We warmly welcome a new Working Group in DARIAH on Combining Language Learning with Crowdsourcing Techniques (D4COLLECT). Chaired by Verena Lyding, Post-doc Researcher at the Institute for Applied Linguistics at Eurac Research, and Lionel Nicolas, Senior Researcher, Institute for Applied Linguistics of Eurac Research, this Working Group aims to explore research and innovation trends in the use of crowdsourcing techniques in the domain of language learning, while at the same time opening paths to crowdsource NLP datasets from language learning activities.
Linguistic diversity is enshrined in Article 22 of the Charter of Fundamental Rights of the European Union and the respect for the rights of persons belonging to minorities is a fundamental element of the Charter. At the same time, the increasing demand for language learning materials and the striking diversification of learner profiles poses a challenge for language instructors all over the world. Material devised for learners with a specific linguistic background (e.g. French) can thus be far less effective for learners with a significantly different background (e.g. Hungarian). One-size-fits-all materials are therefore bound to be suboptimal solutions that force learners to make up for the mismatch and thus partially limit their learning progression. The more heterogeneous and numerous the learner profiles become, as is the reality today, the greater the need for new specific materials. Hence the need to explore new ways to produce language-learning material.
Aims of D4COLLECT
D4COLLECT is aimed at exploring research and innovation trends in the use of crowdsourcing techniques in the domain of language learning, while at the same time opening paths to crowdsource NLP datasets from language learning activities. This means that on the production side, R&I players who are working on language-related topics and have laborious and complex tasks that can be approached by crowdsourcing are prospective members of D4COLLECT no matter if they are directly interested in language learning or rather in the crowdsourcing workforce it can unleash through its learners and teachers.
As such, D4COLLECT is meant to sustain and move forward the outcomes of the COST Action enetCollect, which was funded from 2017-2021, and to serve as a flexible and dynamic bottom-up institutional framework for knowledge exchange, research coordination and capacity building beyond the end of the COST Action.
To that end, D4COLLECT will bring together language teachers and experts in linguistics, computational linguistics, educational sciences, software engineering and digital humanities to explore digital workflows, tools, and solutions for deploying implicit and explicit crowdsourcing methods in the creation of language-learning materials and the collection of language datasets. For instance, explicit crowdsourcing can be used to collaboratively devise lesson content and evaluate its effectiveness by comparing the performance of different samples of learners; while implicit crowdsourcing can be used to generate exercise content from language resources (e.g., lexica) and manually validate automatically generated content, while at the same time improve and extend the underlying datasets.
We warmly welcome the D4COLLECT Working Group in DARIAH and look forward to fruitful collaborations!
If you would like to join the WG or be kept up to date on its activities, visit their page or email the chairs.