge building, without a corresponding remuneration, "[by] tap[ping] the latent talent of the crowd" • Game mechanics are instrumental in triggering engagement and in increasing motivation in non-game environments. • The risk of crowdsourcing is often the exploitation of the results of users' efforts and a profit-making mechanism in favour of the knowledge seeker. Full paper: Zourou, K., & Lamy, M. (2013). Social networked game dynamics in web 2.0 language learning communities. Alsic, 16. Retrieved from http://alsic.revues.org/2642
  12. 12. Thankyou! #enetcollect @web2learn_eu Slides available at https://www.slideshare.net/Web2Learn_eu

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