Identification of the Students Learning Process During Education Robotics Activities - Scaradozzi, David; Cesaretti, Lorenzo; Screpanti, Laura; Mangina, Eleni (Frontiers in Robotics and AI, 2020)
This paper presents the design of an assessment process and 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 utilised 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, to carry out more complex and inspirational projects: the integration of sensors on a robotic prototype is a 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 artefact. 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 analysed 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.