Abstract
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.
Highlights
Engagement is a concept widely investigated in human– robot interaction (HRI) and yet still elusive [52]
The behavior of these robots is driven by the effects it will have on the user’s learning, via the effect it has on the user’s engagement, inspired by the findings in the fields of Educational HRI and Multi-modal Learning Analytics about the existence of a link between engagement and learning
Fundamental pre-requisites for achieving that goal are that (1) it is possible to compute an approximation of user engagement which is devoid of human intervention, to allow for its automatic online extraction (RQ2); (2) the operationalization of engagement obtained in step 1 preserves the link with user learning (RQ1)
Summary
Engagement is a concept widely investigated in human– robot interaction (HRI) and yet still elusive [52]. The definition by Corrigan et al in [22] seems to be in line with the social/task distinction in the HRI engagement literature with regards to the nature of the HRI scenario/context They define engagement in terms of three contexts as follows: “task engagement where there is a task and the participant starts to enjoy the task he is doing, social engagement which considers being engaged with another party of which there is no task included and social-task engagement which includes interaction with another (e.g., robot) where both cooperate with each other to perform some task”.
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