Persuasive social robots have the ability to influence human behaviour through social interaction, which makes them a valuable technological solution for all applications aiming to support a person's behaviour change. The recently proposed Child-Robot Relational Norm Intervention (CRNI) model intro-duces a new approach for persuasive social robotics, leveraging children's reluctance to disturb robots to promote behaviour change. Unlike traditional methods that rely on direct feedback or reminders, CRNI encourages children to self-monitor and self-correct improper behaviour, by making the robot express mild distress whenever the child exhibits the incorrect behaviour. This paper proposes the first implementation of the CRNI approach in a real HRI context and evaluates its effectiveness in improving children's handwriting posture. The evaluation includes two user studies: (i) a multi-session study with five children investigating the long-term impact of the approach, (ii) a controlled study with 29 children comparing CRNI to direct robot reminders. The results indicate that the CRNI model leads to more sustained posture correction compared to direct interventions. More broadly, our findings suggest that relational norm-based approaches can offer an effective yet less intrusive method for fostering positive behaviours in children.

Jupyter is a web-based, interactive computing environment that supports many commonly-used programming languages. It has been widely adopted in the CS education community and is now rapidly expanding to other STEM disciplines due to the growing integration of programming in STEM education. However, unlike other educational platforms, there is currently no integrated way to capture, analyze, and visualize student interaction data in Jupyter notebooks. This means that teachers have limited to no visibility into student activity, preventing them from drawing insights from these data and providing timely interventions on the fly. In this paper, we present Jupyter Analytics, an end-to-end solution for teachers to collect, analyze, and visualize both synchronous and asynchronous learning activities in Jupyter. The Jupyter Analytics system consists of two JupyterLab extensions connected via a cloud-based backend. On the student side, we introduce the Jupyter Analytics Telemetry extension to anonymously capture students' interaction activity with more structure and higher granularity than log data. On the teacher side, we introduce the Jupyter Analytics Dashboard extension, which visualizes real-time student data directly in the notebook interface. The Jupyter Analytics system was developed through an iterative co-design process with university instructors and teaching assistants, and has been implemented and tested in several university STEM courses. We report two use cases where Jupyter Analytics impacted teaching and learning in the context of exercise sessions, and discuss the potential value of our tools for CS education.

Reading is an essential skill that plays a crucial role in education, and it involves mastering various sub-skills, such as decoding tokens and acquiring phonological awareness. Research indicates that practising music in an educational setting can enhance reading skills, but implementing such interventions can be challenging. Interestingly, studies have found a correlation between individuals’ rhythm capabilities and reading sub-skills, which opens the door to more targeted and easy-to-implement educational activities. Digitalised rhythmic activities could be an effective solution, but only a few studies have explored their impact on learning to read. Specifically, several aspects have been left unexplored, and this paper aims to tackle them: the different aspects of rhythm and how to design them best to boost the interaction with children, especially in a school setting. We aim to investigate and evaluate the design of digital activities that can help us understand the connection between reading learning and rhythm. The paper presents the design of six activities and plans to conduct long-term experiments to validate the findings. The initial subjective validation with ten students in a school indicates promising outcomes, as the students were highly engaged and found the design intuitive.

Persuasive social robots employ their social influence to modulate children’s behaviours in child-robot interaction. In this work, we introduce the Child-Robot Relational Norm Intervention (CRNI) model, leveraging the passive role of social robots and children’s reluctance to inconvenience others to influence children’s behaviours. Unlike traditional persuasive strategies that employ robots in active roles, CRNI utilizes an indirect approach by generating a disturbance for the robot in response to improper child behaviours, thereby motivating behaviour change through the avoidance of norm violations. The feasibility of CRNI is explored with a focus on improving children’s handwriting posture. To this end, as a preliminary work, we conducted two participatory design workshops with 12 children and 1 teacher to identify effective disturbances that can promote posture correction.

This paper investigates the potential impact of deep generative models on the work of creative professionals. We argue that current generative modeling tools lack critical features that would make them useful creativity support tools, and introduce our own tool, generative.fashion1, which was designed with theoretical principles of design space exploration in mind. Through qualitative studies with fashion design apprentices, we demonstrate how generative.fashion supported both divergent and convergent thinking, and compare it with a state-of-the-art text-based interface using Stable Diffusion. In general, the apprentices preferred generative.fashion, citing the features explicitly designed to support ideation. In two follow-up studies, we provide quantitative results that support and expand on these insights. We conclude that text-only prompts in existing models restrict creative exploration, especially for novices. Our work demonstrates that interfaces which are theoretically aligned with principles of design space exploration are essential for unlocking the full creative potential of generative AI.

Maintaining a proper body posture during interactions with educational tablet applications is crucial for children’s physical well-being and task performance, especially considering digital tablet’s increasingly pervasive use in classrooms. In this work we propose WriteUpRight, an interaction system for children’s self-regulation of posture while writing on a tablet. The system relies on slowly deforming visual stimuli appearing on the tablet screen and compares two posture correction strategies: the Error Amplification method (see Figure 1) seeks to induce self-correction by amplifying the postural error, while the Error Correction method seeks to unobtrusively nudge the child towards the correct posture. Through a formative design and a user study with 42 children, we demonstrate the effectiveness of our solution and the advantages of the Error Amplification method with respect to the Error Correction method. The system shows potential for helping children maintain a proper head-screen distance and head roll angle during reading and writing tasks on tablets.

Children’s retention of a proper body posture while interacting with educational tablet applications is important for both their physical health and task performance. In this work, we propose a new approach to unobtrusively induce postural changes in children by applying a slowly deforming visual stimulus appearing on the tablet screen. To preliminarily validate our approach we designed a reading-and-writing tablet application for children, during which 8 different slow visual stimuli would be provided, and monitored the children’s posture via a vision-based automated posture tracking system. Results from 10 children aged 6-11 suggest that the proposed approach is suitable for unobtrusively changing children’s postures and will stand as the basis for the future design of an adaptive unobtrusive posture regulation system.

To make social robots effective in education, they need to be autonomous both in terms of assessing the student’s engagement state as well as intervening effectively in soft real-time when necessary. Hidden Markov Model (HMM) is an interpretable machine learning technique for modeling temporal data that is commonly used post-hoc to analyse latent learning processes. In this paper, we contribute by proposing an HMM-based intervention methodology for assessing and classifying the state of the student as either productive or unproductive in soft real-time. The system identifies and tracks states and patterns not conducive to learning, and a robot intervention is triggered whenever a too-high non-productive engagement is detected. In a pilot study with 22 children, we evaluate this methodology in terms of both 1) the effectiveness of the interventions on the students’ learning gains and on behaviors found conducive to learning, and 2) the students’ perception of the robotic interventions. Results suggest that the robot interventions have a positive effect on the post-test scores relative to the baseline robot, although there isn’t a significant difference in the learning gains. Moreover, interventions that try to induce reflective behaviors are most effective in inducing the required learning behavior, followed by communication-inducing interventions. Lastly, students’ perception of intervention usefulness does not reflect their actual effectiveness.

Handwriting practising, as any other repetitive task, often leads the practiser to an overconcentration state where their performance might be affected by postural and mental fatigue. Short breaks to perform unrelated activities, especially relaxation exercises, have shown to be a simple alternative to soften or postpone this phenomenon. Therefore, in this paper we are investigating the immediate effects of different types of short-duration relaxation exercises in the handwriting and posture of children aged from 8 to 10 in handwriting training. We divided 40 children in two groups performing the sessions, guided by a social robot, with small exercises of mindfulness or stretching in the middle of their training. Additionally, we analysed participants’ perceptions towards the robot leading these interactions. Results showed improvements in participants’ handwriting quality and posture maintenance regardless of the condition. Additionally, more positive feedback about the pause was reported from individuals in the mindfulness condition.

Spatial understanding and communication are essential skills in human interaction. An adequate understanding of others’ spatial perspectives can increase the quality of the interaction, both perceptually and cognitively. In this paper, we take the first step towards understanding children’s perspective-taking abilities and their tendency to adapt their perspective to a counterpart while completing a task with a robot. The elements used for studying children’s behaviours are the frame of reference and perspective marking, which we evaluated through a task where players needed to compose instructions to guide each other to complete the task. We developed the interaction with an NAO robot and analyzed the children’s instructions and their performance throughout the game. Our initial findings demonstrated that children tend to compose their first instruction by following the principle of least collaborative effort. Children significantly changed and adapted their perspective, i.e. frame of reference and perspective marking to the robot, mainly when the robot failed to follow their instructions correctly. Additionally, results show that children tend to create a mental model of their counterparts and the robot changing that frame of reference might affect their performance or the flow of the interaction.

The ability to consider a wide range of solutions to a design problem is a crucial skill for designers, and is a major differentiator between experts and novices. One reason for this is that novices are unaware of the full extent of the design space in which solutions are situated. To support novice designers with design space exploration, we introduce Mixplorer, a system that allows designers to take an initial design and mix it with other designs. Mixplorer differs from existing tools by supporting the exploration of ill-defined design spaces through social design space exploration. To evaluate Mixplorer, we conducted (1) an interview study with design instructors who reported that Mixplorer would “help to open the minds” of novice designers and (2) a controlled experiment with novices, finding that the design-mixing functionality of Mixplorer provided significantly better support for creativity, and that participants who mixed designs produced more novel designs.

This article presents the novel modular version of the robotic platform Cellulo, a versatile handheld robot initially designed as an educational robot. The use of Cellulo in different contexts and applications over the years has highlighted the need for modularity. Modularity adds versatility by increasing the spectrum of functionalities of the robot, as well as more robustness. Modulo Cellulo consists of three modules: a main module, a battery module, and an interaction module. We describe the new Modulo Cellulo platform, the different modules design, the mechanical and electrical inter-connectivity between them, the new adaptive controller, and the application development framework. As a show case, we present the addition of the reconfigurable robot Mori as a module for Cellulo, in an activity envisioning the collaboration between reconfigurable swarm robots.

Wizard of Oz, a very commonly employed technique in human-robot interaction, faces the criticism of being deceptive as the humans interacting with the robot are told, if at all, only at the end of their interaction that there was in fact a human behind the robot. What if the robot reveals the wizard behind itself very early in the interaction? We built a deep wizard of Oz setup to allow for a robot to play together with a human against a computer AI in the context of Connect 4 game. This cooperative game interaction against a common opponent is then followed by a conversation between the human and the robot. We conducted an exploratory user study with 29 adults with three conditions where the robot reveals the wizard, lies about the wizard, and does not say anything, respectively. We also split the data based on how the participants perceive the robot in terms of autonomy. Using different metrics, we evaluate how the users interact with and perceive the robot in both the experimental and perceived conditions. We find that while there is indeed a significant difference in the participants willingness to follow robots suggestions between the experimental conditions as well as in the effort they put to prove themselves as humans (reverse Turing test), there isn’t any significant difference in their robot perception. Additionally, how humans perceive whether the robot is tele-operated or autonomous seems to be indifferent to the robot revealing its identity, i.e., the pre-conceived notions may be uninfluenced even if the robot explicitly states otherwise. Lastly, interestingly in the perception based conditions, absence of statistical significance may suggest that, in certain contexts, wizard of oz may not require hiding the wizard after all.

Robot-mediated learning activities are often designed as collaborative exercises where children work together to achieve the activity objectives. Although miscommunications and misunderstandings occur frequently, humans, unlike robots, are very good at overcoming them and converging to a shared solution. With the aim of equipping a robot with these abilities and exploring its effects, in this article we investigate how a humanoid robot can collaborate with a human learner to construct a shared solution to a problem via suggesting actions and (dis)agreeing with each other. Concretely, we designed a learning activity aiming to improve the computational thinking skills of children, in which the robot makes suggestions on what to do, that may be in line with what the human thinks or not. Furthermore, the robot may suggest wrong actions that could essentially prevent them from finding a correct solution. Via a pilot study conducted remotely with 9 school children, we investigate whether the interaction results in positive learning outcomes, how the collaboration evolves, and how these relate to each other. The results show positive learning outcomes for the participants in terms of finding better solutions, suggesting that the collaboration with the robot might have helped trigger the learning mechanisms.

The social aspects of therapy and training are important for patients to avoid social isolation and must be considered when designing a platform, especially for home-based rehabilitation. We proposed an online version of the previously proposed tangible Pacman game for upper limb training with haptic-enabled tangible Cellulo robots. Our main objective is to enhance motivation and engagement through social integration and also to form a gamified multiplayer rehabilitation at a distance. Thus, allowing relatives, children, and friends to connect and play with their loved ones while also helping them with their training from anywhere in the world. As well as connecting therapists to their patients through haptically linking capabilities. This is especially relevant when there are social distancing measures which might isolate the elderly population, a majority of all rehabilitation patients.

Handwriting learning is a long and complex process that takes about ten years to be fully mastered. Nearly one-third of all children aged 4-12 experiences handwriting difficulties and, sadly, most of them are left to fight them on their own, due to the scarcity of tools for the detection and remediation of such difficulties. Building on state-of-the-art digital solutions for automated handwriting assessment and the training of specific handwriting-related skills, in this article we discuss requirements, rationale, and architecture of a system for handwriting training, which relies on a social robot as a mediator agent, offering personalized training and suggestions. The system is envisioned to operate autonomously and to support long-term interactions via personalization. Preliminary validation of the system in an experiment with 31 children showed its potential not only for autonomously guiding handwriting training sessions, but also for its inclusion in the teachers’ practice.

Recent studies with children have pointed out the importance of spatial thinking as an essential factor in determining later success in STEM-related fields. The current study explores the potential of using embodied activities with robots to aid the development of children’s spatial perspective-taking abilities. This research focuses on evaluating children’s spatial perspective-taking abilities and assessing the potential of the designed activity to practice perspective-taking. The activity design is inspired by the dynamic and mental processes involved in remote-controlled cars and racing games, it is developed with a Cozmo robot, and it includes guiding the robot within the maze by considering the robot’s point of view. We evaluated the activity through a user study with 22 elementary school children between the ages of 8 and 9. The findings showed that children’s performance at different angular disparities was aligned with the previous research in developmental psychology. Additionally, most children made fewer mistakes in guiding the robot as they played more. Finally, while we did not observe any performance improvement in the group of children who had access to the robot’s point of view during the game, we learned new insights about how children perceived seeing the maze through the robot’s eyes.

Gamified rehabilitation tackles the problem of keeping patients engaged in, and motivated to do physical rehabilitation to improve its efficacy. However, with respect to standard rehabilitation, patients are freer to move about and may compensate their motion difficulties with parasite movements, which would greatly reduce the efficacy of the rehabilitation. To identify and characterize compensatory motions, we collected and analyzed video data of people playing the "tangible Pacman" game (an upper-limb rehabilitation game in which a patient moves a semi-passive robot, the "Pacman", on a map to collect 6 apples, while being chased by one or two autonomous robots, the "ghosts"). Participants include 10 healthy elderly adults and 10 chronic stroke patients, who played multiple runs of the game, with different sized maps and various game configurations. By analyzing the video recordings we successfully identified higher shoulder and torso lateral tilt compensation in stroke patients and developed a proof-of-concept compensatory motion detection system which relies on a wearable Inertial Measurement Unit and ROS to provide in-game, real-time visual feedback on compensation.

Tangible User Interfaces (TUI) have been found to be relevant tools for collaborative learning by providing a shared workspace and enhancing joint visual attention. Researchers have explored the use of TUIs in a variety of curricular activities and found them particularly interesting for spatial exploration. However, very few studies have explored how TUIs could be used as a collaborative medium for people with visual impairments (VIs). In this study, we investigated the effect of tangible interaction (a small tangible robot) in a spatial collaborative task (a treasure hunt) involving two people with VIs. The aim was to evaluate the impact of the design of the TUI on the collaboration and the strategies used to perform the task. The experiment involved six dyads of people with VIs. The results showed that the collaboration was impacted by the interaction design and open interesting perspectives on the design of collaborative games for people with VIs.

In the Republic of Kazakhstan, the transition from Cyrillic to Latin alphabet raises challenges to training an entire population in writing the new script. This paper presents a CoWriting Kazakh system, an extension of the existing CoWriter system, aiming to implement an autonomous social robot that would assist children in transition from the old Cyrillic alphabet to a new Latin alphabet. With the aim to investigate which learning strategy yields better learning gains, we conducted an experiment with 67 children, aged 8-11 years old, who interacted with a robot in a CoWriting Kazakh learning scenario. Participants were asked to teach a humanoid NAO robot how to write Kazakh words using one of the scripts, Latin or Cyrillic. We hypothesized that a scenario in which the child is asked to mentally convert the word to Latin would be more effective than having the robot perform conversion itself. Results show that the CoWriter was successfully applied to this new script-switching task. The findings also suggest interesting gender differences in the preferred method of learning with the robot.

Inductive reasoning is an important educational practice but can be difficult for teachers to support in the classroom due to the high level of preparation and classroom time needed to choose the teaching materials that challenge students' current views. Intelligent tutoring systems can potentially facilitate this work for teachers by supporting the automatic adaptation of examples based on a student model of the induction process. However, current models of inductive reasoning usually lack two main characteristics helpful to adaptive learning environments, individual differences of students and tracing of students' learning as they receive feedback. In this paper, we describe a model to predict and simulate inductive reasoning of students for a categorization task. Our approach uses a Bayesian model for describing the reasoning processes of students. This model allows us to predict students' choices in categorization questions by accounting for their feature biases. Using data gathered from 222 students categorizing three topics, we find that our model has a 75% accuracy, which is 10% greater than a baseline model. Our model is a contribution to learning analytics by enabling us to assign different bias profiles to individual students and tracking these profile changes over time through which we can gain a better understanding of students' learning processes. This model may be relevant for systematically analysing students' differences and evolution in inductive reasoning strategies while supporting the design of adaptive inductive learning environments.

Collaborative learning appears in a joint intellectual efforts of individuals to understand an object of knowledge collectively. In their search for understanding the problems, meanings, and solutions, learners employ different multi-modal strategies. In this work, we explore the role of force feedback in learners interaction with tangible hand-held robots. We designed a collaborative learning environment to provide embodied intuitions on linear mathematical functions combined with graphical representations and ran a first study involving 24 participants. Our analysis shows a positive learning gain for our learning activity. Moreover, to explore the link between different types of force feedback and learners' collaboration, we designed a focus group study with 12 participants. Our results suggest that the haptic communication channel affects the collaboration dynamic differently according to the nature of the learning task. We finish by proposing design insights for future exploration of haptic in collaborative learning.

Humanoid robots, with a focus on personalised social behaviours, are increasingly being deployed in educational settings to support learning. However, crafting pedagogical HRI designs and robot interventions that have a real, positive impact on participants' learning, as well as effectively measuring such impact, is still an open challenge. As a first effort in tackling the issue, in this paper we propose a novel robot-mediated, collaborative problem solving activity for school children, called JUSThink, aiming at improving their computational thinking skills. JUSThink will serve as a baseline and reference for investigating how the robot's behaviour can influence the engagement of the children with the activity, as well as their collaboration and mutual understanding while working on it. To this end, this first iteration aims at investigating (i) participants' engagement with the activity (Intrinsic Motivation Inventory-IMI), their mutual understanding (IMIlike) and perception of the robot (Godspeed Questionnaire); (ii) participants' performance during the activity, using several performance and learning metrics. We carried out an extensive user-study in two international schools in Switzerland, in which around 100 children participated in pairs in one-hour long interactions with the activity. Surprisingly, we observe that while a teams' performance significantly affects how team members evaluate their competence, mutual understanding and task engagement, it does not affect their perception of the robot and its helpfulness, a fact which highlights the need for baseline studies and multi-dimensional evaluation metrics when assessing the impact of robots in educational activities.

A key element for the success of any game is its ability to produce a different experience at each round, thus keeping the player engagement high. This is particularly important for those games that also have a serious objective, such as gamified rehabilitation systems, aiming at encouraging patients in performing home rehabilitation exercises. In all cases, a game element which is typically static is the workspace, i.e. the "floor" upon which the game takes place. This is especially true for robot-assisted rehabilitation games, where the workspace must satisfy the requirements given by the robot's locomotion and localization systems, as well as the patient's exercise motion requirements.In this article we present a simple yet effective solution for designing dynamic and customizable tangible workspaces, which relies on hexagonal tiles and our previously proposed Cellulo localization system. These "hextiles" can be easily tangibly rearranged at each game round to yield a desired workspace shape and configuration, allowing tabletop mobile robots to move continuously within each new workspace. We ground our solution in the context of robot-assisted rehabilitation, where high adaptability is crucial for the efficacy of the solution, and propose a dynamic extension of our "tangible Pacman" rehabilitation game.Experiments show that the proposed solution allows for adaptation in range of motions, exercise types, physical and cognitive difficulty, besides reducing repetitiveness.

We consider the machine teaching problem in a classroom-like setting wherein the teacher has to deliver the same examples to a diverse group of students. Their diversity stems from differences in their initial internal states as well as their learning rates. We prove that a teacher with full knowledge about the learning dynamics of the students can teach a target concept to the entire classroom using O(min{d,N}log 1/ɛ) exam-ples, where d is the ambient dimension of the problem, N is the number of learners, and ɛ is the accuracy parameter. We show the robustness of our teaching strategy when the teacher has limited knowledge of the learners’ internal dynamics as provided by a noisy oracle. Further, we study the trade-off between the learners’ workload and the teacher’s cost in teaching the target concept. Our experiments validate our theoretical results and suggest that appropriately partitioning the classroom into homogenous groups provides a balance between these two objectives.

The interaction with the various learners in a Massive Open Online Course (MOOC) is often complex. Contemporary MOOC learning analytics relate with click-streams, keystrokes and other user-input variables. Such variables however, do not always capture learners' learning and behavior (e.g., passive video watching). In this paper, we present a study with 40 students who watched a MOOC lecture while their eye-movements were being recorded. We then proposed a method to define stimuli-based gaze variables that can be used for any kind of stimulus. The proposed stimuli-based gaze variables indicate students' attention (i.e., with-me-ness), at the perceptual (following teacher's deictic acts) and conceptual levels (following teacher discourse). In our experiment, we identified a significant mediation effect of the two levels of with-me-ness on the relation between students' motivation and their learning performance. Such variables enable common measurements for the different kind of stimuli present in distinct MOOCs. Our long-term goal is to create student profiles based on their performance and learning strategy using stimuli-based gaze variables and to provide students gaze-aware feedback to improve overall learning process.

This paper presents the design of a novel and engaging collaborative learning activity for handwriting where a group of participants simultaneously tutor a Nao robot. This activity was intended to take advantage of both collaborative learning and the learning by teaching paradigm to improve children's meta-cognition (perception of their own skills). Multiple engagement probes were integrated into the activity as a first step towards fostering long term interactions. As a lot of research targets social interactions, the goal here was to determine whether an engagement strategy focused on the task could be as, or more efficient than one focused on social interactions and participants' introspection. To that effect, two engagement strategies were implemented. They differed in content but used the same multi-modal design in order to increase participants' meta-cognitive reflection, once on the task and performances, and once on participants' enjoyment and emotions. Both strategies were compared to a baseline by probing and assessing engagement at the individual and group level, along the behavioural, emotional and cognitive dimensions, in a between subject experiment with 12 groups of children. The experiments showed that the collaborative task pushed the children to adapt their manner of writing to the group, even though the adopted solution was not always correct. Furthermore, there was no significant difference between the strategies in terms of behaviour on task (behavioural engagement), satisfaction (emotional engagement) or performance (cognitive engagement) as the group dynamics had a stronger impact on the outcome of the collaborative teaching task. Therefore, the task and social engagement strategies can be considered as efficient in the context of collaboration.

In the Republic of Kazakhstan, the transition from Cyrillic towards Latin alphabet raises challenges to teach the whole population in writing the new script. This paper presents a CoWriting Kazakh system that aims to implement an autonomous behavior of a social robot that would assist children in learning a new script. Considering the fact that the current generation of primary school children have to be fluent in both Kazakh scripts, this exploratory study aims to investigate which learning approach provides better effect. Participants were asked to teach a humanoid robot NAO how to write Kazakh words using one of the scripts, Latin vs Cyrillic. We hypothesize that it is more effective when a child mentally converts the word to Latin in comparison to having the robot perform conversion itself. The findings reject this hypothesis, but further research is needed as it is suggested that the way the pre-test was performed might have caused the obtained results.

In this paper, we propose that the data generated by educational robots can be better used by applying learning analytics methods and techniques which can lead to a deeper understanding of the learners' apprehension and behavior as well as refined guidelines for roboticists and improved interventions by the teachers. As a step towards this, we put forward analyzing behavior and task performance at team and/or individual levels by coupling robot data with the data from conventional methods of assessment through quizzes. Classifying learners/teams in the behavioral feature space with respect to the task performance gives insight into the behavior patterns relevant for high performance, which could be backed by feature ranking. As a use case, we present an open-ended learning activity using tangible haptic-enabled Cellulo robots in a classroom-level setting. The pilot study, spanning over approximately an hour, is conducted with 25 children in teams of two that are aged between 11-12. A linear separation is observed between the high and low performing teams where two of the behavioral features, namely number of distinct attempts and the visits to the destination, are found to be important. Although the pilot study in its current form has limitations, e.g. its low sample size, it contributes to highlighting the potential of the use of learning analytics in educational robotics.

In this article, we present a multi-level time scales framework for the analysis of human-robot interaction (HRI). Such a framework allows HRI scientists to model the inter-relation between measures and factors of an experiment. Our final goal with the introduction of this framework is to unify scientific practice in the HRI community for better reproducibility. Our new approach transposes Newell’s framework of human actions to model human-robot interaction. Measures from the interaction are sorted into categories (time scales) corresponding to the temporal constraints proposed by Newell. According to this sorting, a bottom-up or top-down analysis can then be performed to correlate variables which allows a better understanding and explanation of the interaction. The utilization of our method within two experimental use cases is then presented. The first one, a child-robot interaction, involves two robots and one child playing a memory game. The second is based on an analysis of the PInSoRo dataset, involving 30 child-robot pairs in a freeplay interaction. Finally, we introduce clear guidelines to re-use the framework.

In this paper, we present a robotic approach to improve the teaching of handwriting using the tangible, haptic-enabled and classroom-friendly Cellulo robots. Our efforts presented here are in line with the philosophy of the Cellulo platform: we aim to create a ready-to-use tool (i.e. a set of robot-assisted activities) to be used for teaching handwriting, one that is to coexist harmoniously with traditional tools and will contribute new added values to the learning process, complementing existing teaching practices.

The interaction between a child and a robot is not designed the same way in human-robot interaction (HRI) and in educational uses of robotics (R4L). For instance, HRI researchers aim at minimizing misunderstandings between humans and robots, which makes perfectly sense. However, in education, misunderstandings and disagreements are the fuel for learning: learning is the side effect of the cognitive effort - verbal elaborations- required to repair misunderstandings or resolve conflicts. This approach does not make the design of such agents easier, since the robot must be able to perceive them and to repair them, which is not a trivial design task. This additional cognitive effort is close to the concept of 'germane cognitive load', proposed in instructional psychology, i.e. the cognitive load induced by the construction of new schemas. No pain, no gain. I will illustrate it with our work on robots for learning to read and to write, based on the learning-by-teaching paradigm. Despite having escaped from the trap of anthropomorphism and its uncanny valley, educational robotics seem to be still affected by the myth of media richness, i.e. the belief that the more a robot interacts as a human (understands language, perceives emotions), the better it will be for learning. This is not totally false, but what learners learn depends first upon the cognitive activity they have to perform, hence upon the fine tuning of their germane load, and only indirectly on the robot properties.

This paper describes research aimed at supporting children's reading practices using a robot designed to interact with children as their reading companion. We use a learning by teaching scenario in which the robot has a similar or lower reading level compared to children, and needs help and extra practice to develop its reading skills. The interaction is structured with robot reading to the child and sometimes making mistakes as the robot is considered to be in the learning phase. Child corrects the robot by giving it instant feedbacks. To understand what kind of behavior can be more constructive to the interaction especially in helping the child, we evaluated the effect of a deictic gesture, namely pointing on the child's ability to find reading mistakes made by the robot. We designed three types of mistakes corresponding to different levels of reading mastery. We tested our system in a within-subject experiment with 16 children. We split children into a high and low reading proficiency even-though they were all beginners. For the high reading proficiency group, we observed that pointing gestures were beneficial for recognizing some types of mistakes that the robot made. For the earlier stage group of readers pointing were helping to find mistakes that were raised upon a mismatch between text and illustrations. However, surprisingly, for this same group of children, the deictic gestures were disturbing in recognizing mismatches between text and meaning.

Social robots are being used to create better educational scenarios, thereby fostering children»s learning. In the work presented here, we describe an autonomous social robot that was designed to enhance children»s handwriting skills. Exploiting the benefits of the learning-by-teaching method, the system provides a scenario in which a child acts as a teacher and corrects the handwriting difficulties of the robotic agent. To explore the children»s perception towards this social robot and the effect on their learning, we have conducted a multi-session study with children that compared two contrasting competencies in the robot: 'learning' vs 'non-learning' and presented as two conditions in the study. The results suggest that the children learned more in the learning condition compared with the non-learning condition and their learning gains seem to be affected by their perception of the robot. The results did not lead to any significant differences in the children»s perception of the robot in the first two weeks of interaction. However, by the end of the 4th week, the results changed. The children in the learning condition gave significantly higher writing ability and overall performance scores to the robot compared with the non-learning condition. In addition, the change in the robot»s learning capabilities did not show to affect their perceived intelligence, likability and friendliness towards it.

Rehabilitation aims to ameliorate deficits in motor control via intensive practice with the affected limb. Current strategies, such as one-on-one therapy done in rehabilitation centers, have limitations such as treatment frequency and intensity, cost and requirement of mobility. Thus, a promising strategy is home-based therapy that includes task specific exercises. However, traditional rehabilitation tasks may frustrate the patient due to their repetitive nature and may result in lack of motivation and poor rehabilitation. In this article, we propose the design and verification of an effective upper extremity rehabilitation game with a tangible robotic platform named Cellulo as a novel solution to these issues. We first describe the process of determining the design rationales to tune speed, accuracy and challenge. Then we detail our iterative participatory design process and test sessions conducted with the help of stroke, brachial plexus and cerebral palsy patients (18 in total) and 7 therapists in 4 different therapy centers. We present the initial quantitative results, which support several aspects of our design rationales and conclude with our future study plans.

Capturing students' behavioral patterns through analysis of sequential interaction logs is an important task in educational data mining and could enable more effective and personalized support during the learning processes. This study aims at discovery and temporal analysis of learners' study patterns in MOOC assessment periods. We propose two different methods to achieve this goal. First, following a hypothesis-driven approach, we identify learners' study patterns based on their interaction with lectures and assignments. Through clustering of study pattern sequences, we capture different longitudinal activity profiles among learners and describe their properties. Second, we propose a temporal clustering pipeline for unsupervised discovery of latent patterns in learners' interaction data. We model and cluster activity sequences at each time step and perform cluster matching to enable tracking learning behaviours over time. Our proposed pipeline is general and applicable in different learning environments such as MOOC and ITS. Moreover, it allows for modeling and temporal analysis of interaction data at different levels of actions granularity and time resolution. We demonstrate the application of this method for detecting latent study patterns in a MOOC course.

The presented study tries to tackle an intriguing question of how user-generated data from current technologies can be used to reinforce learners' reflections, improve teaching practices, and close the learning analytics loop. In particular, the aim of the study is to utilize users' gaze to examine the role of a mirroring tool (i.e. Exercise View in Eclipse) in orchestrating basic behavioral regulation of participants engaged in a debugging task. The results demonstrated that students who processed the information presented in the Exercise View and acted upon it, improved their performance and achieved higher level of success than those who failed to do it. The findings shed a light how to capture what constitute relevant data within a particular context using gaze patterns, that could guide collection of essential learner-centred analytics for the purpose of designing usable and modular learning environments based on data-driven approaches.

In this paper, we propose a taxonomy of handwriting errors exhibited by children as a way to build adequate strategies for integration with a co-writing peer. The exploration includes the collection of letters written by children in an initial study, which were then revised in a second study. The second study also analyses the "peer-learning" (PL) and "peer-tutoring" (PT) learning methods in an educational scenario, where a pair of children perform a collaborative writing activity in the presence of a robot facilitator. The data obtained in the first two studies allowed us to create a "taxonomy of handwriting errors". A set of writing errors were selected and implemented in an educational activity for validation. This activity constituted a third study, wherein we systematically induced the errors into a Nao robot's handwriting using the {PT} method - A teacher-child corrects the handwriting errors of the learner-robot. The preliminary results suggest that the children in general showed awareness to the writing errors and were able to perceive the writing abilities of the robot.

The Cellulo robots are small tangible robots that are designed to represent virtual interactive point-like objects that reside on a plane within carefully designed learning activities. In the context of these activities, our robots not only display autonomous motion and act as tangible interfaces, but are also usable as haptic devices in order to exploit, for instance, kinesthetic learning. In this article, we present the design and analysis of the haptic interaction module of the Cellulo robots. We first detail our hardware and controller design that is low-cost and versatile. Then, we describe the task-based experimental procedure to evaluate the robot's haptic abilities. We show that our robot is usable in most of the tested tasks and extract perceptive and manipulative guidelines for the design of haptic elements to be integrated in future learning activities. We conclude with limitations of the system and future work.

This article presents a learning activity and its user study involving the Cellulo platform, a novel versatile robotic tool designed for education. In order to show the potential of Cellulo in the classroom as part of standard curricular activities, we designed a learning activity called Windfield that aims to teach the atmospheric formation mechanism of wind to early middle school children. The activity involves a didactic sequence, introducing the Cellulo robots as hot air balloons and enabling children to feel the wind force through haptic feedback. We present a user study, designed in the form of a real hour-long lesson, conducted with 24 children in 8 groups who had no prior knowledge in the subject. Collaborative metrics within groups and individual performances about the learning of key concepts were measured with only the hardware and software integrated in the platform in a completely automated manner. The results show that almost all participants showed learning of symmetric aspects of wind formation while about half showed learning of asymmetric vectorial aspects that are more complex.

In this article, we present Cellulo, a novel robotic platform that investigates the intersection of three ideas for robotics in education: designing the robots to be versatile and generic tools; blending robots into the classroom by designing them to be pervasive objects and by creating tight interactions with (already pervasive) paper; and finally considering the practical constraints of real classrooms at every stage of the design. Our platform results from these considerations and builds on a unique combination of technologies: groups of handheld haptic-enabled robots, tablets and activity sheets printed on regular paper. The robots feature holonomic motion, haptic feedback capability and high accuracy localization through a microdot pattern overlaid on top of the activity sheets, while remaining affordable (robots cost about EUR 125 at the prototype stage) and classroom-friendly. We present the platform and report on our first interaction studies, involving about 230 children.

In this integrated study of dynamics in MOOCs discussion forums, we analyze the interplay of temporal patterns, discussion content, and the social structure emerging from the communication using mixed methods. A special focus is on the yet under-explored aspect of time dynamics and influence of the course structure on forum participation. Our analyses show dependencies between the course structure (video opening time and assignment deadlines) and the over-all forum activity whereas such a clear link could only be partially observed considering the discussion content. For analyzing the social dimension we apply role modeling techniques from social network analysis. While the types of user roles based on connection patterns are relatively stable over time, the high fluctuation of active contributors lead to frequent changes from active to passive roles during the course. However, while most users do not create many social connections they can play an important role in the content dimension triggering discussions on the course subject. Finally, we show that forum activity level can be predicted one week in advance based on the course structure, forum activity history and attributes of the communication network which enables identification of periods when increased tutor supports in the forum is necessary.

This demo submission is associated with the Work-in-Progress submission "Orchestration Graphs: enabling rich social pedagogical scenarios in MOOCs". We present our implementation of a web application for designing, running and orchestrating social pedagogical scenarios. The application is based on Orchestration Graphs, an educational modeling language. We plan to demonstrate our technology by automatically simulating user activity, and offering visitors the opportunity to interact with the graph editor.

One of the initial promises of MOOCs was to enable participants from around the world to learn and build knowledge together, however existing MOOC platforms are very limited in their collaborative functionality. Using a recent educational modeling language which can express a broad diversity of educational scenarios, we present a technical infrastructure design and prototype which enables instructors to design and run pedagogically rich and therefore complex scenarios. We present this as a theoretical and technical contribution to support a broad program of research and innovation related to collaborative learning at scale.

In this paper, we explored the effect of a robot's subconscious gestures made during moments when idle (also called adaptor gestures) on anthropomorphic perceptions of five year old children. We developed and sorted a set of adaptor motions based on their intensity. We designed an experiment involving 20 children, in which they played a memory game with two robots. During moments of idleness, the first robot showed adaptor movements, while the second robot moved its head following basic face tracking. Results showed that the children perceived the robot displaying adaptor movements to be more human and friendly. Moreover, these traits were found to be proportional to the intensity of the adaptor movements. For the range of intensities tested, it was also found that adaptor movements were not disruptive towards the task. These findings corroborate the fact that adaptor movements improve the affective aspect of child-robot interactions (CRI) and do not interfere with the child's performances in the task, making them suitable for CRI in educational contexts.

The CoWriter activity involves a child in a rich and complex interaction where he has to teach handwriting to a robot. The robot must convince the child it needs his help and it actually learns from his lessons. To keep the child engaged, the robot must learn at the right rate, not too fast otherwise the kid will have no opportunity for improving his skills and not too slow otherwise he may loose trust in his ability to improve the robot' skills. We tested this approach in real pedagogic/therapeutic contexts with children in difficulty over repeated long sessions (40-60 min). Through 3 different case studies, we explored and refined experimental designs and algorithms in order for the robot to adapt to the troubles of each child and to promote their motivation and self-confidence. We report positive observations, suggesting commitment of children to help the robot, and their comprehension that they were good enough to be teachers, overcoming their initial low confidence with handwriting.

Measuring “how much the human is in the interaction” - the level of engagement - is instrumental in building effective interactive robots. Engagement, however, is a complex, multi-faceted cognitive mechanism that is only indirectly observable. This article formalizes with-me-ness as one of such indirect measures. With-me-ness, a concept borrowed from the field of Computer-Supported Collaborative Learning, measures in a well-defined way to what extent the human is with the robot over the course of an interactive task. As such, it is a meaningful precursor of engagement. We expose in this paper the full methodology, from real-time estimation of the human's focus of attention (relying on a novel, open-source, vision-based head pose estimator), to on-line computation of with-me-ness. We report as well on the experimental validation of this approach, using a naturalistic setup involving children during a complex robot-teaching task.

Building over algorithms previously developed for digital pens, this article introduces a novel 2D localization technique for mobile robots, based on simple printed patterns. This method combines high absolute accuracy (below 0.3mm), unlimited scalability, low computational requirements (the presented open-source implementation runs at above 45Hz on a low-cost microcontroller) and low cost (below €30 per device at prototype stage). The article first presents the underlying algorithms and localization pipeline. It then describes our reference hardware and software implementations, and finally evaluates the performance of this technique for mobile robots.

We present an omnidirectional ball wheel drive design that utilizes a permanent magnet as the drive roller to generate the contact force. Particularly interesting for novel human-mobile robot interaction scenarios where the users are expected to physically interact with many palm-sized robots, our design combines simplicity, low cost and compactness. We first detail our design and explain its key parameters. Then, we present our implementation and compare it with an omniwheel drive built with identical conditions and similar cost. Finally, we elaborate on the main advantages and drawbacks of our design.

'Teaching analytics' is the application of learning analytics techniques to understand teaching and learning processes, and eventually enable supportive interventions. However, in the case of (often, half-improvised) teaching in face-to-face classrooms, such interventions would require first an understanding of what the teacher actually did, as the starting point for teacher reflection and inquiry. Currently, such teacher enactment characterization requires costly manual coding by researchers. This paper presents a case study exploring the potential of machine learning techniques to automatically extract teaching actions during classroom enactment, from five data sources collected using wearable sensors (eye-tracking, EEG, accelerometer, audio and video). Our results highlight the feasibility of this approach, with high levels of accuracy in determining the social plane of interaction (90%, κ=0.8). The reliable detection of concrete teaching activity (e.g., explanation vs. questioning) accurately still remains challenging (67%, κ=0.56), a fact that will prompt further research on multimodal features and models for teaching activity extraction, as well as the collection of a larger multimodal dataset to improve the accuracy and generalizability of these methods.

Research on learning dashboards aims to identify what data is meaningful to different stakeholders in education, and how data can be presented to support sense-making processes. This paper summarizes the main outcomes of a systematic literature review on learning dashboards, in the fields of Learning Analytics and Educational Data Mining. The query was run in five main academic databases and enriched with papers coming from GScholar, resulting in 346 papers out of which 55 were included in the final analysis. Our review distinguishes different kinds of research studies as well as different aspects of learning dashboards and their maturity in terms of evaluation. As the research field is still relatively young, many of the studies are exploratory and proof-of-concept. Among the main open issues and future lines of work in the area of learning dashboards, we identify the need for longitudinal research in authentic settings, as well as studies that systematically compare different dashboard design options.

In the context of MOOCs, "With-me-ness" refers to the extent to which the learner succeeds in following the teacher, specifically in terms of looking at the area in the video that the teacher is explaining. In our previous works, we employed eye-tracking methods to quantify learners' With-me-ness and showed that it is positively correlated with their learning gains. In this contribution, we describe a tool that is designed to improve With-me-ness by providing a visual-aid superimposed on the video. The position of the visual-aid is suggested by the teachers' dialogue and deixis, and it is displayed when the learner's With-me-ness is under the average value, which is computed from the other students' gaze behavior. We report on a user-study that examines the effectiveness of the proposed tool. The results show that it significantly improves the learning gain and it significantly increases the extent to which the students follow the teacher. Finally, we demonstrate how With-me-ness can create a complete theoretical framework for conducting gaze-based learning analytics in the context of MOOCs.

Research in education has long established how children mutually influence and support each other's learning trajectories, eventually leading to the development and widespread use of learning methods based on peer activities. In order to explore children's learning behavior in the presence of a robotic facilitator during a collaborative writing activity, we investigated how they assess their peers in two specific group learning situations: peer-tutoring and peer-learning. Our scenario comprises of a pair of children performing a collaborative activity involving the act of writing a word/letter on a tactile tablet. In the peer-tutoring condition, one child acts as the teacher and the other as the learner, while in the peer-learning condition, both children are learners without the attribution of any specific role. Our experiment includes 40 children in total (between 6 and 8 years old) over the two conditions, each time in the presence of a robot facilitator. Our results suggest that the peer-tutoring situation leads to significantly more corrective feedback being provided, as well as the children more disposed to self-disclosure to the robot.

In this paper, we present an experiment in the context of a child-robot interaction where we study the influence of the child-robot spatial arrangement on the child's focus of attention and the perception of the robot's performance. In the “Co-Writer learning by teaching” activity, the child teaches a Nao robot how to handwrite. Usually only face-to-face spatial arrangements are tested in educational child robot interactions, but we explored two spatial conditions from Kendon's F-formation, the side-by-side and the face-to-face formations in a within subject experiment. We estimated the gaze behavior of the child and their consistency in grading the robot with regard to the robot's progress in writing. Even-though the demonstrations provided by children were not different between the two conditions (i.e. the robot's learning didn't differ), the results showed that in the side-by-side condition children tended to be more indulgent with the robot's mistakes and to give it better feedback. These results highlight the influence of experimental choices in child-robot interaction.

In this study, we investigated the way users memorize, analyze, collaborate, and learn new concepts on a Tangible User Interface (TUI). Twenty-seven pairs of apprentices in logistics ( N = 54) interacted with an interactive simulation of a warehouse. Their task was to discover efficient design principles for building storehouses. In a between-subjects experimental design, half of the participants used 3D physical shelves, whereas the other half used 2D paper shelves. This manipulation allowed us to control for the “representational effect” of 3D tangibles: the first group saw the warehouse as a small-scale model with realistic shelves, whereas the second group had access to a more abstract layout with rectangular pieces of paper. Both groups interacted with the system in the same way. We found that participants in the first group (i.e., who used 3D realistic shelves) better memorized a warehouse layout, built a more efficient model, and scored higher on a learning test. Additionally, students wore eye-tracking goggles while completing those tasks; preliminary results suggest that 3D interfaces increased joint visual attention, which was found to be a significant predictor for participants’ task performance and learning gains. Implications for designing TUIs in collaborative settings are discussed.

This article presents a comparison of the effects of input-output location (co-located versus discrete) on user performance in a tangible user interface (TUI) system. We conducted a mobile eye-tracking study with two different versions of a TUI system and, despite similar performances in both conditions, our findings revealed differences in the users gaze patterns, shedding new light on the underlying cognitive processes.

We present an eye-tracking study where we augment a Massive Open Online Course (MOOC) video with the gaze information of the teacher. We tracked the gaze of a teacher while he was recording the content for a MOOC lecture. Our working hypothesis is that displaying the gaze of the teacher will act as cues in crucial moments of dyadic conversation, the teacher-student dyad, such as reference disambiguation. We collected data about students’ video interaction behaviour within a MOOC. The results show that the showing the teacher’s gaze made the content easier to follow for the students even when complex visual stimulus present in the video lecture.

This article presents a novel robotic partner which children can teach handwriting. The system relies on the learning by teaching paradigm to build an interaction, so as to stimulate meta-cognition, empathy and increased self-esteem in the child user. We hypothesise that use of a humanoid robot in such a system could not just engage an unmotivated student, but could also present the opportunity for children to experience physically-induced benefits encountered during human-led handwriting interventions, such as motor mimicry. By leveraging simulated handwriting on a synchronised tablet display, a NAO humanoid robot with limited fine motor capabilities has been configured as a suitably embodied handwriting partner. Statistical shape models derived from principal component analysis of a dataset of adult-written letter trajectories allow the robot to draw purposefully deformed letters. By incorporating feedback from user demonstrations, the system is then able to learn the optimal parameters for the appropriate shape models. Preliminary in situ studies have been conducted with primary school classes to obtain insight into children's use of the novel system. Children aged 6-8 successfully engaged with the robot and improved its writing to a level which they were satisfied with. The validation of the interaction represents a significant step towards an innovative use for robotics which addresses a widespread and socially meaningful challenge in education.

Mutual modelling, the reciprocal ability to establish a mental model of the other, plays a fundamental role in human interactions. This complex cognitive skill is however difficult to fully apprehend as it encompasses multiple neuronal, psychological and social mechanisms that are generally not easily turned into computational models suitable for robots. This article presents several perspectives on mutual modelling from a range of disciplines, and reflects on how these perspectives can be beneficial to the advancement of social cognition in robotics. We gather here both basic tools (concepts, formalisms, models) and exemplary experimental settings and methods that are of relevance to robotics. This contribution is expected to consolidate the corpus of knowledge readily available to human-robot interaction research, and to foster interest for this fundamentally cross-disciplinary field.

Presented is PYROBOTS, a new open-source software toolkit for the executive control of complex robotic systems. Borrowing ideas from previous tools like URBI [1], it proposes a lightweight Python framework to develop (preemptive) concurrent and event-based executive controllers. Designed in a bottom-up fashion, out of the need for a practical, unobstrusive tool suitable for rapid prototyping of complex interaction scenarios, PYROBOTS also exposes several simple yet convenient abstractions for physical resources management and pose representation. While middleware-agnostic, it features specific integration with the ROS middleware. Experimental deployments and stress-tests on several robotic platforms (including PR2 and Nao) in dynamic human environments are reported.

In order to explore the impact of integrating a robot as a facilitator in a collaborative activity, we examined interpersonal distancing of children both with a human adult and a robot facilitator. Our scenario involves two children performing a collaborative learning activity, which included the writing of a word/letter on a tactile tablet. Based on the learning-by-teaching paradigm, one of the children acted as a teacher when the other acted as a learner. Our study involved 40 children between 6 and 8 years old, in two conditions (robot or human facilitator). The results suggest first that the child acting as a teacher feel more responsible when the facilitator is a robot, compared to a human; they show then that the interaction between a (teacher) child and a robot facilitator can be characterized as being a reciprocity-based interaction, whereas a human presence fosters a compensation-based interaction.

Studying natural reading and its underlying attention processes requires devices that are able to provide precise measurements of gaze without rendering the reading activity unnatural. In this paper we propose an eye tracking system that can be used to conduct analyses of reading behavior in low constrained experimental settings. The system is designed for dual-camera-based head-mounted eye trackers and allows free head movements and note taking. The system is composed of three different modules. First, a 3D model-based gaze estimation method computes the reader's gaze trajectory. Second, a document image retrieval algorithm is used to recognize document pages and extract annotations. Third, a systematic error correction procedure is used to post-calibrate the system parameters and compensate for spatial drifts. The validation results show that the proposed method is capable of extracting reliable gaze data when reading in low constrained experimental conditions.

We present an eye tracking study to investigate how natural reading behavior and reading comprehension are influenced by in-context annotations. In a lab experiment, three groups of participants were asked to read a text and answer comprehension questions: a control group without taking annotations, a second group reading and taking annotations, and a third group reading a peer-annotated version of the same text. A self-made head-mounted eye tracking system was specifically designed for this experiment, in order to study how learners read and quickly re-read annotated paper texts, in low constrained experimental conditions. In the analysis, we measured the phenomenon of annotation-induced overt attention shifts in reading, and found that: (1) the reader's attention shifts toward a margin annotation more often when the annotation lies in the early peripheral vision, and (2) the number of attention shifts, between two different types of information units, is positively related to comprehension performance in quick re-reading. These results can be translated into potential criteria for knowledge assessment systems.

We present the design approach and evaluation of our prototype called "Ranger". Ranger is a robotic toy box that aims to motivate young children to tidy up their room. We evaluated Ranger in 14 families with 31 children (2-10 years) using the Wizard-of-Oz technique. This case study explores two different robot behaviors (proactive vs. reactive) and their impact on children's interaction with the robot and the tidying behavior. The analysis of the video recorded scenarios shows that the proactive robot tended to encourage more playful and explorative behavior in children, whereas the reactive robot triggered more tidying behavior. Our findings hold implications for the design of interactive robots for children, and may also serve as an example of evaluating an early version of a prototype in a real-world setting.

No abstract available.

Human body-language is one of the richest and most obscure sources of information in inter-personal communication which we aim to re-introduce into the classroom's ecosystem. In this paper we present our observations of student-to-student influence and measurements. We show parallels with previous theories and formulate a new concept for measuring the level of attention based on synchronization of student actions. We observed that the students with lower levels of attention are slower to react then focused students, a phenomenon we named "sleepers' lag".

In co-located meetings, participants create and share content to establish a common understanding. In this paper, we present a collaborative environment that enables group members to create and share content simultaneously by providing them with different kinds of individual input devices and a shared workspace. We also report on an exploratory study to investigate the influence of the input device used on the shared knowledge produced by the group. The results suggest that driven by the affordances, various input devices complement each other. We thus recommend groups to be equipped with multitude of them to support diverse meeting task demands. Additionally, we observed that groupware usage differs across various phases of the problem-solving activity. This provides implications for the design of collaborative environments to assist each of the respective phases of the task, in order to extend their usefulness for the group.

In this paper we give a preview of our system for automatically evaluating attention in the classroom. We demonstrate our current behaviour metrics and preliminary observations on how they reflect the reactions of people to the given lecture. We also introduce foundations of our hypothesis on peripheral awareness of students during lectures.

We present a longitudinal study on the participation regulation effects in the presence of a speech aware interactive table. This study focuses on training meetings of groups of top level managers, whose compositions do not change, in a corporate organization. We show that an effect of balancing participation develops over time. We also report other emerging group-specific features such as interaction patterns and signatures, leadership effects, and behavioral changes between meetings. Finally we collect feedback from the participants and analyze qualitatively the human and social aspects of the participants interaction mediated by the technology.

We present the results of group dynamics and their effect on success in problem solving/decision making meetings. We use a novel multiple input environment for collaboration and data collection, and a hidden profile task given to groups, whose goals are to find the correct solution. We observe that groups elect 0, 1 or 2 leaders, and the best results are obtained by the groups with a single leader. Prior acquaintance (familiarity), does not show any effect on the success or on the group strategies. Groups with a single leader tend to be more successful, and leaders expressed their authority verbally rather than by through the collaborative system.

In meetings, people sometimes come across information in the environment or in the conversation. This kind of accidentally encountered information may be or not be relevant to the main meeting topic, but stirs people's temporary interest and mediates group conversation. In some cases people even need to search these unexpected terms in the Web. This phenomenon is called opportunistic search. We question if digital displays can be designed to support opportunistic search in meetings. Assuming information sharing in collocated meetings may largely come from conversation, we propose to design an ambient tabletop display that provides just-in-time conversational information to support opportunistic browsing and searching. In this paper, we present our design explorations with user study, and answer some of the major design questions for such systems.

We present the design and evaluation of an iPad application that will be used to operate the modular robots “Roombots”. Roombots are the building blocks for adaptive pieces of furniture. The application allows a user to arrange adaptive furniture within a room. We conducted a user study with 24 participants to evaluate our approach and to freely explore people's interaction. Data suggests that the ability to move with the tablet leads to a better precision of the furniture arrangement. No significant difference has been observed between using the application through a virtual representation of the room in contrast to an augmented reality environment, even if participants mentioned in a post-study questionnaire their preference for the augmented condition. Users described the interface as intuitive and easy to use.

We present Flag, an awareness tool that gives information on the presence and status of students in a university leaning center with the aim of promoting informal collaboration among them.

Tabletop systems have become quite popular in recent years, during which there was considerable enthusiasm for the development of new interfaces. In this paper, we establish a comparison between touch and tangible interfaces. We set up an experiment involving several actions like translation and rotation. We recruited 40 participants to take part in a user study and we present our results with a discussion on the design of touch and tangible interfaces. Our contribution is an empirical study showing that overall, the tangible interface is much faster but under certain conditions, the touch interface could gain the upper hand.

In this paper, we analyze complex gaze tracking data in a collaborative task and apply machine learning models to automatically predict skill-level differences between participants. Specifically, we present findings that address the two primary challenges for this prediction task: (1) extracting meaningful features from the gaze information, and (2) casting the prediction task as a machine learning (ML) problem. The results show that our approach based on profile hidden Markov models are up to 96% accurate and can make the determination as fast as one minute into the collaboration, with only 5% of gaze observations registered. We also provide a qualitative analysis of gaze patterns that reveal the relative expertise level of the paired users in a collaborative learning user study.

We aim at merging technologies from information technology, roomware, and robotics in order to design adaptive and intelligent furniture. This paper presents design principles for our modular robots, called Roombots, as future building blocks for furniture that moves and self-reconfigures. The reconfiguration is done using dynamic connection and disconnection of modules and rotations of the degrees of freedom. We are furthermore interested in applying Roombots towards adaptive behaviour, such as online learning of locomotion patterns. To create coordinated and efficient gait patterns, we use a Central Pattern Generator (CPG) approach, which can easily be optimized by any gradient-free optimization algorithm. To provide a hardware framework we present the mechanical design of the Roombots modules and an active connection mechanism based on physical latches. Further we discuss the application of our Roombots modules as pieces of a homogenic or heterogenic mix of building blocks for static structures.

Although many augmented tabletop systems have shown the potential and usability of finger-based interactions and paper-based interfaces, they have mainly dealt with each of them separately. In this paper, we introduce a novel method aimed to improve human natural interactions on augmented tabletop systems, which enables multiple users to use both fingertips and physical papers as mediums for interaction. This method uses computer vision techniques to detect multi-fingertips both over and touching the surface in real-time regardless of their orientations. Fingertip and touch positions would then be used in combination with paper tracking to provide a richer set of interaction gestures that the users can perform in collaborative scenarios.

This paper describes TinkerSheets, a paper-based interface to tangible simulations. The proposed interface combines the advantages of form-based input and paper. Form-based input allows to set an arbitrary number of parameters. Using paper as a medium for the interface keeps the interaction modality consistently physical. TinkerSheets are also used as an output screen to display summarized information about the simulation. A user study conducted in an authentic context shows how the characteristics of the interface shape real world usage. We also describe how the affordances of this control and visualization interface support the co-design of interaction with end-users.

This paper presents an algorithm that detects misunderstandings in collaborative work at a distance. It analyses the movements of collaborators' eyes on the shared workspace, their utterances containing references about this workspace, and the availability of 'remote' deictic gestures. This method is based on two findings: 1. participants look at the points they are talking about in their message; 2. their gazes are more dense around these points compared to other random looks in the same timeframe. The algorithm associates the distance between the gazes of the emitter and gazes of the receiver of a message with the probability that the recipient did not understand the message.

This project incorporates modular robots as building blocks for furniture that moves and self-reconfigures. The reconfiguration is done using dynamic connection / disconnection of modules and rotations of the degrees of freedom. This paper introduces a new approach to self-reconfiguration planning for modular robots based on the graph signature and the graph edit-distance. The method has been tested in simulation on two type of modules: YaMoR and M-TRAN. The simulation results shows interesting features of the approach, namely rapidly finding a near-optimal solution.

Explicit Referencing is a mechanism for enabling deictic gestures in on-line communication. Little is known about the impact of ER on distance problem solving. In this paper, we report on a study where 120 students (60 pairs) had to solve a problem collaboratively, at a distance, using chat tools that differed in the way a user may relate an utterance to the task context. Results indicate that team performance is improved by explicit referencing mechanisms. However, when Explicit Referencing is implemented in a way that is detrimental to the linearity of the conversation, resulting in the visual dispersion or scattering of messages, its use has negative consequences for collaborative work at a distance. The role of a linear message history in the collaboration mechanisms was equally important than that of Explicit Referencing.