Developing pedagogically appropriate language corpora through
                        crowdsourcing and gamification

Zviel-Girshin, Rina; Kuhn, Tanara Zingano; Luís, Ana R.; Koppel, Kristina; Todorovic, B.; Holdt, Špela Arhar; Tiberius, Carole; Kosem, Iztok

doi:10.14705/rpnet.2021.54.1352

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…We have developed the idea, formulated research questions, conducted initial tests with the crowd to establish the suitability of the crowdsourcing task, and used the lessons learned to design both the game flow and a work plan for the implementation. We have presented different stages of this work at conferences, as available in Kuhn et al (2021) and Zviel-Girshin et al (2021). In this paper, we focus on the newest development, namely on the first stage of the game preparation that primarily addresses issues related to the (corpus) data needed for the game.…”

Section: Introductionmentioning

confidence: 99%

Data preparation in crowdsourcing for pedagogical purposes

Kuhn¹,

Holdt

Kosem

et al. 2022

SLO2.0

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One way to stimulate the use of corpora in language education is by making pedagogically appropriate corpora, labeled with different types of problems (sensitive content, offensive language, structural problems). However, manually labeling corpora is extremely time-consuming and a better approach should be found. We thus propose a combination of two approaches to the creation of problem-labeled pedagogical corpora of Dutch, Estonian, Slovene and Brazilian Portuguese: the use of games with a purpose and of crowdsourcing for the task. We conducted initial experiments to establish the suitability of the crowdsourcing task, and used the lessons learned to design the Crowdsourcing for Language Learning (CrowLL) game in which players identify problematic sentences, classify them, and indicate problematic excerpts. The focus of this paper is on data preparation, given the crucial role that such a stage plays in any crowdsourcing project dealing with the creation of language learning resources. We present the methodology for data preparation, offering a detailed presentation of source corpora selection, pedagogically oriented GDEX configurations, and the creation of lemma lists, with a special focus on common and language-dependent decisions. Finally, we offer a discussion of the challenges that emerged and the solutions that have been implemented so far.

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Section: Introductionmentioning

confidence: 99%

Data preparation in crowdsourcing for pedagogical purposes

Kuhn¹,

Holdt

Kosem

et al. 2022

SLO2.0

Self Cite

View full text Add to dashboard Cite

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EnetCollect – European Network for Combining Language Learning with Crowdsourcing Techniques (COST Action CA16105)

Nicolas

Lyding

2022

SLO2.0

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This article reviews the European Network for Combining Language Learning with Crowdsourcing Techniques (enetCollect), an extensive network project created to foster research and innovation (R&I) on the combination of crowdsourcing and language learning. Accordingly, we explain how it began, introduce its overall logic and organization, and discuss its achievements in terms of both (1) creating a new R&I community through a concluded large network project, and (2) fostering R&I on a high-potential and mostly unexplored subject. We also discuss the challenges involved and lessons learned, whether in orchestrating and leading a new R&I community or the challenges we faced and generally observed in the efforts of enetCollect members, as they explored the many facets of such a versatile enterprise.

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Developing pedagogically appropriate language corpora through crowdsourcing and gamification

Cited by 2 publications

References 3 publications

Data preparation in crowdsourcing for pedagogical purposes

Data preparation in crowdsourcing for pedagogical purposes

EnetCollect – European Network for Combining Language Learning with Crowdsourcing Techniques (COST Action CA16105)

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