Intelligent tutoring systems need a model of learning goals for the personalization of educational content, tailoring of the learning path, progress monitoring, and adaptive feedback. This article presents such a model and corresponding interaction designs for the coaches and learners (respectively, a monitor-and-control dashboard and mobile app with supportive communications trough a virtual agent), all deployed and tested in a system for child diabetes self-management training. We developed a domain-independent upper ontology to structure learning goals and related concepts (such as achievements and tasks) and a domain ontology that specifies the knowledge base (for, in our case, diabetes self-management training). With this approach, we relate knowledge elements (e.g., skill) to educational tasks and to learners' knowledge development (e.g., achievements). The ontology was implemented in a multimodal tutoring system consisting of mobile educative games, a health diary, an embodied conversational agent (ECA), and a web application for authoring and monitoring. We show that our model provides a coherent and concise foundation for: 1) the formalization of learning in the diabetes self-management domain, but also for other domains such as mathematics; 2) personal goal setting and thereby personalization of the educational process including ECA's guidance; and 3) creating awareness of progress on the personal educational path. We found that a motivational tutoring system requires a rich set of learning activities and accompanying materials of which a subset is offered to the learner based on personal relevance. The implemented model proved to accommodate the personal agent-guided learning paths of children with diabetes, under different treatments from hospitals in Italy and the Netherlands.

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Social or humanoid robots do hardly show up in “the wild,” aiming at pervasive and enduring human benefits such as child health. This paper presents a socio-cognitive engineering (SCE) methodology that guides the ongoing research & development for an evolving, longer-lasting human-robot partnership in practice. The SCE methodology has been applied in a large European project to develop a robotic partner that supports the daily diabetes management processes of children, aged between 7 and 14 years (i.e., Personal Assistant for a healthy Lifestyle, PAL). Four partnership functions were identified and worked out (joint objectives, agreements, experience sharing, and feedback & explanation) together with a common knowledge-base and interaction design for child's prolonged disease self-management. In an iterative refinement process of three cycles, these functions, knowledge base and interactions were built, integrated, tested, refined, and extended so that the PAL robot could more and more act as an effective partner for diabetes management. The SCE methodology helped to integrate into the human-agent/robot system: (a) theories, models, and methods from different scientific disciplines, (b) technologies from different fields, (c) varying diabetes management practices, and (d) last but not least, the diverse individual and context-dependent needs of the patients and caregivers. The resulting robotic partner proved to support the children on the three basic needs of the Self-Determination Theory: autonomy, competence, and relatedness. This paper presents the R&D methodology and the human-robot partnership framework for prolonged “blended” care of children with a chronic disease (children could use it up to 6 months; the robot in the hospitals and diabetes camps, and its avatar at home). It represents a new type of human-agent/robot systems with an evolving collective intelligence. The underlying ontology and design rationale can be used as foundation for further developments of long-duration human-robot partnerships “in the wild.”@en

A mayor challenge in human-robot interaction is the synthesis of social signals through non-verbal behaviour expression. Appropriate perception and expression of dominance (verticality) is essential for social interaction. In this paper, we present our work on algorithmic modulation of robot bodily movement to control dominance expression. We developed a parameter-based model for body expansiveness. This model was applied to a variety of behaviours and evaluated by human observers in two different studies with respectively static postures (N=772) and gestures (N=31). Modulation of body expansiveness proved to robustly influence perceived dominance independent of behaviour and viewing angles.

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A mayor challenge in human-robot interaction and collaboration is the synthesis of non-verbal behaviour for the expression of social signals. Appropriate perception and expression of dominance (verticality) in non-verbal behaviour is essential for social interaction. In this paper, we present our work on algorithmic modulation of robot bodily movement to express varying degrees of dominance. We developed a parameter-based model for head tilt and body expansiveness. This model was applied to a variety of behaviours. These behaviours were evaluated by human observers in two different studies with respectively static pictures of key postures (N=772) and realtime gestures (N=31). Overall, specific behaviours proved to communicate different levels of dominance. Further, modulation of body expansiveness and head tilt robustly influenced perceived dominance independent of specific behaviours and observer viewing height and angle. The modulation did not influence perceived valence, but it did influence perceived arousal. Our study shows that dominance can be reliably expressed by both selection of specific behaviours and modulation of behaviours.

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Children will only benefit from educational technologies and e-coaches when they understand the long-term consequences and are (intrinsically) motivated to use these support systems. This paper presents an Objective Dashboard that integrates educational achievements, goals and tasks with gamification features (such as challenges, scores and rewards) to advance the interests and engagements of children with type 1 diabetes when using the Personal Assistant for a healthy Lifestyle (PAL) system. By linking in-app activities (e.g., play a quiz or keep a diary) to relevant educational achievements, and to skills and knowledge required in daily life, we aim to increase intrinsic motivation and thereby usage. We designed a dashboard displaying personalised achievements, learning goals and tasks in the domain of diabetes self-management education. We used common user interface design patterns such as layering, colouring, and iconic presentation to organise complex information and reinforce the relations between concepts. Subsequently, we conducted a usability evaluation with twelve children, on the basis of which we refined our design. We found that, colouring and layering were to some extent effective, however, iconic representations were insufficient. Therefore, we recommend to provide short, descriptive labels at any time.@en

Educational technology needs a model of learning goals to support motivation, learning gain, tailoring of the learning process, and sharing of the personal goals between different types of users (i.e., learner and educator) and the system. This paper proposes a tree-based learning goal structuring to facilitate personal goal setting to shape and monitor the learning process. We developed a goal ontology and created a user interface representing this knowledge-base for the self-management education for children with Type 1 Diabetes Mellitus. Subsequently, a co-operative evaluation was conducted with healthcare professionals to refine and validate the ontology and its representation. Presentation of a concrete prototype proved to support professionals' contribution to the design process. The resulting tree-based goal structure enables three important tasks: ability assessment, goal setting and progress monitoring. Visualization should be clarified by icon placement and clustering of goals with the same difficulty and topic. Bloom's taxonomy for learning objectives should be applied to improve completeness and clarity of goal content.@en

Social robots are entering the private and public domain where they engage in social interactions with nontechnical users. This requires robots to be socially interactive and intelligent, including the ability to display appropriate social behaviour. Progress has been made in emotion modelling. However, research into behaviour style is less thorough; no comprehensive, validated model exists of non-verbal behaviours to express style in human-robot interactions. Based on a literature survey, we created a model of non-verbal behaviour to express high/low warmth and competence—two dimensions that contribute to teaching style. In a perception study, we evaluated this model applied to a NAO robot giving a lecture at primary schools and a diabetes camp in the Netherlands.
For this, we developed, based on expert ratings, an instrument measuring perceived warmth, competence, dominance and affiliation. We show that even subtle manipulations of robot behaviour influence children’s perceptions of the robot’s level of warmth and competence.@en

This paper describes ongoing work carried out in the European project PAL which will support childre in their diabetes self-management as well as assist health professionals and parents involved in the diabete regimen of the child. Here, we will focus on the construction of the PAL ontology which has been assemble from several independently developed sub-ontologies and which are brought together by a set of hand-writte interface axioms, expressed in OWL.We will describe in detail how the triple model of RDF has been extende towards transaction time in order to represent time-varying data. Examples of queries and rules involvin temporal information will be presented as well. The approach is currently been in use in diabetes camps.@en

The PAL project1 is developing an embodied conversational agent (robot and its avatar), and applications for child-agent activities that help children from 8 to 14 years old to acquire the required knowledge, skills, and attitude for adequate diabetes selfmanagement. Formal and informal caregivers can use the PAL system to enhance their supportive role for this self-management learning process. We are developing a common ontology (i) to support normative behavior in a flexible way, (ii) to establish mutual understanding in the human-agent system, (iii) to integrate and utilize knowledge from the application and scientific domains, and (iv) to produce sensible human-agent dialogues. The common ontology is constructed by relating and integrating partly existing separate ontologies that are specific to certain contexts or domains. This paper presents the general vision, approach, and state of the art.@en