Identification of the Students Learning Process During Education Robotics Activities

Abstract

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.

Systematic Review on Which Analytics and Learning Methodologies Are Applied in Primary and Secondary Education in the Learning of Robotics Sensors

“…All articles consider as a learning goal the construction of a robotic device, where the sensors and the programming of the robot are fundamental for its achievement. Therefore, Piaget’s and Papert’s constructivist theories are present in all articles [ 35 , 51 , 52 ].…”

“…We found only two. One expressly applies learning analytics using machine learning techniques, specifically the Elbow Method, performing a k-means clustering [ 51 ]. The other uses robotic sensors to collect environmental data [ 65 ], where students learn data literacy by analyzing the resulting data and making objective decisions.…”

“…The constructivist learning theory proposed by Piaget and Papert [ 35 , 51 , 52 , 53 , 54 ] is the basis of ER. In an ER activity or lesson, students design, build, program, debug and share their robotic constructions.…”

“…Therefore, Learning Analytics in ER helps to improve teaching and learning science-based and art concepts and skills [ 67 , 78 , 79 , 80 , 81 , 82 ]. However, we found only two references that converge ER, robotics sensors, and Learning Analytics [ 51 , 65 ]. Notwithstanding the few references, the positive results in both pieces of research foster to continue integrating Learning Analytics in the teaching and learning processes during robotic sensors activities.…”

Tecnologías avanzadas para afrontar el reto de la innovación educativa

“…El uso de la robótica en el aula es una tendencia con un impacto indudable en la enseñanza de materias STEAM dotadas de un componente creativo importante (Gdansky et al, 2020;Scaradozzi et al, 2020) y para estimular la creatividad (Hendrick et al, 2020). Un trabajo sobre robótica, STEM y creatividad es el de Casado y Checa (2020) o también el de Hurtado y Santamaría (2019).…”