Effects of interactivity of written practice on incidental vocabulary acquisition

Dialogue-based computer-assisted language learning (CALL) allows a learner to practice an L2 by discussing with a computer. By offering a fully controllable meaning-focused learning environment, it also allows to conduct research on the effectiveness of different modalities of interaction. In this study, we compare the effects of the interactivity of the writing activity on the incidental vocabulary acquisition. Interactive learning activities are often assumed to generate more learning outcomes than non-interactive ones (Chi, 2009). We know that negotiation of meaning and interactionally modified output are beneficial for incidental vocabulary learning (Newton, 1995; Ellis & He, 1999). Is it possible however that, at identical input and output opportunities, the level of interactivity offered by a dialogue activity affects incidental vocabulary learning? In a randomized controlled experiment, 160 learners used two versions of a dialogue-based CALL game. Their receptive (form-meaning mapping) and productive (collocational use) knowledge of specific French lexical items was tested before and after these sessions. In the treatment, participants had to maintain several task-based conversations with in-game characters. Both conditions presented the same input and had the same output opportunities, but the difference lied in the interactivity of the conversational practice: in the “dialogue system” group, the system-controlled interlocutor answered dynamically to each participant, while in the “dialogue completion” group, all answers from the interlocutor were visible at the start of the activity. Preliminary results show that the “dialogue system” group (Mgain = +10.9%, d = .55) significantly outperforms the “dialogue completion” group (Mgain = +8.8%, d = .35). These findings are consistent with previous studies (e.g., Newton, 1995; Ellis & He, 1999) and with the Task Involvement Hypothesis (Laufer & Hulstijn, 2001). They put in evidence the importance of spontaneous interactive production activities and the inherent limitations of dialogue completion exercises. For tutorial CALL, they also show the necessity of developing interactive dialogue systems to allow for autonomous conversational practice of the L2

The Role of Cognate Vocabulary in CEFR-based Word-level Readability Assessment

Cognate vocabulary is known to have a facilitating effect on foreign language (L2) lexical development (de Groot and Keijzer, 2000; Elgort, 2013). Because of their cross-lingual semiotic transparency, cognates are known to be easier to comprehend and learn. As a result, cognate status has been considered an important feature when modeling L2 vocabulary learning (Willis and Ohashi, 2012) or when assessing L2 lexical readability (Beinborn et al., 2014). Although the latter readability-focused user study has shown a positive effect of cognates on decontextualized word comprehension, not many studies seem to have focused on how cognate vocabulary is distributed in reading texts of different L2 levels, such as reading materials found in textbooks graded along the CEFR (Common European Framework of Reference) scale (Council of Europe, 2001). Our aim is therefore to examine whether the presupposed increasing difficulty of the lexical stock attested in such texts is somehow related to cognate density. To this end, we will focus on French and Dutch L2 and will use two lexical databases, viz. FLELex (Francois et al., 2014) and NT2Lex (Tack et al., 2018), respectively. These resources have been compiled from a corpus of L2 reading materials targeted towards a specific CEFR level, including expert-written texts found in textbooks or readers. The lexicons thus describe word frequency distributions observed along the CEFR scale and therefore inform us about the lexical stock that should be understood a priori at a given level. In these CEFR-graded word distributions, cognate vocabulary in Dutch and French will be automatically identified, drawing on recent machine translation methods (Beinborn et al., 2013; Mitkov et al., 2007). As a parallel reference dataset, we will use the Dutch-French alignments of the Dutch Parallel Corpus (Paulussen et al., 2006)