An important step in children’s socio-cognitive development is learning how to engage in coordinated conversations. This requires not only becoming competent speakers but also active listeners. This paper studies children’s use of backchannel signaling (e.g., ”yeah!” or a head nod) when in the listener’s role during conversations with their caregivers via Zoom. While previous work had found backchannel to be still immature in middle childhood, our use of both more natural/spontaneous conversational settings and more adequate controls allowed us to reveal that school-age children are strikingly close to adult-level mastery in many measures of backchanneling. The broader impact of this paper is to highlight the crucial role of social context in evaluating children’s conversational abilities.

Understanding children's conversational skills is crucial for understanding their social, cognitive, and linguistic development, with important applications in health and education. To develop theories based on quantitative studies of conversational development, we need (i) data recorded in naturalistic contexts (e.g., child-caregiver dyads talking in their daily environment) where children are more likely to show much of their conversational competencies, as opposed to controlled laboratory contexts which typically involve talking to a stranger (e.g., the experimenter); (ii) data that allows for clear access to children's multimodal behavior in face-to-face conversations; and (iii) data whose acquisition method is cost-effective with the potential of being deployed at a large scale to capture individual and cultural variability. The current work is a first step to achieving this goal. We built a corpus of video chats involving children in middle childhood (6–12 years old) and their caregivers using a weakly structured word-guessing game to prompt spontaneous conversation. The manual annotations of these recordings have shown a similarity in the frequency distribution of multimodal communicative signals from both children and caregivers. As a case study, we capitalize on this rich behavioral data to study how verbal and non-verbal cues contribute to the children's conversational coordination. In particular, we looked at how children learn to engage in coordinated conversations, not only as speakers but also as listeners, by analyzing children's use of backchannel signaling (e.g., verbal “mh” or head nods) during these conversations. Contrary to results from previous in-lab studies, our use of a more spontaneous conversational setting (as well as more adequate controls) revealed that school-age children are strikingly close to adult-level mastery in many measures of backchanneling. Our work demonstrates the usefulness of recent technology in video calling for acquiring quality data that can be used for research on children's conversational development in the wild.

The study of how children develop their conversational skills is an important scientific frontier at the crossroad of social, cognitive, and linguistic development with important applications in health, education, and child-oriented AI. While recent advances in machine learning techniques allow us to develop formal theories of conversational development in real-life contexts, progress has been slowed down by the lack of corpora that both approximate naturalistic interaction and provide clear access to children’s non-verbal behavior in face-to-face conversations. This work is an effort to fill this gap. We introduce ChiCo (for Child Conversation), a corpus we built using an online video chat system. Using a weakly structured task (a word-guessing game), we recorded 20 conversations involving either children in middle childhood (i.e., 6 to 12 years old) interacting with their caregivers (condition of interest) or the same caregivers interacting with other adults (a control condition), resulting in 40 individual recordings. Our annotation of these videos has shown that the frequency of children’s use of gaze, gesture, and facial expressions mirrors that of adults. Future modeling research can capitalize on this rich behavioral data to study how both verbal and non-verbal cues contribute to the development of conversational coordination

Conversation requires cooperative social interaction between interlocutors. In particular, active listening through backchannel signaling (hereafter BC) i.e., showing attention through verbal (short responses like "Yeah") and non-verbal behaviors (e.g. smiling or nodding) is crucial to managing the flow of a conversation and it requires sophisticated coordination skills. How does BC develop in childhood? Previous studies were either conducted in highly controlled experimental settings or relied on qualitative corpus analysis, which does not allow for a proper understanding of children's BC development, especially in terms of its collaborative/coordinated use. This paper aims at filling this gap using a machine learning model that learns to predict children's BC production based on the interlocutor's inviting cues in child-caregiver naturalistic conversations. By comparing BC predictability across children and adults, we found that, contrary to what has been suggested in previous in-lab studies, children between the ages of 6 and 12 can actually produce and respond to backchannel inviting cues as consistently as adults do, suggesting an adult-like form of coordination.

The study of how children develop their conversational skills is an important scientific frontier at the crossroad of social, cognitive, and linguistic development with important applications in health, education, and child-oriented AI. While recent advances in machine learning techniques allow us to develop formal theories of conversational development in real-life contexts, progress has been slowed down by the lack of corpora that both approximate naturalistic interaction and provide clear access to children's non-verbal behavior in face-to-face conversations. This work is an effort to fill this gap. We introduce ChiCo (for Child Conversation), a corpus we built using an online video chat system. Using a weakly structured task (a word-guessing game), we recorded 20 conversations involving either children in middle childhood (i.e., 6 to 12 years old) interacting with their caregivers (condition of interest) or the same caregivers interacting with other adults (a control condition), resulting in 40 individual recordings. Our annotation of these videos has shown that the frequency of children's use of gaze, gesture and facial expressions mirrors that of adults. Future modeling research can capitalize on this rich behavioral data to study how both verbal and non-verbal cues contribute to the development of conversational coordination. CCS Concepts: • Applied computing → Psychology.