走向社会和参与同伴学习：预测反向通道和脱离儿童
Towards Social & Engaging Peer Learning: Predicting Backchanneling and Disengagement in Children
摘要：
社交机器人和交互式计算机应用程序有可能通过充当同伴学习伙伴来促进幼儿的早期语言发展。然而，研究发现，孩子们只信任那些表现自然和人际交往方式的机器人。为了帮助机器人成为有吸引力和专注的同伴学习伙伴，我们开发了模型来预测听者是否会失去注意力（离线预测，LDP）以及机器人在接下来的几秒钟内应该产生反向通道响应的程度（反向通道程度预测，BEP）。我们将LDP和BEP作为时间序列分类问题，并进行了几次实验，以评估不同的时间序列特征和特征集对模型预测性能的影响。使用统计学和机器学习，我们还研究了哪些社会人口因素影响孩子们花在反向引导和倾听同伴的时间。为了使我们的模型具有可解释性，我们还分析了负责其预测性能的关键特征。我们的实验揭示了多模态特征的实用性，例如瞳孔扩张，眨眼率，头部运动，面部动作单元，这些特征以前从未使用过。我们还发现，时间序列特征的动态是听众脱离和反向通道的丰富预测因子。
英文摘要：
Social robots and interactive computer applications have the potential to foster early language development in young children by acting as peer learning companions. However, studies have found that children only trust robots which behave in a natural and interpersonal manner. To help robots come across as engaging and attentive peer learning companions, we develop models to predict whether the listener will lose attention (Listener Disengagement Prediction, LDP) and the extent to which a robot should generate backchanneling responses (Backchanneling Extent Prediction, BEP) in the next few seconds. We pose LDP and BEP as time series classification problems and conduct several experiments to assess the impact of different time series characteristics and feature sets on the predictive performance of our model. Using statistics & machine learning, we also examine which socio-demographic factors influence the amount of time children spend backchanneling and listening to their peers. To lend interpretability to our models, we also analyzed critical features responsible for their predictive performance. Our experiments revealed the utility of multimodal features such as pupil dilation, blink rate, head movements, facial action units which have never been used before. We also found that the dynamics of time series features are rich predictors of listener disengagement and backchanneling.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
