Educational Robotics is rapidly gaining attention as an effective methodology to develop skills and engage students preserving their peculiar style of learning. It is often tied together with two other methodologies, Coding and Tinkering, characterized by a similar hands-on approach. In order to fully exploit their inclusive features, teachers need to be prepared to introduce them into classroom. It is often noticed that in service teachers are not yet fully prepared to face this challenge. Many actions have been established to recover this situation, but a proper method for assessing whether these actions are successful or not is not yet developed. This paper presents a methodology for introducing in-service teachers to Educational Robotics, Coding and Tinkering and for assessing the outcomes. 184 in-service teachers were assessed and results analysed. Final considerations draw a picture of the situation amongst the sample chosen for the present study, observing that the intervention seemed to be successful in providing key notions and examples, and improving teachers' self-confidence.

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This paper presents the design of an assessment process and its outcomes to investigate the impact of Educational Robotics activities on students' learning. Through data analytics techniques, the authors will explore the activities' output from a pedagogical and quantitative point of view. Sensors are utilized in the context of an Educational Robotics activity to obtain a more effective robot-environment interaction. Pupils work on specific exercises to make their robot smarter and to carry out more complex and inspirational projects: the integration of sensors on a robotic prototype is crucial, and learners have to comprehend how to use them. In the presented study, the potential of Educational Data Mining is used to investigate how a group of primary and secondary school students, using visual programming (Lego Mindstorms EV3 Education software), design programming sequences while they are solving an exercise related to an ultrasonic sensor mounted on their robotic artifact. For this purpose, a tracking system has been designed so that every programming attempt performed by students' teams is registered on a log file and stored in an SD card installed in the Lego Mindstorms EV3 brick. These log files are then analyzed using machine learning techniques (k-means clustering) in order to extract different patterns in the creation of the sequences and extract various problem-solving pathways performed by students. The difference between problem-solving pathways with respect to an indicator of early achievement is studied.

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