Long-term child-robot interaction (CRI) in kindergartens is key for integrating robots into children’s daily lives and supporting their development. This unsupervised 4-weeks study explores how the robot Pepper can support phonological awareness (PA) in kindergarten children through the ‘Sound Game’, leveraging robot logs, questionnaires and open feedback by teachers and experts’ analysis of audio recordings. Our findings reveal a generally high engagement, with the game being played 12 times, for a total of 233 minutes. Teachers played a crucial role in mediating the game, especially for younger children, and noted its potential for long-term use. The game proved beneficial across age groups, highlighting its potential as a first step for sustained, effective PA development and the transformative impact of social robots in early childhood education.

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.

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.

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.

MOTIVATION: Whole exome sequencing (WES) studies for autism spectrum disorder (ASD) could identify only around six dozen risk genes to date because the genetic architecture of the disorder is highly complex. To speed the gene discovery process up, a few network-based ASD gene discovery algorithms were proposed. Although these methods use static gene interaction networks, functional clustering of genes is bound to evolve during neurodevelopment and disruptions are likely to have a cascading effect on the future associations. Thus, approaches that disregard the dynamic nature of neurodevelopment are limited. RESULTS: Here, we present a spatio-temporal gene discovery algorithm, which leverages information from evolving gene co-expression networks of neurodevelopment. The algorithm solves a prize-collecting Steiner forest-based problem on co-expression networks, adapted to model neurodevelopment and transfer information from precursor neurodevelopmental windows. The decisions made by the algorithm can be traced back, adding interpretability to the results. We apply the algorithm on ASD WES data of 3871 samples and identify risk clusters using BrainSpan co-expression networks of early- and mid-fetal periods. On an independent dataset, we show that incorporation of the temporal dimension increases the predictive power: predicted clusters are hit more and show higher enrichment in ASD-related functions compared with the state-of-the-art. AVAILABILITY AND IMPLEMENTATION: The code is available at http://ciceklab.cs.bilkent.edu.tr/st-steiner. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.