In educational HRI, it is generally believed that a robots behavior has a direct effect on the engagement of a user with the robot, the task at hand and also their partner in case of a collaborative activity. Increasing this engagement is then held responsible for increased learning and productivity. The state of the art usually investigates the relationship between the behaviors of the robot and the engagement state of the user while assuming a linear relationship between engagement and the end goal: learning. However, is it correct to assume that to maximise learning, one needs to maximise engagement? Furthermore, conventional supervised models of engagement require human annotators to get labels. This is not only laborious but also introduces further subjectivity in an already subjective construct of engagement. Can we have machine-learning models for engagement detection where annotations do not rely on human annotators? Looking deeper at the behavioral patterns and the learning outcomes and a performance metric in a multi-modal data set collected in an educational human–human–robot setup with 68 students, we observe a hidden link that we term as Productive Engagement. We theorize a robot incorporating this knowledge will (1) distinguish teams based on engagement that is conducive of learning; and (2) adopt behaviors that eventually lead the users to increased learning by means of being productively engaged. Furthermore, this seminal link paves way for machine-learning models in educational HRI with automatic labelling based on the data.

在教育性HRI中，通常认为机器人行为会直接影响用户与机器人的互动，手头的任务以及他们在合作活动中的伙伴。然后，增加这种参与将对增加学习和生产力负有责任。现有技术通常假设机器人的行为与最终目标：学习之间存在线性关系，而研究机器人的行为与用户的行为状态之间的关系。但是，假设最大程度地学习需要最大化参与度是正确的吗？此外，传统的监督参与模型需要人工注释者获得标签。这不仅费力，而且在已经很主观的参与结构中引入了更多的主观性。在注释不依赖人工注释者的情况下，我们可以提供用于参与度检测的机器学习模型吗？在由68名学生组成的教育性人机交互环境中，深入研究行为模式和学习成果以及多模式数据集中的绩效指标，我们发现了一个隐藏的链接，我们称之为生产参与。我们从理论上讲，结合了这一知识的机器人将（1）根据有助于学习的参与度来区分团队；（2）采取最终导致用户通过富有成效的参与来增加学习的行为。此外，此开创性链接为教育型HRI中的机器学习模型铺平了道路，该模型基于数据自动标记。