Keep on Moving! Exploring Anthropomorphic Effects of Motion during Idle Moments

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

Unsupervised extraction of students navigation patterns on an EPFL MOOC

How do students learn in MOOCs? This project aims at answering this question by analyzing the activities of thousands of students registered on EPFL Scalaa MOOC hosted by Coursera. With the rapid growth of MOOCs, Education Science has entered the Big Data bubble, bringing new opportunities to study and improve learning technologies. We are interested in studying students navigation patterns which are the short sequences of learning activities that a students perform on the MOOC platform. In our case, the learning activities are one of watching a video lecture, reading or posting on the forum and submitting assignments. In this project we use unsupervised machine learning techniques to extract the main navigation patterns of students and gain insights on their behavior. We produce a simple and efficient visualization tool in order to provide feedback to teachers to help them understand the potential difficulties encountered by their students during the course and, if necessary, take actions accordingl

Bringing letters to life: handwriting with haptic-enabled tangible robots

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. To maximize our potential contributions to this learning process, we focus on two promising aspects of handwriting: the visual perception and the visual-motor coordination. These two aspects enhance in particular two sides of the representation of letters in the mind of the learner: the shape of the letter (the grapheme) and the way it is drawn, namely the dynamics of the letter (the ductus). With these two aspects in mind, we do a detailed content analysis for the process of learning the representation of letters, which leads us to discriminate the specific skills involved in letter representation. We then compare our robotic method with traditional methods as well as with the combination of the two methods, in order to discover which of these skills can benefit from the use of Cellulo. As handwriting is taught from age 5, we conducted our experiments with 17 five-year-old children in a public school. Results show a clear potential of our robot-assisted learning activities, with a visible improvement in certain skills of handwriting, most notably in creating the ductus of the letters, discriminating a letter among others and in the average handwriting speed. Moreover, we show that the benefit of our learning activities to the handwriting process increases when it is used after traditional learning methods. These results lead to the initial insights into how such a tangible robotic learning technology may be used to create cost-effective collaborative scenarios for the learning of handwriting

Iterative Classroom Teaching

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/eps) examples, where d is the ambient dimension of the problem, N is the number of learners, and eps 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

Analysis and Remediation of Handwriting difficulties

Even with correct training, up to 25% of children never master handwriting like their peers. While research shows a correlation between handwriting difficulties and school failure, these difficulties can also impact children in their self esteem and behavioral development. Since the mastery of handwriting requires a lot of different skills, it is never easy to understand where a given child is facing difficulties, nor how exactly to help him/her overcome them. For this reason, it is of prime importance to detect and understand childrenâs handwriting difficulties the earliest possible in order to propose the most effective remediation possible. In this thesis, we first introduce a modernize version of the currently adopted handwriting tests, that show clear limitations in the era of digitalization. Indeed, the nature itself of these tests, conducted on paper, restricts them to the analysis of the final static aspect of handwriting. Its dynamics, found to be very important, is therefore hidden and cannot be taken into consideration. For this reason, we designed in collaboration with therapists several features that describe different aspects of handwriting, which are not limited to static but also capture kinematic, pressure and tilt. The designed features have the main advantage to describe very low level aspects of handwriting, which makes them quite independent of the writing content. We verified this hypothesis by giving the proof of concept that our model for automatic detection of handwriting difficulties can be translated from the latin to the the cyrillic alphabet. In the same way, we demonstrated that our model can also, given retraining, be used on paper or directly on digital tablets, like iPads. Finally, we introduced our iPad-based test allowing to extract the multidimensional handwriting profile of the child. This test aims to answer the first of the afore-discussed problems, by allowing to extract the specific strengths and weaknesses of a child, in less than a minute, on different aspects and at different granularities. The second part of this thesis tackles the problem of designing remediation activities for handwriting difficulties. We designed activities specifically targeting the handwriting aspects identified by the model and obtained a preliminary proof of concept that serious games targeting specific skills of handwriting (e.g. pressure, kinematic, tilt, ...) can have a positive impact on the overall quality of handwriting. Finally the two last chapters of this thesis tackle two corollary, but still crucial questions related to handwriting remediation. After the integration of these remediation activities in a Child-Robot Interaction scenario, in which the child is the teacher of the robot, we gave the proof of concept of the importance of the design of robot behaviors towards social acceptance with children, something especially important knowing the importance of the childâs perception of the robot and the interaction with it in such a scenario. Finally, in the last Chapter, we investigated whether it is possible to "remediate some handwriting difficulties by preventing them", i.e., by supporting pre-school children in the acquisition of the fundamental visuo-motor coordination skills required by handwriting

Extending the Spectrum of Dysgraphia: A Data Driven Strategy to Estimate Handwriting Quality

This paper proposes new ways to assess handwriting, a critical skill in any child’s school journey. Traditionally, a pen and paper test called the BHK test (Concise Evaluation Scale for Children’s Handwriting) is used to assess children’s handwriting in French-speaking countries. Any child with a BHK score above a certain threshold is diagnosed as ‘dysgraphic’, meaning that they are then eligible for financial coverage for therapeutic support. We previously developed a version of the BHK for tablet computers which provides rich data on the dynamics of writing (acceleration, pressure, and so forth). The underlying model was trained on dysgraphic and non-dysgraphic children. In this contribution, we deviate from the original BHK for three reasons. First, in this instance, we are interested not in a binary output but rather a scale of handwriting difficulties, from the lightest cases to the most severe. Therefore, we wish to compute how far a child’s score is from the average score of children of the same age and gender. Second, our model analyses dynamic features that are not accessible on paper; hence, the BHK is useful in this instance. Using the PCA (Principal Component Analysis) reduced the set of 53 handwriting features to three dimensions that are independent of the BHK. Nonetheless, we double-checked that, when clustering our data set along any of these three axes, we accurately detected dysgraphic children. Third, dysgraphia is an umbrella concept that embraces a broad variety of handwriting difficulties. Two children with the same global score can have totally different types of handwriting difficulties. For instance, one child could apply uneven pen pressure while another one could have trouble controlling their writing speed. Our new test not only provides a global score, but it also includes four specific score for kinematics, pressure, pen tilt and static features (letter shape). Replacing a global score with a more detailed profile enables the selection of remediation games that are very specific to each profile

The transferability of handwriting skills: from the Cyrillic to the Latin alphabet

Do handwriting skills transfer when a child writes in two different scripts, such as the Latin and Cyrillic alphabets? Are our measures of handwriting skills intrinsically bound to one alphabet or will a child who faces handwriting difficulties in one script experience similar difficulties in the other script? To answer these questions, 190 children from grades 1–4 were asked to copy a short text using both the Cyrillic and Latin alphabets on a digital tablet. A recent change of policy in Kazakhstan gave us an opportunity to measure transfer, as the Latin-based Kazakh alphabet has not yet been introduced. Therefore, pupils in grade 1 had a 6-months experience in Cyrillic, and pupils in grades 2, 3, and 4 had 1.5, 2.5, and 3.5 years of experience in Cyrillic, respectively. This unique situation created a quasi-experimental situation that allowed us to measure the influence of the number of years spent practicing Cyrillic on the quality of handwriting in the Latin alphabet. The results showed that some of the differences between the two scripts were constant across all grades. These differences thus reflect the intrinsic differences in the handwriting dynamics between the two alphabets. For instance, several features related to the pen pressure on the tablet are quite different. Other features, however, revealed decreasing differences between the two scripts across grades. While we found that the quality of Cyrillic writing increased from grades 1–4, due to increased practice, we also found that the quality of the Latin writing increased as well, despite the fact that all of the pupils had the same absence of experience in writing in Latin. We can therefore interpret this improvement in Latin script as an indicator of the transfer of fine motor control skills from Cyrillic to Latin. This result is especially surprising given that one could instead hypothesize a negative transfer, i.e., that the finger controls automated for one alphabet would interfere with those required by the other alphabet. One interesting side-effect of these findings is that the algorithms that we developed for the diagnosis of handwriting difficulties among French-speaking children could be relevant for other alphabets, paving the way for the creation of a cross-lingual model for the detection of handwriting difficulties

Designing Configurable Arm Rehabilitation Games: How Do Different Game Elements Affect User Motion Trajectories?

For successful rehabilitation of a patient after a stroke or traumatic brain injury, it is crucial that rehabilitation activities are motivating, provide feedback and have a high rate of repetitions. Advancements in recent technologies provide solutions to address these aspects where needed. Additionally, through the use of gamification, we are able to increase the motivation for participants. However, many of these systems require complex set-ups, which can be a big challenge when conducting rehabilitation in a home-based setting. To address the lack of simple rehabilitation tools for arm function for a home-based application, we previously developed a system, Cellulo for rehabilitation, that is comprised of paper-supported tangible robots that are orchestrated by applications deployed on consumer tablets. These components enable different features that allow for gamification, easy setup, portability, and scalability. To support the configuration of game elements to patients’ level of motor skills and strategies, their motor trajectories need to be classified. In this paper, we investigate the classification of different motor trajectories and how game elements impact these in unimpaired, healthy participants. We show that the manipulation of certain game elements do have an impact on motor trajectories, which might indicate that it is possible to adapt the arm remediation of patients by configuring game elements. These results provide a first step towards providing adaptive rehabilitation based upon patients’ measured trajectories

“It Is Not the Robot Who Learns, It Is Me.” Treating Severe Dysgraphia Using Child–Robot Interaction

Writing disorders are frequent and impairing. However, social robots may help to improve children's motivation and to propose enjoyable and tailored activities. Here, we have used the Co-writer scenario in which a child is asked to teach a robot how to write via demonstration on a tablet, combined with a series of games we developed to train specifically pressure, tilt, speed, and letter liaison controls. This setup was proposed to a 10-year-old boy with a complex neurodevelopmental disorder combining phonological disorder, attention deficit/hyperactivity disorder, dyslexia, and developmental coordination disorder with severe dysgraphia. Writing impairments were severe and limited his participation in classroom activities despite 2 years of specific support in school and professional speech and motor remediation. We implemented the setup during his occupational therapy for 20 consecutive weekly sessions. We found that his motivation was restored; avoidance behaviors disappeared both during sessions and at school; handwriting quality and posture improved dramatically. In conclusion, treating dysgraphia using child–robot interaction is feasible and improves writing. Larger clinical studies are required to confirm that children with dysgraphia could benefit from this setup