dialogue-based CALL

Abstract BACKGROUND. Interactivity has been defined in various ways: in instructed SLA, as distinguishing dialogic tasks from monologic ones (Ellis et al., 1994); in game design, as involving agency and user control.

Abstract The number of times a word appears in the input, i.e., the frequency of occurrence, is a well-known factor influencing incidental vocabulary acquisition (Webb, 2020). However, paradoxically, the frequency of occurrence has only a medium predicting power in vocabulary learning, around r = .

Las inteligencias artificiales conversacionales—es decir, los chatbots, los robots parlantes, los agentes conversacionales y los sistemas de diálogo—ofrecen una posibilidad prometedora para el aprendizaje de idiomas: un entorno de baja ansiedad para la práctica comunicativa e interactiva, que puede además controlarse en cuanto a complejidad y exposición específica, estrategias de andamiaje y retroalimentación correctiva.

A meta-analysis of effectiveness studies on dialogue-based CALL, based on 17 studies and $k = 100$ individual effect sizes. We use Morris and DeShon’s (2002) formulas to compute comparable effect sizes across designs, and analyse them through a multilevel random-effects model. Results confirm that dialogue-based CALL practice has a significant medium effect size on L2 proficiency development ($d = 0.59$). Extensive moderator analyses on several learning outcomes of different types and features of dialogue-based CALL (type of interaction, modality, agent embodiment, etc.) confirm the effectiveness of form-focused and goal-oriented systems, system-guided interactions, corrective feedback provision, and gamification features. Significant effects for lower proficiency learners, and on vocabulary, morphosyntax, holistic proficiency and accuracy are established.

Dialogue systems, such as chatbots and dialogue-based games, provide an opportunity for safe, meaningful conversational practice to …

Dialogue systems allow a user to interact, orally or in writing, with an automated interlocutor, whether it is referred to as a …

Automatizing testing and computation of multiple variations (pruning, normalization…) of L2 utterance fluency metrics, we collected pre/post oral interview responses from N=215 learners of French, and compared each metric’s variation with external proficiency estimates, to determine which operationalizations best predict proficiency and detect very-short-term developmental changes in L2 fluency.

Preguntas de investigación ¿En qué medida es posible medir de manera valida, confiable y precisa efectos de aprendizaje de corta duración en lengua extranjera en términos de fluidez oral de manera semiautomática?

Les systèmes de dialogue pour l’apprentissage des langues regroupent différentes applications permettant à un apprenant de langue étrangère de converser, oralement ou par écrit, avec un interlocuteur automatisé (chatbot, robot, interface vocale, personnage non-joueur, etc.