Educational Robots are gaining popularity in classrooms but can increase the load on teachers compared to the use of more traditional technologies. Providing support to teachers can make teachers confident in including robots in their teaching routines. In order to support teachers in managing robotic activities in the classroom, it is important to first understand the challenges they face when engaging with these activities. To investigate these challenges, we observed three teachers managing robotic activities across fifteen standard school sessions, followed by retrospective interviews. In these sessions, students performed group activities on assembling and programming different robotic platforms. The results highlight a) how managing the additional technical complexity of the robotic activity is challenging for teachers b) teachers interventions focus on supporting students make connections between their programs and their robot behaviour in the real-world. Building on our results, we discuss how orchestration tools may be designed to help alleviate teachers challenges and support teachers interventions in robotic classrooms.

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.

Bringing robots into classrooms presents a new set of challenges for classroom management and teacher support compared to traditional technology-enhanced learning and has been left almost unexplored by the research community. In this paper, we present the opportunities and challenges of orchestrating Educational Robotics (ER) activities in classrooms. To support our discussion, we present a case study of 25 students working in pairs using handheld robots to engage in a computational thinking activity. While performing the activity, students' behavioral information was sent from the robots to an orchestration dashboard that was used in a debriefing activity. Although this work is in its preliminary stages, it contributes to framing the challenges that need to be addressed to realistically scale-up usage of ER in classrooms.