Abstract. This paper presents the EU EASEL project, which explores the potential impact and relevance of a robot in educational settings. We present the project objectives and the theorectical background on which the project builds, briefly introduce the EASEL technological developments, and end with a summary of what we have learned from the evaluation studies carried out in the project so far.

This paper describes a longitudinal study in which children could interact unsupervised and at their own initiative with a fully autonomous computer aided learning (CAL) system situated in their classroom. The focus of this study was to investigate how the mindset of children is affected when delivering effort-related praise through a social robot. We deployed two versions: a CAL system that delivered praise through headphones only, and an otherwise identical CAL system that was extended with a social robot to deliver the praise. A total of 44 children interacted repeatedly with the CAL system in two consecutive learning tasks over the course of approximately four months. Overall, the results show that the participating children experienced a significant change in mindset. The effort-related praise that was delivered by a social robot seemed to have had a positive effect on children’s mindset, compared to the regular CAL system where we did not see a significant effect.

Abstract. In collaborative play, children exhibit different levels of engagement. Some children are engaged with other children while some play alone. In this study, we investigated multimodal detection of individual levels of engagement using a ranking method and non-verbal features: turn-taking and body movement. Firstly, we automatically extracted turn-taking and body movement features in naturalistic and challenging settings. Secondly, we used an ordinal annotation scheme and employed a ranking method considering the great heterogeneity and temporal dynamics of engagement that exist in interactions. We showed that levels of engagement can be characterised by relative levels between children. In particular, a ranking method, Ranking SVM, outperformed a conventional method, SVM classification. While either turn-taking or body movement features alone did not achieve promising results, combining the two features yielded significant error reduction, showing their complementary power.

Since children (5-9 years old) are still developing their emotional and social skills, their social interactional behaviors in small groups might differ from adults' interactional behaviors. In order to develop a robot that is able to support children performing collaborative tasks in small groups, it is necessary to gain a better understanding of how children interact with each other. We were interested in investigating vocal turn-taking patterns as we expect these to reveal relations to collaborative and conflict behaviors, especially with children behaviors as previous literature suggests. To that end, we collected an audiovisual corpus of children performing collaborative tasks together in groups of three. Through automatic turn-taking analyses, our results showed that speaker changes with overlaps are more common than without overlaps and children seemed to show smoother turn-taking patterns, i.e., less frequent and longer lasting speaker changes, during collaborative than conflict behaviors.

Abstract. This paper presents the design and validation of a measurement instrument for children's perceptions of robots' social competence. The need for a standardized validated instrument has emerged as a requisite for meta-analyses and comparisons among various studies in the field of child-robot interaction. We report on the development of the instrument and its validation, which adopted a design-based method with two iterations. We used construct validity, which was formed by divergent and convergent validity. Children's perceptions of three different robotic platforms were examined in two empirical studies with 78 children aged 7-9 years, which was based on semi-structured interviews with qualitative thematic content analysis. The results indicated that children differentiate their perception of social competence depending on the perceived intentionality of the robot and they ascribe discrete categorizations to the robot such as a machine, social artifact and social agent. The findings are discussed in relation to existing literature.