7 December 2021, online
The strong relationship between vocabulary knowledge and reading comprehension has been thoroughly researched and confirmed (Laufer, 1992; Hsueh-Chao, & Nation, 2000). Readers should ideally know between 95% and 98% of the words in a text to adequately comprehend it (Laufer and Ravenhorst-Kalovski, 2010). Given that the larger the vocabulary, the better the comprehension of texts, foreign language curriculums should stress the need to teach new vocabulary items. As learners cannot easily learn thousands of words, it is crucial for foreign language curriculum designers, publishers of educational materials and textbooks, or even teachers to identify the most important words to teach at each stage of the learning process.
The most common answers to that challenge have been (1) to use frequency lists obtained from a large corpus of texts intended for native readers (Kucera and Francis, 1967; Leech et al., 2001; Brysbaert and New, 2009; etc.) and (2) to rely on expert knowledge, such as teacher expertise or linguists’ recommendations (Beacco et al., 2008; Capel, 2010). The advantage of the latter approach is its ability to relate words to some difficulty scale, in particular that of the CEFR, but its empirical foundations have been challenged. More recently, new trends have been investigated, such as frequency lists projected onto the CEFR scale (Kilgariff et al., 2014) or resources in which word frequencies are estimated on texts intended for L2 readers (François et al., 2014; Dürlich and François, 2018). However, these new trends only focus on the lexicon, whereas the CEFR Reference Level Descriptors (Beacco et al., 2008; O’Keeffe and Mark, 2017) may include grammatical structures, functions, pattern of spelling development, morphemic patterns, etc.
Recent efforts have opened new avenues for research in the field of graded resources for language learning. Lindström et al. (2021) aim at developing data-driven CEFR-graded resources for grammatical or morphological patterns. In parallel, new lexical resources have used meanings as the main entry instead of the spelling form (Alfter, 2021; Tack et al. 2018).
This workshop therefore aims at bringing together all current efforts about manual or automatic building of language resources with CEFR labels and at strengthening current initiatives by building a community around these topics.
Important dates
15 September22 September: Abstract submission deadline- 20 October: Notification of acceptance
- 15 October: Registration opens
- 7 December: Workshop
- 15 January: Full paper submission deadline
- 15 March: Notification of acceptance
Registration
Attendance will be free of charge but registration is compulsory.
Invited speakers
The workshop will include four invited talks, opening with a presentation of the CEFRLex project:
- Damien De Meyere & Thomas François (UCLouvain)
- Núria Gala (Aix Marseille Université)
- Elena Volodina (University of Gothenburg)
- Bill McDowell (DuoLingo)
Keynote speakers and summaries of invited talks
Scientific committee
- David Alfter (UCLouvain, Belgium)
- Adrien Bibal (UCLouvain, Belgium)
- Serge Bibauw (UCLouvain and KULeuven, Belgium)
- Rémi Cardon (UCLouvain, Belgium)
- Barbara De Cock (UCLouvain, Belgium)
- Damien De Meyere (UCLouvain, Belgium)
- Cédrick Fairon (UCLouvain, Belgium)
- Thomas François (UCLouvain, Belgium)
- Nuria Gala (Aix-Marseille Université, France)
- Maribel Montero (UGent, Belgium)
- Ann-Sophie Noreillie (KU Leuven, Belgium)
- Ferran Suñer Muñoz (UCLouvain, Belgium)
- Kaori Sugiyama (Seinan Gakuin University, Japan)
- Amalia Todirascu (Strasbourg University, France)
- Patrick Watrin (UCLouvain, Belgium)
- Nathan Vandeweerd (UCLouvain and VUB, Belgium)
- Elena Volodina (Gothenburg University, Sweden)
Organizing committee
- David Alfter
- Barbara De Cock
- Damien De Meyere
- Thomas François
- Nuria Gala
- Patricia Kerres
- Ferran Suñer Muñoz
- Alice Pintard
- Elena Volodina
- Patrick Watrin
- Nami Yamaguchi
Contact
If you have further questions, please contact David Alfter <david.alfter@uclouvain.be>.
Funding
